abstracts of the 26th world congress of psychiatric...

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Abstracts of the 26th World Congress of Psychiatric Genetics

(WCPG): Oral Abstracts

Friday, October 12, 2018

Oral Session: Depression

1:30 p.m. - 3:00 p.m.

1

GENETIC COMORBIDITY BETWEEN DEPRESSION AND

CARDIO-METABOLIC DISEASE, STRATIFIED BY AGE AT

ONSET

Saskia Hagenaars 1 , ∗, Jonathan Coleman 1 , Helena Alexandra Gaspar 1 , Karen Hodgson 1 , The PGC MDD

Consortium, METASTROKE Consortium

2 , Gerome Breen 1 , Cathryn Lewis 1

1 King’s College London

2 University of Virginia

Background: The association between major depressive disorder (MDD) and cardio-metabolic disease is well established. MDD increases the risk of cardio-metabolic disease onset and mortality, but cardio-metabolic disease itself can also increase risk of developing MDD. However, the underlying mechanisms of the association between MDD and cardio- metabolic disease remain elusive. A possible mechanism

may be represented by cerebrovascular disease and its risk factors predisposing MDD in later life, according to the ‘vas- cular depression’ hypothesis. Methods: Polygenic risk score (PRS) analysis was used to examine how coronary artery disease, stroke, and type 2 diabetes predict MDD status, stratified by early and late age at onset in the Psychiatric Genomics Consortium and UK Biobank (N early onset MDD ∼ 16K, late onset MDD ∼16K, controls ∼67K). Genome-wide association studies were performed with MDD cases stratified by early and late age at onset, in order to calculate genetic correlations between cardio-metabolic traits and early and late age at onset for MDD.

Results: All PRS significantly predicted MDD status. When stratified by early and late onset, PRS for coronary artery disease, stroke, BMI, and type 2 diabetes also significantly predicted late onset MDD, with coronary artery disease, BMI, and type 2 diabetes also predicting early onset MDD. Significant genetic correlations were identified between MDD and the cardio-metabolic traits. I will also discuss findings from age at onset stratified genetic correlations. Discussion: This study provides possible support for the vas- cular depression hypothesis by showing a genetic association between late onset MDD and cardio-metabolic traits.

Disclosure: Nothing to disclose.

doi: 10.1016/j.euroneuro.2018.08.008

2

DOES HIGH BMI IN THE ABSENCE OF METABOLIC

CONSEQUENCES CAUSE DEPRESSION?

Jess Tyrrell 1 , ∗, Anwar Mulugeta 2 , Andrew Wood 1 , Ang Zhou 2 , Robin Beaumont 1 , Marcus Tuke 1 , Samuel Jones 1 , Katherine Ruth 1 , Hanieh Yaghootkar 1 , Cathryn Lewis 3 , Tim Frayling 1 , Elina Hypponen 2

1 University of Exeter 2 University of South Australia 3 King’s College London

Background: Depression is more common in obese than non- obese individuals, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used Mendelian randomisation to provide evidence that higher body mass index (BMI) causes depression but have not tested whether this relationship is driven by the metabolic consequences of BMI or differs between men and women. Methods: We performed a Mendelian randomisation study using 48 791 individuals with depression and 291 995 controls in the UK Biobank to test for causal effects of higher BMI on depression. We used two genetic instruments, both

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representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to “un- couple” the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately. Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomisation provided evidence that higher BMI partly causes depression, with a genetically determined 1 SD higher BMI (4.9 kg/m2) associated with higher odds of depression in all individuals (Odds ratio (OR) 1.18 [95%CI: 1.09-1.28], P = 0.00007) and women only (OR: 1.24 [95%CI: 1.11-1.39], P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls, from the Psychiatric Genomics Consortium

(PGC) strengthened the statistical confidence of the findings in all individuals. Using a metabolically favourable adi- posity genetic risk score, and meta-analysing data from UK Biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26 [95%CI: 1.06-1.50], P = 0.010. Discussion: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.


doi: 10.1016/j.euroneuro.2018.08.009

3

GENOME-WIDE META-ANALYSIS OF DEPRESSION

David Howard 1 , ∗, Mark J. Adams 1 , Toni-Kim Clarke 1 , Jonathan D. Hafferty 1 , Jude Gibson 1 , Jonathan R.I. Coleman 2 , Ian J. Deary 1 , 23andMe Research Team, Major Depressive Disorder Working Group, Daniel J. Smith 3 , Patrick F. Sullivan 4 , Naomi R. Wray 5 , Gerome Breen 2 , Cathryn M. Lewis 2 , Andrew M. McIntosh 1

1 University of Edinburgh


3 University of Glasgow

4 University of North Carolina at Chapel Hill 5 University Of Queensland

Background: Depression is a heritable and polygenic condition that causes extensive periods of disability and increases the risk of suicide. Previous genetic studies have identified a number of common risk variants which have increased in number in line with increasing sample sizes. The availability of summary statistics from these studies represents an opportunity to conduct the largest genome-wide meta-analysis study of depression to date. Methods: We examined the effect of 8,098,588 genetic variants on depression by performing a meta-analysis of the summary statistics from the Hyde et al. (2016) analysis of 23andMe, the Wray et al. (2018) analysis of the Major Depressive Disorder Working Group of the Psychiatric Ge- nomics Consortium data, and the Howard et al. (2018) analysis of UK Biobank. We used the broad depression phenotype from UK Biobank which shares a high genetic correlation

(0.87, s.e. = 0.04) with Wray et al (2018). This provided a total of 807,553 individuals (246,363 cases and 561,190 controls) with which to conduct the meta-analysis and further downstream analyses. Results: The genome-wide meta-analysis of the three contributing cohorts revealed a total of 118 independent significant (P < 5 × 10 −8 ) variants associated with depression. The estimate of the heritability on the liability scale was 0.089 (0.003) with genetic correlations identified for 33 other traits, including bipolar disorder, schizophrenia, smoking and triglyceride levels. We identified 269 genes and 14 gene-sets that were significantly associated with depression, including gene-sets involved in synaptic transmission. Discussion: This research presents the results from a relatively well-powered genetic analysis of depression, by com- bining previous studies of the condition. A marked increase in the number of associated variants and genes was observed which will enable additional comprehension of the underlying biological aetiology. The gene-sets identified as significant typically relate to brain function and may ultimately allow us to identify the molecular mechanisms underlying genetic predisposition to depression.


doi: 10.1016/j.euroneuro.2018.08.010

4

DETERMINING THE RELATIONSHIP BETWEEN CANNABIS USE AND MAJOR DEPRESSION IN UK BIOBANK

Karen Hodgson ∗, Saskia Hagenaars , Kirstin Purves , Helena Alexandra Gaspar, Kylie Glanville, Jonathan Coleman, Marta Di Forti, Gerome Breen, Paul O’Reilly, Cathryn Lewis

King’s College London

Background: Cannabis is the most widely used drug worldwide. With many countries engaged in debates about the legalisation of cannabis, it is important to fully understand any potential relationships between drug use and mental health outcomes. Psychosis is often the focus of work in this field, but cannabis use has also been linked with both depression and self-harm behaviours (Hodgson et al 2017, Agrawal et al 2017, Smolkina et al 2017). Here, we take advantage of successful genome-wide association studies for both cannabis use (Stringer et al 2016, Pasman et al 2018) and depression (Wray et al 2018), to examine the relationships between these prevalent phenotypes with much greater statistical power. Specifically, we determine the phenotypic and genetic relationship of cannabis use with depression and self-harm, within the population-based UK Biobank. Methods: In UK Biobank, the mental health questionnaire includes items assessing lifetime history of cannabis use, depression and self-harm. Focusing on unrelated individuals of European ancestry with high quality genotypic data available (n = 126,291), n = 28,282 report a life-time history of cannabis use, n = 30,075 report depression and n = 5,520 report self-harm.



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We examined the phenotypic relationships between these traits. Then, using LD score regression and polygenic risk scoring with recently available summary statistics (Wray et al 2018, Stringer et al 2016), we tested the genetic relationship between phenotypes. Finally using Mendelian Randomi- sation, we test evidence for the causal direction of effects. Results: Within UK Biobank, cannabis use is associated with an increased likelihood of depression (OR = 1.64 95%

CI = 1.59-1.70, p = 1.19 × 10-213), and amongst those with depression, is associated with recurrence of depression (OR = 1.28, 95% CI = 1.22-1.35, p = 3.42 × 10-20). Cannabis use is also associated with an increased likelihood of self- harm (OR = 2.85, 95% CI = 2.69-3.01, p = 3.46 × 10-304).

Using LD score, we observe significant genetic correlations between cannabis use and both depression (rg = 0.283, SE = 0.055, p = 2.00 × 10-7) and self-harm (rg = 0.304, SE = 0.075, p = 5.44 × 10-5). We will also present results using polygenic risk scores calculated from available summary statistics (Wray et al 2018, Stringer et al 2016) to predict both cannabis and depression-related phenotypes.

Finally, we discuss the use of Mendelian Randomisation to probe the nature of the causal pathways linking these phenotypes. Discussion: In this study, we observe phenotypic and genetic associations between cannabis use with both depression and self-harm. Stronger phenotypic relationships are observed for more severe phenotypes of recurrent depression and persistent cannabis use, echoing previous literature.

We note that the observed effect sizes are larger than those reported previously for psychosis-related phenotypes. For example, in a large phenotypic meta-analysis, Moore et al (2007) report OR = 1.41, 95% CI 1.20–1.65 between cannabis use and psychotic outcomes, whilst Verweij et al (2017) calculate a genetic correlation of rg = 0.22 (SE = 0.07) between cannabis use and schizophrenia.

Given the strength of both phenotypic and genetic associations for cannabis use with depression and self-harm, together with the high prevalence of these traits, further research into understanding the causality of these associations is important in the light of changing attitudes to cannabis use.


doi: 10.1016/j.euroneuro.2018.08.011

5

THE ROLE OF NEURODEVELOPMENTAL COPY NUM- BER VARIANTS IN DEPRESSION

Kimberley Kendall 1 , ∗, Elliott Rees 2 , Matthew Bracher-Smith 1 , Lucy Riglin 3 , Stanley Zammit 3 , Michael O’Donovan 3 , Michael Owen 1 , Ian Jones 3 , George Kirov 3 , James T.R. Walters 3

1 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University 2 MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neuro- sciences, Cardiff University 3 Cardiff University

Background: Large, rare copy number variants (CNVs) are associated with neurodevelopmental disorders but it is unclear whether such CNVs also contribute to the risk of depression. A substantial proportion of the CNV enrichment in schizophrenia is explained by CNVs associated with neurodevelopmental disorders. Depression shares genetic risk with schizophrenia and frequently occurs comorbid with neurodevelopmental disorders. We, therefore, aimed to examine the relationship between CNVs and depression in the UK Biobank sample. We hypothesised that (i) neurodevelopmental CNVs are associated with increased rates of depression and, (ii) after excluding CNVs relevant to the primary hypothesis, there is a residual unexplained CNV burden. Methods: We called CNVs using PennCNV-Affy in 455,913 individuals from UK Biobank who reported white British or Irish ethnicity (37-73 years, 54% female) and annotated 53 CNVs associated with neurodevelopmental disorders. We defined depression as a self-report of having received a depression diagnosis (24,575 cases, 5.87%) and then repeated our analyses in two alternative depression variables – self- reported depression and antidepressant prescription at visit 1 (15,748 cases, 3.76%) and hospital discharge diagnosis of depression (11,395 cases, 2.94%). In addition, we used data gathered from an online follow-up mental health questionnaire to further characterise the depression phenotype. Results: The group of 53 neurodevelopmental CNVs was associated with self-reported depression (OR 1.36, 95% CI 1.22 – 1.51, p 1.61 × 10-8) and this result was consistent when repeated in the two alternative depression variables. The association was partially explained by social deprivation, a known risk factor for depression. There was a nominally significant association between carrier status of CNVs greater than or equal to 500kb and depression, but this result did not survive Bonferroni correction for 5 tests. Exploratory analyses found neurodevelopmental CNVs to have a stronger effect on risk of depression in females (OR 1.48, 95% CI 1.30 - 1.69, p 3.07 × 10-9) compared to males (OR 1.16, 95% CI 0.96 – 1.39, p 0.12) for the primary depression phenotype. Discussion: We report the largest study of CNVs in depression to date and the first to robustly demonstrate association between CNVs and depression. Four CNVs were individually associated with depression at levels of significance which survived Bonferroni correction for the 53 CNVs tested (1q21.1 duplication, PWS duplication, 16p13.11 deletion, 16p11.2 duplication). In addition, our study identifies a link between neurodevelopmental CNVs and social deprivation, a potential target that may have a beneficial impact on rates of depression in CNV carriers. Altogether, these findings indicate that neurodevelopmental CNVs may offer important insights into the causes of and pathways to depression.


doi: 10.1016/j.euroneuro.2018.08.012



4 Abstracts

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6

A MULTI-TISSUE NETWORK-BASED ANALYSIS OF RISK

GENES FOR MAJOR DEPRESSION

Zachary Gerring 1 , ∗, Eric Gamazon 2 , 23andMe Research Team, Eske Derks 3

1 QIMR Berghofer Medical Research Institute 2 Vanderbilt Genetics Institute, Vanderbilt University 3 QIMR Berghofer

Background: Major Depression (MD) is a highly disabling mental health disorder that accounts for a sizable proportion of the global burden of disease. MD has a multifacto- rial molecular background, driven in part by a highly polygenic mode of inheritance. A recent genome-wide association study (GWAS) of 130,664 MD cases and 330,470 controls identified 44 single nucleotide polymorphism (SNP) loci associated with the disorder (Wray et al., 2018). Detailed functional studies showed SNP loci are enriched in multiple brain tissues, but not peripheral tissues, and contain SNPs that regulate the expression of multiple genes with putative roles in neurite growth and synaptic plasticity. How- ever, the functional gene mechanisms underlying the statistical associations remain unknown. Pathogenic mechanisms underlying MD risk are hypothesised to involve hundreds of genes across multiple loci that interact with each other in large co-expression networks. Thus, the study of multi- tissue gene co-expression networks may identify functional genes and processes in MD. Methods: Normalised RNA-Seq data for 13 brain tissues and whole blood were downloaded from the Genotype-Tissue Expression (GTEx) study portal (version 7). MD GWAS summary statistics for 10,468,942 autosomal SNPs (Wray et al., 2018) were provided by the Psychiatric Genomics Consor- tium (PGC). First, we built unsigned gene co-expression networks for 13 brain tissues and whole blood using a weighted gene co-expression network analysis (WGCNA). Each tissue- specific gene co-expression network was divided into modules (groups) of correlated genes using hierarchical clustering. Second, we characterised biological pathways and processes in gene modules using g:Profiler. Third, we used MAGMA (v1.06) to assign MD SNP associations to genes (risk genes) and subsequently test for the enrichment of risk genes within the tissue-specific gene modules. Finally, we assessed the preservation (replication) of gene modules across brain tissues and whole blood using the module Preservation function of WGCNA. Results: Hierarchical clustering of tissue-specific gene co- expression networks formed highly organised gene modules with enrichments in specific biological processes. Gene- based analyses identified 137 genes associated with MD

risk (P < 2.77 × 10-7). MD risk genes were enriched in a single module in 8 out of 13 brain tissues, with the strongest association in anterior cingulate cortex (corrected P = 1.00 × 10-4). No enrichment of risk genes was observed in brain cerebellar tissues or whole blood. MD

gene modules contained genes involved in synaptic signalling (P < 4.07 × 10-66) and nervous system development (P = 1.58 × 10-51). Gene co-expression modules were strongly preserved across brain tissues, with the exception

of brain cerebellar tissues, while weak preservation of modules was observed between brain and whole blood. Discussion: Genes are known to interact with one another in highly organised networks. We show the network-based analysis of multi-tissue gene expression data and the subsequent integration of GWAS summary statistics for MD provides a new and biologically meaningful approach for the interpretation of disease-associated loci. The identification of networks and biological processes can be further integrated with drug repositioning strategies, and may thereby identify molecular targets for the development of diagnostic tests and precision treatment strategies for MD.


doi: 10.1016/j.euroneuro.2018.08.013

Oral Session: Schizophrenia

1:30 p.m. - 3:00 p.m.

7

SCHIZOPHRENIA POLYGENIC RISK SCORE ANALYSIS IN 22Q11.2 DELETION SYNDROME

Thomas Monfeuga 1 , Michael P. Epstein 2 , Aaron M. Holleman 2 , Isabelle Cleynen 3 , Henry Johnston 2 , Yingjie Zhao 4 , Donna M. McDonald-McGinn 5 , Raquel E. Gur 6 , Stephen T. Warren 2 , Joris Vermeesch 3 , Beverly S. Emanuel 5 , Bernice E. Morrow

4 , Anne S. Bassett 7 , Nigel Williams 1 , ∗

1 Cardiff University 2 Emory University 3 KU Leuven

4 Albert Einstein College of Medicine 5 Children’s Hospital of Philadelphia 6 University of Pennsylvania 7 University of Toronto

Background: It is now well established that individuals with 22q11.2DS are at an increased risk of psychosis, however as only ∼40% of adults with 22q11.2DS develop psychotic symptoms, the increased risk is clearly not fully penetrant. Multi- ple hypotheses have been proposed for this incomplete penetrance, including breakpoint heterogeneity, environmental contributions, and an effect of other genetic variants either within the remaining allele and/or outside the 22q11.2 locus. Polygenic Risk Score (PRS) analysis allows the risk conferred by common genetic variants associated with a disorder to be considered in aggregate. Studies of large cohorts of idiopathic schizophrenia have demonstrated that cases have a significantly greater PRS composed of schizophrenia risk alleles than unaffected controls. In this study we used analysed schizophrenia PRS to investigate the effect of common genetic variants in the aetiology of psychosis in 22q11.2DS patients. Methods: Whole-genome sequencing (WGS) and genome- wide genotyping array data was provided by the 22q11.2DS Brain and Behaviour Consortium. WGS data was available for 435 samples with known psychosis status (214 with psychosis, 221 without), of which 374 had also been genotyped


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using Affymetrix arrays (178 with psychosis, 196 without). We calculated PRS in both datasets with the software PRSice (v2), using the schizophrenia GWAS of the Psychiatric Ge- nomics Consortium 2 (PGC2) as the discovery sample. Re- gression analyses were performed to test if psychosis status could be discriminated by PRS, after correcting for gender and population stratification. Results: Using the genotypes from WGS data only (n = 435), the 22q11.2DS individuals with psychosis had a significantly higher schizophrenia PRS than 22q11.2DS patients with no known history of psychosis (p < 10e-6, Nagelkerke’s R2 ∼0.8 at SNP p-value threshold pT = 0.01. Conducting PRS analysis SNP array genotypes for the 374 samples generated analogous results, confirming the robustness of the method. Discussion: This study has used the largest available sample of 22q11.2DS patients to demonstrate that 22q11.2DS patients with psychosis have a significantly increased PRS composed of common schizophrenia risk alleles. This suggests that genetic variants outside of the 22q11.2 deleted region also have an important role in mediating the increased risk to schizophrenia in 22q11.2DS. Current work aims at modelling whether the schizophrenia liability in 22q11.2DS patients is due to the deletion and common variants additively. A better understanding of the molecular mechanisms leading to psychiatric symptoms in this syndrome will help early diagnostic and intervention.

Presented on behalf of the Genomics Group, and the In- ternational 22q11.2 Deletion Syndrome Brain Behavior Con- sortium.


doi: 10.1016/j.euroneuro.2018.08.014

8

PREVALENCE OF CLINICALLY WELL-ESTABLISHED

CNV SYNDROMES WITHIN A TORONTO SCHIZOPHRENIA

PATIENT POPULATION

Venuja Sriretnakumar 1 , ∗, Brianna Barsanti-Innes 2 , Syed M. Wasim

3 , Clement Zai 1 , James L. Kennedy 1 , Joyce So 3

1 Centre for Addiction and Mental Health

2 University of Toronto 3 University Health Network, Mount Sinai Hospital

Background: Many rare genetic syndromes are known to phenotypically manifest with psychiatric symptoms that can be indistinguishable from primary psychiatric disorders. While the majority of ongoing research in psychiatric genetics has been dedicated to the identification and characterization of genes involved in primary psychiatric disorders, there has been little research to determine the extent to which rare genetic variants/syndromes contribute to the overall psychiatric disease load. In our study, we aim

to investigate the prevalence and role of clinically well- characterized pathogenic copy number variant (CNV) syndromes within a schizophrenia patient population.

Methods: A total of 466 schizophrenia patients recruited at the Centre for Addiction and Mental Health (CAMH) (Toronto, Canada) were sequenced using the Affymetrix SNP Array 6. 0 assay. CNVs were called using two algorithms, including the Canary Software and PennCNV. Only overlapping CNV calls from both algorithms were used for further analysis to increase the confidence of each CNV called. Currently used clinical CNV size thresholds of 200kbp and 500kbp were used to filter all called deletions and duplications, respectively. All CNVs were individually assessed to characterize the presence of known, clinically well-characterized CNV syndromes with the use of the UCSC Genome browser, DECI- PHER GRCh37, and GeneReviews® database. Results: A total of 861 deletions and 171 duplications were called on 348 schizophrenia patients, which passed quality control and size threshold filtrations. Upon CNV analysis, a total of 14 patients were identified with a total of 18 CNVs associated with previously recognized CNV syndromes. A further 30 patients were identified to have one or more genes of interest associated with brain-related phenotypes within 37 CNVs. Taken together, in our SCZ cohort, 4.02% (14/348) of patients were identified to have known pathogenic CNVs and 8.62% (30/348) have candidate brain-related CNVs, for a combined total of 12.64%

(44/348) of SCZ patients with CNVs of interest. CNVs associated with known syndromes include: del 1q21.1, del 16p11.2, del 22q11.2, del 2p16.3, del 5q35.3, del 15q11.1- q12, del 16p13.3, del 16p13.11-p12.3, del 17p12, and dup Xp22.12. All of the detected CNVs within these syndrome regions are associated with one or more of the following neu- rocognitive phenotypes: schizophrenia, autism, intellectual disability, developmental delay, brain anomalies, learning disabilities, behavioural abnormalities, and/or seizures Discussion: We observed a greater than expected number of syndromic CNVs amongst the schizophrenia cohort (14/348, 4.02%), particularly CNVs already hypothesized or known to associated with neurodevelopmental disorders (30/348, 8.62%). Screening for these rare genetic disorders could lead to better understanding of the pathophysiology of psychiatric disorders, as well as the prevalence of these syndromic CNVs within various psychiatric population subtypes. Cor- rectly identifying syndromic CNVs within psychiatric populations can improve patient prognosis to other non-psychiatric systemic symptoms, and phenotypic characterization of CNV carriers versus non-carriers will provide a baseline diagnosis criteria for clinicians. Further analyses will be undertaken to define specific phenotypes associated with their respective CNVs to better characterize potential genetic effects on the phenotypic presentation of SCZ patients.


doi: 10.1016/j.euroneuro.2018.08.015



6 Abstracts

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9

IDENTICAL BY DESCENT SEGMENTS ASSOCIATES WITH

SCHIZOPHRENIA

Anders Rosengren 1 , ∗, Alicia Martin 2 , Vivek Appadurai 3 , Thomas Als 4 , iPSYCH-BROAD Working Group 1 , Alfonso Buil 5 , Thomas Werge 6

1 Research Institute of Biological Psychiatry, PC Sct Hans, iPSYCH

2 Massachusetts General Hospital 3 Research Institute of Biological Psychiatry 4 iPSYCH, iSEQ, Aarhus University 5 Institute of Biological Psychiatry 6 Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services - Copenhagen

Background: Rare genetic variants are thought to contribute to the risk of mental disorders including Schizophre- nia. These are however difficult to detect. Traditional methods such as GWAS are now routinely run on even very large samples, but they may still lack the power to detect rare variants, or may even lack the variants themselves as in these are only available as a Priori selected or subsequently imputed into the data using some non exhaustive reference.

In this study we use clusters of Identical By Descent Segments. Leveraging only genotyped common SNPS to approach rare variants. Methods: Our sample consist of the 70500 people of Euro- pean descent in the Danish iPSYCH Study.

We use the refined IBD method of Beagle to detect all autosomal pairwise IBD segment of minimum length 2cM. From

these we form clusters consisting of 3 to hundreds of people all sharing one specific IBD segment using the Efficient Multiple-IBD algorithm, EMI. We then apply the framework of fast-lmm and LEAP to do association on each individual cluster. We associated clusters and Schizophrenia in a subset consisting of roughly 3000 cases and 20000 controls. We account for their relatedness as a random effect and include age and gender as covariates. Results: We identify 34 clusters that exceed our Bonferroni threshold of 1e-8 with an average length of 1.3cM and ranging from 20 to 167 persons corresponding to MAFs of 0.1%- 0.01%

The clusters cover 10 distinct genomic regions each cov- ering from one to tens of genes.

These include candidate genes such as NRXN3 and ITSN2, but also genes apparently not already implicated.

The combined set of genes is enriched for Schizophrenia genes as listed by the GWAS Catalog. Discussion: Here we do a direct test of individual IBD clusters and are able to detect clusters highly associated with Schizophrenia.

Trying to finemap any single causal variant within in the associated IBD clusters segment using the subset of samples also Whole Exome Sequenced have so far been unsuccessful. Since no population rare variant of high cluster frequency has been shown to itself be associated with Schizophrenia in the cohort adjusted for multiple testing. Hopefully this will possible as a more specific and detailed sample reference

is obtained either directly for the clusters or as imputation reference.

The validity of the method still needs further testing, and we approach this on various levels.

