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Mutations Modifying the Age of Onset of Dementia Vélez et al. 2015

Supplementary Material for:

APOE*E2 Allele Delays Age of Onset in PSEN1 E280A Alzheimer’s Disease

JI Vélez1,2,*, F Lopera2,*, D Sepulveda-Falla2,3,*, HR Patel1, AS Johar1, A Chuah4, C Tobón2, D Rivera2, A Villegas2, Y Cai1, K Peng5, R Arkell6, FX Castellanos7, SJ Andrews9, MF Silva Lara1, PK Creagh1,

S Easteal9, J de Leon8, ML Wong10, J Licinio10*, CA Mastronardi1,10*& M Arcos-Burgos1,2,*,#

1. Genomics and Predictive Medicine Group, Department of Genome Sciences, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia.

2. Neuroscience Research Group, University of Antioquia, Medellín, Colombia.3. Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.4. Genome Discovery Unit, Department of Genome Sciences, John Curtin School of Medical Research, The

Australian National University, Canberra, ACT, Australia.5. Biomolecular Resource Facility, John Curtin School of Medical Research, The Australian National University,

Canberra, ACT, Australia.6. Early Mammalian Development Laboratory, Research School of Biology, The Australian National University,

Canberra, ACT, Australia.7. NYU Child Study Center, NYU Langone Medical Center, New York, NY, US, and Nathan Kline Institute for

Psychiatric Research, Orangeburg, NY, US.8. Mental Health Research Center at Eastern State Hospital, University of Kentucky, Lexington, Kentucky, US.9. Genome Diversity and Health Group, Department of Genome Sciences, John Curtin School of Medical Research,

The Australian National University, Canberra, ACT, Australia. 10. South Australian Health and Medical Research Institute and Department of Psychiatry, School of Medicine,

Flinders University, Adelaide, Australia.

* These authors contributed equally to this work.

# Correspondence to be directed to:

Mauricio Arcos-Burgos, M.D., Ph.D.Associate Professor and Group Leader, Genomics and Predictive Medicine GroupJohn Curtin School of Medical Research,The Australian National University, Building 131 Garran Road, Canberra, ACT, Australia.E-mail: [email protected]

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mailto:[email protected]


Clinical assessment: The modified CERAD evaluation battery

As part of our clinical program of AD, patients as well as carriers and non-carriers family members

(either from the same geographical region as the E280A pedigree or carrying the PSEN1 E280A

mutation) underwent clinical, neurological and neuropsychological assessment at the Group of

Neurosciences AD Clinic. Patients were defined as affected by MCI based on Petersen’s criteria, by

AD if the DSM-IV criteria were met, 1, 2 and using a Spanish version of the CERAD evaluation battery3

adapted for the cultural and linguistic characteristics specific to this population,4-7 which includes the

following measures:

(i) Semantic Fluency (Animal Naming): Individuals are requested to name as many animals as

they can in 1 min. The dependent variable is the number of unique animals named.

(ii) Modified Boston Naming Test: Consists in asking individuals to identify 15 line drawings of

increasing complexity (high, medium and low frequency) with a maximum of 10 s for each drawing.

One point is awarded for each correct response. The dependent variable is the total number of correct

naming.

(iii) Mini-mental State Exam (MMSE): A modified version of the MMSE by Folstein (1975)8

was used. The spelling backward subtest was excluded because the spelling ability involved in this task

is not a common skill for Spanish-speaking people. Instead, we replaced it with subtraction by threes,

which is more appropriate for this population. The total score of this test was 30 points. The dependent

variable was the total number of correct points.

(iv) Word List Memory (WLM): This test assesses the ability to recall newly learned

information. Individuals are presented 10 printed words on a card at the rate of one every 2 s. They are

immediately asked to recall as many words as possible from the list. This procedure is repeated in three

consecutive trials. The maximum number of correct words is 30 for the three trials. The number of

correct words identified serves as the dependent variable.

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(v) Constructional Praxis: Individuals are presented with four line drawings of figures of

increasing complexity (circle, rhombus, pentagon and cube), one at a time, and asked to copy them.

Maximum time allowed for copying each figure is 2 min. Reproductions are scored according to

predetermined criteria and the dependent measure is the total score for the four drawings.

(vi) Word List Recall: This test assesses delayed memory by requesting individuals to recall the

list of ten words presented in the WLM task. The maximum time allowed for this recall is 90 s. One

point is awarded for each word recalled correctly with maximum of 10 points if all the words are

recalled.

