Detection of gene-gene interactions in genome-wide association studies

1. Detection of gene-gene interactions in genome-wide association studies Manuel A R Ferreira Center for Human Genetic Research Massachusetts General Hospital Harvard Medical School

2. What is epistasis or GxG? Phenotype 5 3 Locus B 1 0 1 1 Locus A Epistasisdefined as the extent to which the joint contribution oftwo locitowards a phenotype deviates from that expected under a purely additive model. Fisher (1918)

3. Is it expected to be important for complex traits/diseases? Not much evidence Growing evidence Model organisms Model organisms Brem et al. 2005 Nature 436: 701–703 [Yeast] Li et al. 1997 Genetics 145, 453–465 [Rice] Montooth et al. 2003 Genetics 165, 623–635 [Drosophila] Carlborg et al. 2003 Genome Res. 13, 413–421 [Chicken] Shimomura et al. 2001 Genome Res. 11, 959–980 [Mice] Xu & Jia 2007 Genetics 175, 1955-1963 [Barley] Zeng et al. 2000 Genetics 154, 299–310 [Drosophila] Flint et al. 2004 Mamm. Genome 15, 77–82 [Mice] Humans Maller et al. 2006 Nat Genet. 38:1055-9 Humans Schadt & Lum 2006 J Lipid Res 47: 2601–2613 (Gjuvsland et al. 2006 Genetics 175: 411–420) Can we detect it in genome-wide association studies? Technically challenging Astronomical number of tests (how to perform, analyze and correct for them, power) Plausible for certain models of interaction Marchini et al. 2005 Nat Genet 37, 413–417 Evans et al. 2006 PLoS Genet 2, e157 No reports as yet (in humans)

4. Traditional methods to detect epistasis 1. Regression Flexible framework Slow y = m1.LocusA + m2.LocusB + m3. (LocusA×LocusB) 2. “Linkage Disequilibrium” or allelic-association Powerful (eg. case-only) Less flexible, phasing a × d OR = b × c 3. Transmission distortion More robust Less powerful All allele-based!

5. New methods Allele-based test Faster standard tests (eg. logistic regression), useful for whole-genome screens Collapse B Collapse A a × d OR = b × c ORcases≠ ORcontrols Test for epistasis

6. New methods Gene 1 Gene 2 A B 1 C 2 35 allele-based tests SNPs D 3 E 4 F 5 A B 1 C 2 A single gene-based test D 3 E 4 F 5

7. New methods 2. Gene-based test Reduce # tests, capture “haplotypic” variation, analysis of pathways or networks Case-only sample Powerful Less robust 1. Canonical correlation analysis of Gene 1 and Gene 2 p canonical correlations 2. Estimate the significance of all correlations using Bartlett’s (1941) test Case-control sample Flexible Less powerful 1. Canonical correlation analysis of Gene 1 and Gene 2 Store composite variables for Gene 1 and Gene 2 associated with the largest canonical correlation 2. Test for interaction between these composite variables using standard linear or logit regression + m3. (Gene1×Gene2) y = m1.Gene1 + m2.Gene2

8. New methods  3. Performance Gene 1 Gene 2 Type-1 error (α = 0.05)

9. New methods  3. Performance Gene 1 Gene 2 Power (α = 0.05)

10. http://pngu.mgh.harvard.edu/~purcell/plink/

11. Application to a bipolar disease GWAS Poster 560 New Bioinformatic and Computational Methods 3.15 – 5.00pm

12. Acknowledgements MGH University College London Shaun Purcell Pamela Sklar Mark Daly Ed Scolnik Laurie Weiss Douglas Ruderfer Yan Meng Jennifer Stone Matt Ogdie Hugh Gurling WTCCC Nick Craddock Funding NHMRC Sidney Sax post-doctoral fellowship STEP-BD Jordan Smoller Roy Perlis Vishwajit Nimgaonkar Nan Laird Matt McQueen Steve Faraone