We also detect IBD clusters highly associated with ADHD, ID, Bipolar, Anorexia, recurrent depression and Autism.


doi: 10.1016/j.euroneuro.2018.08.016

10

FIRST GENOME-WIDE ASSOCIATION STUDY OF

SCHIZOPHRENIA IN AN INDIAN POPULATION REVEALS A

NOVEL SUSCEPTIBILITY LOCUS

Sathish Periyasamy 1 , Sujit John 2 , Raman Padmavati 2 , Preeti Rajendren 2 , Priyadarshini Thirunavukkarasu 2 , Jacob Gratten 3 , Elizabeth Holliday 4 , Andrew Bakshi 5 , Lynn Jorde 6 , Matthew Brown 7 , Naomi Wray 3 , Rachel Suetani 8 , Jean Giacomotto 8 , Rangaswamy Thara 2 , Bryan Mowry 8 , ∗

1 Queensland Brain Institute 2 Schizophrenia Research Foundation

3 University of Queensland

4 University of Newcastle 5 Peter MacCallum Cancer Centre 6 University of Utah

7 Queensland University of Technology 8 Queensland Brain Institute, The University of Queensland

Background: Polygenic variation predisposes to schizophrenia. Genome-wide association studies (GWAS) mostly in Eu- ropean populations have identified over 100 schizophrenia- associated loci. We report the first schizophrenia GWAS in a unique Indian population. Methods: Our sample of 3092 Tamil ancestry individuals from Chennai, Tamil Nadu consists of schizophrenia families and unrelated cases and controls (1321 affected – 816 case-control, 505 family; 1771 controls – 900 unrelated case-control, 871 -family). The GWAS was conducted using Genome-wide Complex Trait Analysis. Polygenic risk scores, heritability analysis, gene-set analysis, runs of homozygosity, copy number variant analysis, cross-trait genetic correlation for SCZ between India and Europe, eQTL analyses, SMR analysis, pathway analyses, and network connectivity- enrichment analysis were conducted to further investigate this dataset. We investigated the regulatory effects of our top SNP, highlighting one gene for functional testing. Results: Our sample was characterized by a uniform ethnicity ( > 97% Tamil), a degree of inbreeding (F_HET mean = 0.026 [-0.02 to 0.02]) and a homogeneous schizophrenia phenotype with very low rates of alcohol and illicit substance abuse. We observed a genome-wide significant association between schizophrenia and a chromosome 8q locus (OR: 1.06; P = 4.35 × 10-8), with support from the schizophrenia PGC2 (OR: 1.03; PGC P = 7.56 × 10-4). eQTL analysis revealed that this locus was regulating a single gene downstream of our top SNP. PGC2-generated genetic profile scores predicted schizophrenia in our sample (maximum R2


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(observed scale) = 0.05; P = 1 × 10-37), as did the frequency of runs of homozygosity (FROH) burden (P = 3.357 × 10-5). For network connectivity-enrichment analysis of brain regions the putamen generated the highest enrichment score, similar to what was observed in the PGC2 schizophrenia dataset. For the MHC locus we observed no evidence for replication (P < 0.05) in Tamil Nadu samples; for other PGC

genome-wide significant loci, 31/97 PGC regions replicated (P-value < 0.05) in our TN dataset, with the chromosome 2q32.3 locus (rs59979824; ranked 66 in PGC) having the best P-value (0.0007298) in our dataset. We investigated gene expression at our top locus and observed that only one gene showed a dose-response association with our index SNP. Pre- liminary zebrafish data further suggests that partial loss-of- function of this gene leads to abnormal brain development. Discussion: This first schizophrenia GWAS in an Indian population –characterized by features which are advantageous for genome-wide analyses - has identified a genome-wide significant locus that has also attracted PGC2 support. Fur- ther we provide evidence for genome-wide sharing of common genetic variation for schizophrenia between Europe and India. Moreover, the frequency of runs of homozygosity also predicted schizophrenia in our Indian population.

Larger Indian samples than those currently available will be required to replicate the associations identified here and to discover more population-specific variants associated with disease.


doi: 10.1016/j.euroneuro.2018.08.017

11

GENE-SETS GOING FORWARD: THE ROLE OF GENE- SETS IN THE CONTEXT OF THE OMNIGENIC MODEL OF

SCHIZOPHRENIA

Alexandros Rammos 1 , ∗, Lara A. Neira Gonzalez 2 , Schizophrenia Working Group 3 , Daniel R. Weinberger 4 , Kevin J. Mitchell 1 , Kristin K. Nicodemus 2

1 Trinity College Dublin


3 Psychiatric Genomics Consortium

4 Lieber Institute for Brain Development

Background: The genetic architecture of schizophrenia involves both rare and common variation in many genes. Re- cent theories developed by the Pritchard group (1) have suggested an even broader omnigenic background for the disorder. Within that context, genes can be split into core and peripheral genes. Core genes may be identified by rare variant or Genome-Wide Association studies and can be used as a basis to identify peripheral genes, which work in common networks with them.

The aim of this study was to quantitatively assess whether a number of experimentally-derived gene-sets based on core genes would make a larger than expected contribution to polygenic risk, as proposed by the omnigenic model. The main hypothesis of this study was that sets based on schizophrenia core genes would be associated with schizophrenia at a greater level than expected under an equally distributed omnigenic effect. Random genic and non-genic sets, as well as two sets associated with cardiac disease and cancer, were selected as comparisons to the schizophrenia core gene sets. Methods: Polygenic profile scoring was performed with SNPs derived from six recently validated putative core gene- sets centred on four schizophrenia genes identified either by rare variant or GWAS (TCF4, FMRP, miR-137, CHD8) in the data available for secondary analysis from the PGC2 schizophrenia case-control cohort (N = 29,125 cases and 34,836 controls). A leave-one-out polygenic risk score analysis was implemented across the 39 different studies, and combination of p-values from independent ‘left out’ sets was performed using Stouffer’s z test. Results: We observed a robust polygenic signal using SNPs from each of the TCF4 (p-value = 1.18 × 10- 46), FMRP (p-value = 1.66 × 10-33), miR-137 upregulated (p-value = 3.28 × 10-23) and CHD8 down-regulated (p- value = 1.91 × 10-33) gene-sets. Additional analyses using randomly selected sets of genic and non-genic SNPs of the same size showed a consistent floor effect in amount of variance explained, more pronounced when using genic SNPs, which was also the case when non-schizophrenia specific sets were used. Discussion: Our findings indicated a significant association between the gene-sets based on putative core genes and schizophrenia. Additionally, we report the existence of a floor effect, linked with the underlying omnigenic model, which needs to be taken into consideration when interpret- ing the results of a polygenic score study on complex traits. Finally, the method developed allows a quantification of the contribution of specified core gene-sets as well as potentially identifying peripheral genes, and should be practically applicable in the selection of sets of SNPs that yield the greatest signal to noise ratio in the construction of a polygenic score.


Reference

Boyle, EA, et al., 2017. Cell 169 (7), 1177–1186.

doi: 10.1016/j.euroneuro.2018.08.018



8 Abstracts

ARTICLE IN PRESS


12

COMPARATIVE GENETIC ARCHITECTURES OF

SCHIZOPHRENIA IN EAST ASIAN AND EUROPEAN POPU- LATIONS

Max Lam

1 , ∗, Chia-Yen Chen 2 , Shengyin Qin 3 , Pak Sham

4 , Nakao Iwata 5 , Kyung Hong 6 , Sibylle Schwab 7 , Weihua Yue 8 , Ming Tsuang 9 , Jianjun Liu 10 , Xiancang Ma 11 , René Kahn 12 , Yongyong Shi 13 , Hailiang Huang 2 , PGC-EAS Schizophrenia Working Group

1 Institute of Mental Health

2 Massachusetts General Hospital 3 Collaborative Innovation Center, Jining Medical University 4 Brain and Cognitive Sciences, Centre for Genomic Sci- ences, University of Hong Kong 5 Fujita Health University School of Medicine 6 Sungkyunkwan University School of Medicine, Samsung Medical Center 7 Centre for Medical and Molecular Bioscience, Illawarra Health and Medical Research Institute, University of Wol- longong 8 National Clinical Research Center for Mental Disorders, Ministry of Health, Peking University 9 University of California, San Diego 10 Genome Institute of Singapore 11 Clinical Research Center for Mental Disease of Shaanxi Province, The First Affiliated Hospital of Xi’an Jiaotong University 12 Icahn School of Medicine at Mount Sinai 13 The First Teaching Hospital of Xinjiang Medical University

Background: Schizophrenia is a severe psychiatric disorder with a lifetime risk of approximately 1% worldwide. Most large-scale schizophrenia genetic studies have studied individuals of primarily European ancestry missing out on important biological insights. Here, we compile a sizable schizophrenia sample of East Asian descent to further elucidate the genetic architecture of the illness. Methods: 22,778 schizophrenia cases and 35,362 controls from samples from Singapore, Japan, Indonesia, Korea, Hong Kong, Taiwan, and mainland China were compiled. European samples (56,418 cases and 78,818 controls) were then included for comparison. Fixed-effect inverse variance meta-analysis was used to combine GWAS summary statistics first from the EAS samples and then with the EUR samples. Downstream analyses included genetic correlation, partitioned heritability, gene-set, natural selection, trans- ethnicity finemapping and polygenic risk score conducted to draw further insights from the genome-wide data. Results: 21 significant GWAS associations at 19 loci were identified within the EAS samples. 15 of these associations have marked allele frequency deviations from EUR MAF. Ge- netic correlations and gene set analysis were largely consistent with EUR based schizophrenia studies. Population differentiation did not appear to drive the effects in these observed loci. When meta-analyzed with EUR schizophrenia, 208 independent variants across 176 loci were identified, 53 loci were novel. EUR-EAS trans-ethnicity finemapping improved identification of causal variants compared to EUR alone. The median size of the 95% credible set decreased

from 57 to 34, the number of associations mapped to < = 5 variants increased from 8 to 15, the number of associations mapped to a single variant with greater than 50% probability increased from 16 to 20, and median size of the genomic regions the associations mapped decreased from 277 Kb to 111 Kb. Polygenic risk score modeling demonstrated that variance explained across various p-value thresholds provides a proxy for the signal-to-noise ratio, which differs by training population - relative to EUR training data, variants from the EAS training data with more permissive p-values improve EAS prediction accuracy, indicating that larger EAS studies will be needed to explain similar case/control variance as currently explained in EUR individuals. We dd not find significant EAS association in the MHC. The key SNP rs13194504 reported in EUR, has < 1% MAF in EAS, and EUR-specific LD

patterns within MHC region might account for signals reported in previous reports. Discussion: The large-scale EAS sample allowed the empirical evaluation of congruence of the genetic architecture of schizophrenia between EAS and EUR. In spite of cross- population genetic correlation indistinguishable from 1, we found that polygenic risk models trained in one population have reduced performance in the other population due to differential allele frequency distributions and LD-structure. This highlights the importance of including all major ances- tral groups in genomic studies both as a strategy to improve power to find disease associations and to ensure the findings have maximum relevance for all populations.


doi: 10.1016/j.euroneuro.2018.08.019

Oral Session: Neurogenetics & Ethics

1:30 p.m. - 3:00 p.m.

13

ENGAGING MENTAL HEALTH SERVICE USERS AND

THEIR FAMILIES IN THE GENETICS TESTING IN MENTAL

HEALTH AND MENTAL HEALTH IN GENETIC TESTING

David Crepaz-Keay ∗

Mental Health Foundation

Background: Psychiatric genetic testing is often thought of as an objective endeavour that increases our knowledge and understanding. This presentation will challenge the notion of objective neutrality and explore the following themes:

• Current understanding of genetic testing among mental health service users and their families

• Common questions raised about genetics and genetic testing as it relates to mental health

• Ethical and psychological impact of genetic testing and ways of enabling people to make informed decisions pre and post genetic testing

Methods: The presentation will explore the benefits and risks associated with genetic testing from the perspectives of patients, families and other lay interests.


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In order to explore these potential impacts of genetic testing, I will draw analogies with experiences of responses to existing diagnostic processes in psychiatry and how these have changed alongside other social changes, using existing literature and previous research. I will also draw on discus- sions with service user academics and activists and family supporters. Results: The positive opportunities offered for accurately identifying and supporting people who are at elevated risk need to be balanced with the potential stigma, including self-stigma and discrimination, both direct and indirect ex- perienced by people with a psychiatric diagnosis, their families and broader communities. Discussion: The presentation will consider the broader public health and public mental implications of genetic testing and how information is related to testing is shared and used. This will include ethical issues around resource allocation and an exploration of the benefits and risks of measures de- signed to prevent conditions that may develop, where those interventions have risks associated with them and hence the importance of assessing people’s values and understanding how the might be applied to choices about genetic testing, data sharing and advanced decision-making.


doi: 10.1016/j.euroneuro.2018.08.020

14

GENETIC META-ANALYSIS IDENTIFIES 9 NOVEL LOCI AND FUNCTIONAL PATHWAYS FOR ALZHEIMER’S DIS- EASE RISK

Danielle Posthuma 1 , ∗, Iris Jansen 1 , Jeanne Savage 1 , Kyoko Watanabe 1 , Julien Bryois 2 , Dylan M. Williams 2 , Wiesje M. van der Flier 3 , Richard Dobson 4 , Lea K. Davis 5 , Hreinn Stefansson 6 , Kari Stefansson 6 , Nancy L. Pedersen 2 , Stephan Ripke 7 , Ole A. Andreassen 8 , PGC-Alzheimer Workgroup

1 VU Amsterdam

2 Karolinska Institutet 3 VU University Medical Center 4 King’s College London

5 Vanderbilt University Medical Center 6 deCODE Genetics/Amgen

7 Broad Institute of MIT and Harvard

8 University of Oslo

Background: Late onset Alzheimer’s disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls) aiming to further characterize the genetic architecture of AD. Methods: We meta-analysed genetically informative data from AD cases (N = 24,087) and controls (N = 55,058) and 47,793 by-proxy cases/328,320 proxy controls. AD-by-proxy

was determined based on parental AD status weighted by parental age. We conducted extensive functional annotation of GWAS results, including positional, eQTL and chromatin interaction mapping. We conducted pathway analyses, tissue enrichment and cell-type analyses as well as genetic correlation analyses. Mendelian randomization was performed to detect direct effects between AD and other traits. Results: Results from the AD-by-proxy status and clinical AD

showed strong genetic correlation (rg = 0.81). Genetic meta- analysis identified 29 risk loci, of which 9 novel, and implicated 215 potential causative genes. Independent replication further supported these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. Discussion: These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new

drug development.


doi: 10.1016/j.euroneuro.2018.08.021

15

POSITIVE DOSE RESPONSE OF THE 1Q21.1 DISTAL

CNV ON ICV THROUGH AN EFFECT ON CORTICAL

SURFACE AREA

Ida Sonderby 1 , ∗, Omar Gustafsson 2 , Dennis van der Meer 3 , Nhat Trung Doan 4 , Derrek Hibar 5 , Lars Tjelta Westlye 4 , Srdjan Djurovic 6 , Sébastien Jacquemont 7 , Paul Thompson 8 , Ole Andreassen 9 , ENIGMA-CNV Working Group

1 NORMENT, KG Jebsen Centre for Psychosis Research

2 deCODE genetics 3 Norwegian Centre for Mental Disorders Research

4 NORMENT

5 Janssen Research & Development, LLC

6 Oslo University Hospital 7 University of Montreal 8 Imaging Genetics Center, Keck School of Medicine, Univer- sity of Southern California 9 University of Oslo

Background: The 1q21.1 CNV predisposes to e.g. delayed development, schizophrenia, congenital heart defects and obesity and there is an increased prevalence of macro- cephaly in duplications carriers and microcephaly in deletion carriers. Frequency of 1q21.1 in the UK biobank: 0.027 % (deletion) and 0.044 % (duplication). Methods: Structural T1-MRI data from 16,078 subjects from 34 research samples world-wide as well as data from the UK biobank were analyzed (FreeSurfer) and CNVs called (PennCNV). List of ENIGMA-CNV datasets:



10 Abstracts

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http://enigma.ini.usc.edu/ongoing/enigma-cnv/enigma- cnv- co- authors/ . Brain measures were normalized by an inverse normalization of the residual of a linear regression correcting for age, age squared, gender and scanner. Dose response (deletion = 1, non-carrier = 2, duplication = 3) was analyzed in a linear model on the normalized brain values. Results below 0.005 (p = 0.05/10 regions) were considered significant. Results: Subcortical volumes, cortical area and thick- ness and ICV of deletion (n = 25) and duplication carriers (n = 16) of the 1q21.1 CNV were compared to non-carriers (n = 19,711). We found a positive dose-response effects of copy total surface area ( β= 1.72, P = 3.0 ∗10-9) and intracra- nial volume ( β= 1.49, P = 1.1 ∗10-21). In addition, a small, significant negative dose response on caudate was observed ( β= -0.45, P = 0.0045). Discussion: These findings suggest that the mechanism be- hind the head circumference change in 1q21.1 distal CNV carriers is an increase in cortical surface area and may indicate an effect on early development of the (dorsal) telen- cephalon. This may explain the various biological and neurodevelopmental phenomena in 1q21.1 distal carriers and underlines the value of large-scale collaboration such as ENIGMA-CNV for studies of rare genetic variants implicated in brain pathology.


doi: 10.1016/j.euroneuro.2018.08.022

16

GENOME-WIDE META-ANALYSES REVEAL NOVEL LOCI FOR VERBAL SHORT-TERM MEMORY AND LEARNING AND

SHOW GENETIC CORRELATION WITH SCHIZOPHRENIA, ALZHEIMER’S DISEASE, AND TYPE 2 DIABETES

Jari Lahti 1 , ∗, Samuli Tuominen 1 , Qiong Yang 2 , Sarah Medland 3 , Jodie Painter 3 , Zachary Gerring 3 , CHARGE Consortium Cognitive Working Group, Jan Bressler 4 , Thomas Mosley Jr. 5 , Cornelia van Duijn 6 , Lenore Launer 7 , Stephanie Debette 8 , Ian Deary 9 , Sudha Seshadri 2 , Katri Räikkönen 1

1 University of Helsinki 2 Boston University 3 QIMR Berghofer Medical Research Institute 4 University of Texas 5 University of Mississippi Medical Center 6 Erasmus Medical Center University Medical Center 7 National Institutes of Health

8 University of Bordeaux 9 University of Edinburgh

Background: Understanding the biological basis of memory functions may help combat neurodegenerative disorders. Despite moderate heritability in twin studies, consistent findings on the molecular genomic basis of memory functions are scarce. This reflects low power in most of the studies. In a large genome-wide association study (GWAS) based on the CHARGE consortium we investigated common

genetic variants associated with short-term verbal memory and learning in adults. Methods: We conducted meta-analyses for verbal memory immediate recall test scores and verbal learning scores in a discovery sample across 27 cohorts (N = 44,874) and 22 cohorts (N = 28,909), respectively. Respective sample sizes of the replication samples were N = 8763 and N = 3853. All participants were adults of European descent and were free of dementia and stroke. Results: For verbal memory immediate recall test scores, we observed a genome-wide significant signal in an intron of CDH18 and for verbal learning, we observed a signal peak in the 3p21.1 region with a large LD block in/nearNT5DC2, STAB1, ITIH1, ITIH4, and PBRM1. For both phenotypes, we also observed a signal nearAPOC1. Associations of lead SNPs in all regions replicated (p < .05). MAGMA gene-based analyses implicated an additional nine genes for verbal learning (CALN1, TOMM40, SMIM4, NEK4, GNL3, AGXT2, MUSTN1, GLT8D1, and ITIH3). Several SNPs in the 3p21.1 region are eQTLs for POC1A, GNL3, GLYCTK, DUSP7, ITIH4, PPM1M, and GLT8D1 in brain tissues in the GTeX or Braineac databases. S- PrediXcan transcriptome-wide analyses showed that POC1A expression in the putamen was associated with lower verbal learning scores. Finally, we show with LDscore regression a negative genetic correlation of schizophrenia and Type 2 Diabetes (T2D) to both memory traits; a negative genetic correlation with anxiety and neuroticism for verbal immediate recall; and with Alzheimer’s disease (AD) for verbal learning. Discussion: We showed novel genomic regions in cadherin 18 (CDH18) and 3p21.1 that associated with verbal short- term memory and learning, respectively, and we replicated a previously reported finding in APOC1. Cadherins are crucial in synaptic plasticity and the 3p21.1 region has been implicated in psychiatric disorders in previous studies. Our results may also partly explain phenotypic links between memory and psychiatric disorders, T2D, and AD.


doi: 10.1016/j.euroneuro.2018.08.023

17

AN EPIGENOME-WIDE ASSOCIATION STUDY (EWAS) OF ALCOHOL CONSUMPTION REVEALS DNA METHYLA- TION SIGNATURES OF ALCOHOL USE

Toni Clarke 1 , ∗, Stewart W. Morris 2 , Rosie Walker 1 , Heather Whalley 1 , Kathryn Evans 1 , Andrew McIntosh 1


2 Institute of Genetics and Molecular Medicine, University of Edinburgh

Background: Genetic studies of alcohol consumption have revealed several loci that are robustly associated with alcohol consumption. Despite this, the proportion of variance in alcohol consumption that is explained by SNP genetic effects is low. Recent epigenetic studies have shown that around 12% of the variance in alcohol consumption can be explained by patterns of CpG methylation across the

http://enigma.ini.usc.edu/ongoing/enigma-cnv/enigma-cnv-co-authors/



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genome; a remarkable advance on the 0.6% of variance explained by SNP genetic effects using polygenic risk scores (McCartney et al, 2018). Methods: We performed an EWAS study of alcohol consumption in 5101 individuals in Generation Scotland: the Scot- tish Family Health Study. Using the Infinum MethylationEPIC

BeadChip 860,926 CpG probes were assessed for their association with alcohol consumption in current drinkers. Results: Significant differential methylation was observed at several loci including the SLC7A11 gene. This encodes a sodium-independent cystine-glutamate antiporter protein that is involved in regulating extra-synaptic receptors and modulating glutamate signalling. Interestingly, SLC7A11 is implicated in several neurodegenerative disorders and has a mechanistic role in cocaine-withdrawal in animal models. Discussion: Exposure to alcohol leaves an epigenetic signature across the genome which can act as a biomarker of heavy consumption. Furthermore, studying these epigenetic marks may help to identify novel genes and pathways involved in the biological response to alcohol consumption.


doi: 10.1016/j.euroneuro.2018.08.024

18

THE EFFECT OF CARRYING SCHIZOPHRENIA PENE- TRANT CNVS ON SUBCORTICAL BRAIN ANATOMY

Xavier Caseras 1 , ∗, Anthony Warland 2 , Kimberley Kendall 3 , Elliott Rees 4 , George Kirov 5

1 Cardiff University School of Medicine 2 MRC Centre for Neuropsychiatric Genetics and Genomics 3 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University 4 MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neuro- sciences, Cardiff University 5 Cardiff University

Background: Recent imaging research from large consortia has highlighted the existence of brain anatomical differences between schizophrenia patients and healthy controls. Patients have reduced hippocampus, thalamus, amygdala and accumbens, and enlarged lateral ventricles and pallidum. If volumetric abnormalities represent a genetically driven risk factor for the disorder, non-affected carriers at higher genetic risk for schizophrenia should have similar differences relative to non-affected carriers of lower genetic risk. In the present study we investigated the association between rare schizophrenia-associated CNVs and volume of subcortical structures in ∼10,000 non-affected adult participants from UK Biobank. We also examine whether any potential associations found had an impact in participant’s cognitive performance. Methods: There were 9292 participants with no known pathogenic CNV and 49 carriers of at least one schizophrenia associated CNV. Linear regression models were used to investigate the association between the volume of subcortical structures and CNVs after accounting for the effects

of age, gender and total brain volume. False discovery rate correction for multiple comparison was applied. Significant associations were subject to a mediation analysis to examine the potential effect of those over cognitive performance (i.e. scores in a Fluid Intelligence [FI] test). Results: In carriers of SZ-CNV, reductions in right thalamus, pallidum, hippocampus and accumbens, and left putamen were associated with schizophrenia CNVs (after correction for multiple comparison). Four of the above FDR corrected brain areas showed a significant association with FI and were included in the mediation analysis – i.e. thalamus, hippocampus and pallidum right and putamen left. The results suggest a direct effect of SZ-CNVs on FI (B = -0.46, p = 0.002) as well as an effect mediated by volume (B = -0.07, p < 0.001) which accounted for almost 14% (p = 0.006) of the variance shared between SZ-CNV carrying status and FI. Discussion: SZ-CNVs are, in non-affected adults, associated with alterations in subcortical brain volumes in a man- ner consistent with reduced hippocampus, thalamus, amygdala and accumbens being potentially pathogenic, although pleiotropy is also possible. The divergence of effects in cases and in CNV carriers suggests enlarged pallidum is un- likely causally related to schizophrenia. Some, but not all, of the effects of CNVs on cognition in the general population are likely mediated through an influence on sub-cortical volumes.


doi: 10.1016/j.euroneuro.2018.08.025

Oral Session: Animal & Cell Based Models

1:30 p.m. - 3:00 p.m.

19

TRANSCRIPTOMIC ANALYSIS OF CORTICAL NEURONS DERIVED FROM PSYCHIATRIC PATIENTS CARRYING A

T(1;11) TRANSLOCATION, AND A CORRESPONDING

MOUSE MODEL

Shane O’Sullivan 1 , ∗, Marion Bonneau 1 , Philippe Gautier 1 , Yasmin Singh 2 , Helen Torrance 1 , Daniel L. McCarthney 1 , Hansjuergen Volkmer 2 , Maarten Loos 3 , Kathyrn L. Evans 1 , Colin A. Semple 1 , Siddharthan Chandran 1 , Michel Didier 4 , David J. Price 1 , David J Porteous 1 , J. Kirsty Millar 1

1 The University of Edinburgh

2 The University of Tübingen

3 Sylics Amsterdam

4 Sanofi

Background: A chromosomal translocation in a Scottish pedigree is linked to high risk for schizophrenia and affective disorders. The translocation disrupts the DISC1 and DISC2 genes on chromosome 1, and fuses DISC1 with the disrupted DISC1FP1 gene on chromosome 11. It is likely that one or more of these gene disruptions contributes to the mechanism by which the translocation increases disease risk. It is also possible that positional effects of the



12 Abstracts

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translocation adversely influence expression of neighbour- ing genes. Methods: We have carried out RNASeq and quantitative RT- PCR analysis of induced pluripotent stem cell (IPSC)-derived cortical neurons from translocation family members, and of brain cortex from a corresponding mouse model that recapitulates the effects of the translocation upon DISC1 expression. The data were subsequently evaluated for differential gene expression and gene ontology predictions, and compared to independent models of psychiatric disorders. Results: The set of dysregulated genes in the human cortical neurons is enriched for matches to genes whose expression is altered by independent DISC1 mutations in IPSC-derived human neurons. A substantially overlapping set of genes is dysregulated in both human cortical neurons and mutant mouse cortex. Moreover, gene ontology and pathway analysis predict largely the same major dysregulated functions in both datasets, including excitatory synapse dysfunction. Multiple putative schizophrenia genes identified through recent large-scale genome-wide association and copy number variant studies are dysregulated in the human cortical neurons and mutant mouse cortex. A number of dysregulated genes have been confirmed at the qPCR level, including the historic schizophrenia risk gene NRG1, and the putative risk genes DRD2, ERBB4 and PDE4B. Discussion: Altogether these observations indicate that i) many of the gene expression changes in the human cortical neurons are due to disruption of DISC1, ii) DISC1 disruption may act as a trigger for common shared disease pathways in schizophrenia, and iii) defective excitatory synapse function and plasticity due to the translocation may be a disease mechanism.


doi: 10.1016/j.euroneuro.2018.08.026

20

EVALUATING GENETIC CAUSATION AND PERSONALIZED

PHARMACOLOGICAL TREATMENT OF AN ULTRA-RARE

DISEASE ASSOCIATED WITH DELETION OF CACNG2

Morgan Kleiber 1 , Timothy Chapman 1 , M.L. Maile 1 , O. Hong 1 , R. Purcell 2 , A. Lippa 2 , Amanda Roberts 3 , Jonathan Sebat 1 , ∗

1 University of California, San Diego 2 RespireRx Pharmaceuticals, Inc. 3 Scripps Research Institute

Background: The characterization of genomic sequence variants in autism spectrum disorder (ASD) and neurodevelopmental disorders (NDD) studies has led to the identification of causal genes that may provide novel opportunities for individualized treatment strategies. Effective treatments for most ASDs and NDDs have been elusive due to a lack of understanding of how a genetic variant within a patient plays a causal role, as well as the establishment of an intervention that can demonstrably target the underlying molecular pathology and improve cognitive function in vivo.