(vii) Word List Recognition. This test consists of recognition of the 10 words from the WLM

task, presented in a list of 20 words (including 10 additional distractor words). A forced-choice

paradigm is used so that subjects must respond with YES to the words they consider correct (i.e., in the

list of words previously read), and NO to the words that were not on that list. One point is awarded for

each word correctly recognized. To adjust by chance, the subject’s score is calculated as the total

number of correct answers minus 10; if the result is less than 0, a score of 0 is given.

(viii) Line-Drawing Recall. Individuals are asked to recall the drawings they copied previously

and draw them from free recall on a blank sheet of paper. This tests serves to assess visual memory.

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Supplementary Figure 1. (a) Histogram and probability density plots for the ADAOO in 71 patients

with PSEN1 E280A AD. Analysis of the ADAOO distribution disclosed the presence of two hidden

groups with an average ADAOO of ~45 and ~57 years old, respectively. Box- and violin-plots for the

ADAOO by (b) gender and (c) education group. No difference in the average ADAOO was found in

either case. (d) ADAOO as a function of the years of education. ADAOO. AD = Alzheimer’s disease;

ADAOO = Alzheimer’s disease age of onset.

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Supplementary Figure 2. (a) Histogram and probability density plots for the ADAOO in Results 54

patients with sporadic AD. Analysis of the ADAOO distribution disclosed the presence of two hidden

groups with an average ADAOO of ~59 years and ~69 years, respectively. Box- and violin-plots for the

ADAOO by (b) gender and (c) education group. No difference in the average ADAOO was found in

either case. (d) ADAOO as a function of the years of education. Observe that individuals with < 3

years of education in the sample seem to have an earlier ADAOO. AD = Alzheimer’s disease; ADAOO

= Alzheimer’s disease age of onset.

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Supplementary Figure 3. (a) Partition of phenotypic variance in the multi-locus LMEM for each

forward inclusion (steps 1 to 10) and backward elimination (10 steps after the dotted line). The yellow

vertical line marks the selected model based on the highest multiple posterior probability of association

(mPPA). (b) Manhattan plot for the CEFVs included for analysis. Markers with –log10(P)>5.08 were

significant after FDR correction. The red horizontal line corresponds to the Bonferroni threshold of –

log10(P)=6.89. (c) Beanplots for the ADAOO in variants with PFDR<0.05 (see Table 1). Pink, blue and

dotted horizontal lines correspond, respectively, to the within genotype average ADAOO, the

individuals’ ADAOO and the global average ADAOO in our 71 patients with PSEN1 E280A

Alzheimer’s disease. ADAOO: Alzheimer’s disease age of onset. CEFVs: common exonic functional

variants. LMEM: linear mixed-effects model.

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Rare Functional Exonic Variants Modifying ADAOO

Supplementary Table 1. Results for the association analysis of rare variants using (a) regression- and

(b) permutation-based KBAC. (c) Top 20 genes associated genes from the SKAT-O9 analysis of rare

variants.

(a)

Chr Start Stop Gene m G T P5 31,799,031 32,111,038 PDZD2 7 8 3.395 1.80x10-2

11 108,093,559 108,239,826 ATM 4 5 2.423 2.40x10-2

10 98,741,041 98,745,585 C10orf12 3 4 1.817 5.00x10-2

(b)

Chr Start Stop Gene m G T P

5 31,799,031 32,111,038PDZD2 7 8 0.200 1.40x10-2

11 108,093,559 108,239,826 ATM 4 5 0.143 2.13x10-2

17 71,330,523 71,640,227 SDK2 3 4 0.107 5.29x10-2

(c)

