Genotypes and phenotypes in Anorexia Nervosa

A gene-association study

Outline

1. Genotypes and phenotypes in AN – general questions

2. Current works – analysis of 14 BMI-related SNPs – Genome-wide association study of BMI– Why to look at BMI-SNPs in ED?– Research questions– Results– Interpretation and conclusions

3. Bonus (if enough time) – article on real-time fMRI

Abbreviations

• ED – Eating disorders• ANR – Anorexia nervosa restrictive type• GWAS – Genome wide association study• SNP – Single Nucleotide Polymorphism• BMI – Body mass index

General questions

• Are variations in particular genes associated with susceptibility to ED?

• Are these genes associated with subsets of ED patients?

• Are these genetic variations predictive of the outcome and course of the treatment?

• To verify if variation in a gene is associated with a disease:– Genotype the candidate gene/polymorphism in cases and

controls– Count genotypes in both groups– Compare frequencies of genotypes– By the way: Samples have to be large!

Current works – GWAS of BMI

• „Six New Loci Associated with BMI Highlight a Neuronal Influence on Body Weight Regulation” – Willer et al., Nature Genetics, January 2009

• Metaanalysis of previous findings plus replication with new samples (~88k subjects)

• Unfortunately, it wasn’t done by us • Confirmed previous loci (FTO, MC4) and identified

new ones (p<5 x 10-8)• 14 SNPs all together associated with BMI

14 SNPs associated with BMI in normal population

• FTO• MC4R (2 SNPs)• BDNF (2 SNPs)• TMEM18• GNPDA2• SH2B1• KCTD15• MTCH2• NEGR1• ETV5• 2 SNPs not close to any gene

The Idea!

• Take these 14 SNPs associated with BMI in normal population

• Genotype them in the sample of ED and controls• Analyze – show association or lack of it• With the results, address some of the questions

perturbing ED field

• ED types can be divided in a couple of subcategories

• Here, we focus only on one subcategory: ANR

Why to look at these SNPs - Questions

• In the ED field:– Is high BMI a protective factor against ANR?– Is ANR on the extreme end of the BMI dimension or is it

qualitatively different?

• In the current study:– Are variants associated with high BMI in normal

population underrepresented in ANR?– Are these variants associated with BMI in ANR sample

(Is low BMI in ANR etiologically distinct from low BMI in normal population?)

Materials and methods

• A sample– 205 female ANR patients and 1674 female healthy controls

(population sample)– Some removed for low genotyping (more than 1 missing

genotype)– Resulting sample consisted of 173 ANR and 1571 control

subjects

• SNPs– rs2844479 excluded because of significant differences in

missingess rate bt. cases and controls – Remaining 13 SNPs passed the quality check

• Analyses were performed with Plink, UNPHASED, SPSS

Results: case-control

• To find out if SNPs are associated with ANR caseness• A case-control analysis of frequencies of genotypes and

alleles showed no difference bt. groups.

Results: counting effect alleles

CountsMean nr of

effect alleles F Sig.

Controls 1208 12.437 0.002 0.963

ANR Cases(only complete genotypes) 131 12.427

• To find out if BMI-increasing alleles are protective against ANR

• How to do it?

1. Make sure which allele in the GWAS study was the BMI-increasing allele (effect allele) in each SNP

2. Calculate how many of effect alleles each individual in the current study has

3. Compare means bt. cases and controls

• No difference in number of effect alleles bt. cases and controls

Results: BMI in ANR

• To find out if investigated SNPs affect BMI in ANR sample

• rs925946 (BDNF gene) significantly associated with BMI in ANR sample, p-value=0.008

• No association for other SNPs

Genotypes Count Freq. Additive ValueConf. Int.95%Low

Conf. Int.95%High

G/G 79 48.47% 0 0 0

G/T 68 41.72% -0.004 -0.048 0.040

T/T 16 9.82% 0.188 0.012 0.364

Summary and discussion

• The aim of this study was to find out whether genetic variants affecting BMI in normal population are protective against ANR

• Results show that this is not the case• Additionally, except for one, SNPs influencing BMI in normal

population didn’t predict BMI in the ANR group, suggesting that their effects are overruled by presence of other, putative genetic risk factors for ANR

• Genetic etiology of low BMI in ANR is distinct from that in normal population

• It suggests that ANR is qualitatively different from other types of weight related disorders

Teaser

• There are also interesting results for the other subcategory of ED

• 4 SNPs associated with bingeing-purging category (ANP + BNP + NAO-P)

• That’s another story

• Roger Adan• Judith Hendriks

• Annemarie van Elburg• Unna Danner

The Research Training Network INTACT

Acknowledgements

Real – time fMRI

Traditional fMRI

– BOLD signal (Blood-oxygen-level dependent)– Brain activity via oxygen absorption from hemoglobin

(change in magnetic properties)– Delay: detectable oxygenation change after 3-5 seconds– Takes time to analyse

Real-time fMRI

• Sophisticated software to analyse BOLD signal on-the-fly

• Real-time chagnes of activity observable (still with 3-5s delay)

• Visualized on the screen• Adaptive experiments

Neurofeedback

• Improved biofeedback…• Voluntary modulation of brain activity in chosen

regions• Quick to grasp• Subjects choos their own strategy and region

(what works best)

Some applications

• Brain-pong (controlling external devices)• Locked-in syndrome (brain injury, stroke, ALS)

– Efficient communication, Brain-read

• Pain perception• Empathy in children (learning brain states)• Lie detection (possibly)• Therapeutic applications?

– Stroke rehabilitation, epilepsy, depression, addiction treatment– Augmentation of psychotherapy

• Possible implications… let loose your imagination

Caveats

• Many limitations– Practical– Neuroethics– Still in its infancy

• Nature Reviews Neuroscience – „Applications of real-time fMRI”, deCharms, 2008