Methods: To demonstrate the feasibility of personalized medicine for a rare ASD/NDD, we have generated and evaluated a mouse model using CRISPR/Cas9 genome editing. This model recapitulates a previously reported case of autosomal dominant mental retardation 10 (MRD10, OMIM: 614256) and is characterized by an exon 2 in-frame deletion of the AMPA- receptor binding domain of Cacng2 (Cacng2 �e2), a transmembrane AMPA receptor regulatory protein that mediates rapid glutamatergic excitatory signaling facilitating receptor trafficking, gating, and developmental synaptic plasticity. In Cacng2 �e2/ �e2 mice, mRNA is expressed at normal levels in the cerebellum; however, no protein is detectable in cerebellar whole-cell lysate or post-synaptic fractions, suggesting that the mutant protein may be targeted to the lysosome for degradation. Results: Behaviourally, homozygous Cacng2 �e2/ �e2 C57BL/6J mice present with severe motor ataxia. Heterozy- gous Cacng2 + / �e2 males and females show significant cognitive and behavioral phenotypes such as hyperactivity, decreased anxiety-related behaviour, decreased startle response, deficits in reversal learning, and heightened male aggression. Maternal care deficits in female Cacng2 + / �e2 carriers resulted in a dramatic loss of fecundity ( < 40% pup survival by postnatal day 5). Low survival rates in offspring of heterozygous females is consistent with this mutation being under strong negative selection. Discussion: With the goal of reversing cognitive impairments associated with MRD10, we have initiated treatment studies using the ampakine compound CX717 at doses of 0.3 and 3 mg/kg. Given the function of CX717 as a positive al- losteric modulator of AMPA receptor activity, this represents a valuable opportunity to evaluate a potential treatment for MRD10.


doi: 10.1016/j.euroneuro.2018.08.027

21

ABERRANT NRXN1 α SPLICING ARISING FROM 2P16.3

HETEROZYGOUS DELETIONS IMPACTS NEURONAL

FUNCTION

Erin Absherty 1 , ∗, Shijia Zhu 1 , Esther Cheng 1 , Alesia Antoine 1 , Madeline Halpern 1 , Gintaras Deikus 1 , Peter Gochman 2 , Judith Rapoport 2 , Gabriel Hoffman 1 , Nadejda Tsankova 1 , John Crary 1 , Robert Sebra 1 , Gang Fang 1 , Kristen Brennand 1

1 Icahn School of Medicine at Mount Sinai 2 National Institute of Mental Health, National Institutes of Health

Background: Intragenic heterozygous deletions in NRXN1, a highly alternatively spliced presynaptic cell-adhesion protein essential for synaptic function, are strongly associated with schizophrenia (SZ) and autism spectrum disorder (ASD). Homozygous NRXN1 KO mice display behavioral deficits and electrophysiological deficits consistent with these neurodevelopmental disorders. Moreover, synaptic deficits observed in the NRXN1 KO mice were recapitulated in human induced



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pluripotent stem cell (hiPSC)-derived neurons with conditional heterozygous NRXN1 α deletions. Single molecule long read sequencing of mouse prefrontal cortex has identified more than 200 NRXN1 α isoforms alone. It remains unclear to what extent rodent NRXN1 alternative splicing patterns are conserved in humans and the mechanism by which specific transcripts contribute to neuropsychiatric disease remains unknown. Methods: Here, we apply new methods to integrate long read and short read sequencing data, in order to not only catalogue but also quantify, NRXN1 α isoform reper- toires in human post-mortem and hiPSC-derived neurons. Using a rare cohort of human induced pluripotent stem

cell (hiPSC) neurons from four individuals with heterozygous NRXN1 deletions, we investigate the impact of patient- specific NRXN1 deletions on neuronal function using the multi-electrode array. Results: We have cataloged and quantified hundreds of unique transcripts arising from alternative splicing of the neuropsychiatric risk gene NRXN1 in post-mortem adult dlPFC(154), fetal PFC (65) and hiPSC forebrain neurons (102), demonstrating for the first time that hiPSC derived neurons can, to a large extent, recapitulate the in vivo splice repertoire. We quantify the differential transcript expression and identify novel transcripts in two 2p13.1 psychosis patients with 3’ deletions in NRXN1. Functionally, NRXN1 deletion hiPSC neurons show decreased neuronal activity, which can be partially ameliorated by overexpression of a single differentially expressed NRXN1 α transcript Discussion: We present hiPSCs neurons as a platform for gaining insight into how the typical transcriptional repertoire is impacted by patient specific deletions in NRXN1, a CNV associated with many neuropsychiatric disorders. Iden- tification of the repertoire and abundance of NRXN1 α transcriptional variants is important for understanding protein diversity at human synapses. Our objective is to dissect what role specific transcriptional variants may play in neuronal function how this contributes to neuropsychiatric disease risk.


doi: 10.1016/j.euroneuro.2018.08.028

22

THE PSYCHIATRIC RISK GENE NT5C2 REGULATES PRO- TEIN TRANSLATION IN HUMAN NEURAL PROGENITOR

CELLS, AND IS INVOLVED IN LOCOMOTOR BEHAVIOUR

IN DROSOPHILA MELANOGASTER

Rodrigo R.R. Duarte 1 , ∗, Nathaniel D. Bachtel 2 , Ioannis Eleftherianos 2 , Douglas F. Nixon 3 , Robin M. Murray 1 , Nicholas J. Bray 4 , Timothy R. Powell 1 , Deepak P. Srivastava 1


2 George Washington University 3 Cornell University 4 Cardiff University

Background: Genome-wide association studies (GWAS) of schizophrenia revealed hundreds of genes implicated in this disorder, and one approach to elucidate their relevance to neurobiology is functional genetics. We have previously reported that the top schizophrenia risk variants on chromosome 10q24 (rs11191419 and ch10_104957618_I) exert cis- regulatory effects on the expression of the positional candidate genes BORCS7, AS3MT, and NT5C2, in the adult and developing human brain. More specifically, we observed that NT5C2 was the only gene for which both risk alleles were associated with concordant effects on gene expression, in which case its expression was reduced. The NT5C2 gene (5’-nucleotidase cytosolic II, cN-II) has been previously implicated in susceptibility to spastic paraplegia, intellectual disability and Parkinson’s disease. It encodes an enzyme that cleaves phosphate from purine nucleotides, although its function during neurodevelopment and distribution in the adult brain remains unknown. Methods: The distribution of the NT5C2 protein in the human DLPFC and in cortical human neural progenitor cells (hNPCs) was determined using immunostaining. To probe the effects of a loss-of-function, the expression of NT5C2 was transiently reduced in hNPCs, and in the central nervous system (CNS) of Drosophila melanogaster, using RNA interference (RNAi). Gene Ontology (GO) analysis was applied to the transcriptomic signature elicited by the knockdown in hNPCs, and motility behaviour was assessed in the model organism. Results: Immunostaining of NT5C2 revealed a ubiquitous expression of this protein in hNPCs, neurons and a fraction of glial cells in the DLPFC. The knockdown of NT5C2 in hN- PCs was associated with an over-activation of adenosine monophosphate-activated protein kinase (AMPK) signalling, which is partly involved in protein translation; and downreg- ulated GO terms involved in cytoskeleton remodelling and protein translation. Reduced expression of CG32549 (NT5C2 homologue) in Drosophila melanogaster elicited a reduction in motility behaviour. Discussion: Our work describes the hitherto unknown pattern of NT5C2 expression in human neuronal cells and post- mortem brain tissue. We implicate loss of NT5C2 function in the regulation of the cytoskeleton, protein translation, and AMPK signalling in hNPCs, and demonstrate that expression of CG32549 in the CNS is critical for motility behaviour in flies.


doi: 10.1016/j.euroneuro.2018.08.029



14 Abstracts

ARTICLE IN PRESS


23

MODELING A GENETIC RISK FOR SCHIZOPHRENIA: PHENOTYPIC DIFFERENCES IN HUMAN NEURAL PRE- CURSORS AND CEREBRAL ORGANOIDS FROM PATIENTS WITH CHR16P13.11 MICRODUPLICATIONS

Mandy Johnstone ∗, Navneet Vasistha , Miruna Barbu , O. Dando, Karen Burr, E. Christopher, S. Glen, Eve C. Johnstone, Heather Whalley, Andrew McIntosh, Stephen Lawrie, Siddharthan Chandran

The University of Edinburgh

Background: Schizophrenia (SCZ) and other major mental illnesses including classic neurodevelopmental disorders are highly heritable. Large-scale studies have shown that genomic variation, in the form of copy number variants (CNVs), accounts for a significant portion of risk. CNVs in the disrupted in schizophrenia 1 (DISC1)-interactor and nuclear distribution factor E-homolog 1 gene (NDE1) on chromosome 16q13.11, that lead to SCZ and neurodevelopmental disorders are proposed to result in abnormal neuronal precursor cell (NPC) proliferation and differentiation. Methods: We have tested this hypothesis by generating a platform of human iPSCs from patients with schizophrenia, and other neurodevelopmental disorders, who are known to have CNVs affecting NDE1. We have differentiated these iPSCs into NPCs in vitro and have undertaken comparative studies between mutant and control cell lines. In parallel we have also studied the effects of NDE1 on developmental pathways in ‘cerebral organoids’; a three-dimensional tissue culture of human iPSC that mimics early stages of human cortical development. Studying neurodevelopmental disorders in three-dimensional in vitro cultures can teach us fun- damental aspects of the development of the human cortex, that are beyond reach in current animal model systems. Results: Human brain imaging of affected carriers of the 16p13.11 microduplication showed reduced brain volume. IPSC-derived brain organoids from these patients were smaller and showed reduced neuronal progenitor cell proliferation. Transcriptomic and proteomic data shows deficits in key intracellular signaling pathways associated with proliferation which we have been able to rescue both genetically and pharmacologically. Discussion: This is the first study demonstrating a biologically relevant, potentially ameliorable, signaling pathway underlying chromosome 16p13.11 microduplication syndrome in patient-derived neuronal precursor cells and provides mechanistic insight into the underlying basis of this mutation.


doi: 10.1016/j.euroneuro.2018.08.030

24

RNA-SEQ ANALYSIS IN HIPSC-DERIVED NEURONS FROM

PATIENTS WITH SCHIZOPHRENIA

Laura Stertz 1 , Guangsheng Pei 1 , Peilin Jia 1 , Shenglan Li 1 , Henriette Raventos 2 , Ying Liu 1 , Zhongming Zhao 1 , Consuelo Walss-Bass 1 , ∗

1 University of Texas Health Science Center at Houston

2 University of Costa Rica

Background: Human induced pluripotent stem cells (hiPSC) have provided a new way of studying schizophrenia (SZ), by allowing the establishment of brain cellular models accounting for the patient genetic background. Here we conducted an exploratory RNA-sequencing profiling study of cell lines derived from hiPSCs generated from lymphoblastoid cell lines (LCL) of subjects from the population isolate of the Central Valley of Costa Rica (CVCR) Methods: Five cell lines (LCL, hiPSC, NPC, cortical neurons and astrocytes) derived from 6 healthy controls and 7 SZ patients from the CVCR were generated using standard methodology. RNA from these cells was sequenced using Illu- mina HiSeqTM2500. Normalization and differential expression (DE) analysis were performed using DESeq2 (|FC| >

1.5 e BH corrected p-value < 0.05). Functional enrichment analysis was performed using DAVID 6.8. Results: hiPSC-derived neurons were responsible for 94.4%

of the variance seen on DE analyses. We found 454 genes dif- ferently expressed on neurons differentiated from SZ compared to HC. Noteworthy, one of these genes was ZNF804A

(FDR = 0.032), a strong candidate gene for schizophrenia susceptibility, with solid evidence of association from GWAS. Discussion: ZNF804A is a zinc finger protein has been shown to regulate neurite outgrowth, dendritic spine maintenance and activity-dependent structural plasticity. It is expressed broadly throughout the brain, especially in the developing hippocampus and the cortex, as well as in the adult cerebellum. A great advantage of using iPS-derived cells is that the effect of outside environmental influences, such as use of medications, is removed and only the effects of genetic composition, which is unchanged by transformation, are left. The DE of ZNF804A only on hiPSC-neurons highlight the crucial role that this protein can have on SZ pathology during neuronal development. Functional studies will help us further characterize this mechanism.


doi: 10.1016/j.euroneuro.2018.08.031



Abstracts 15

ARTICLE IN PRESS


Saturday, October 13, 2018

Oral Session: Statistics

1:15 p.m. - 2:45 p.m.

25

EXPLORING THE GENETIC ARCHITECTURE OF PSYCHI- ATRIC DISORDERS USING PARTITIONED HERITABILITY

APPROACHES

Luke Evans 1 , ∗, Richard Border 1 , Alta du Pont 1 , Naomi Friedman 1 , Emma Johnson 2 , Jian Yang 3 , Peter Visscher 3 , Matthew Keller 4

1 Institute for Behavioral Genetics, University of Colorado 2 Washington University in St. Louis 3 University of Queensland

4 University of Colorado Boulder

Background: Complex trait genetic architecture has important implications for the understanding of psychiatric disorders and for the utility of marker-based genetic risk prediction. Narrow-sense heritability (h2) quantifies the role of genetic variation in disorder liability, and also is the upper limit of theoretical risk prediction accuracy from genetic markers. Partitioned heritability approaches extend these summaries to explore the relative contribution of variants across the allele frequency spectrum or relevant annotations, and can be used explore genetic correlations among traits. These approaches use imputed variants across the full allele frequency spectrum to estimate SNP-based h2 (h2SNP) within partitioned bins. Methods: We used partitioned heritability approaches with imputed data to explore the genetic architecture of four psychiatric disorders using the UK Biobank (imputed to the Haplotype Reference Consortium sequence data) and Psy- chiatric Genomics Consortium (PGC; imputed to the TOPMed whole genome sequence reference panel) datasets. We applied genomic relatedness matrix (GRM) restricted maximum likelihood (GREML) with linkage disequilibrium (LD)- and minor allele frequency (MAF)-stratified (LDMS) GRMs in unrelated individuals to estimate partitioned heritability of major depression (MDD), generalized anxiety disorder (GAD), bipolar disorder (BPD), and schizophrenia (SCZ), particularly to estimate the contribution of rare variation, and to explore genetic architecture. We extended the LDMS approach to incorporate close relatives and cohabitants along with MAF and LD partitioning. Results: Using GREML-LDMS in unrelated individuals, we estimated total ( + /-SE) GAD h2SNP = 0.16 + /-0.02, MDD

h2SNP = 0.12 + /-0.03, and BPD h2SNP = 0.21 + /-0.01. No- tably, approximately 10% and 13% of the GAD and BPD h2SNP, respectively, was due to variants with MAF < 0.01, though none of MDD h2SNP was due to rare variants. When cohabitation and close relatives were included in the analysis, over 30% of GAD phenotypic variance was attributed to factors shared between close relatives (possibly even rarer genetic or non-genetic) and 7.5% to cohabitation of individuals.

Discussion: Our analyses, using some of the largest imputation reference panels and case/control datasets available, suggest a possible and previously underappreciated role of rare variation for GAD and BPD. Furthermore, extended GREML-LDMS models support a substantial influence of shared environments of cohabiting individuals or close relatives in GAD diagnosis. We are currently examining these same models in MDD, BPD, and SCZ, and will estimate genetic correlations among disorders within LDMS-partitioned marker bins.


doi: 10.1016/j.euroneuro.2018.08.032

26

POLYGENIC LIABILITY AND DEPRESSION IN THE

IPSYCH2012 COHORT

Katherine Musliner 1 , ∗, Preben Bo Mortensen 2 , John McGrath 3 , David Hougaard 4 , Jonas Bybjerg-Grauholm

4 , Marie Bækvad-Hansen 5 , Carsten B. Pedersen 6 , Marianne Pedersen 2 , Ole Mors 7 , Merete Nordentoft 8 , Anders Børglum

2 , Thomas Werge 9 , Nis Suppli 10 , Esben Agerbo 11

1 National Centre for Register-based Research, Aarhus Uni- versity 2 Aarhus University 3 QLD Brain Institute 4 Statens Serum Institut 5 Danish Center for Neonatal Screening, Statens Serum In- stitute 6 Centre for Integrated Register-based Research National Centre for Register-based Research, Aarhus University 7 Aarhus University Hospital, Risskov 8 Mental Health Centre Copenhagen, University of Copen- hagen

9 Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services - Copenhagen

10 Psychiatric Center Copenhagen

11 CIRRAU - Centre for Integrated Register-based Research, National Centre for Register-based Research, Aarhus Uni- versity

Background: Although the usefulness of polygenic risk scores as a measure of genetic liability for major depressive disorder (MDD) has been established, their capacity to predict who will develop depression in the general population remains relatively unexplored. Our goals were to evaluate whether polygenic risk scores for MDD, bipolar disorder (BPD) and schizophrenia (SCZ) can predict depression in the general population, and explore whether these polygenic liabilities are associated with heterogeneity in age at onset and severity at initial depression diagnosis. Methods: Data were obtained from the iPSYCH2012 sample, a case-cohort sample comprised of a subcohort of 30,000 individuals randomly sampled from the Danish population and all additional individuals diagnosed in a psychiatric hospital with five different psychiatric disorders, including depression (ICD-10 codes F32, F33). For this study we included all


16 Abstracts

ARTICLE IN PRESS


genotyped individuals from the subcohort with Danish ancestry (N = 20,158) along with all additional genotyped depression cases with Danish ancestry (N = 18,030). Severity at depression diagnosis was assessed using the ICD-10 severity specifier (mild, moderate, severe without psychotic symptoms, severe with psychotic symptoms), and treatment set- ting (outpatient, inpatient, emergency). Age at onset (AAO) was operationalized as each individual’s age in years at first depression diagnosis. Polygenic risk scores were trained using the most recent results from the PGC as discovery datasets. Hazard of depression was estimated using Cox regressions modified to accommodate the case-cohort design. Case-only analyses were conducted using linear and multi- nomial regressions. All models were adjusted for sex, birth year and the first 4 principal components. Results: In this nationally-representative sample of the Dan- ish population, a one standard deviation increase in polygenic liability for MDD was associated with a 32% increase in hazard for depression (p < .0001). The corresponding increases associated with PRS-BPD and PRS-SCZ were 10% and 12%, respectively (p < .0001 for both). Hazard of depression was 2.65 times higher among individuals in the top decile of PRS-MDD relative to individuals in the bottom decile. PRS-MDD was not associated with differences in either AAO

or severity, however among depression cases, PRS-BPD and PRS-SCZ showed small associations with earlier age at diagnosis (p = .013 for both), and PRS-BPD was associated with increased odds of inpatient (OR = 1.05, 95% CI = [1.01-1.09]) and emergency (1.04, [1.00-1.08]) treatment. Discussion: Polygenic liability for MDD trained using prevalent samples of MDD cases predicts depression in the general population, suggesting that these scores are tapping in to an underlying liability for developing depression, not just risk for maintaining the disorder. The fact that PRS-BPD and PRS- SCZ also predict depression, although to a lesser extent than PRS-MDD, is consistent with prior results suggesting a degree of common genetic overlap among these disorders. Variation in polygenic loading for BPD and SCZ may contribute slightly to heterogeneity in clinical presentation at first diagnosis among depression cases.


doi: 10.1016/j.euroneuro.2018.08.033

27

GENOME-WIDE ASSOCIATION STUDY OF RESILIENCE

TO LIFETIME TRAUMA IN THE UK BIOBANK

Karmel Choi 1 , ∗, Murray Stein 2 , Chia-Yen Chen 1 , Tian Ge 1 , Erin Dunn 1 , Kristen Nishimi 3 , Karestan Koenen 3 , Jordan Smoller 4

1 Massachusetts General Hospital 2 University of California, San Diego 3 Harvard T.H. Chan School of Public Health

4 Massachusetts General Hospital, Harvard Medical School

Background: Not all individuals exposed to trauma expe- rience poor mental health. People who demonstrate resilience are expected to show low levels of psychological

distress and high levels of subjective wellbeing despite lifetime trauma exposure. Although genome-wide association studies (GWAS) have now identified loci associated with a range of psychiatric conditions, resilience has been un- derstudied. Here, we present the first GWAS of psychological resilience as a multidimensional continuous trait conditioned on trauma exposure, in 89,914 individuals of European ancestry from the UK Biobank (UKB Application #25163). Methods: Participants self-reported their lifetime trauma exposure (i.e., childhood trauma, partner violence, and other traumatic events) as well as current psychological symptoms (i.e., depression, anxiety, and PTSD) and subjective wellbeing (i.e., happiness, and meaning). Us- ing confirmatory factor analysis, we tested a theoretically based latent factor model of psychological health consisting of symptom and wellbeing sub-factors, with good resulting fit: X2(3) = 810.06, p < .00001, RMSEA = .07, and CFI/TLI = .99/.97. Notably, latent factor models capture common variance across traits and reduce measurement error by design. For individuals who endorsed at least one lifetime trauma exposure (83%, N reported above), latent factor scores were regressed on lifetime trauma variables; resilience was thus operationalized as residuals from this model, such that higher resilience scores reflected greater levels of psychological health than predicted by one’s lifetime trauma exposure. Using imputed dosage data, we conducted a GWAS of resilience using SNPTEST, and examined heritability, genetic correlations, and gene-based results. Results: Resilience was modestly heritable (h2 = 6%). No genome-wide significant loci were identified, although three independent suggestive SNPs (p < 1x10-6) were detected. Interestingly, gene-based analyses in MAGMA identified two significant genes (MEIS2, MYO1H) previously linked to behavior and/or psychopharmacological response. Con- sistent with its derivation across multiple domains of psychological health, our resilience phenotype showed high but not complete genetic correlations with subjective wellbeing (r = 0.80), depressive symptoms (r = -0.76), and neuroticism

(r = -0.70). Despite evidence that heritability of trauma- related phenotypes can be higher in females, sex-stratified GWAS of resilience in females (N = 48,753) revealed similar heritability (h2 = 7%). Discussion: Resilience—defined as broad psychological health despite lifetime trauma—is partially heritable, genetically correlated but somewhat distinct from existing GWAS phenotypes, and has gene-based signals relevant for psychological processes. However, no genome-wide significant loci have yet been identified. Efforts to meta-analyze across further cohorts will clarify whether unique genome- wide signals emerge for this resilience phenotype.


doi: 10.1016/j.euroneuro.2018.08.034



Abstracts 17

ARTICLE IN PRESS


28

GENETIC ASSOCIATION ANALYSES IN DIVERSE AN- CESTRY POPULATIONS

Karoline Kuchenbaecker 1 , ∗, Theresa Reiker 2 , Arthur Gilly 3 , Deepti Gurdasani 3 , Lorraine Southam

3 , Gershim Asiki 4 , Janet Seeley 4 , Anatoli Kamali 4 , George Dedoussis 5 , Manjinder Sandhu 3 , Eleftheria Zeggini 3 , Erin Dunn 6 , Cathryn Lewis 7

1 University College London

2 Swiss Tropical and Public Health Institute 3 Wellcome Trust Sanger Institute 4 Medical Research Council/Uganda Virus Research Institute (MRC/UVRI) 5 Harokopio University of Athens 6 Harvard Medical School 7 King’s College London

Background: Most studies investigating the genetic basis of psychiatric disorders use data from individuals of European ancestry, raising questions about the genetic factors that shape disease risk in individuals with non-European ancestry and whether genetic risk factors are the same across populations. Our goal was to address these knowledge gaps by performing a trans-ethnic analysis and constructing what will be the largest mega-analysis of individuals with and without major depressive disorder (MDD). Methods: Our first aim was to determine whether complex traits generally showed consistent genetic associations across populations. We used imputed genotypes from

genome-wide arrays and measures of blood biomarkers from

samples from the UK (N = 9,961), two isolated Greek populations (N = 1,641 and N = 1,945), and Ugandan samples (N = 4,778). Using linear mixed models, we tested associations between established lipid-associated SNPs identified through European samples and polygenic scores based on these variants. Secondly, we created a database of MDD in samples with diverse or admixed ethnicity and established an analysis pipeline to assess variant associations. Results: Few of the established lipid loci displayed nominally significant associations with their target biomarker in the Greek and Ugandan samples. The polygenic scores were associated with highly consistent effects across the Euro- pean populations (p < 3x10-4 in each case). For the Ugan- dan cohort, there were significant associations of the HDL (p = 4x10-26) and LDL scores (p = 4x10-20) but no evidence of association for the triglyceride score (p = 0.14). This suggests that some traits might share fewer genetic factors across populations.

For MDD, we identified 28 studies, totalling more than 20,000 affected and 300,000 unaffected individuals with non-European ancestry to be analysed through the Psychi- atric Genomics Consortium MDD working group. Our pipeline is based on a single mixed model analysis rather than stratifying analyses by ancestry. Mixed models can account for population structure and the presence of relatives. How- ever, standard implementation for genetic studies are de- signed for continuous outcomes. Therefore, we use logistic mixed model implementations. Results can be combined across studies through trans-ethnic meta-regression.