Chr Start Stop Gene m T P11 108,093,559 108,239,826 ATM 4 0.004 6.94x10-3

5 31,799,031 32,111,038 PDZD2 7 0.005 9.15x10-3

19 46,298,968 46,318,605 RSPH6A 3 0.011 1.95x10-2

14 105,190,534 105,213,647 ADSSL1 3 0.011 1.95x10-2

6 28,962,594 28,973,037 ZNF311 3 0.011 1.96x10-2

1 109,255,556 109,285,367 FNDC7 3 0.011 1.96x10-2

3 38,035,078 38,048,676 VILL 3 0.011 1.96x10-2

8 71,581,600 71,648,177 XKR9 3 0.011 1.97x10-2

6 161,123,225 161,175,085 PLG 3 0.011 1.97x10-2

3 69,156,039 69,171,746 LMOD3 3 0.011 1.97x10-2

1 178,482,212 178,492,635 TEX35 3 0.011 1.97x10-2

16 31,085,743 31,094,833 ZNF646 3 0.011 1.97x10-2

11 64,591,662 64,612,041CDC42BPG 3 0.011 1.98x10-2

14 105,767,148 105,864,484 PACS2 3 0.011 1.98x10-2

11 61,891,445 61,920,635 INCENP 3 0.011 1.98x10-2

10 98,741,041 98,745,585 C10orf12 3 0.011 1.98x10-2

3 38,738,837 38,835,501 SCN10A 3 0.011 1.98x10-2

17 71,330,523 71,640,227 SDK2 3 0.011 1.98x10-2

6 170,151,718 170,181,680 ERMARD 3 0.011 1.9x10-2

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Chr: Chromosome; m: number of markers; G: number of multi-marker genotypes; T: one-side KBAC test statistic; P: one-sided P-value; KBAC: Kernel-based adaptive cluster; SKAT-O: Optimized Sequence Kernel Association Test. Highlighted genes in (c) were also found using KBAC.

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Supplementary Table 2. GO process and disease annotations for the network involving AOO modifier genes.

GO Processes and Disease Annotations (P-value) Gene(s) involvedPSEN1

APOE*

TRIM22

IFI16

RC3H1*

DFAN5

GO ProcessNegative regulation of cell proliferation (3.367x10-7)Cell differentiation (1.4x10-5)Regulation of apoptotic process (2.69x10-5)Positive regulation of apoptotic process (1.57x10-3)Regulation of neuron apoptotic process (2.37x10-4)Regulation of intrinsic apoptotic signalling pathway (2.68x10-4)Intrinsic apoptotic signalling pathway by p53 class mediator (2.96x10-4)Negative regulation of neuron death (5.22x10-3)Positive regulation of IL-1 β production (7.56x10-5)Immune response (2.32x10-4)T cell activation involved in immune response (4.30x10-4)Positive regulation of NIK/NK-kappa B signaling (1.66x10-3)

DiseasesAlzheimer's disease (3.34x10-5)EOAD (1.68x10-2)Delerium, Dementia, Amnestic, Cognitive disorders (3.13x10-5)Frontotemporal lobar degeneration (4.3x10-4)Frontotemporal lobar dementia (4.3x10-4)Neurodegenerative diseases (5.85x10-4)

* Age of onset decelerators.

ConventionsApoptotic-related processesImmunological-related processes

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Supplementary Table 3. GO process and disease annotations for the network involving AOO modifier genes.

Genes involved (by Network Analysis) Processes P PFDR

PSEN1, APOE Organic hydroxy compound transport 4.40x10-5 1.68x10-3

Positive regulation of protein catabolic process 4.74x10-5 1.68x10-3

Regulation of axonogenesis 4.92x10-5 1.68x10-3

Positive regulation of proteolysis 2.08x10-5 1.40x10-3

Regulation of coagulation 2.13x10-5 1.40x10-3

Positive regulation of protein processing 3.13x10-5 1.63x10-3

Regulation of synaptic plasticity 8.48x10-5 2.02x10-3

Positive regulation of cellular catabolic process 9.55x10-5 2.07x10-3

PSEN1, RC3H1 T cell activation involved in immune response 4.89x10-6 6.53x10-4

Lymphocyte activation involved in immune response 1.84x10-5 1.40x10-3

Cell activation involved in immune response 6.07x10-5 1.83x10-3

Leukocyte activation involved in immune response 6.07x10-5 1.83x10-3

IFI16, RC3H1 Negative regulation of B cell proliferation 3.63x10-5 1.63x10-3

IFI16, PSEN1 Myeloid leukocyte differentiation 4.74x10-5 1.68x10-3

IFI16, PSEN1, RC3H1 Leukocyte differentiation 3.71x10-5 1.63x10-3

IFI16, PSEN1 Autophagy 3.70x10-5 1.63x10-3

Cellular response to starvation 5.10x10-5 1.69x10-3

Antigen receptor-mediated signaling pathway 7.38x10-5 1.93x10-3

IFI16, PSEN1, RC3H1, TRIM22 Immune effector process 4.33x10-6 6.53x10-4

PSEN1, RC3H1, TRIM22, DFNA5 Positive regulation of intracellular signal transduction 3.40x10-5 1.63x10-3

PSEN1, RC3H1, APOE, TRIM22, DFNA5 Regulation of intracellular signal transduction 7.02x10-5 1.93x10-3

IFI16, AOAH, PSEN1, RC3H1, APOE, FCRL5 Negative regulation of response to stimulus 7.37x10-7 4.04x10-4