Discussion: Our findings suggest that at least some genetic factors may not be shared across populations for a given complex trait. Thus, caution may be needed before general- ising findings from European ancestry to non-European populations. The rising numbers of biobanks and cohorts with health record linkage provide a unique and cost-effective opportunity to build a resource of diverse ancestry samples from existing studies. Novel mixed model approaches provide a powerful analysis tool that addresses population structure, relatedness and imbalance in the number of cases and controls.


doi: 10.1016/j.euroneuro.2018.08.035

29

GENETIC ASSOCIATIONS BETWEEN PSYCHIATRIC DIS- ORDER RISK AND PHENOTYPIC FACTORS IN THE UK

BIOBANK

Caitlin Carey 1 , ∗, Rebecca Shafee 2 , Raymond Walters 3 , Duncan Palmer 4 , Liam Abbott 1 , Daniel Howrigan 4 , Claire Churchhouse 1 , Benjamin Neale 4 , Elise Robinson 4

1 Broad Institute 2 Harvard Medical School 3 Stanley Center for Psychiatric Research and Program in

Medical and Population Genetics, Broad Institute of Har- vard and MIT

4 Massachusetts General Hospital

Background: Common polygenic variation strongly contributes to risk for psychiatric disorders such as attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia (SCZ). Every individual has some degree of polygenic risk for each of these conditions. Biobanks with large-scale phenotypic and genomic data, such as the UK Biobank (UKBB), can thus be leveraged to better understand the full spectrum of outcomes associated with that risk. Such analyses can clarify how genetic risk for psychiatric disorders varies in phenotypic presentation and highlight clinically useful associations. Methods: Beginning with more than 2500 traits measured in the UKBB, we identified 350 traits with significant common- variant heritability in the white British UKBB subsample (N = 333,554) using LD Score Regression (LDSR). To improve interpretability, we next conducted factor analysis on these 350 traits in the same subsample and identified a 40- factor solution yielding cohesive and interpretable factors. The factors included a wide range of physical (e.g., pain, asthma), behavioral (e.g., neuroticism, sleep), and cognitive outcomes (e.g., intelligence/education). We then performed a GWAS of each factor, controlling for age, sex and principal components. To evaluate the relationship of these factors with psychiatric disorders, we estimated the genetic correlation of each with ADHD, ASD, and SCZ using LDSR with the latest Psychiatric Genomic Consortium GWAS for each disorder. Results: All three psychiatric disorders were moderately genetically associated with poorer overall mental (e.g., life


18 Abstracts

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dissatisfaction factor: rg = .30 to.39) and physical (e.g., general health factor: rg = -.18 to -.53) health, as well as with certain behavioral factors (e.g., sleep issues: rg = .22 to .56). In contrast, divergent profiles of genetic correlation across disorders were observed for factors such as alcohol use (ADHD only rg = .57), weight (ADHD rg = .37, vs. SCZ rg = - .12), and wearing glasses (ADHD rg = -.27, vs. ASD rg = .28; all ps < 1e-9). Discussion: It has been clearly established that the common polygenic influences on risk for neuropsychiatric disorders partially overlap. While polygenic risk for each disorder is defined by association to its clinical features (e.g. behavioral problems), we highlight how this polygenic risk can also differ substantially in their biomedical and cognitive correlates. These results suggest that the shared and unshared polygenic influences on risk for psychiatric disorders can be used to distinguish elements of the disorders’ underlying biology.


doi: 10.1016/j.euroneuro.2018.08.036

30

ENVIRONMENTAL FACTORS ARE OFTEN HERITABLE: DOES THIS BIAS POLYGENIC GENE-BY-ENVIRONMENT

INTERACTION ANALYSES?

Wouter J. Peyrot 1 , ∗, Matthew Keller 2 , Wouter van Rheenen 3 , Naomi Wray 4 , Brenda Penninx 1

1 VU University Medical Center 2 University of Colorado Boulder 3 UMC Utrecht Brain Center Rudolf Magnus 4 University Of Queensland

Background: In the era of genome-wide genotype data, research in gene-by-environment interaction (GxE) has shifted its focus from candidate genes to polygenic methods, such as analyses of polygenic risk scores (PRS) and the genetic relatedness matrix (GRM). This progress acknowledges the highly polygenic nature of psychiatric disorders, but also in- troduces new challenges. In particular, most environmental factors tested in GxE analyses have a genetic component, such as smoking, cannabis use and even exposure to childhood trauma (CT). We hypothesize that commonly used polygenic GxE analyses may be biased when analyzing heritable environmental factors. Methods: Polygenic data were simulated using population parameters representative of Major Depressive Disorder (MDD), i.e., heritability of 35% and lifetime risk 15%. In addition, we simulated CT for the same individuals, assuming a cumulative CT risk of 20%, and an odds ratio for MDD of 2.5 for exposed individuals (CT = 1) compared to unexposed individuals (CT = 0). We simulated 5 scenarios: (1) no interaction & CT not heritable (CT-h2l = 0), (2) different direction of genetic effect on MDD in CT = 1 compare to CT = 0 &

CT-h2l = 0, (3) larger genetic effects on MDD in CT = 1 compared to CT = 0 & CT-h2l = 0, (4) no interaction & CT-h2l = 50%

with no genetic correlation with MDD (other than via the

causal effect of CT on MDD), and (5) no interaction & CT- h2l = 50% with genetic correlation with MDD. On these simulated data, we performed PRS analyses (logistic regression of MDD on PRSxCT) and GRM-based analyses (cross-product Haseman-Elston regression to estimate (1) the genetic correlation between MDD in CT = 1 and MDD in CT = 0, and (2) the difference in magnitude of the liability-scale heritably of MDD in CT = 1 compared to CT = 0). Results: The PRS- and GRM-based analyses provided non- biased results for scenarios 1-4. For scenario 5, a slight bias was introduced when simulating a relatively strong genetic correlation between MDD and CT of 0.53 (MDD-h2l of 0.31 in CT = 1 and 0.33 in CT = 0). Discussion: Contrary to our hypothesis, these simulations suggest that polygenic GxE analyses generally provide re- liable results. However, GxE results should be interpreted with caution when a heritable environmental factor is studied with a strong genetic correlation with the disorder. A limitation of our work is that we cannot rule out potential bias from other genetic architectures or polygenic methods than those studied here.


doi: 10.1016/j.euroneuro.2018.08.037

Oral Session: Autism Disorders

1:15 p.m. - 2:45 p.m.

31

DISCOVERY AND CHARACTERIZATION OF 102 GENES ASSOCIATED WITH AUTISM FROM EXOME SEQUENCING

OF 37,269 INDIVIDUALS

Frederick Satterstrom

1 , ∗, Jack Kosmicki 2 , Jiebiao Wang 3 , Ryan Collins 2 , Silvia de Rubeis 4 , Michael Breen 4 , Sherif Gerges 2 , Anders Børglum

5 , Joseph Buxbaum

6 , David Cutler 7 , Bernie Devlin 8 , Kathryn Roeder 3 , Stephan Sanders 9 , Michael Talkowski 2 , Mark Daly 2

1 Broad Institute 2 Center for Genomic Medicine, Massachusetts General Hos- pital, Stanley Center for Psychiatric Research, Broad Insti- tute of Harvard and MIT, Harvard Medical School 3 Carnegie Mellon University 4 Seaver Autism Center for Research and Treatment, Icahn

School of Medicine at Mount Sinai 5 iPSYCH, The Lundbeck Foundation Initiative for Integra- tive Psychiatric Research, iSEQ, Centre for Integrative Se- quencing, Aarhus University 6 Seaver Autism Center for Research and Treatment, Fried- man Brain Institute, & Mindich Child Health and Develop- ment Institute, Icahn School of Medicine at Mount Sinai 7 Emory University School of Medicine 8 University of Pittsburgh School of Medicine 9 UCSF Weill Institute for Neurosciences

Background: The genetic architecture of autism spectrum

disorders (ASDs) involves the interplay of common and rare variation, with hundreds of genes conferring risk to the dis-



Abstracts 19

ARTICLE IN PRESS


order. Identifying the genes associated with ASD and understanding their effects in the context of ASD’s heterogeneous phenotypic presentation have both been longstanding goals. Previous studies have focused almost exclusively on de novo (newly arising) variation for gene discovery and have not in- corporated other sources of variation or utilized genic constraint metrics. Methods: On behalf of the Autism Sequencing Consortium, we present the largest exome sequencing study in ASD to date, with 37,269 samples from 31 sampling sources across large sequencing studies supported by SFARI, NHGRI, and NIMH, including the iPSYCH study in Denmark. To identify ASD-associated genes, we use a Bayesian framework that in- corporates both de novo and case-control variation and also leverages gene (pLI) and regional (MPC) constraint metrics. Results: We discover 26 genome-wide significant genes and 102 genes (FDR < 0.1) associated with ASD. Thirteen of the 102 genes overlap 12 large recurrent copy number variants, indicating that these genes may contribute to the autism associations in 1p36.3, 2p15, 2q37.3, 11q13, 15q11.2, 15q24, and 16p11.2. The 102 genes are also enriched in both gene expression regulators expressed primarily during the prenatal/fetal period and neuronal communication genes expressed during later postnatal development.

Meta-analyzing our results with published de novo variants from 5264 intellectual disability / developmental delay (ID/DD) trios indicates that 47 of the 102 ASD-associated genes are more strongly associated with ID/DD than ASD, as evidenced by a higher rate of de novo variants in ascertained ID/DD individuals than in ascertained ASD individuals. Of the remaining 55 genes discovered, 52 are preferential towards ASD, and 3 are equally associated with both. We demonstrate that comorbid ASD-ID/DD genes are markedly different from ASD-preferential genes in terms of degree of negative selection, phenotypic presentation, and expression from single-cell RNA sequencing in multiple cell types. Among ASD probands, individuals with variants in comorbid ASD-ID/DD genes on average walk 2.6 months later (P = 6e-4) and have an IQ 11.7 points lower (P = 5e-04) than those with a variant in ASD-preferential genes. Discussion: Our data constitute the most comprehensive description of the genetic architecture of ASD to date. The 102 ASD-associated genes are a substantial increase from

the 65 (at an equal FDR of 0.1) identified in the previous effort from our consortium. With the division of the 102 genes into those that are preferential for comorbid ID/DD

and those that are not, this work lends evidence that different biological processes underlie ASD-associated genes and the phenotypic presentation of ASD probands.


doi: 10.1016/j.euroneuro.2018.08.038

32

MOST EXOME SEQUENCING IDENTIFIED AUTISM SPEC- TRUM DISORDER GENES CONFER GREATER RISK TO

INTELLECTUAL DISABILITY / DEVELOPMENTAL DELAY

THAN ASD

Jack Kosmicki 1 , ∗, Liu He 2 , Jeffrey Barrett 2 , Mark Daly 3

1 Harvard University 2 Wellcome Trust Sanger Institute 3 Analytic and Translational Genetics Unit

Background: Studies of de novo variation, which is identified by exome sequencing parent-offspring families, have successfully identified over 100 genes associated with neurodevelopmental disorders, most notably intellectual disability / developmental delay (ID/DD) or autism spectrum

disorders (ASDs). Mutations in the genes first discovered in de novo studies of ASD were specifically enriched in those ASD individuals with comorbid ID/DD, and these genes were commonly assumed to affect both traits. As such, some crit- icized these studies for failing to separate ID/DD genes from

those genes that confer risk to ASD without ID/DD. Through a meta-analysis of de novo variants from 3981 probands ascertained for ASD (1179 with comorbid ID/DD) and 5264 probands ascertained for ID/DD (503 with comorbid ASD) we show that previously associated ASD genes are more often mutated in individuals with ID/DD who do not have ASD. Methods: We meta-analyzed of 16,454 published de novo variants from 9,245 families. We leveraged detailed phenotyping on 85.9% of the probands to increase power to both identify associated genes and calculate the degree of risk each gene confers for different disorders. Results: To identify associated genes, we compared the number of observed de novo missense and protein- truncating variants (PTVs) to the number expected from a null mutational model across three sets: ASD + ID/DD, ASD

only, and ID/DD only. This yielded a total of 107 significant genes. The contribution of evidence towards each gene’s association is overwhelmingly provided by ID/DD compared to ASD (OR = 4.9; P < 9e-58), with only 10 of the 107 genes possessing more de novo missense and PTVs in ASD cases than expected by chance; the remaining 97 genes are associated with ID/DD. Of the 10 ASD-associated genes, only 3, ANK2, DSCAM, and CHD8, are significantly more strongly associated with ASD than ID/DD, with the de novo variants in DSCAM and ANK2 observed solely in ASD probands. De novo missense and PTVs in the remaining 7 ASD-associated genes are observed 2.9 times more in 4789 ID/DD cases who do not have ASD (P < 7e-7). This same finding extends to prior published ASD gene lists (OR = 1.8; P < 0.002), indicating that most ASD-associated genes from previous de novo studies are more strongly associated with ID/DD. Furthermore, ascertained ASD cases harboring an ID/DD de novo variant have an IQ 20 points lower, begin walking 4 months later, and are 5 times more likely to have seizures than the rest of the ASD population. Discussion: Studying de novo variation has been extremely successful for identifying disease-associated genes, with ASD being arguably the absgship trait for this study design. However, the presence of comorbid ID/DD in ASD probands


20 Abstracts

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with associated de novo variants begged the question as to how much risk each gene confers to the different disorders. Leveraging detailed phenotyping in a meta-analysis of 3981 ASD and 5264 ID/DD individuals, we identified 107 disease- associated genes, of which only 3 are more strongly associated with ASD than ID/DD. Furthermore, genes previously associated with ASD are more often observed in ID/DD cases without comorbid ASD; ASD cases with de novo variants in ID/DD genes are phenotypically more similar to ID/DD cases than the rest of the ASD population. Therefore, researchers should be cautious with using published lists of ASD genes for follow-up functional studies - especially if the goal is to learn about the genetic etiology of core ASD features in the absence of cognitive impairment - namely social and behavioral impairments.


doi: 10.1016/j.euroneuro.2018.08.039

33

EXPLORING THE DEVELOPMENTAL GENETIC ARCHI- TECTURE OF SOCIAL BEHAVIOUR: EVIDENCE FOR

GENETIC OVERLAP WITH ASD AND ADHD

Fenja Schlag 1 , Jan Buitelaar 2 , Jakob Grove 3 , Ellen Verhoef 1 , Chin Yang Shapland 1 , Ditte Demontis 3 , George Davey Smith 4 , Dheeraj Rai 5 , Simon E. Fisher 6 , Anders Børglum

3 , Beate St Pourcain 7 , ∗

1 Max Planck Institute for Psycholinguistics 2 Radboud University 3 Aarhus University 4 University of Bristol 5 School of Social and Community Medicine, University of Bristol 6 Max Planck Institute for Psycholinguistics, Donders Insti- tute for Brain, Cognition and Behaviour, Radboud Univer- sity 7 Max Planck Institute for Psycholinguistics; MRC Integrative Epidemiology Unit (MRC IEU), University of Bristol

Background: The acquisition of social skills represents an important developmental milestone that most children mas- ter effortlessly, while developmental delays and deviations in social behaviour often have long-reaching effects on later mental health and wellbeing. The spectrum of social abilities is phenotypically complex. For example, prosocial behaviours capture positive interactions, while socially dis- ruptive behaviours may lead to problematic peer relations. The developmental genetic architecture of social behaviour and its genetic overlap with psychiatric disorders is, however, largely unknown. Here, we investigate age-, trait- and reporter-specific changes in genetic factors underlying social skills in the general population and study shared genetic influences with Autism Spectrum Disorders (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). Methods: Two social traits capturing low prosociality (LPS) and peer problems (PP) were repeatedly assessed in participants of a UK birth cohort (ALSPAC; N ≤6174, 4 to 17 years), using either mother- and/or teacher-report. Single

Nucleotide Polymorphism (SNP)-heritability (SNP-h2) for social traits was estimated with linkage disequilibrium score (LDSC) regression, based on genome-wide quasi Poisson regression analyses modelling right skewed data. Genome- wide summary statistics for ASD and ADHD were obtained through the PGC/iPSYCH consortium. The association between polygenic risk for disorder and social traits was assessed with polygenic scoring (PGS, p-threshold < 0.1), also using a quasi Poisson regression framework. Estimates were combined with random-effects meta-regression. Results: We identified opposite developmental trends in LDSC-h2 estimates for mother-reported LPS and PP respectively (trait x age interaction, p = 0.0002). While LPS SNP-h2 decreased with progressing age, with the highest estimate at age 4 years (SNP-h2 = 0.18(SE = 0.08)), PP SNP-h2 increased, with the highest estimate at age of 17 years (SNP-h2 = 0.43(SE = 0.12)). Teacher-rated LDSC-h2 was strongest at 11 years, both for LPS (0.21(SE = 0.09)) and PP (0.15(SE = 0.10)), although the data was too sparse to model developmental patterns. Using a meta-regression framework, we identified a positive association between ASD

polygenic risk and PP, irrespective of age and rater (beta- PGS = 0.05 (SE = 0.01), p = 0.0002), but not for LPS. Similarly, we observed evidence for a developmentally stable positive genetic overlap between ADHD polygenic risk and social behaviour. This was observed for mother-rated (beta- PGS = 0.05 (SE = 0.01), p < 10-4) and teacher-rated (beta- PGS = 0.11(SE = 0.02) p < 10-4) PP, as well as teacher-rated LPS (beta-PGS = 0.07 (SE = 0.02), p < 10-4), but not mother- rated LPS. Discussion: Social traits have complex genetic architectures that follow trait-specific developmental patterns, but show

genetic overlap with both ASD and ADHD that is developmentally stable. However, the strength of genetic links with psychiatric disorder can differ by rater and/or type of social behaviour.


doi: 10.1016/j.euroneuro.2018.08.040

34

NOVEL INSIGHT INTO THE AETIOLOGY OF AUTISM

SPECTRUM DISORDER GAINED BY INTEGRATING FUNC- TIONAL DATA WITH GENOME-WIDE ASSOCIATION

SUMMARY STATISTICS

Oliver Pain ∗, Andrew Pocklington, Richard Anney

Cardiff University

Background: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders affecting 1% of individuals with core features typically occurring in early childhood. ASD are highly heritable, with common genetic variation explaining ∼18% of the variance between cases and controls. Genome-wide association studies (GWAS) have identified several loci associated with ASD. However, associated loci are often difficult to interpret functionally. To gain insight into the functional consequence of ASD-associated loci, and identify novel ASD-associated genes, we integrated gene



Abstracts 21

ARTICLE IN PRESS


expression data with the summary statistics of the largest ASD GWASs to date. The results were then used to highlight gene-sets dysregulated in ASD, and to estimate the variance in ASD attributable to differential gene expression. Methods: We performed a transcriptome-wide association study (TWAS) of ASD by integrating 14 gene expression datasets including adult brain, blood and adi- pose tissues with the largest ASD GWAS (Ncases = 18,381, Ncont. = 27,969) (Grove et al. 2017, bioR χ iv). Analyses were performed using the software FUSION. 14,943 independent genes were tested for differential expression in ASD. Gene-set enrichment analysis of the TWAS results was performed using 135 brain relevant gene-sets, as well as 1,329 canonical pathway gene-sets. Stratified linkage disequilibrium score regression (S-LDSC) was used to estimate the proportion of ASD SNP-heritability that can attributed to differential gene expression. Results: TWAS identified 12 genes associated with ASD at transcriptome-wide significance, of which 11 did not overlap with genome-wide significant loci identified by the GWAS. Several of the associated genes have been implicated in ASD, including PTGDR (prostaglandin D2 receptor). However, a 1Mb region on chromosome 17 contained 7 transcriptome-wide significant genes, highlights the need for further analysis to infer causal associations. Gene-set enrichment analysis identified several canonical pathways as enriched, the two most significant being the amyloid and telomere end packaging pathways. The proportion of ASD- SNP-heritability explained by gene-expression was strongly dictated by the size of the sample used to derive the gene- expression SNP-weights, with the CommonMind Consortium

prefrontal cortex SNP-weights explaining the most variance in ASD (12% of SNP-heritability). Discussion: This study has identified several differentially expressed genes in ASD, supporting previous findings and highlighting novel genetic associations. PTDGR has been previously reported as differentially expressed in ASD by two studies using observed gene-expression data, supporting the validity of the TWAS approach in our study. Gene- set enrichment highlighted the importance of amyloid and telomere-related pathways congruent with previous findings of increased beta-amyloid levels in individuals with ASD and shorter telomere length in families with high risk of ASD. Partitioned heritability estimates further demonstrated the utility of the TWAS approach for understanding ASD, and that this approach will become more powerful as SNP-weights derived from larger samples become available.


doi: 10.1016/j.euroneuro.2018.08.041

35

OPPOSITE GENETIC EFFECTS FOR POLYGENIC ASD

RISK SHARED WITH AND INDEPENDENT OF ADHD: EVIDENCE FOR A CANCELLING-OUT HYPOTHESIS STUDYING GENETIC OVERLAP WITH LANGUAGE AND

LITERACY

Ellen Verhoef 1 , ∗, Jakob Grove 2 , Chin Yang Shapland 1 , Ditte Demontis 3 , Stephen Burgess 4 , iPSYCH-Broad-PGC ASD

Consortium

5 , George Davey Smith 5 , Dheeraj Rai 6 , Simon E. Fisher 7 , Anders D. Børglum

3 , Beate St Pourcain 8

1 Max Planck Institute for Psycholinguistics 2 The Lundbeck Foundation Initiative for Integrative Psychi- atric Research, iPSYCH, Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark; Department of Biomedicine - Human Genetics, Aarhus University 3 The Lundbeck Foundation Initiative for Integrative Psychi- atric Research, iPSYCH, Centre for Integrative Sequencing, iSEQ, Aarhus University 4 University of Cambridge 5 University of Bristol, School of Social and Community Medicine 6 Bristol Medical School, University of Bristol 7 Max Planck Institute for Psycholinguistics, Nijmegen, Don- ders Institute for Brain, Cognition and Behaviour, Radboud

University, Nijmegen

8 Max Planck Institute for Psycholinguistics, University of Bristol, Donders Institute for Brain, Cognition and Be- haviour, Radboud University, Nijmegen

Background: Autism Spectrum Disorders (ASD) share genetic liability with complex cognitive skills, such as verbal intelligence quotient (IQ) scores, and thus genetic overlap with highly related literacy- and language-related abilities (LRAs) is expected. However, recent evidence suggests that such genetic overlap can differ by ASD subgroup. Especially ASD with comorbid Attention-Deficit/Hyperactivity Disor- der (ADHD) symptoms may genetically correlate more with impaired than enhanced cognitive performance. Moreover, multi-trait scores such as IQ may conceal association patterns for individual subskills. Here, we study genetic overlap between ASD and a wide range of LRAs. Specifically, we disentangle polygenic links into ASD genetic effects shared with and independent of ADHD using methodologies robust to collider bias. Methods: Thirteen LRAs capturing oral comprehension, reading, spelling, phonological awareness/memory and verbal IQ were studied in 7 to 13-year-old children from a UK birth cohort (ALSPAC; N ≤5,919; SNP-h2 > 0.25). Genome- wide summary statistics for 1) ASD excluding individuals comorbid for ADHD (cases:10,321; controls: 22,664), and 2) ADHD (cases:14,584; controls:22,492) were obtained through the iPSYCH consortium. To disentangle polygenic relationships between ASD and LRAs, ASD genetic effects on LRAs were modelled as effects shared with and independent of ADHD using multivariable regressions (MVR), analogous to Mendelian Randomization (MR). ASD instruments were selected by applying conservative (p < 5x10 ̂ -8), typical MR (p < 0.0015) and common polygenic-scoring (PGS, p < 0.05) thresholds.


22 Abstracts

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Results: Studying PGS-comparable instruments and without implying causality, we identified positive genetic associations between ASD risk and all LRAs, conditional on ADHD, passing an experiment-wide multiple testing threshold (p < 0.007). In contrast, polygenic ASD risk shared with ADHD showed an inverse association, based on the same set of ASD instruments. This pattern was observed for stan- dardised reading and spelling instruments, verbal IQ and oral comprehension, but not phonological memory, phone- mic awareness and study-specific LRAs. The strongest effects were present for reading speed at age 9 with a 0.02 Z-score increase (SE = 0.003, P = 6x10 ̂ -10) and a 0.038 de- crease (SE = 0.007, P = 2x10 ̂ -8) in performance per log odds in ASD and ADHD liability respectively (latter capturing shared ASD/ADHD effects). Using typical MR instruments, this genetic overlap was attenuated, but still present for several reading and spelling abilities, and completely abol- ished when analysed with conservative ASD instruments. Discussion: Subthreshold genetic variants capturing ASD liability may show opposite polygenic effects on reading, spelling and reasoning for ASD with and without ADHD

symptoms, although implicating the same genomic regions. Polygenic ASD risk was associated with increased reading, spelling and reasoning performance conditional on ADHD. However, the same genomic regions were also associated with lower reading, spelling and reasoning performance via ADHD, as shared ASD/ADHD effects. This has implications for the presence of null effects in ASD collections with large proportions of patients comorbid for ADHD and detectable genetic correlations between clinical ASD and ADHD.


doi: 10.1016/j.euroneuro.2018.08.042

36

SINGLE CELL RNA SEQUENCING OF EMBRYONIC MOUSE

CORTEX REVEALS OVERLAPPING AND DISTINCT PAT- TERNS OF ASD RISK GENE EXPRESSION

Rebecca Muhle ∗, Kristina Yim , Martina Krenzer , Guillermina Hill-Teran, Kathleen Malison, James Noonan

Yale University

Background: Autism Spectrum Disorder (ASD) is a lifelong and often debilitating neurodevelopmental condition. Ge- netic risk factors for ASD exhibit locus heterogeneity, and researchers have identified dozens of genes associated with ASD risk at genome-wide significance. Deciphering how

these genes might contribute to ASD risk is still a major task. While many ASD risk genes encode proteins with known neuronal functions, other ASD risk genes encode proteins that regulate the expression of other genes. Regulatory ASD

risk genes may act as ASD risk gene coordinators, regulating the expression of other ASD risk genes during neurodevelopment. Consistent with this, we previously showed in a model of early brain development that decreased levels of the ASD

risk-associated regulatory gene CHD8 dysregulates the expression of other ASD risk genes. The developing brain has a complex and tightly orchestrated composition of cell types,

however, and which cell types express one or more ASD risk genes, at what stage of neurodevelopment, and in which cellular compartment are unanswered questions. Methods: To establish the neurodevelopmental trajectory of ASD risk genes during brain development, we have performed single cell transcriptome analysis (scRNA-seq) of embryonic cortices harvested at stages spanning corticogenesis. We use the developing mouse brain as a model due to a number of advantages: we can investigate in parallel a multitude of cell types analogous to those found in the human developing brain, ASD risk genes are largely conserved between mouse and human species, and robust genetic models exist for follow-up studies of cell-types with specific ASD risk gene expression. Dissociated, viable samples were individually loaded onto the 10X Chromium Con- troller for single-cell encapsulation and barcoding. Gene expression values were imputed using the program MAGIC

(Markov Affinity-based Graphical Imputation of Cells) to re- cover dropout values, and we used PHATE (Potential Heat- diffusion Affinity-based Trajectory Embedding) to visualize the latent trajectory structure. A principle curve was fit to the main trajectory of variation, followed by correlation analysis of ASD risk gene expression. Results: The first two components of variation capture the trajectory of corticogenesis, with progenitor cells and ma- ture neurons separating along the principle curve. This relationship is present within each time point and persists when all times points are merged. ASD risk genes parti- tion into subgroups based on correlation of expression with marker genes specific for cortical regions and cell types. Immunohistochemistry of selected ASD risk genes in sec- tioned mouse cortices confirms the developmental pattern and compartmentalization of expression found in our single cell analysis. Discussion: These investigations will yield insight into the role of cell types in the development of ASD. Cell types showing co-expression of multiple ASD risk genes will be further assessed using mouse lines genetically engineered for conditional labelling to identify cell type specific ASD

risk gene target networks. ASD risk gene mutant mouse lines will be used to assess the effect of loss of function alleles on cell types with high ASD risk gene expression. By deciphering how individual ASD risk genes impact brain development, we can better understand how individual risk genes may contribute to risk for ASD in order to spark new investigations for novel therapeutics.


doi: 10.1016/j.euroneuro.2018.08.043



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Oral Session: Affective Disorders

1:15 p.m. - 2:45 p.m.

37

ASSOCIATION OF THE POLYGENIC RISK SCORE FOR

MAJOR PSYCHIATRIC DISORDERS WITH CLINICAL FEA- TURES IN BIPOLAR DISORDER AND CONTROLS

Jie Song 1 , ∗, Lu Yi 1 , Sarah Bergen 1 , Erik Smedler 2 , Mikael Landen 2

1 Karolinska Institutet 2 University of Gothenburg

Background: Despite the overlap in genetic etiology and clinical presentations, bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) display distin- guishing characteristics. Whether genetic factors contribute to these clinical features remains to be investigated. Here, we test whether polygenic risk scores (PRSs) of BD, SCZ and MDD associate with clinical presentations of BD and a control sample. Methods: This study included participants from Swedish Bipolar Collection Study (SWEBIC) that have been genotyped and interviewed regarding their diagnoses, clinical characteristics, medical intervention, and outcomes. Clinical features of BD, including full inter-episodic remission, global assessment of functioning (GAF), severity in symptoms during follow-up, psychotic features during mood episodes, alcohol and drug abuse, and hospitalization due to suicide attempt/deliberate self-harm were collected from telephone interviews, the bipolar quality assurance registry and the National Patient Registry. The final sample consists of 5099 patients with BD and 3331 controls. Standardized PRSs of BD, SCZ and MDD were computed using summary statistics from the genome-wide association studies of SCZ, BD and MDD working group from the Psychiatric Genomics Consor- tium. Linear, logistic and ordinal logistic regression models were used to estimate associations between PRS and clinical outcomes among the participants, with adjustment for sex, age, the first six population principal components and genotyping platforms. With respect to the outcomes suicide, drug and alcohol abuse, the effects of PRS were evaluated both in the combined cohort and in cases and controls separately. Results: PRS of BD and PRS of SCZ/MDD showed associations with clinical symptoms of BD in opposite directions. Higher BD-PRS was associated with full inter-episodic remission, higher GAF and less severe symptoms, while higher PRSs of SCZ and MDD showed associations with lower rate of inter- episode remission, lower GAF, and more severe symptoms. PRS of SCZ was significantly associated with psychotic symptoms. PRSs of BD, SCZ and MDD were positively associated with hospitalization due to suicide attempt/self-harm, but the associations did not hold after adjustment of BD status. Finally, PRS of MDD associated with diagnoses of alcohol and drug abuse regardless of adjustment for BD status.

Discussion: This study provides evidence that the differential polygenetic liabilities between BD, SCZ and MDD partly account for different clinical presentations of BD.


doi: 10.1016/j.euroneuro.2018.08.044

38

WHOLE EXOME SEQUENCING OF MULTIPLEX BIPOLAR

DISORDER FAMILIES AND FOLLOW-UP RESEQUENCING

IMPLICATE RARE VARIANTS IN CELL ADHESION GENES CONTRIBUTING TO DISEASE ETIOLOGY

Anna Maaser 1 , ∗, Fabian Streit 2 , Kerstin U. Ludwig 3 , Anna C. Koller 3 , Franziska Degenhardt 3 , Holger Thiele 4 , José Guzman Parra 5 , Fabio Rivas 5 , Fermín Mayoral 5 , Stefan Herms 6 , Per Hoffmann 7 , Sven Cichon 7 , Marcella Rietschel 8 , Markus M. Nöthen 9 , Andreas J. Forstner 6

1 Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn

2 Central Institute of Mental Health, University Medical Center Mannheim/University of Heidelberg 3 Institute of Human Genetics, University of Bonn

4 Cologne Center for Genomics, University of Cologne 5 University General Hospital of Málaga, Biomedical Re- search Institute of Málaga IBIMA

6 Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Human Genomics Re- search Group, University of Basel 7 Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, University of Bonn, University of Basel, Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany 8 Central Institute of Mental Health, University Medical Center Mannheim/University of Heidelberg 9 Institute of Human Genetics, University of Bonn

Background: Bipolar disorder (BD) is a complex psychiatric disorder affecting around 1% of the general population. The disease is characterized by recurrent episodes of mania and depression and has a high heritability of around 70%. Since the cumulative impact of common variants with small effect may only explain 25-38% of the phenotypic variance for BD, rare variants of high penetrance were suggested to confer risk for BD. Methods: In the present study, whole exome sequencing (WES) was performed on 226 individuals of 68 large multi- ply affected families of European origin. We selected two to five affected individuals with BD or recurrent major depression from each family. WES was executed on the Illumina HiSeq2500 platform. For data analysis, we used the Var- bank pipeline (varbank.ccg.uni-koeln.de). We filtered for rare (minor allele frequency < 0.1%), nonsynonymous, potentially functional and segregating variants. Subsequently, we applied the Residual Variation Intolerance Score and performed an enrichment analysis with the genes that were ranked among the 20% most intolerant genes in the genome.


24 Abstracts

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For follow up analyses, we prioritized candidate genes that were either implicated in at least two unrelated families or previously reported in NGS studies or GWAS of BD. In addition, we enclosed genes that were predominantly driving the significant pathways in our gene enrichment analysis. Using the single molecule molecular inversion probes (smMIPs) technology targeted resequencing of the 42 most promising candidate genes is currently performed on the HiSeq2500 in an independent case/control cohort. Results: In total, WES identified 1214 rare, segregating and potentially functional variants implicating 1122 different genes. A significant enrichment for a total of 18 pathways was observed (e.g. neuron projection, post-synapse and cell adhesion; p < 0.001). The 42 prioritized candidate genes include several cadherin genes (e.g. CDH7 and CDH23). Inter- estingly, CDH23 encodes a calcium-dependent cell adhesion protein that may play a role in neurogenesis and that was previously implicated in risk for BD and other major psychiatric disorders. Discussion: Our preliminary results strongly suggest that rare and highly-penetrant variants in neuronal and cell adhesion genes contribute to BD etiology. The results of our follow-up resequencing of a large case/control sample will provide further statistical evidence for an involvement of particular genes and pathways.


doi: 10.1016/j.euroneuro.2018.08.045

39

GENOME-WIDE ASSOCIATION STUDY IDENTIFIES NOVEL

LOCI ASSOCIATED WITH BIPOLAR DISORDER

Niamh Mullins 1 , ∗, Gio Pan 2 , Manuel Mattheisen 3 , Vassily Trubetskoy 2 , Stephan Ripke 2 , Laura Sloofman 1 , Alexander Charney 1 , Bipolar Disorder Psychchip Investigators, Bipolar Disorder Working Group 4 , Eli Stahl 1

1 Icahn School of Medicine at Mount Sinai 2 Charite 3 Aarhus University 4 Psychiatric Genomics Consortium

Background: Bipolar disorder (BD) is a common and severe mood disorder with a high heritability. Thirty genomic loci have previously been associated with BD in a large in- ternationally collaborative genome-wide association study (GWAS) conducted by the Psychiatric Genomics Consortium

(PGC). Here, we extend this work, by adding over 29,000 additional samples genotyped on the Psychchip array to identify novel associations with BD. Methods: We have performed GWAS meta-analysis of 48 cohorts to date, totaling 35,398 BD cases and 60,842 controls of European descent. Samples include 11,297 cases and 17,805 controls genotyped on the custom Psychchip array and 1,758 cases, 5,937 controls from a recent GWAS in the iPSYCH cohort. LD Score regression was used to investigate the genetic correlation between BD and a range of human diseases and traits. A transcriptome-wide association study (TWAS) was conducted using data on gene expression in the

prefrontal cortex from the CommonMind Consortium, to test whether predicted gene expression in the brain is associated with bipolar disorder. Work is currently underway to incorporate additional BD cohorts from population biobanks to this GWAS and perform genomic imputation of all cohorts to the Haplotype Reference Consortium panel. Results: Preliminary results from this GWAS indicate 48 independent genomic loci associated with BD (P < 5e-8). Of these loci, 24 were significant in the most recent BD GWAS conducted by the PGC, replicating regions including the CACNA1C, ANK3, TRANK1 and SCN2A genes. Two additional loci were previously reported in other GWAS on BD, including the MAD1L1 locus. The 22 novel regions from this study encompass genes encoding ion channels or neurotransmitter receptors such as KCNN3, CACNB2 and GRINA, and for the first time identifies the major histocompatibility complex (MHC) as genome-wide significant for bipolar disorder. TWAS found 7 genes in the novel loci where BD-associated SNPs significantly influence gene expression in the prefrontal cortex, four of which are in the MHC region. Genetic correlations were observed between BD and adult-onset psychiatric disorders, as well as behavioural traits such as risk taking (rG = 0.31, P = 2e-21) and smoking (rG = 0.20, P = 6e-13). Discussion: This is the largest GWAS on bipolar disorder to date and has thus far found 48 loci reaching genome-wide significance. Future work will examine the custom content of the Psychchip array, including low frequency variants, for association with BD. Bioinformatics approaches which leverage multi-omics data will be used to dissect the biological mechanisms underlying associated loci and prioritise causal genes and variants.


doi: 10.1016/j.euroneuro.2018.08.046

40

COMMON SNPS AND RARE CNVS CONFER RISK FOR

DIFFERENT CLASSES OF PSYCHIATRIC SYMPTOMS

Alexander Charney ∗, Pamela Sklar

Icahn School of Medicine at Mount Sinai

Background: Genetic studies of bipolar disorder (BD) over the past decade have shown that risk is conferred through many common single nucleotide polymorphisms (SNPs) of relatively weak effects, while evidence for a role of rare copy number variants (CNVs) has been found in some studies but not others. The putative lack of CNV burden stands as a point of contrast between BD and schizophrenia (SCZ), a clinically similar disorder with which BD has a very high SNP- based genetic correlation. Here, we perform a genome-wide CNV study in a cohort of 6,353 BD cases and 8,656 controls, a case sample size approximately 2.5-fold larger than earlier work. In addition to our primary analyses of CNV burden in BD compared to controls, we integrate CNV, SNP and clinical data from each subject in order to assess if different classes of genetic variation confer risk to different classes of psychiatric symptoms.



Abstracts 25

ARTICLE IN PRESS


Methods: For our primary analyses we tested CNV burden in BD cases compared to controls at various size and frequency thresholds and followed-up all previous reported associations of CNV burden in BD. Next, we tested if common SNPs and rare CNVs contribute to different psychiatric symptoms within BD using CNV burden and polygenic risk scores (PRS) derived from previous genome-wide association studies of schizophrenia (SCZ) and educational attainment (EDU), stratifying cases according to history of psychosis, age of onset, and severity of clinical course. Results: Primary analyses of CNV burden in 6,353 BD cases compared to 8.656 controls found no significant differences. In cases diagnosed with the bipolar subtype of schizoaffective disorder (SAB), an increased rate of large ( > 500KB), rare ( < 1% of sample) CNVs was observed relative to both controls (p-value = 0.001) and bipolar I disorder (BD I) cases (p-value = 0.0002). By definition, all SAB cases have a history of psychosis. To determine if CNV burden influenced risk for psychotic symptoms, we stratified BD I cases into a group with and a group without psychosis. CNV burden and SCZ PRS were significantly higher in SAB compared to either BD I group. In contrast, CNV burden did not differ between BD I groups stratified by psychosis, while SCZ PRS was higher in the group with psychosis compared to the group without. EDU PRS did not differ between SAB and either BD I group, or between the BD I groups. We next stratified cases into those with childhood onset ( < 12 years old) and average onset (18-24 years old). Lower EDU PRS was seen in childhood compared to average onset cases in both SAB and BD I, while the SCZ PRS was not associated with age of onset. In SAB a higher CNV burden was seen in childhood compared to average onset cases, while in both SAB and BD I the childhood onset cases with symptoms continuous throughout the life course had higher CNV burden compared to those with an episodic course. Discussion: In the largest study of CNVs in BD to date, we do not find evidence of CNV burden in 6,353 BD compared to 8,656 controls. However, we show that a more nuanced picture emerges when BD cases are stratified according to clinical phenomenology. By integrating CNV and SNP data with high-dimensional clinical data we begin to dissect the contribution of different classes of genetic variation to different symptom profiles in BD.


doi: 10.1016/j.euroneuro.2018.08.047

41

GENETIC RISK SCORES AND POSTPARTUM PSYCHIATRIC

DISORDERS

Anna Bauer 1 , ∗, Xiaoqin Liu 2 , Enda Byrne 3 , Patrick Sullivan 4 , Naomi Wray 3 , Esben Agerbo 5 , Mette Nyegaard 6 , Katja Ingstrup 2 , Benedicte Johannsen 2 , Merete Mægbæk 2 , Yunpeng Wang 7 , Merete Nordentoft 8 , Preben Bo Mortensen 6 , Trine Munk-Olsen 2 , Samantha Meltzer-Brody 4

1 UNC School of Medicine

2 NCRR-National Centre for Register-Based Research, Aarhus University 3 University of Queensland

4 University of North Carolina at Chapel Hill 5 CIRRAU - Centre for Integrated Register-based Research, National Centre for Register-based Research, Aarhus Uni- versity 6 Aarhus University 7 NORMENT, University of Oslo and Institute of Biological Psychiatry, St. Hans Hospital 8 Mental Health Centre Copenhagen, University of Copen- hagen

Background: The postpartum period is a particularly vul- nerable time for psychiatric disorders, with greater heritability than major depression (MD) outside of the perina- tal period. How genetic liability of postpartum psychiatric disorders varies by other significant risk factors is unknown. A single study has considered genetic risk scores (GRS) and risk of postpartum depression and found that GRS for bipolar disorder (BD) was more strongly associated with postpartum

depression than major depression outside the postpartum

period. We aimed to: 1) estimate associations of GRS for MD, BD, and schizophrenia (SCZ) with postpartum psychiatric disorders, and 2) examine differences by prior psychiatric history. Methods: We conducted a case-control study based on Dan- ish population-based registers. Association analyses were conducted between MD, BD, and SCZ GRS and case-control status of a postpartum psychiatric disorder. The study consisted of all women in the iPSYCH2012 cohort who had given birth before December 31, 2015 (n = 8,931). Cases were women with a diagnosed psychiatric disorder or a filled psy- chotropic prescription within one year after delivery (n =

6,516 cases, 2,334 controls). Genome-wide data were obtained from neonatal biobanks. Information on case status, family psychiatric history, personal psychiatric history, and other demographic factors were obtained from national registers. Results from meta-analyses of MD, BD, and SCZ from

the Psychiatric Genomics Consortium including 23andMe and excluding individuals in the iPSYCH2012 cohort were applied to calculate genetic risk scores. Results are presented for SNPs with P < 0.05. Results: Previously identified risk factors including primi- parity and family history of psychiatric disorders were confirmed. Parental psychiatric history was associated with postpartum psychiatric disorders among women with previous psychiatric history (OR, 1.14; 95% CI 1.02 – 1.28) but not without psychiatric history (OR, 1.08; 95% CI: 0.81 – 1.43). GRS for MD was associated with an increased risk of postpartum psychiatric disorders in both women with (OR, 1.44; 95% CI: 1.19 – 1.74, per 10-decile increase) and without (OR, 1.88; 95% CI: 1.26 – 2.81, per 10-decile increase) psychiatric history. GRS for BD and SCZ were associated with risk of psychiatric disorders at any time point, but not with postpartum psychiatric disorders in our study. Discussion: Our cases predominantly consisted of depression diagnoses and women prescribed antidepressants, thus, it is encouraging that MD GRS predicts psychiatric episodes in our target sample of postpartum women. We did not find an expected association between GRS BD and postpartum


26 Abstracts

ARTICLE IN PRESS


psychiatric disorders; however, this may be due to case selection and lower power of the BD GRS discovery sample. In conclusion, genetic liability for major depression was associated with postpartum psychiatric disorders, suggesting GRS can provide additional information about risk not en- compassed solely in simple measures of family history.


doi: 10.1016/j.euroneuro.2018.08.048

42

THE COST OF ADVANCED LANGUAGE ABILITY: IN- CREASED RISK FOR MENTAL ILLNESS?

Tanner Koomar 1 , Bruce Tomblin 1 , James Potash 2 , Jacob Michaelson 1 , ∗

1 University of Iowa 2 Johns Hopkins University

Background: Emerging results from our whole genome sequencing studies of language suggest that so-called damaging variation in specific brain genes and pathways actually leads to increased language ability. These same genes were found to be enriched (P = 0.003; OR = 1.7) for genomic interactions with human accelerated regions (HARs), which are conserved non-coding elements showing human-specific accelerated substitution rates. Taken together, these findings suggest there may be human-specific selective pressures in favor of hypomorphic alleles in these pathways, resulting in overall improved abilities in language and social communication. It remains unclear, however, what brain and behavior trade-offs may be incurred as a result of the accumu- lation of these hypomorphic alleles. Such a tradeoff might manifest in individuals with superior language and social communication skills who also have a corresponding eleva- tion in risk for mental illness. Previous reports in the literature have noted these patterns in performance artists such as actors and comedians. Methods: In light of these observations, we sought to directly test the hypothesis that genes and pathways that contribute positively to language ability with increasing genetic burden ("burden-positive", N = 472 genes from our pathway burden model presented elsewhere at this meeting) show a corresponding excess of risk for neuropsychiatric conditions, such as bipolar disorder and schizophrenia. Results: When examining the results of a study of rare variation in bipolar disorder, we found that predicted language ability (based on genetic factors) was significantly and positively correlated with affected status (logistic regression P = 0.003; Z = 2.93). Further examination revealed that this trend was disproportionately driven by the presence of damaging variation (singleton variants with CADD > 15) in our burden-positive language genes, lending support to the "trade off" hypothesis (logistic regression P = 0.004; Z = 2.85). Examination of summary statistics from genome-wide association studies (GWAS) of bipolar disorder further supported burden-positive language genes having significantly elevated risk for bipolar disorder (P = 0.003; OR = 4.17 at a gene-wise median bipolar risk threshold of OR > 1.12 and

P < 0.01). Similar findings were obtained when examining schizophrenia rare and common variant studies. In contrast, we have observed no such risk relationship for burden- positive language genes in neurodevelopmental conditions (such as ADHD or autism), suggesting that the relationship may be specific to adult-onset conditions (like bipolar disorder and schizophrenia). Discussion: Together, these findings suggest an intimate relationship between the genetic potentiators of human language and the risk mechanisms for major mental illness.


doi: 10.1016/j.euroneuro.2018.08.049


1:15 p.m. - 2:45 p.m.

43

RARE COPY NUMBER VARIATIONS ARE ASSOCIATED

WITH POORER COGNITION IN SCHIZOPHRENIA

Leon Hubbard 1 , ∗, Elliott Rees 2 , Derek Morris 3 , Amy Lynham

1 , Sophie Legge 1 , Denise Harold 4 , Stanley Zammit 1 , Aiden Corvin 4 , Michael Gill 4 , Michael O’Donovan 1 , Michael Owen 5 , Gary Donohoe 6 , George Kirov 1 , Andrew Pocklington 1 , James T.R. Walters 1

1 Cardiff University 2 MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neuro- sciences, Cardiff University 3 Neuroimaging and Cognitive Genomics (NICOG) Centre, School of Psychology, National University of Ireland Galway 4 Trinity College Dublin

5 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University 6 National University of Ireland Galway

Background: Rare copy number variations (CNVs) are associated with increased risk of neuropsychiatric disorders and lower cognitive ability in healthy individuals. Their contributions to cognitive functioning in people with schizophrenia have not been clearly defined. We compared cognitive ability in schizophrenia patients with and without known schizophrenia CNVs. Methods: 479 participants with schizophrenia recruited in the UK were used in a within-case cross-sectional design in the Cardiff Cognition in Schizophrenia sample (Cardif- fCOGS). We sought replication in an independent Irish sample of 519 participants with schizophrenia, schizoaffective disorder or schizophreniform psychosis. We investigated the relationship between schizophrenia CNV status and general cognitive ability, measured using the MATRICS composite z- score and estimated premorbid IQ in CardiffCOGS using linear regression. In the Irish sample, general cognitive ability was measured using full-scale IQ. Results: In CardiffCOGS, individuals with a schizophrenia CNV (N = 8) performed over one standard deviation below cases without a schizophrenia CNV (N = 471) on the MATRICS composite z-score ( β= -1.16., 95%CI = -2.06



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to -0.25, p = 0.011). In the Irish sample individuals with schizophrenia CNVs (N = 7) performed 12 full-scale IQ points lower than cases without a schizophrenia CNV (n = 512) ( β= -0.96, 95%CI = -1.82 to -0.11, p = 0.027), replicating the direction and magnitude of effect observed in Cardif- fCOGS. Meta-analysis of the two samples showed those with schizophrenia CNVs (n = 15) performed approximately one standard deviation below those with no schizophrenia CNV (n = 983) ( β= -1.06, 95%CI = -1.67 to -0.44, p = 8x10-4). We also observed schizophrenia CNV carriers in CardiffCOGS were also impaired on a measure of estimated premorbid IQ (NART; 9 IQ points difference, β= -1.01, 95%CI = -1.71 to -0.32, p = 0.004). Discussion: In the largest combined schizophrenia sample to date with cognition data, we have demonstrated that individuals with the disorder who are carriers of schizophrenia CNVs display clinically important levels of cognitive impairment.


doi: 10.1016/j.euroneuro.2018.08.050

44

A TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY

ASSESSING A CAUSAL ASSOCIATION BETWEEN NEU- ROTICISM AND SCHIZOPHRENIA

Hannah Jones 1 , ∗, George Davey Smith 1 , Michael O’Donovan 2 , Michael Owen 2 , James Walters 2 , Stanley Zammit 2

1 University of Bristol 2 Cardiff University

Background: Anxiety is a prominent feature of schizophrenia, present in the prodromal phase of the illness. There is strong evidence that the personality trait neuroticism, an underlying factor strongly associated with anxiety, is genetically correlated with schizophrenia, implying that neuroticism and schizophrenia share genetic risk factors in common. However, a genetic correlation may also suggest a possible role of neuroticism in the pathogenesis of schizophrenia. We therefore performed a Mendelian randomization (MR) analysis using publicly available data to investigate the potential casual association between neuroticism and schizophrenia. Methods: We performed bi-directional two-sample MR between neuroticism and schizophrenia using the most recent publicly available summary-level genome-wide data. Independent, genome-wide significant (p ≤ 5e-8) single nucleotide polymorphisms (SNPs) associated with neuroticism

(91 SNPs) and schizophrenia (88 SNPs) were combined using an inverse-variance-weighted (IVW) multiplicative random effects approach. Impact of potential MR assumption violations were explored using weighted median, weighted mode and MR Egger methods. The MR assumption of no measurement error in the SNP exposure associations (NOME assumption) was assessed using FGX (IVW) and I2GX (MR Egger) statistics and presence of heterogeneity was assessed using

Cochran’s (IVW) and Rücker’s (MR Egger) Q tests. All analyses were performed using the TwoSampleMR R package. Results: The IVW MR method provided evidence of a casual effect of genetically instrumented neuroticism on risk of schizophrenia (p = 0.018). This causal association was also evident when using the weighted median (p = 0.002) and weighted mode (p = 0.018) approaches. However, the MR Egger intercept provided strong evidence of presence of horizontal pleiotropy (p < 0.001) and Q tests provided strong evidence of presence of heterogeneity in the effect estimates (p < 0.001). There was also evidence of a causal effect of schizophrenia on neuroticism (IVW p = 0.001), however evidence was weaker when using the weighted median (p = 0.148) and weighted mode (p = 0.150) approaches. The I2GX statistic indicated potential violation of the NOME assumption and Q tests again provided strong evidence of presence of heterogeneity in the effect estimates (p <

0.001). Discussion: Assuming certain MR assumptions are met, our results provide evidence of a causal effect of neuroticism

on schizophrenia, and also of schizophrenia risk on neuroticism. However, MR Egger analyses provided evidence of horizontal pleiotropy and Q tests provided strong evidence of presence of heterogeneity. A potential implication of our evidence supporting a causal effect of neuroticism on schizophrenia is that a greater focus on treatment of anxiety in high-risk subjects could reduce risk of transition to psychosis, however further work is needed, potentially using biologically relevant sub-sets of anxiety specific SNPs, to establish whether the associations between anxiety and schizophrenia is causal.


doi: 10.1016/j.euroneuro.2018.08.051

45

INTERROGATING THE EVOLUTIONARY PARADOX OF

SCHIZOPHRENIA: A NOVEL EVIDENCE FROM NEAN- DERTHALS AND DENISOVANS

Chenxing Liu 1 , ∗, Christos Pantelis 1 , Ian Paul Everall 2 , Chad Bousman 3

1 The University of Melbourne 2 Institute of Psychiatry, Psychology and Neuroscience, King’s College London

3 University of Calgary

Background: Schizophrenia is a psychiatric disorder with a worldwide prevalence of 1%. The high heritability and reduced fertility among schizophrenia patients have raised an evolutionary paradox: why has negative selection not elimi- nated schizophrenia associated alleles during the evolution process? Methods: To address this question, we examined evolutionary markers, known as modern-human-specific sites and archaic-human-specific sites, using existing genome- wide association study data from 34,241 individuals with schizophrenia and 45,604 healthy controls included in the Psychiatric Genomics Consortium.