PSEN1, RC3H1, APOE, FCRL5, TRIM22, DFNA5 Regulation of signal transduction 1.00x10-4 2.07x10-3

IFI16, AOAH, PSEN1, RC3H1, APOE, FCRL5, TRIM22, DFNA5 Regulation of response to stimulus 2.56x10-6 5.61x10-4

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Calculation of Performance Measures for the ARPA-based predictive models

Supplementary Table 4. (a) Possible results when the real and predicted early- and late-onset

Alzheimer’s disease (AD) status are compared. Here, a is the number of individuals with early-onset

AD (EO) that are correctly classified, b is the number of EO individuals classified as late-onset AD

(LO), c corresponds to the number of LO cases classified as EO, and d to the number of LO cases

correctly classified. (b) Expressions for calculating the performance measures used to quantitatively

compare the CART, Random Forest and TreeNet strategies.

(a)

Phenotype PredictionEOAD LOAD

EO (AOO < 48) a bLO (AOO ≥ 48) c d

(b)Measure ExpressionSensitivity a / (a+c)Specificity d / (c+d)Precision d / (c+d)Classification rate (CR) (a+d) / (a+b+c+d)

AOO: age of onset.

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Gender-specific effects of genetic variants modifying ADAOO

We performed a 2-based association analysis to assess gender-effects in the nine genetic variants

reported to modify ADAOO in our 71 patients with PSEN1 E280A AD. Results of this analysis are

presented below. Overall, no specific effects of these genetic variants by gender or education group

were found.

Supplementary Table 5. Gender-specific effects of genetic variants modifying ADAOO.

SNP Gene 2 df P

rs7412 APOE 0.014 1 0.906rs36092215 GPR20 1.055 2 0.590rs12364019 TRIM22 0.000 1 1.000rs16838748 FCRL5 0.000 1 1.000rs12701506 AOAH 1.019 2 0.601rs2682585 PINLYP 2.002 2 0.367rs62621173 IFI16 0.291 1 0.590rs10798302 RC3H1 0.000 1 1.000rs754554 DFNA5 1.530 2 0.465

2 = Test statistic; df = degrees of freedom; P = P-value.

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Number of variants per gene after filtering

Supplementary Figure 4. Distribution of the number of common functional exonic variants (CEFVs)

within genes. On average, ~2.83 CEFVs are located within each gene (standard deviation = 3.23).

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Power analysis

Oligogenic model for detecting ADAOO modifiersOur power analysis using the pwr10 package in R11 suggests that, for a k=3 group design, 71 individuals would be sufficient to detect 80% true positives and a large effect (defined by the Cohen’s d parameter; d=0.82, Supplementary Figure 4) when m=100,000 variants are tested for association (a value that certainly overcomes the final number of variants used during the LMEM analyses). The selection of such effect is based on our hypothesis that variants of large effect (i.e., mutations) modify ADAOO in individuals with PSEN1 E280A AD (see also the β̂ coefficients in Table 1 of the manuscript); k is selected based on the maximum number of possible genotypes for a biallelic genetic marker; and m, as mentioned before, to be conservative.

Supplementary Figure 5. (a) Sample size (n) as a function of the Cohen’s effect size (d) and power. The type I error probability used for calculations is 0.05/100,000. In (b) the red dot corresponds to n=71, power = 80% and a large effect d = 0.82.

It is also worth mentioning that d implies that d% of the ADAOO variance is explained by the m independent variables (i.e., ~100,000 genetic variants being tested). Our LMEMs estimates show that the percentage of ADAOO variance explained with only nine CEFVs (see Table 1 of the manuscript) tested for association is > 90% for the n=71 individuals. Under these conditions (d=0.9, n=71, m=40,000), the post hoc power estimate is of 96%, but for nine CEFVs the post hoc power estimate is of 99.9%.

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Testing differences in ADAOO using extreme phenotypes from Genetic IsolateIn addition, we have performed a power analysis to assess what the resulting sample size would be when a different split of the groups (in terms of the AOO) is used. As before, we utilized the pwr10 package in R11 to evaluate the power of a sample of n1 individuals with an ADAOO < 48 years and n2

individuals with ADAOO ≥ 48 years, using a two-sample t-test, for a wide range of effect sizes (f). Cohen (1988)12 suggests that values of f=0.2, 0.5, and 0.8 represent small, medium, and large effect sizes, respectively, where f is calculated as

f =|μ1−μ2

σ | (1)