28 Abstracts

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Results: By testing the distribution of schizophrenia SNPs with risk and protective effects in the human-specific sites, we observed a positive selection of protective alleles for schizophrenia in modern humans relative to archaic humans (Neanderthal and Denisovans). Such findings indicated that risk alleles of schizophrenia have been gradually removed from the modern human genome due to negative selection pressure. Discussion: Our study, for the first time, provides experimental evidence supporting the role of negative selection in eliminating risk alleles for schizophrenia. The present findings expand on the notion of schizophrenia as a ‘by product’ of obtaining high-level cognition, contribute to our understanding of the genetic origins of schizophrenia, and should assist future research aimed at unraveling the genetic architecture of the disorder.


doi: 10.1016/j.euroneuro.2018.08.052

46

USING PLEIOTROPY TO DISSECT FUNCTIONAL PATH- WAYS IN COGNITION, EDUCATION, AND SCHIZOPHRENIA

Max Lam

1 , ∗, Todd Lencz 2

1 Institute of Mental Health

2 Zucker Hillside Hospital

Background: Multiple studies have demonstrated an inverse correlation between the genetic architectures of cognition and schizophrenia (Rg ∼.20), and a positive genetic correlation between education and cognition (Rg ∼ .70). Paradox- ically, however, a modest (though consistent) positive association between schizophrenia and educational has also been reported. Here, we leverage prior results of multiple GWASs on cognition, education, and schizophrenia to investigate this phenomenon, in an attempt to parse biological mechanisms that might shed light on the etiopathogenesis of schizophrenia. Methods: GWAS summary statistics for cognition (N = 107,207), education (N = 328.917), and schizophrenia (N = 77,096) were meta-analyzed using a technique known as Analysis based on SubSets (ASSET), which allows pleiotropic signals across the entire genome to be pooled using a two-tailed z-test approach. Genomic loci were consolidated within subsets representing overlaps between the three phenotypes. These subsets were subjected to further downstream analyses including S-Predixcan brain tissue expression profile analysis (GTex and CommonMind); a series of MAGMA competitive and GENE2FUNC hyper- geometric pathway analyses; and LD-score regressions for genetic correlations with a range of other phenotypic traits. Results: ASSET analysis revealed 300 lead SNPs across 235 loci met the genome-wide significance threshold of p < 5e-8. Of these, 103 loci were novel when compared with the initial input GWAS. We focused downstream analyses on comparing two key subsets of variants: i) SNPs which showed effect concordance, where alleles conferred advantages to cognitive performances, and education while being protec-

tive for schizophrenia; ii) SNPs demonstrating paradoxical effects between schizophrenia and education, where alleles confer risk to schizophrenia but also advantage to education. “Concordant” variants were more likely to implicate early neurodevelopment pathways such as CHD8, cell adhesion, cell projection, and cell metabolic processes. By contrast, “paradoxical” variants were associated with adult neuronal function pathways such as voltage-gated ion channel signaling, synaptic transmission, and postsynaptic activity. Genetic correlation analyses suggest that the paradoxical subset of variants may be elucidating a disease con- tinuum that implicates BMI, ADHD, cardiovascular disease, mood and bipolar disorder. Discussion: Pleiotropic analysis successfully identified more than 100 novel loci underlying brain function, many of which are in the process of validation by larger, more recent single-phenotype GWAS. Using a subset-based technique, we were able to isolate subsets of genomic variants that could not have been otherwise investigated using standard inverse-variance based meta-analysis or genome- wide based multi-trait analysis. Leveraging the joint genetic correlations of cognition, education, and schizophrenia, we were able to identify two broad biological mechanisms –early neurodevelopmental and adulthood synaptic pruning pathways that contribute not only to the etiopathogenesis of schizophrenia, but broader biological dimensions that are implicated in both general health and psychiatric illness. Further work is necessary to further identify specific aspects of how these biological mechanisms could be implicated in the disease process.


doi: 10.1016/j.euroneuro.2018.08.053

47

TRANSCRIPTOME-WIDE ASSOCIATION ANALYSIS OF

SCHIZOPHRENIA IMPLICATES PROCESSES INVOLVED

IN SYNAPTIC DEVELOPMENT AND PLASTICITY, AND

IMPAIRED LONG-TERM POTENTIATION

Lynsey Hall 1 , ∗, Christopher Medway 1 , Antonio Pardiñas 1 , Elliott Rees 2 , Valentina Escott-Price 2 , Andrew Pocklington 1 , James Walters 1 , Peter Holmans 1 , Michael Owen 2 , Michael O’Donovan 1

1 Cardiff University 2 MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University

Background: Schizophrenia is a complex highly heritable disorder. Genome-wide association studies have identified multiple loci that influence the risk of developing schizophrenia, although the causal variants driving these associations and their impacts on specific genes are largely unknown. Here we seek to link genetic findings to changes in gene expression in human brain by performing a transcriptome-wide association study in which we integrate the largest published genome-wide association dataset (40,675 schizophrenia cases and 64,643 controls) of



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schizophrenia with publicly available post-mortem expression data from the dorsolateral prefrontal cortex (DLPFC). Methods: TWAS was performed using FUSION software, using genome-wide summary statistics from the PGC2 + CLOZUK schizophrenia meta-analysis and DLPFC expression weights from the Common Mind Consortium for genes with significantly non-zero cis-heritable expression. A competitive gene-set enrichment analysis, where gene-set membership is a linear predictor of absolute TWAS z-score, was performed using MAGMA. This analysis was carried out for two different gene-sets; i) 10 significant gene-sets reported in the CLOZUK + PGC2 manuscript and ii) 6677 data- driven gene-sets. Results: We identify a significant correlation between schizophrenia risk and expression at 89 genes in DLPFC, including 42 genes not identified in earlier similar studies of this tissue. Genes whose expression correlate with schizophrenia were enriched for those involved in CNS synaptic transmission and long-term potentiation. 33 significant TWAS genes, corresponding to 28 distinct loci, fell outside established schizophrenia risk loci as defined by the CLOZUK + PGC2 meta-analysis. Given that TWAS might have greater power than GWAS to detect disease-associated genes when multiple variants influence the phenotype through changes in expression, we anticipate that among these loci will be true schizophrenia associated genes. For gene-set analysis, of the 10 significant CLOZUK + PGC2 gene- sets, 2 were significantly enriched for TWAS signal; abnormal long-term potentiation (p = 6.03E-05) and genes that are intolerant to loss-of-function mutations (loss-of-function intolerance; p = 1.73E-04). In the broader data driven analysis of 6159 gene sets, significant enrichment was found for 5 gene sets related to nervous system development, abnormal synaptic transmission, abnormal long-term potentiation, reduced long term potentiation, and calcium-dependent cell- cell adhesion, with all but abnormal long term potentiation remaining significant after conditional analysis. Discussion: Previous genetic studies have implicated post-synaptic glutamatergic and gabaergic processes in schizophrenia; here we extend this to include molecules that regulate presynaptic transmitter release. We identify large numbers of specific candidate genes to which we assign predicted directions of effect in terms of expression level, facilitating downstream experimental studies geared towards a better mechanistic understanding of schizophrenia pathogenesis.


doi: 10.1016/j.euroneuro.2018.08.054

48

AN EVOLUTIONARY EPIGENETICS APPROACH TO

SCHIZOPHRENIA

Niladri Banerjee ∗, Tatiana Polushina , Stephanie Le Hellard

University of Bergen

Background: Schizophrenia is a psychotic disorder with an estimated lifetime prevalence of ∼1% worldwide. Despite

reduced fecundity of patients, this rate is stable across population groups separated by geography and time. To explain this stable occurrence of the disorder, the ‘Evolution- ary hypothesis of schizophrenia’ has been gaining ground. The most well-known version of the hypothesis was proposi- tioned by TJ Crow in 1998. The key assertion made by Crow

was the emergence of schizophrenia as a by-product of human evolution via language.

In the past two decades, the emergence of genomic technologies and resources has made it possible to test the evolutionary aspect of the hypothesis. There is a growing body of evidence from the field of genomics that supports the notion that human evolution may have played a role in schizophrenia.

Developments within the last five years have allowed researchers to trace the evolution of epigenomes as well, giv- ing an unprecedented window on gene-environment (GxE) interactions of the past several thousand to millions of years. As part of my PhD, I undertook a body of work that looks at the evolutionary question of schizophrenia from this new field of evolutionary epigenetics. Methods: We first investigate whether human-specific differentially methylated regions (DMRs), determined in comparison to Neanderthals and Denisovans (Gokhman et al, 2014) are enriched for schizophrenia markers. These methylated regions represent at least 750,000 years of evolution since the last common ancestor diverged from Ne- anderthals and Denisovans. We utilized summary statistics from genome-wide association studies (GWAS) of schizophrenia and 12 other phenotypes and tested for enrichment of association in human, neanderthal & denisovan DMRs using a polygenic enrichment testing pipeline (Schork et al, 2013). This was followed up by investigating primate- methylated regions that represent at least 13MYA of epige- nomic evolution. Finally, we investigate whether human- specific methylated regions, as defined in the first study are amenable to methylation variation in actual patients with schizophrenia using a candidate evidence-based approach. Results: We find evidence that recent evolution denoted by human-specific methylated regions tracing ∼750,000 years of methylation development are enriched for schizophrenia markers (Banerjee et al, 2018). Primate methylation markers are not enriched for schizophrenia variants with the exception of the extended Major-Histocompatibility Region (MHC) region (Paper II, Schizophrenia Research, under re- view). Finally, we find evidence of methylation disruption in brain samples of patients with schizophrenia in regions that underwent human-specific methylation evolution (Paper III, manuscript). Discussion: Our results provide support that recent evolution, denoted by methylation changes since the divergence of the common ancestor of humans, Neanderthals and Denisovans, may have played a role in susceptibility to schizophrenia at a group-level.


doi: 10.1016/j.euroneuro.2018.08.055



30 Abstracts

ARTICLE IN PRESS


Sunday, October 14, 2018


12:30 p.m. - 2:00 p.m.

49

DYSFUNCTION OF CACNA1I IMPAIRED SLEEP SPIN- DLES DURING NREM

Ayan Ghoshal 1 , david Uygun 2 , lingling Yang 1 , Violeta Lopez-Huerta 1 , Mario Arias Garcia 1 , Zhanyan Fu 1 , James McNally 2 , Qiangge Zhang 3 , Xiaohong Mao 4 , Thomas Nicholson 4 , Guoping Feng 3 , Robert Strecker 2 , Shaun Purcell 5 , Qian Pan 1 , ∗

1 Broad Institute of Harvard and MIT

2 Harvard Medical School 3 MIT

4 Novartis Institute of Biomedical Research

5 Icahn School of Medicine at Mount Sinai

Background: Schizophrenia affects 1% of the population yet has no effective treatments. Antipsychotic medications can interrupt positive symptoms such as hallucinations in a subset of individuals, but they have little impact on negative or cognitive symptoms. Novel and more effective treatments for schizophrenia patients depend on emerging clues from the recent psychiatric genetics that are expected to converge on neurobiological pathways. CACNA1I is a schizophrenia risk gene identified in GWAS. It encodes the pore-forming human CaV3.3 α1 subunit, a subtype of voltage-gated calcium channel that contributes to T-type currents. CaV3.3 expresses abundantly in Thalamic Reticu- lar Nucleus (TRN) and mediate rebound bursting in TRN that plays a key role in sleep spindle generation. Previously, we reported biophysical and biochemical deficits of a missense variant R1346H of hCaV3.3, a de novo mutation discovered in a schizophrenia patient, when expressed in human cell lines. Methods: We have generated CaV3.3-knockout and CaV3.3- R1305H knock-in animals using CRISPR-cas9 genome editing and studied the neurobiological phenotypes of these mice. We studied the whole-cell patch clamp of TRN neurons, as well as TRN excitability using ex vivo brain slice recording. Subsequently, we utilized in vivo EEG recording of naturally wake/sleep animals to investigate the impact of CaV3.3 on sleep spindles. Results: We showed that R1346H is associated with lower CaV3.3 membrane-bound levels, together with lower whole cell T-type current density in TRN neurons. Reduction in CaV3.3 function was also accompanied by reduced rebound burst firing of TRN neurons. This reduction in TRN function was reflected in a significant reduction in NREM specific spindle oscillations in both the KO and R1346H mice, detected using a novel automated wavelet algorithm, without alternation in the overall sleep architecture. Discussion: As sleep spindle density in schizophrenia patients have been shown to be reduced, our data suggest a potential causal link between CACNA1I and certain neu-

rophysiological deficits in schizophrenia patients. Together with additional genetics clues, CaV3.3 may represent a novel target of schizophrenia where modifying thalamocor- tical connection could be a novel therapeutic strategy for treating schizophrenia.


doi: 10.1016/j.euroneuro.2018.08.056

50

DE NOVO LOF AND DAMAGING MISSENSE VARIANTS ASSOCIATED WITH SCHIZOPHRENIA ARE CONCEN- TRATED IN LOF INTOLERANT GENES

Elliott Rees 1 , ∗, Jun Han 2 , Madeleine Duffield 2 , Sarah Tosato 3 , Celso Arango 4 , Micha Gawlik 5 , Jim van Os 6 , Andrew Pocklington 7 , Valentina Escott-Price 2 , George Kirov 7 , Peter Holmans 7 , James Walters 7 , Michael Owen 2 , Michael O’Donovan 7

1 MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neuro- sciences, Cardiff University 2 MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University 3 University of Verona 4 Universitario Gregorio Marañón

5 University of Wuerzburg 6 University of Utrecht 7 Cardiff University

Background: Exome sequencing studies have shown de novo coding variants to be important risk factors for neuropsychiatric disorders, such as intellectual disability (ID), autism spectrum disorder (ASD), and schizophrenia. The risk from de novo LoF variants for ASD and ID has been refined to those not observed as standing variation in large population reference exomes and occurring in LoF intolerant genes. A similar non-significant trend has been observed for schizophrenia, although current trio samples are smaller than in studies of ID and ASD. Here we analysed de novo variants in a new schizophrenia sample (n = 624) and combined it with published data (total n = 1,763) to refine the risk for schizophrenia from de novo coding variants to specific genes. Methods: We performed exome sequencing of 675 new proband-parent schizophrenia trios using Illumina HiSeq4000 instruments. A validation experiment was performed through Sanger sequencing 150 putative de novo variants. To refine de novo risk variants, we followed published methods by comparing the rate of de novo variants not observed/observed in ExAC and occurring in LoF intolerant/LoF tolerant genes between schizophrenia and unaffected siblings from published ASD studies. The rate of synonymous de novo variants was used as a negative control. Single Gene and gene-set enrichment tests compared the observed number of de novo variants to that expected based on per-gene mutation rates. Results: After stringent sample and variant quality control, we observed 650 coding de novo variants in 624 trios, corre-


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sponding to a coding de novo rate (1.04 per proband) consistent with the literature. A high validation rate was observed for both SNVs (0.95) and indels (0.94). The rate of synonymous de novo variants was not significantly different between schizophrenia and controls for any test. Although the rate of LoF de novo variants (both observed and unobserved in ExAC) did not differ between schizophrenia and controls in LoF tolerant genes, a significant excess of schizophrenia variants not observed in ExAC was found in LoF intolerant genes (risk ratio (RR) = 1.86, P = 0.00053). The rate of damaging missense de novo variants (defined as variants with MPC scores > 2) not observed in ExAC in LoF intolerant genes was also increased in schizophrenia (RR = 2.13, P = 0.0032). In an analysis of 134 central nervous system gene sets, FMRP targets were associated with nonsynonymous de novos after correction for multiple testing. No single gene remained significant after correction for multiple testing. Discussion: Our new exome sequencing sample substantially increases the number of de novo variants studied in schizophrenia, thus increasing our power to identify risk variants. Following previous observations in studies of ASD

and ID, we find significant evidence that schizophrenia de novo variants not observed as standing variation in ExAC are enriched in LoF intolerant genes. We also provide novel evidence that damaging missense de novo variants in these genes confer similar levels of risk as LoF de novo variants. However, no novel genes were associated with schizophrenia after correction for multiple testing, suggesting even larger samples are required for novel gene discovery.


doi: 10.1016/j.euroneuro.2018.08.057

51

IDENTIFICATION OF NEW GENES ASSOCIATED WITH

CHILDHOOD-ONSET SCHIZOPHRENIA: ATP1A3 AND THE

FXYD GENE FAMILY

Boris Chaumette 1 , ∗, Vladimir Ferrafiat 2 , Amirthagowri Ambalavanan 1 , Alice Goldenberg 2 , Alexandre Dionne-Laporte 1 , Dan Spiegelman 1 , Patrick Dion 1 , Priscille Gerardin 2 , Claudine Laurent 3 , David Cohen 3 , Judith Rapoport 4 , Guy Rouleau 1

1 McGill University 2 CHU Rouen

3 Hopital Pitié Salpêtrière 4 NIMH

Background: Childhood-onset schizophrenia (COS) is a severe form of schizophrenia defined as onset before age of 13. This juvenile form of schizophrenia has similar disease manifestations to adult-onset schizophrenia with which it shares a number of genetic risk factors. It is a rare disease with a prevalence estimated to be of 0.03%. The rate of comorbidity of other neurodevelopmental disorders and medical conditions are higher than in forms of schizophrenia with a later onset. Methods: We identified two unrelated cases diagnosed with both COS and alternating hemiplegia of childhood (AHC).

AHC is a rare disease starting typically before the age of 18 months and the clinical presentation is characterized by repeated episodes of hemiplegia that alternately affects one side of the body. A few genes are known to be associated with AHC and these guided our genetic exploration of the two patients using targeted sequencing of the ATP1A3 gene. Then, we replicated our findings in an independent cohort of 17 trios of unrelated COS cases recruited in the NIMH. We performed whole exome sequencing (WES) and specifically investigated this gene and its known brain-expressed inter- actors. Only variants in exonic positions, with a frequency < 0.01 in the 1000 Genome project and ExAC database, identified as possibly damaging by at-least three algorithms, and in a phylogenetically-conserved position were retained. Results: In the cases with comorbid AHC, two distinct pathogenic de novo variants were identified in the ATP1A3 gene. In the replication cohort, we identified a third case with a possibly damaging missense variant in the same exon of ATP1A3. Three other cases presenting missense variants predicted to be deleterious in genes from the FXYD family (FXYD1, FXYD6, and FXYD6-FXYD2 readthrough) were observed. ATP1A3 encodes the α-subunit of a neuron- specific ATP-dependent transmembrane sodium-potassium

pump. FXYD genes encode proteins that modulate the ATP- dependent pump function. Their patterns of brain expression are compatible with the age of onset. Discussion: Our report is the first to identify variants in the same pathway for COS. It illustrates the interest of exploring medical comorbidities and stratifying a complex condition according to the age of onset for the identification of deleterious missense variants. ATP1A3 is a replicated gene in rare neuropediatric diseases and we extended the phenotype to a pure psychiatric presentation. The association with rare variants in FXYD gene family is novel. Finally, our study highlights the interest of DNA sequencing in psychiatry and opens the way to genetic counseling.


doi: 10.1016/j.euroneuro.2018.08.058

52

SCHIZOPHRENIA AND URBAN LIVING: A STUDY ON

HALF A MILLION PEOPLE FROM THREE COUNTRIES

Lucia Colodro Conde 1 , ∗, Baptiste Couvy-Duchesne 2 , John Whitfield 1 , Fabian Streit 3 , Loic Yengo 2 , Maciej Trzaskowski 2 , Eveline de Zeeuw

4 , Michel Nivard 4 , David Whiteman 1 , Dorret Boomsma 4 , Jian Yang 2 , Marcella Rietschel 3 , John McGrath 5 , Sarah E. Medland 1 , Nick Martin 6

1 QIMR Berghofer Medical Research Institute 2 University of Queensland

3 Central Institute of Mental Health

4 VU University 5 QLD Brain Institute 6 Queensland Institute of Medical Research

Background: Schizophrenia is more prevalent in urban than in rural areas. This association has been observed in differ-



32 Abstracts

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ent countries and remains significant after controlling for possible confounders (OR [95% CI] = 1.72 [1.53, 1.92] according to meta-analysis). A large body of research has been generated on this topic after the classic study from Faris and Dunham in 1931. The stress of urban living has been proposed as a risk (and causal) factor for the disease.

We have investigated an alternative (but not incompat- ible) explanation: that people with higher genetic risk for schizophrenia tend to live in more urbanized areas due to selective migration in either past or current generations. Methods: We used data of genotyped adults individuals from

four independent non clinical cohorts. Analyses performed in the discovery cohort (QIMR, Australia, N = 15,544) were replicated in participants from the UKB (United Kingdom, N = 456,426), the NTR (The Netherlands, N = 16,434) and QSKIN (Australia, N = 15,726).

We calculated polygenic risk scores (PRS) of schizophrenia for our participants, using the summary statistics of the GWAS on schizophrenia published in 2014 by the Psychiatric Genomics Consortium. We fit linear mixed models with population density of the area of residence as the outcome and the PRS as a predictor, controlling for demographic variables and ancestry principal components.

Using multi-instrument Mendelian Randomisation (MR) we tested the hypothesis that having a higher propensity to schizophrenia causes a person to live in a denser and less remote area. Although with reduced statistical power, we also tested the reverse causation hypothesis that population density induces the onset of schizophrenia. Results: Our polygenic risk score analysis showed that PRS for schizophrenia predicted population density in our discovery and replication cohorts, so a higher genetic risk for the disease was associated with a denser area of residence. This association remained significant after controlling for socio-economic status (SES) of the area of residence. We also tested whether PRS for schizophrenia predicted SES of the area of residence. Results were only significant in the UKB, so a higher genetic risk would be associated with living in more deprived neighbourhoods.

Our MR results suggested that schizophrenia could be a causal factor for living in denser areas. These results were only significant in the UKB, our largest cohort. Results are suggestive of a reverse positive causal relationship between population density and schizophrenia, with an effect size five times higher than the effect observed for schizophrenia causing living in more populated areas (0.20 vs. 0.04). Discussion: Our study investigates the association between genetic risk for schizophrenia and characteristics of where people live with the aim of increasing our knowledge of why this disease is more prevalent in cities. We used data on where people live collected as part of four studies, from

three countries (Australia, UK, Netherlands) for a total number of 504,130 participants.

Our results show that the distribution of the genetic risk for the disorder is not uniform and concentrates in more populated, providing empirical evidence that the increased schizophrenia prevalence in urbanized areas is not only due to the environmental stressors of the city but also on the genetic risk for the disease. Altogether, our results support the selective migration hypothesis.


doi: 10.1016/j.euroneuro.2018.08.059

53

CONTROLLING TYPE 1 ERROR RATE IN CNV GENE- SET ENRICHMENT TESTS

Andrew Pocklington 1 , ∗, Elliott Rees 2 , George Kirov 1 , James Walters 1 , Peter Holmans 1 , Michael Owen 2 , Michael O’Donovan 1

1 Cardiff University 2 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University

Background: Gene-set enrichment analysis of case-control CNV data has been instrumental in identifying association between the genetic perturbation of synaptic function and schizophrenia risk (Kirov et al. 2012, Szatkiewicz et al. 2014, Pocklington et al. 2015, Marshall et al. 2017). The regression models used in these analyses belong to the class of self-contained tests, which are known to inabste gene-set enrichment statistics in common variant analyses (de Leeuw

et al. 2015). More recent evidence indicates that inabstion is also present in CNV gene-set enrichment statistics (Singh et al. 2017). Here we evaluate the extent to which inabsted type 1 error rates confound synapse gene-set association in schizophrenia case-control CNV analyses. Methods: Analyses were carried out on the combined ISC, MGS and CLOZUK CNV dataset of (Rees et al. 2016). A standard logistic regression-based enrichment test (Pocklington et al. 2015) was performed on randomly generated gene- sets to evaluate the rate of type 1 errors in this dataset. We then investigated methods for overcoming p-value inabstion based upon comparison with random gene-sets or through fitting a generalised least-squares regression model. These were used to generate unbiased p-values for synaptic gene- sets previously associated with schizophrenia via CNVs. Results: The p-values for randomly generated, biologically uninformative gene-sets diverged strongly from the expected null distribution, showing clear evidence of inabstion. A number of synaptic gene-sets previously reported to be enriched in CNVs from individuals with schizophrenia survive correction for inabstion of the test statistic. Discussion: First noted in gene-set enrichment analyses of common polymorphisms (de Leeuw et al. 2015), susceptibility to p-value inabstion is an inherent weakness of self-contained tests that must be taken into account when analysing other classes of variant. In the case of CNVs, inabstion is driven by rare (MAF < 0.1%) and highly multi-genic CNVs (Singh et al, 2017), a class which contains all robustly associated loci. Attempting to ameliorate inabstion by removing these loci prior to analysis (equivalent to removing all genome-wide significant loci from a GWAS) is not a sat- isfactory solution. While a self-contained test can in principle be converted to a competitive test through comparison to random gene-sets, this is typically a time-intensive pro- cedure and alternative methods capable of accounting for correlation between genes are preferable. Enrichment of


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risk variants in synapse-related gene-sets remains one of the most consistent genetic findings in schizophrenia to date.


doi: 10.1016/j.euroneuro.2018.08.060

54

EXOME SEQUENCING OF 23,851 CASES IMPLICATES NOVEL RISK GENES AND PROVIDES INSIGHTS INTO THE

GENETIC ARCHITECTURE OF SCHIZOPHRENIA

Tarjinder Singh ∗, Benjamin Neale , Mark Daly , SCHEMA Consortium

Massachusetts General Hospital

Background: Schizophrenia, a debilitating psychiatric disorder, has a substantial genetic component with common intergenic and rare coding variants contributing to risk. De- spite the discovery of hundreds of common risk loci, only a handful of associations have resulted in validated functional variants that pinpoint novel biology underlying disease pathogenesis. This central challenge is shared with most complex polygenic disorders. To address this short- coming, sequencing studies of rare coding variants can complement existing approaches by pinpointing likely causal genes overlapping common risk loci, and complete the allelic spectrum in disease genes. However, success to this end has been hampered by power limitations. Methods: The Schizophrenia Exome Sequencing Meta- Analysis (SCHEMA) Consortium is a global effort to analyze whole-exome and genome sequencing data to advance gene discovery. We have sequenced 23,851 cases and 50,996 controls, which include individuals of European, Latin Amer- ican, East Asian, Ashkenazi Jewish, and African American ancestry. We performed extensive quality control steps on all data jointly, with consideration of coverage differences between capture technologies. We similarly processed an additional 56,100 non-psychiatric samples from gnomAD for use as population controls. Our expanded data set consists of 23,851 cases and over 100,000 controls, one of the largest sequencing analyses to date. Results: We first implicate protein-truncating variants (PTVs) in two novel genes, TRIO and HERC1, as conferring substantial risk for schizophrenia, and replicate a known association in SETD1A. After adding external controls, we identify additional novel genes at exome-wide significance, including NMDA receptor subunit GRIN2A, a target of psy- choactive drugs. We discuss signs of convergence with common schizophrenia loci and risk genes for neurodevelopmental disorders, showing an allelic series in GRIN2A and an association of de novo mutations in SETD1A and TRIO to broader neurodevelopmental phenotypes. After excluding novel risk genes, schizophrenia cases still carry a substantial excess of rare PTVs, suggesting that more remain to be discovered. Finally, we present an online browser that dis- plays variant- and gene-based results. Discussion: In summary, analyses of whole exomes complement those of common variants in expanding our understanding of schizophrenia, and the combined approach can

serve as a roadmap for inferring the biology of disease in other disorders.


doi: 10.1016/j.euroneuro.2018.08.061

Oral Session: Functional Genomics

12:30 p.m. - 2:00 p.m.

55

EXPRESSION QUANTITATIVE TRAIT LOCI IN THE DE- VELOPING HUMAN BRAIN AND THEIR ENRICHMENT IN

NEUROPSYCHIATRIC DISORDERS

Heath O’Brien 1 , Eilis Hannon 2 , Matthew Hill 3 , Carolina Toste 1 , Matthew Robertson 1 , Joanne Morgan 1 , Gemma McLaughlin 4 , Cathryn Lewis 4 , Leonard Schalkwyk 5 , Antonio Pardinas 1 , Michael Owen 3 , Michael O’Donovan 3 , Jonathan Mill 6 , Nick Bray 1 , ∗

1 Cardiff University School of Medicine 2 University of Exeter Medical School 3 Cardiff University 4 King’s College London

5 University of Essex 6 University of Exeter

Background: Genetic influences on gene expression in the human fetal brain plausibly impact upon a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. However, to date, there have been no studies that have mapped genome-wide expression quantitative trait loci (eQTL) specifically in the human prenatal brain. Methods: We performed strand-specific, whole transcriptome sequencing of total RNA derived from brain tissue from

120 human fetuses aged 12-19 post-conception weeks, de- riving expression measures for 144,448 Ensembl transcripts, annotated to 28,875 genes. Genomic DNA from each sample was genotyped for approximately 710,000 single nucleotide polymorphisms (SNPs), followed by genotype imputation using the Haplotype Reference Consortium r1.1 panel. Cis- eQTL were identified by linear regression of allele dosage against gene expression measures, adjusted for PEER factors and other covariates, using FastQTL. Results: We identified high confidence cis-eQTL for > 1300 genes and > 3000 individual transcripts (FDR < 0.05). Fe- tal brain eQTL were found to be enriched among risk variants for attention deficit hyperactivity disorder, schizophrenia and bipolar disorder. We further identified changes in gene expression within the prenatal brain that potentially mediate risk for neuropsychiatric traits, including increased expression of C4A in association with genetic risk for schizophrenia, increased expression of LRRC57 in association with genetic risk for bipolar disorder and altered expression of multiple genes within the chromosome 17q21 inversion in association with variants influencing the personality trait of neuroticism. Discussion: We have mapped eQTL operating in the human fetal brain, providing evidence that these confer risk to



34 Abstracts

ARTICLE IN PRESS


certain neuropsychiatric disorders, and identifying gene expression changes in the prenatal brain that could mediate susceptibility to these conditions.


doi: 10.1016/j.euroneuro.2018.08.062

56

INTEGRATIVE ANALYSIS OF GENOME-WIDE AS- SOCIATION STUDY RESULTS OF ATTENTION- DEFICIT/HYPERACTIVITY DISORDER (ADHD) AND

HUMAN FETAL BRAIN METHYLATION DATA REVEALS NOVEL GENES ASSOCIATED WITH ADHD

Anke Hammerschlag 1 , ∗, Enda Byrne 2 , ADHD Group 3 , Meike Bartels 1 , Naomi Wray 2 , Christel M. Middeldorp 2

1 Vrije Universiteit Amsterdam

2 University of Queensland


Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental psychiatric disorder affecting 5% of children and 2.5% of adults. ADHD is highly heritable and recently the first genome-wide associated genetic variants have been identified. The next step is to identify the functional implications of the identified variants. As methylation status is associated with gene expression, methylation is one of the possible regulatory mechanisms through which genetic variants can exert their effects. Methods: We apply the analytical paradigm SMR (summary data–based Mendelian randomization) and HEIDI (heterogeneity in dependent instruments) to integrate summary- level data from genomic, transcriptomic and methylomic studies to detect DNA methylation sites that are associated with gene expression and ADHD through shared genetic effects (i.e., pleiotropy). Because ADHD is thought to be a neurodevelopmental disorder, we investigate methylation quantitative trait loci (mQTLs) measured in human fetal brain tissue. In addition, we investigate mQTLs and expression quantitative trait loci (eQTLs) measured in adult human brain to increase statistical power as these datasets are several magnitudes larger. We integrate this data with genomic data from the currently largest ADHD genome-wide association study (GWAS) on ADHD diagnosis including 55,374 individuals. Results: Our preliminary results reveal several DNA methylation sites in fetal and adult human brain samples that show

associations with ADHD through pleiotropy at shared genetic variants. These sites influence the expression levels of multiple genes, including PTPRF. This gene is within the chromosome 1 locus identified by the ADHD GWAS including many genes, and our analysis suggests that this might be the gene of interest at the locus. In addition, we found associations between gene expression levels measured in adult human brain and ADHD through pleiotropy at shared genetic variants, including TIE1 and FOLH1 that have not been reported in the ADHD GWAS, likely because of the lack of power.

Discussion: We identified several methylation sites and genes that may provide important leads for future functional studies to get insight in the mechanisms through which genetic variants influence ADHD risk at an early developmental stage. We are going to follow up on these results by analyzing the currently largest ADHD GWA meta-analysis on ADHD diagnosis and symptoms including 149,290 individuals to increase statistical power. In addition, we will analyze a large dataset of blood samples to increase statistical power as well, as it has been shown that eQTLs and mQTLs of brain and blood highly correlate. With these datasets we expect to identify additional DNA methylation sites and genes associated with ADHD.


doi: 10.1016/j.euroneuro.2018.08.063

57

METHYLOMIC BIOMARKERS OF SCHIZOPHRENIA AND

ANTIPSYCHOTIC MEDICATION EXPOSURE

Eilis Hannon 1 , Emma Dempster 2 , Georgina Mansell 2 , Leo Schalkwyk 3 , Robin Murray 4 , Andrew McQuillin 5 , Kaarina Kowalec 6 , David St. Clair 7 , Derek Morris 8 , Patrick Sullivan 9 , Michael O’Donovan 10 , James MacCabe 11 , CRESTAR Consortium, David Collier 12 , Jonathan Mill 2 , ∗

1 University of Exeter Medical School 2 University of Exeter 3 University of Essex 4 Institute of Psychiatry 5 University College London

6 Karolinska Institutet 7 University of Aberdeen

8 Neuroimaging and Cognitive Genomics (NICOG) Centre, School of Psychology, National University of Ireland Galway 9 University of North Carolina at Chapel Hill 10 Cardiff University 11 King’s College London

12 Eli Lilly and Company

Background: There is growing interest in the role of developmentally regulated epigenetic variation in the molecular etiology of schizophrenia, with studies of disease- discordant monozygotic twins, clinical sample cohorts and post-mortem brain tissue identifying methylomic variation associated with disease. Leveraging on the considerable in- vestment in genome-wide association studies (GWAS), we are examining genome-wide patterns of DNA methylation across multiple cohorts with the aim of undertaking an integrated genetic-epigenetic approach to schizophrenia. Although effective in many patients, approximately one- third of schizophrenia cases are resistant to commonly prescribed antipsychotic medications. To date, the atypical antipsychotic drug clozapine is the only evidence-based treatment for these individuals, although its use is often associated with severe side-effects. Clozapine is known to influence chromatin remodelling and has previously been associated with global hypomethylation in the leukocytes of schizophrenic patients. In addition to identifying peripheral



Abstracts 35

ARTICLE IN PRESS


blood methylomic signatures of schizophrenia, this study aimed to identify epigenetic variation associated with antipsychotic medication exposure. Methods: DNA methylation was profiled in whole blood samples from i) multiple schizophrenia case-control cohorts using the Illumina 450K HumanMethylation array (3,172 cases and 2,453 controls) and ii) a cohort of chronic and first- episode schizophrenia patients (n = 159) who were compared to matched samples prescribed alternative medications (n = 439). Following stringent quality control, an epigenome-wide association study was performed to i) identify differentially methylated positions (DMPs) associated with schizophrenia and ii) to compare schizophrenia patients prescribed clozapine to those prescribed alternative medications. Results: Widespread schizophrenia-associated DMPs were identified in our EWAS of schizophrenia, with a meta- analysis across cohorts identifying robust methylomic signatures of disease. Of note, we identified 27 DMPs associated with clozapine exposure (P < 1x10-7); 26 of these 27 sites were associated with hypermethylation in patients prescribed clozapine, significantly more than expected by chance (P = 4.17x10-7). These DMPs were subsequently tested in two replication cohorts (n = 268; 289) with a highly significant enrichment of consistent directions of effect (P = 0.000311 and P = 4.17x10-7). Finally, we show

that at many sites where differential DNA methylation is associated with schizophrenia there is a considerable overlap with medication effects. Discussion: We identify multiple differentially methylated positions associated with schizophrenia, which overlap those associated with clozapine exposure. Given the dif- ficulties in establishing causal associations in epigenome- wide association studies, these data demonstrate the utility of pharmacoepigenetic studies to aid the interpretation of schizophrenia case control studies.


doi: 10.1016/j.euroneuro.2018.08.064

58

DRUG TARGETOR: AN ONLINE TOOL TO VISUALIZE

GENETICS-DRIVEN DRUG-TARGET NETWORKS

Helena Alexandra Gaspar ∗, Gerome Breen

King’s College London

Background: Genome-wide association studies (GWASs) have shown their ability to identify drug targets, and statistical techniques have been developed to impute expression levels in different tissues. This heterogeneous data can be used to prioritize drugs and targets. We present Drug Targe- tor v1.0 (drugtargetor.com) a web application that draws on both fields of chemistry and genetics to visualize interactions between drugs and genes. Methods: Drug Targetor orders drugs and genes by GWAS association, and connects drugs to genes by type of interaction. GWAS summary statistics were collected for over 500 phenotypes. S-PrediXcan was used to impute expres-

sion levels and MAGMA for pathway analysis. Interactions between drugs and targets (or genes) were mined from

various databases: ChEMBL v23, DGIdb, PubChem, Ki DB, DSigDB, and PHAROS. We present results for 3 phenotypes: schizophrenia (PMID 25056061), inabsmmatory bowel diseases (PMID 26192919), and Alzheimer’s disease (PMID

24162737). Results: CACNA1C is the top schizophrenia-associated gene in the top drugs network. S-PrediXcan predicts negative regulation for CACNA1C in the cerebellar hemisphere (zs- core = -4.5) and substantia nigra (z-score = -4.9). Within the antipsychotics-only network, top genes are CACNA1I, CHRM4, DRD2, ABCB1 and HTR5A. CACNA1I is connected to penfluridol, a T-type calcium channel blocker. For inabsmmatory bowel diseases, gene hubs are JAK2, GPX1, IL10, STAT3 and TNF. JAK2, IL10, STAT3 and TNF are implicated in anti-inabsmmatory cytokine pathways. JAK2 is significantly upregulated in esophageal mucosa (z-score = + 5.2), and GPX1 in the terminal ileum (z-score = + 5.2). GPX1 encodes glutathione peroxidase 1, an antioxidant enzyme. The first drug hit connected to GPX1 is tocopherol (vitamin E). For Alzheimer’s disease, the top gene is GPX4. Top drugs for Alzheimer’s disease are ACE inhibitors (not significant). Interestingly, tocopherol is also in the top drug hits. Discussion: With Drug Targetor networks, we found drug suggestions consistent with previous knowledge: anti- inabsmmatory or antioxidant drugs for inabsmmatory bowel disease, calcium blockers and D2 antagonists for schizophrenia, and ACE inhibitors for Alzheimer’s disease. Although results highly depend on the GWAS study sample size, Drug Targetor might help to better understand drug mechanisms using genetic evidence.


doi: 10.1016/j.euroneuro.2018.08.065

59

GENOME-EDITING OF THE RERE SUPER-ENHANCER

IN HUMAN NEURAL PRECURSOR CELLS ALTERS EXPRES- SION OF NEURODEVELOPMENTAL AND SCHIZOPHRENIA

RISK GENES

Cathy Barr 1 , ∗, Yu Feng 1 , Karen Wigg 1 , Maria Carol Marchetto 2 , Fred Gage 2

1 The Krembil Research Institute 2 Salk Institute for Biological Sciences

Background: The majority of associated markers for complex genetic traits reside in gene regulatory regions, particularly enhancers and super-enhancers. Enhancers can reside megabases from the gene they regulate (target gene) and their targets are often not the nearest gene. Thus, the assumption that the gene nearest a GWAS-significant marker will be the risk gene will in many cases be incorrect. Methods: To identify the target genes of enhancers with GWAS significant markers, we analyzed our Capture-HiC

data selecting enhancers for functional studies using CRISPR/Cas9 in human neural precursor cells (hNPCs) derived from embryonic stem cells. The impact on expression



36 Abstracts

ARTICLE IN PRESS


was measured by digital droplet PCR and RNA-seq in the edited versus the mock-transfected cells. Results: We selected the super-enhancer spanning the 3’ end of the RERE gene for study, the site of GWAS significant SNPs for schizophrenia and major depression. Fur- ther, RERE is a co-repressor/co-activator involved in retinoic acid signaling and the key gene in 1p36 deletion syndrome, a developmental disorder with autism spectrum

symptoms. Capture-HiC data indicate interactions of the super-enhancer with RERE, PARK7 (Parkinsons 7, protects neurons from oxidative stress and regulates dopamine neurotransmission) and PER3 (Period 3). These 3 genes are transcription co-regulators or transcription factors. Us- ing CRISPR/Cas9, we deleted a 2kb region of the super- enhancer in hNPCs and analyzed the transcriptome by RNA- seq. We identified 107 genes that were differentially expressed, including 14 regulated by retinoic acid. Impor- tantly, 3 of these are located in independent GWAS regions for schizophrenia including RAI1. RAI1 was previously implicated in psychiatric and cognitive disorders prior to the psychiatric GWAS finding because this gene resides in the locus for the developmental syndromes, Smith–Magenis (hap- loinsufficiency) and Potocki-Lupski (duplications). Smith–Magenis syndrome is characterized by intellectual disability, behavioral abnormalities, hyperactivity, speech delay and circadian abnormalities and Potocki-Lupski syndrome is characterized by autism and intellectual disability. Discussion: Capture-HiC provides important new leads in pinpointing the target genes of enhancer-mediated regulation emanating from the GWAS findings and functional studies confirm altered expression of interacting genes. The finding of altered expression of genes in independent genome regions is an important new lead in understanding the regulation of psychiatric disorder risk genes and syndromic developmental disorders. We are currently differen- tiating the CRISPR/Cas9 edited NPCs to neurons to examine the impact of the edits on differentiation and neuronal phenotypes. Further, to understand the role of this region in brain development, we have created clonal edited ES cell lines for the creation of cerebral organoids.


doi: 10.1016/j.euroneuro.2018.08.066

60

A FUNCTIONAL GENETIC SCREEN FOR CELLULAR

MECHANISMS OF LITHIUM RESPONSE IDENTIFIES AN

INTERPLAY BETWEEN PROTEIN SECRETION, EXTRA- CELLULAR MATRIX, AND ENDOSOMAL/LYSOSOMAL

PATHWAYS

Benjamin Pickard 1 , ∗, David Beattie 1 , Emma Craib 1 , Adil Abbasi 1 , Chloe Littlejohn 1 , Runqi Wang 1 , David Sims 2

1 University of Strathclyde Glasgow

2 University of Oxford

Background: Revealing the pathways and processes through which the mood stabiliser lithium generates a therapeutic response will enable the design of safer and more specific

treatments, targeted to those patients who will gain most benefit. The SH-SY5Y neuroblastoma cell line shows reduced proliferation in media supplemented with 8.5mM lithium

chloride. We have leveraged this phenotype to carry out a functional genetic screen for mutants with altered proliferation rates when treated with lithium. We hypothesised that such mutations would occur in genes mediating the cellular actions of lithium, with potential relevance to the variation in therapeutic response observed in patients. Methods: We used a ‘gene trap’ vector transfection methodology to create a library of over 10,000 different heterozygous loss-of-function mutant SH-SY5Y cells. This library was cultured in parallel for 12 cell doublings in the presence or absence of lithium. High-throughput sequencing was applied to both cell populations to quantify longitudinal changes in individual mutant allele frequency (via altered proliferation rate) that were specific to lithium selection pressure - that is, alleles not in Hardy-Weinberg equilibrium. Subsequent mutation of key genes by CRISPR and analysis by immunofluorescence enabled validation and characteri- sation of these genes. Results: Thirty mutated genes showed significant allele frequency changes, and a some of these cognate ‘lithium response genes’ have been subsequently validated by CRISPR- mediated mutation of cells. The identified genes include those associated with the known GSK3B/B-catenin lithium

response pathway (BTRC, PALMD, CDH4), and those with links to psychiatric disorders (EGR1, PTPRE, FAT4). Surpris- ingly, a large number of secreted protein genes were identified (CCL2, NPY, SST, SPARCL1, IGFBP3, IL32, and SEMA3A) which, given the nature of the screen, formally requires them to act in a local, autocrine fashion. The identification of mutations in extracellular/perineuronal net proteins (VCAN, TNC, COL3A1) offers a potential solution - they are able to create and maintain a microenvironment of secreted proteins close to the cell surface and its receptors. VCAN is a close ortholog of NCAN, one of the top GWAS ’hits’ for bipolar disorder. Moreover, VCAN and the screen’s strongest lithium resistance mutant gene, NGS2/HMP19, are co-localised in the endosomal/lysosomal pathway – a pathway vital for receptor, matrix and membrane flux, and one that is known to be inhibited by lithium. Discussion: Our functional screen approach has revealed causative cellular processes in lithium response/action and we believe their role in pharmacological response in the CNS can be justifiably inferred. Importantly, within the gene set there is a clear overlap with known lithium actions, and established psychiatric risk factors – as would be hoped from

any therapeutic agent. The discovery that secretion and trafficking pathways are over-represented within the mutant gene set defines novel targets for future experimenta- tion as well as selective drug design in the context of bipolar pathology and treatment.


doi: 10.1016/j.euroneuro.2018.08.067



Abstracts 37

ARTICLE IN PRESS


Oral Session: Heritable Neuropsychiatric

Traits

12:30 p.m. - 2:00 p.m.

61

ITEM-LEVEL STUDY OF NEUROTICISM REVEALS GE- NETIC HETEROGENEITY

Mats Nagel 1 , ∗, Kyoko Watanabe 2 , Sven Stringer 2 , Sophie van der Sluis 2 , Danielle Posthuma 3

1 VU Medical Centre 2 VU University 3 Center for Neurogenomics and Cognitive Research, Neuro- science Campus Amsterdam, VU University Amsterdam, VU

University Medical Center

Background: Genome-wide association studies (GWAS) of psychological traits, like neuroticism and depression, are typically conducted on composite scores (e.g. sums of items or symptoms, case-control status). However, the items or symptoms collectively operationalizing one trait can be very diverse in nature, which would affect the statistical power to detect genetic variants. The aim of the current study was to test the genetic homogeneity of items used to measure the neuroticism personality trait, and to possibly identify genetically homogenous subdimensions of neuroticism, which may provide a better neurobiological understanding of this trait. Methods: We conducted large-scale GWAS on 12 neuroticism

items in two UK biobank subsamples (first and second release; total N > 366,726). We then computed inter-item genetic correlations within the two subsamples to assess heterogeneity, and subsequently meta-analyzed both subsamples to maximize statistical power for the discovery of genetic variants. Results: The item-level GWAS in both subsamples showed substantial variation in genetic signal between items. Inter- item genetic correlations within the two subsamples ranged from 0.38 to 0.91, revealing genetic heterogeneity in the full set of items. Meta-analyses identified 255 genome- wide significant independent genomic regions, of which 138 were item-specific. Using hierarchical clustering analysis, we identified two clusters of four genetically homogeneous items, labeled depressed affect and worry. Extensive follow-up analyses including genetic correlations to external traits, functional annotation and Mendelian randomization analyses, support genetic differences between items and confirm the genetic distinctness of the clusters. In addition, the validity of the depressed affect cluster was supported by comparing the genetic signal to that of a not previously published depression meta-analysis. Discussion: Our study demonstrates that the items used to measure neuroticism are genetically heterogeneous. These findings suggest that studying the genetic heterogeneity of items or symptoms used as indicators of other complex psychological traits, like depression, is desirable. In conclusion, we argue that item- and symptom-level analyses are a useful strategy for studying genetic homogeneity, as well as for

constructing coherent subdimensions that can be used as targets of investigation in future studies.


doi: 10.1016/j.euroneuro.2018.08.068

62

SIGNIFICANT SHARED HERITABILITY UNDERLIES SUICIDE

ATTEMPT AND CLINICALLY PREDICTED PROBABILITY

OF ATTEMPTING SUICIDE

Douglas Ruderfer 1 , ∗, Colin Walsh 1 , Matthew Aguirre 2 , Yosuke Tanigawa 2 , Jessica Ribeiro 3 , Joseph Franklin 3 , Manuel Rivas 2

1 Vanderbilt University Medical Center 2 Stanford University School of Medicine 3 Florida State University

Background: Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. Methods: We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype from genotyped samples in the UK Biobank (337,199 participants, 2,433 cases). The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3,250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. Results: We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12x10-4) and the clinically predicted phenotype from

VUMC (h2SNP = 0.046, p = 1.51x10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75x10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34 - 0.81) as well as several psychiatric disorders (rg = 0.26 - 0.79). Discussion: This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.


doi: 10.1016/j.euroneuro.2018.08.069



38 Abstracts

ARTICLE IN PRESS


63

THE EFFECT, DETECTION AND ADJUSTMENT OF SAM- PLE OVERLAP IN PRS STUDIES

Shing Wan Choi 1 , ∗, Jonathan Coleman 1 , Kylie Glanville 1 , Pak Sham

2 , Paul O’Reilly 1


2 The University of Hong Kong

Background: Sample overlap is known to cause test statistic inabstion in polygenic risk score (PRS) studies, but the extent of the problem has not been well characterised. There are also presently no methods to detect or correct for the bias introduced by sample overlap when the overlap is partial and the base genotypes are unavailable. Here, we pro- pose a novel approach to detect sample overlap between the base and target data using summary statistics, and a method for correcting for the bias in the PRS results. Methods: We first produced base and target real data, and a permuted null trait, by stratifying the UK Biobank samples. Next, we included a varying degree of base samples in the target sample to generate overlap. PRS analyses were then performed using the base Genome-Wide Association Study (GWAS) results and the target genotype data and the results assessed for goodness-of-fit. In an attempt to control for sample overlap when detected, we contrast the LD

score regression (LDSC) intercept derived from the separate base and target GWAS summary statistics with that from the meta-analysed GWAS results. Results: We first present results characterizing the extent of the problem of sample overlap in PRS studies, finding, for example, that 10% of overlapped sample can lead to a 0.1 increase in the R2. Next, we demonstrate that we can detect potentially inabsting levels of sample overlap when the pooled GWAS summary has an LDSC intercept inabstion higher than the sum of the LDSC intercept generated from

individual GWAS. By exploiting the LDSC intercept across individual and pooled GWAS, we can infer the level of sample overlap between the base and target samples. Additionally, for target data with large sample sizes ( > 10k), an un-biased coefficient of determination (R2) for the PRS can be calculated. Discussion: Even a small degree of sample overlap can result in false-positive findings in PRS studies, especially if the target data is small. We have developed an algorithm to detect sample overlap between the base and target data, as well as a method based on LDSC for adjusting the bias introduced in terms of R2, and demonstrate their performance and utility using UK Biobank real data.


doi: 10.1016/j.euroneuro.2018.08.070

64

GENOME-WIDE ASSOCIATION STUDY OF SCHOOL

GRADES INFORMS COGNITIVE GENETIC ARCHITECTURE

OF SIX MAJOR PSYCHIATRIC DISORDERS

Veera Rajagopal 1 , ∗, Andrea Ganna 2 , Jonathan R.I. Coleman 3 , Esben Agerbo 4 , Jakob Grove 5 , Thomas D. Als 5 , Henriette T. Horsdal 4 , Liselotte Petersen 4 , iPSYCH-Broad Consortium

6 , Gerome Breen 3 , Anders Børglum

5 , Ditte Demontis 5

1 Aarhus University 2 Center for Genomic Medicine, Massachusetts General Hos- pital, Broad Institute of Harvard and MIT

3 Social, Genetic and Developmental Psychiatry Centre, In- stitute of Psychiatry, Psychology and Neuroscience, King’s College London, National Institute for Health Research

Biomedical Research Centre, South London and Maudsley National Health Service Trust 4 National Center for Register Based Research, Aarhus Uni- versity, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Center for Integrative Se- quencing 5 Aarhus University, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Center for Inte- grative Sequencing 6 The Lundbeck Foundation Initiative for Integrative Psychi- atric Research, iPSYCH, Center for Integrative Sequencing, Aarhus University, Broad Institute of Harvard and MIT

Background: Education attainment (EA) correlates strongly with psychiatric disorders—both phenotypically and genetically. The extent to which the genetic architecture of EA in individuals with psychiatric disorders differs from the genetic architecture of EA in the general population is not known. To address this, we performed a GWAS of school grades in individuals with psychiatric disorders in the iPSYCH

cohort, the first of its kind. Methods: We conducted GWAS of ninth level grades given at Danish and mathematics exit-examinations in the municipal schools in Denmark. We studied ∼35000 individuals: attention deficit hyperactivity disorder (ADHD, N = 4298), autism

spectrum disorder (ASD, N = 3466), schizophrenia (SCZ, N = 1074), major depressive disorder (MDD, N = 10,140), bipolar disorder (BPD, N = 607), anorexia nervosa (AN, N = 1798) and controls (N = 11,273, i.e., without any of the above six diagnoses). School grades and psychiatric diagnoses were extracted from Danish national registers. We analyzed ∼8M variants using a linear regression adjusted for age, sex, genotyping waves and ten principal components. We analyzed all samples together (with psychiatric diagnoses as a covariate) and each psychiatric group, as well as controls, separately. In addition, we analyzed ∼500,000 UK Biobank (UKBB) individuals to (a) replicate genome- wide significant (GWS) loci (b) to measure the variance explained by genome-wide polygenic scores (GPS) trained on our GWASs and (c) to study how GPS for school grades correlate with GPS for psychiatric disorders in the general population. For (a) and (b) the analyzed traits were college completion and verbal and numerical reasoning (VNR) scores.