In our context, μ1and μ2are the population ADAOO for individuals with early- and late-onset AD, respectively, and σ 2 is the common variance. We estimated μ1and μ2 with the correspondent sample means (i.e., 44.2 and 53.5, respectively) and σ 2as

σ̂ 2=S1

2

n1+

S22

n2 (2)

In this formula, S12and S2

2 are the sample estimates of the variance for the group of individuals with early- and late-ADAOO, respectively (i.e., 2.482 and 5.132 as reported in the second paragraph of the “E280A Pedigree” section). Thus, the sample effect size is f=8.936 (which corresponds to an extremely large effect size). The observed power of our study design is >99% when a type I error probability of 5% is used. Now, assuming a separation of eight years, no changes in the variance estimates and a sample size of n=71 individuals, the estimated effect size is f=7.687. The corresponding power would then be > 99%.

In order to further study the power of our design under other conditions, we used a type I error probability of 5%, varied the sample sizes as n1 = n2 ={20, 40, 60, 80, 100} and f in the interval (0.1, 3) to cover small, medium, large and extremely large effect sizes (Cohen, 1988). The results are presented in Supplementary Figure 5 below. Overall, our study design has a power of >90% to detect effect sizes f > 0.8, which is a tenth of that actually observed. Hence, the ADAOO can be safely tested using our sample size, and our ad hoc separation of early and late onset.

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Supplementary Figure 6. Power analysis as a function of n1 and n2, the number of individuals with ADAOO<48 years and ADAOO ≥ 48 years, respectively, for a type I error probability of 5% and effect sizes (a) f = 0.4, (b) f = 0.8 and (c) f =1.2. The red dot corresponds to the power of our study design (n=71, n1=43, n2=28) for the effect size shown.

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Supplementary Figure 7. Squared error loss (left) and relative importance (right) for the best fitting generalized boosting regression model (GBRM) when the (a) out-of-bag, (b) test data set and (c) cross-validation error are used, and the ADAOO is introduced as a continuous variable. Based on the minimum mean squared error, a measure of model fitting, model (b) was selected. The black, red, green and blue lines in the left panel correspond to the training data set, the test data set, the cross-validation, and the best performing model, respectively. Note that the variable importance plots on the right are similar to those obtained when the ADAOO was introduced as a binary variable.

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REFERENCES

1. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive

impairment: clinical characterization and outcome. Arch Neurol 1999; 56(3): 303-308.

2. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders.

Fourth edn: Washington, D.C., 2000.

3. Morris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G et al. The Consortium

to Establish a Registry for Alzheimer's Disease (CERAD). Part I. Clinical and

neuropsychological assessment of Alzheimer's disease. Neurology 1989; 39(9): 1159-1165.

4. Fleisher AS, Chen K, Quiroz YT, Jakimovich LJ, Gomez MG, Langois CM et al. Florbetapir

PET analysis of amyloid-beta deposition in the presenilin 1 E280A autosomal dominant

Alzheimer's disease kindred: a cross-sectional study. Lancet neurology 2012; 11(12): 1057-

1065.

5. Reiman EM, Quiroz YT, Fleisher AS, Chen K, Velez-Pardo C, Jimenez-Del-Rio M et al. Brain

imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant

Alzheimer's disease in the presenilin 1 E280A kindred: a case-control study. Lancet neurology

2012; 11(12): 1048-1056.

7


6. Reiman EM, Langbaum JB, Fleisher AS, Caselli RJ, Chen K, Ayutyanont N et al. Alzheimer's

Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J

Alzheimers Dis 2011; 26 Suppl 3: 321-329.

7. Acosta-Baena N, Sepulveda-Falla D, Lopera-Gomez CM, Jaramillo-Elorza MC, Moreno S,

Aguirre-Acevedo DC et al. Pre-dementia clinical stages in presenilin 1 E280A familial early-

onset Alzheimer's disease: a retrospective cohort study. Lancet Neurol 2011; 10(3): 213-220.

8. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the

cognitive state of patients for the clinician. Journal of psychiatric research 1975; 12(3): 189-

198.

9. Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA et al. Optimal unified

approach for rare-variant association testing with application to small-sample case-control

whole-exome sequencing studies. Am J Hum Genet 2012; 91(2): 224-237.

10. Champely S. pwr: Basic Functions for Power Analysis. R package version 1.1-2. URL:

http://CRAN.R-project.org/package=pwr. 2015.

11. R Core Team. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. URL: http://www.R-project.org. 2015.

8

http://www.R-project.org/

http://CRAN.R-project.org/package=pwr


12. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. edn. Hillsdale, NJ:

Lawrence Erlbaum1998.

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