Abstracts 39

ARTICLE IN PRESS


Results: Gender and psychiatric diagnoses were strongly associated with grades: females performed better; AN scored the highest; ADHD scored the lowest. First, we performed a GWAS of Danish and math (DM), totaled to a single score per individual. We identified three GWS loci (3p21.31, 5p13.2 and 6q16.1), two were GWS in the latest EA-GWAS from SSGAC consortium. The SNP-heritability (h2) was 0.26 (SE = 0.01). Genetic correlation with the previous EA-GWAS was 0.91 (SE = 0.02). GPS for EA explained up to 7.5% variance (adjusted R2) in DM. In the analyses stratified by psychiatric disorders, we found one MDD-specific GWS locus (7p15.2). Maximum variance explained by EA-GPS differed between groups: controls and BPD showed > 10% variance. Second, we performed a GWAS of Danish and math separately. Since Danish and math are correlated (70%), we aimed to isolate the genetic signals specific to Danish and math. Hence, we did Danish-GWAS with math as a covariate (DadjM) and math-GWAS with Danish as a covariate (MadjD). The h2 was ∼0.18 in all four. We observed, however, differences in (a) GWS loci and (b) genetic correlations with psychiatric disorders. These differences were stronger in DadjM and MadjD. We identified two MadjD-specific GWS loci (6p22.1 and 11q23.2). In the UKBB individuals, we found a strong negative correlation of GPS for all psychiatric disorders with GPS for math, MadjD and IQ. On the other hand, we found a strong positive correlation (except ADHD) with GPS for EA, DM, Danish and DadjM. Phenotypes followed the same trend: college completion correlated with Danish- GPSs and VNR correlated with math-GPSs. Discussion: Our findings showed that a substantial portion of the genetic variants influencing EA phenotypes (EA, DM, Danish and DadjM) shared with psychiatric disorders, are not cognitive—but, rather, we speculate, behavioral.


doi: 10.1016/j.euroneuro.2018.08.071

65

GENOME-WIDE ANALYSIS OF INSOMNIA AND SLEEP- RELATED TRAITS IN OVER 1 MILLION INDIVIDUALS IDENTIFIES NOVEL GENES AND PATHWAYS

Philip Jansen 1 , ∗, Kyoko Watanabe 1 , Sven Stringer 1 , Nathan Skene 2 , Julien Bryois 2 , Ana Muñoz-Manchado 2 , Joyce Tung 3 , David Hinds 3 , Vladimir Vacic 3 , Patrick Sullivan 4 , Tinca Polderman 1 , August Smit 1 , Jens Hjerling-Leffler 2 , Eus van Someren 5 , Danielle Posthuma 1

1 VU University Amsterdam

2 Karolinska Institutet 3 22andMe, Inc. 4 University of North Carolina at Chapel Hill 5 VU GGZ InGeest/Netherlands Institute Neuroscience

Background: Insomnia, i.e. problems falling or staying asleep, is highly common in the general population, with strong negative consequences on mental and physical health, and lack of effective treatments. Moreover, sleep- problems are highly prevalent in psychiatric disorders, suggesting a possible shared aetiology between sleep-problems and these disorders. Despite a substantial role of genetic factors in the aetiology of insomnia, the number of identified risk loci, genes and associated neurobiological pathways remains limited. Methods: Here, we perform large-scale genome-wide meta- analysis of insomnia in over 1 million individuals by com- bining genome-wide association (GWAS) results in the UK Biobank (N = 386,533) and 23andMe, Inc. (N = 944,477), leading to a total sample of 1,331,010 individuals. We perform

extensive functional annotation of the genome-wide results using the FUMA online annotation platform. We integrate data from tissue-expression and novel single-cell gene expression to find pathways, and cell- and tissue types related to insomnia. In addition, we analyse the genetics of six sleep-related phenotypes in UK Biobank that are known to correlate with insomnia, including sleep duration, chrono- type, snoring, narcolepsy, daytime napping and ease of get- ting up. Results: We identified 202 independent genome-wide significant loci in insomnia, including two loci on the X chromosome. Gene-based association test and gene mapping in FUMA, including positional mapping, eQTL and chromatin interactions, identified 679 genes, many of which have been previously linked to psychiatric disorders. We observed sex- specific effects, and identify genes significantly related to insomnia in males or females only. Gene-set analysis showed that these genes were highly enriched in gene-sets related to neuronal function and axonal growth. Analysis of gene-expression data showed that the identified genes were highly expressed in basal ganglia of the brain, and single-cell RNA sequencing identified several neuronal cell types, including hypothalamic neurons, known to regulate circadian rhythm, and pyramidal neurons in the claustrum, a brain structure suggested to play a role in consciousness. Genetic correlations indicated a strong genetic overlap between our GWAS results and depression, anxiety and schizophrenia, and several metabolic traits. Using Mendelian Randomiza-


40 Abstracts

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tion, we showed that insomnia has a causal effect on obesity and cardiovascular disease, and bidirectional effects between insomnia and psychiatric disorders. In addition, we find 362 loci for six sleep-related phenotypes, of which many show overlap with insomnia. Discussion: The wealth of results from this largest GWAS study of insomnia to date in over 1 million individuals implicates novel genes, gene-sets and cell-types associated with insomnia, and provide novel targets for future research in sleep disorders. We show clear genetic overlap with a wide variety of psychiatric disorders. In addition, our results show

how large-scale biobanks and big data are rapidly leading to new discoveries and transform the field of psychiatric genetics.


doi: 10.1016/j.euroneuro.2018.08.072

66

ANALYSES OF DISEASE-ASSOCIATED AND LIKELY FUNC- TIONAL VARIANTS FROM PSYCHARRAY IMPLICATE

GENES INVOLVED IN RISK FOR COMPLETED SUICIDE

Emily DiBlasi 1 , ∗, Qingqin Li 2 , John Anderson 1 , W. Brandon Callor 3 , Erik Christensen 3 , Leslie Jerominski 1 , Rob Sargent 1 , Douglas Gray 1 , Nicola Camp 1 , Andrey Shabalin 1 , Anna Docherty 4 , Hilary Coon 1

1 University of Utah School of Medicine 2 Janssen Research & Development LLC

3 Utah State Office of the Medical Examiner 4 University of Utah School of Medicine, Virginia Institute for Psychiatric & Behavioral Genetics

Background: Almost 800,000 people a year die by suicide worldwide, and it is the second leading cause of death among people under 40. This global health crisis is of critical importance. Heritability of completed suicide is estimated at 50%, suggesting that genetics plays a significant role in this extreme phenotype. To find genetic risk factors for suicide, The Utah Suicide Research Study (USRS) has collected a unique genetic resource of > 5,500 population- ascertained DNA samples from completed suicides. This genetic resource is obtained through a collaboration with the Utah State Office of the Medical Examiner. Methods: Using cases from the USRS, we genotyped ∼1300 individuals with the Illumina Infinium PsychArray platform. To complement traditional common variant association study approaches, we prioritized 13,000 PsychArray nonsynonymous variants with documented disease associations and putative functional consequences based on SIFT and Polyphen annotations. Single-variant and gene-based tests were performed using Utah completed suicide cases and Eu- ropean ancestry controls. Results: Preliminary results provide support for APH1B: rs745918508 (p = 5.04E-29) and SUCLA2: rs121908538 (p = 3.14E-12) as potential variants elevating risk for completed suicide. To explore the specificity of genetic risk factors with completed suicide, comparisons of this dataset

with available external genetic resources of individuals with psychiatric diagnoses is ongoing. Discussion: Genetic risk factors in our resource of individuals who died by suicide may be associated with co-occurring psychiatric illness, possibly reflecting extreme lethal forms these illnesses. Alternatively, genetic risk for suicide may span multiple diagnostic categories. In this case, focusing on death by suicide independent of diagnosis may allow for the detection of a more generalizable genetic risk for suicide that can ultimately aid prevention efforts.

Disclosure: Janssen Research & Development LLC - Re- search, Self

doi: 10.1016/j.euroneuro.2018.08.073

Oral Session: ADHD

12:30 p.m. - 2:00 p.m.

67

MULTIVARIATE GWA META-ANALYSIS IN OVER 500K

OBSERVATIONS ON AGGRESSION AND ADHD SYMPTOMS

Michel Nivard ∗, Dorret Boomsma

VU University

Background: We present the results of a multivariate genome-wide association (GWA) study of the developmental genetic etiology of aggression (AGG) and attention- deficit/hyperactivity disorder (ADHD). The project involves a collaboration in over 20 international cohorts from Eu- rope, Australia, New-Zealand and the USA. The cohorts are characterized by repeated measures of Aggression and ADHD symptoms at different ages and assessment by multiple informants and multiple instruments. In total, the meta- analysis included ∼526,000 observations from over 200 GWA studies. Methods: First, a series of univariate GWA studies was performed for every available combination of age, informant and instrument within each cohort. This resulted in 1 to 36 analyses per cohort, with sample sizes ranging between 309 and 10,812. Next, results were pooled into age-by-rater combinations (e.g. mother-rated aged 3-5, teacher-rated 8- 11, etc.) that resulted in an excess of 10,000 independent observations, and then meta-analyzed. Genetic correlations between the age-by-rater combinations, both within and across AGG and ADHD, were estimated with LD Score Re- gression. Finally, we performed a meta-regression analysis across all GWA studies, correcting for the fact that repeatedly measured subjects were included in the analyses. We then estimated the genetic correlation between our meta- analysis and several somatic and psychiatric traits. Results: We obtained an average SNP-heritability of 6.3%

and 8.3% for AGG and ADHD, respectively, across the age- by-rater meta-analyses. Within phenotype, the highest average genetic correlations were seen between maternal ratings and self-reports: 0.49 (AGG) and 0.85 (ADHD). Genetic correlations between mother and teacher ratings was 0.39 for AGG and 0.74 for ADHD. Interestingly, the genetic correlation between teacher ratings and self-report approached



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zero for both traits. Averaged across raters and age, the genetic correlation across AGG and ADHD was 0.75.

We estimate several significant correlations between either AGG and/or ADHD and cognitive/psychiatric/health outcomes. Most notably were the genetic correlations with childhood IQ (-0.61), age at first birth (-0.58), depressive symptoms (0.51), smoking initiation (0.46) and parental age at death (-0.36), indicating the presence of a relation between AGG/ADHD and poor (health) outcomes. Follow up analysis subset the relation between childhood ADHD symptoms and poor health outcomes may be mediated by educational attainment and social economic status. Discussion: The present work is, to our knowledge, the first repeated measures multi rater genome wide association study of psychiatric phenotype. Our works helps further the understanding of prodromal, and subclinical psychiatric symptoms during development. SAAGY (Study of Aggression and ADHD trait Genetics in Youth) includes multiple international cohorts (ALSPAC, CATSS, FinnTwin12, GenR, GINI/LISA, INMA, NFBC 1966/1986, NTR, QIMR, RAINE, TCHAD, TEDS, Dunedin, MOBA, MTFS, TRAILS, Young Finns Study, ABCD, MUSP, BREATHE, E-Risk, Add Health Michigan State University, Understanding Society).


doi: 10.1016/j.euroneuro.2018.08.074

68

THE ROLE OF DELETERIOUS ULTRA-RARE VARIANTS IN

ADHD RISK

Ditte Demontis 1 , ∗, F. Kyle Satterstrom

2 , Jinjie Duan 1 , Francesco Lescai 1 , Søren Dinesen Østergaard 3 , Klaus-Peter Lesch 4 , Thomas Werge 5 , Preben Bo Mortensen 1 , Simon Glerup 1 , Barbara Franke 6 , David Hougaard 7 , Andreas Reif 8 , Mark Daly 9 , Benjamin Neale 10 , Anders Børglum

1

1 Aarhus University 2 Broad Institute 3 Aarhus University Hospital 4 University of Würzburg 5 Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services - Copenhagen

6 Radboud University Medical Center, Donders Institute 7 Statens Serum Institut 8 University Hospital Frankfurt 9 Analytic and Translational Genetics Unit 10 Massachusetts General Hospital

Background: Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioural disorder affecting 3-6% of school-age children, and has a heritability of around 0.76. The SNP heritability, estimating the amount of risk attributed to common genetic variation, has been estimated to be 0.22. The difference between SNP heritability and total heritability indicates that the genetic risk component of ADHD also includes the effects of rare variants. The role of ultra-rare deleterious variants has been established for schizophrenia and autism, and

recently it was unexpectedly found that ADHD and autism

have similar significant excesses of constrained rare protein truncating variants compared to controls, and that these variants occur in similar sets of genes. Methods: Here we present results from extended analyses of the role of ultra-rare variants in ADHD. The study is based on whole-exome sequencing of three cohorts of ADHD cases and controls: 1) a Danish cohort with samples identified in the Danish Newborn Screening Biobank (DNSB) comprising ∼4,400 cases and ∼4,700 controls (DNSB1 sample), 2) a sample consisting of ∼1,100 clinically ascertained cases from

Germany and the Netherlands and ∼1,700 controls with Ger- man and Dutch ancestry (clinical sample), and finally 3) an additional ∼4,400 cases and ∼5,300 controls from the DNSB (DNSB2 sample), which we have just received and will be included in our analyses, bringing our total sample to 9,900 cases and 11,700 controls. Results: Preliminary results from meta-analyses of the DNSB1 and the clinical samples demonstrate a significant overrepresentation of deleterious ultra-rare variants (dURVs) in cases compared to controls (OR = 1.23; P = 8.21x10-10) in evolutionary constrained genes intolerant to loss of function (LoF) variation (pLI > 0.9). For comparison, counts of ultra-rare synonymous variants in highly constrained genes showed no difference between cases and controls (OR = 0.99, P = 0.28). When restricting to dURVs in constrained genes highly expressed in the brain, the odds ratio increased even further (OR = 1.51; P = 5,67x10-7), supporting the idea that dURVs play a considerable role in ADHD

risk, especially when located in genes intolerant to LoF variation. Discussion: We will present updated results from analyses of the impact of dURVs on ADHD risk based on meta-analyses of DNSB1 + 2 and the clinical samples. We will especially focus on the burden of these variants in ADHD in evolution- arily constrained genes as well as in specific gene sets relevant to ADHD, such as “synaptic genes”, “neurite outgrowth genes”, “genes bound by the fragile X mental retardation gene FMRP” and “genes with high brain expression”. Fur- thermore, we will evaluate whether ADHD risk genes identified based on common variant analyses also carry an increased burden of dURVs, and we will conduct gene-based association analyses to identify the genes most frequently hit by dURVs in ADHD cases compared to controls.


doi: 10.1016/j.euroneuro.2018.08.075

69

ASSOCIATION OF AGGRESSION AND ADHD SUBSCALES IN CHILDREN AND ADULTS

Dorret Boomsma 1 , ∗, Conor Dolan 1 , Meike Bartels 1 , Michel Nivard 1 , Toos van Beijsterveldt 1 , Tom Claassen 2 , Jeffrey Glennon 3

1 VU University 2 Radboud University Nijmegen

3 Radboud University, Nijmegen Medical Centre



42 Abstracts

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Background: Based on Bayesian machine learning analysis performed in the MATRICS (Multidisciplinary Approaches to Translational Research In Conduct Syndromes) consortium, in clinical adolescent ADHD and population cohorts, differential associations between aggression and hyperactivity and between aggression and inattention were suggested. Methods: We aimed to replicate these findings in the AC- TION (Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies) consortium, using the database from the large population- based Netherlands Twin Register. Analyses were done for data obtained during in childhood (age 7-16 years) and adulthood, employing both cross-sectional and longitudinal regression analyses. In children and in adults, outcome and predictor variables were assessed by comparable instruments. Aggression was assessed by the Achenbach System of Empirically Based Assessment (ASEBA) age-appropriate in- ventories, namely the Child behavior Check List (CBCL), and the Youth or the Adult Self Report (YSR / ASR). Hyperactivity and inattention were assessed by the Conners’ Parent Rat- ing Scale-Revised: Short version (CPRS-R:S) and the Conners’ Adult ADHD Rating Scales (CAARS). The data were analyzed in children and adult by linear regression and multivariate genetic structural equation modeling. Results: Based on linear regression analyses in which hyperactivity and inattention predicted aggression, we observed different results in children and in adults. In children, hyperactivity was a stronger predictor of aggression than inattention. However, in adults, inattention which tends to show

stronger persistence into adulthood than hyperactivity, was the stronger predictor. Multivariate genetic structural equation modeling in twin families confirmed that in children hyperactivity was the stronger predictor for aggression, with the predictive power mainly due to the genetic associations. In adults, predictive power of Inattention also was mainly due to genetic associations. Discussion: We obtained empirical evidence in independent samples for differential associations between aggression and hyperactivity and between aggression and inattention.


doi: 10.1016/j.euroneuro.2018.08.076

70

GENETIC INFLUENCES CONTRIBUTING TO ATTENTION- DEFICIT/HYPERACTIVITY DISORDER ACROSS THE LIFES- PAN: EVIDENCE FROM GENOME-WIDE ASSOCIATION

STUDIES

Paula Rovira 1 , ∗, María Soler Artigas 2 , Cristina Sanchez-Mora 3 , Iris Garcia-Martínez 4 , Mireia Pagerols 4 , Ditte Demontis 5 , Anders Børglum

5 , Barbara Franke 6 , Stephen V. Faraone 7 , Jan Haavik 8 , Andreas Reif 9 , Claiton Bau 10 , Miguel Casas 11 , Josep Antoni Ramos-Quiroga 12 , Marta Ribasés 4

1 Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Mental Health Hospital Universi- tari Vall d’Hebron

2 Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona 3 Institut de Recerca Hospital University Vall d’Hebron Re- search Institute 4 Vall d’Hebron Research Institute (VHIR) 5 Aarhus University 6 Radboud University Medical Center, Donders Institute 7 Medical Genetics Research Center, SUNY Upstate Medical University 8 University of Bergen

9 University Hospital of Frankfurt 10 UFRGS 11 Hospital Universitari Vall d’Hebron

12 Hospital Universitari Vall d’Hebron, Centro de Investi- gación Biomédica en Red de Salud Mental (CIBERSAM), Insti- tuto de Salud Carlos III, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona

Background: Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder whose impairing symptoms persist into adulthood in around 65% of the diagnosed children. Being able to predict ADHD

persistence in childhood based on genetic load could be a first step towards prevention of ADHD persistence, which is a key clinical concern. The aim of the present study was to compare the genetic background of children and adult ADHD patients and to elucidate whether a subset of children with ADHD exists which is more genetically similar to adult patients, as well as identifying new loci associated with ADHD. Methods: Genomic data from the International Multi-centre persistent ADHD CollaboraTion (IMpACT) and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) were used to conduct a genome-wide association (GWA) meta-analysis of 6,619 adult ADHD patients and 15,976 controls. A meta-analysis of all childhood ADHD

GWAS data available at the time, within the Psychiatric Ge- nomics Consortium (PGC) and iPSYCH, was also conducted. We estimated the genetic correlation between children and adult ADHD and also between adult ADHD and other traits using LD-score regression. Moreover, we examined whether a subgroup of children was more genetically similar to adult patients, who could therefore be hypothesized to be at risk of persistent ADHD, using the well-powered BUHMBOX method. In addition, we ran a combined children and adult meta-analysis of 17,236 patients and 32,513 controls. Results: The top hits of the adult meta-analysis (P < 2.92E- 07) included genes previously associated with neuronal migration. We observed significant genetic correlation between adult ADHD and major depressive disorder (P = 8.01E- 05), neuroticism (P = 5.73E-05), risk taking (P = 2.57E-16), years of schooling (P = 1.76E-26) and intelligence (P = 6.91E- 13), among others. A strong correlation between genetic variants contributing to adult and childhood ADHD (rg = 0.81 (S.E. = 0.08); P = 5.10E-21) was found and no evidence for genetically different subgroups of children with ADHD was detected. In the combined meta-analysis of adults and children, we identified three new genome-wide significant loci, present near genes related to neuronal migration and to monoamine and neurotrophin neurotransmission.


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Discussion: The results support the hypothesis of a shared genetic background between children and adult ADHD and point to new genetic variants associated with the disorder. Prospective studies exploring the genetic background of children with ADHD will allow us to elucidate the genetic impact on the persistence and remittance of ADHD symptoms.


doi: 10.1016/j.euroneuro.2018.08.077

71

ATTENTION-DEFICIT/HYPERACTIVITY DISORDER AND

LIFETIME CANNABIS USE: GENETIC OVERLAP AND

CAUSALITY

María Soler Artigas 1 , ∗, Cristina Sánchez-Mora 2 , Paula Rovira 2 , Vanesa Richarte 2 , Iris García-Martínez 2 , Mireia Pagerols 2 , ADHD Group 3 , International Cannabis Consortium, Miguel Casas 2 , Josep Antoni Ramos-Quiroga 2 , Marta Ribasés 2

1 Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona 2 Hospital Universitari Vall d’Hebron


Background: Attention-deficit/hyperactivity disorder (ADHD) is a severely impairing neurodevelopmental disorder where comorbid conditions play a key role in symptom

progression, disorder course and outcome. ADHD is associated with a significantly increased risk for substance use, abuse and dependence. ADHD and cannabis use are partly determined by genetic factors; the heritability of ADHD

is estimated at 70-80% and of cannabis use initiation at 40-48%. In this study we aimed to gain insights into the genetic overlap and causal relationship of these two traits. Methods: We used summary statistics from the largest available meta-analyses of genome-wide association studies (GWAS) of ADHD (n = 53 293) and lifetime cannabis use (n = 32 330) to run LD score regression and estimate their genetic correlation, undertake a cross-trait analysis to identify new loci and use a two-sample Mendelian randomization approach to infer a causal relationship between these traits. Results: We estimated a genetic correlation of r2 = 0.29 (P = 1.63x10-5) and identified four new genome-wide significant loci: two in a single variant association analysis (rs145108385, P = 3.30x10-8 and rs4259397, P = 4.52x10- 8) and two in a gene-based association analysis (WDPCP, P = 9.67x10-7 and ZNF251, P = 1.62x10-6). We found support that ADHD is causal for lifetime cannabis use, with an odds ratio of 7.9 for cannabis use in individuals with ADHD in comparison to individuals without ADHD (95% CI (3.72, 15.51), P = 5.88x10-5).

Discussion: These results are in line with the temporal relationship between ADHD and future cannabis use, reinforce the need to consider substance misuse in the context of ADHD in clinical intervention, and highlight the need for future genetic studies to provide insight into the shared biological mechanisms underlying both conditions.


doi: 10.1016/j.euroneuro.2018.08.078

72

GENOMIC SEM: A POWERFUL FRAMEWORK TO IN- TERROGATE THE CAUSES AND CONSEQUENCES OF

PSYCHIATRIC DISEASE

Michel Nivard 1 , ∗, Andrew Grotzinger 2 , Mijke Rhemtulla 3 , Ronald de Vlaming 1 , Stuart Ritchie 4 , Travis Mallard 2 , W David Hill 4 , Hill Fung Ip 1 , Andrew McIntosh 4 , Ian J. Deary 4 , Dorret Boomsma 1 , Philipp Koellinger 1 , Paige Harden 2 , Elliot Tucker-Drob 2

1 VU University 2 The University of Texas at Austin

3 University of California, Davis 4 University of Edinburgh

Background: Methods which leverage GWAS summary statistics to estimate genetic correlations between pairwise combinations of traits have produced ’atlases’ of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across phenotypes. Especially psychiatric disorders, personality and behavioral phenotypes exhibit strong genetic correlations. We introduce genomic structural equation modeling (Ge- nomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM syn- thesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Methods: We use genomic structural equation modeling to study whether the relationship between attention deficit hyperactivity disorder (ADHD) and unhealthy or risky behaviors (smoking behaviors, drinking, speeding, eating related behaviors) is causal and whether the causal relation is mediated by educational attainment. (EA) and income. Our analysis is based on the latest GWAS and our technique is robust for the fact that these GWAS have considerable sample overlap. We further explore whether ADHD liability is a common cause which contribute between the strong genetic relationship between EA and mortality. To test the causal directions implied in these models we introduce genetic instruments (both individual SNPs, and Inferred effects of gene expression from SMR/TWAS).



44 Abstracts

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Results: Our results imply a causal effect of ADHD on unhealthy or risky behaviors and that the relationship is mediated by EA, suggesting an educational intervention may reduce the impact of ADHD on physical health. We contrast and compare our findings with those based on pairwise and multivariable Mendelian randomization (MR), randomized trials, and natural experiments. Results obtained with genomic SEM are largely consistent with results obtained using MR, randomized trials and natural experiments.

Discussion: The ability to leverage genetics to identify mod- erators on the causal path from psychiatric disease to adverse outcome and physical health can enable the development of preventive treatments and program which dampen the adverse effects which follow from ADHD.


doi: 10.1016/j.euroneuro.2018.08.079


abstracts of the 26th world congress of psychiatric...

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