Biomedical Master Introduction to genome-wide association studies Metabolic diseases (B. Thorens)

1. Biomedical MasterIntroduction to genome-wide association studiesMetabolic diseases (B. Thorens) Sven Bergmann University of Lausanne & Swiss Institute of Bioinformatics http://serverdgm.unil.ch/bergmann Biomedical Master: Metabolic diseasesLausanne, November 8, 2010

2. Large (genomic) systems • many uncharacterizedelements • relationships unknown • computational analysis should: • improve annotation • reveal relations • reduce complexity • Small systems • elements well-known • many relationships established • quantitative modeling of systems properties like: • Dynamics • Robustness • Logics A Systems Biology approach

6. Overview • Population stratification • Our whole genome associations • New Methods and Approaches

7. ATTGCAATCCGTGG...ATCGAGCCA…TACGATTGCACGCCG… ATTGCAAGCCGTGG...ATCTAGCCA…TACGATTGCAAGCCG… ATTGCAAGCCGTGG...ATCTAGCCA…TACGATTGCAAGCCG… ATTGCAATCCGTGG...ATCGAGCCA…TACGATTGCACGCCG… ATTGCAAGCCGTGG...ATCTAGCCA…TACGATTGCAAGCCG… Genetic variation in SNPs (Single Nucleotide Polymorphisms)

8. Phenotypes Genotypes 159 measurement 144 questions 500.000 SNPs CoLaus = Cohort Lausanne 6’189 individuals Collaboration with:Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)

9. Analysis of Genotypes only Principle Component Analysis reveals SNP-vectors explaining largest variation in the data

10. Ethnic groups cluster according to geographic distances PC2 PC2 PC1 PC1

11. PCA of POPRES cohort

12. Predicting location according to SNP-profile ...

13. … is pretty accurate!

14. The Swiss segregate according to language

15. PC-Analysis of genotypic profile • Is surprisingly accurate! • Is useful for forensic purposes or for individuals interested in their ancestry • Is useful for population stratification in Genome-wide Association studies

16. Phenotypic variation:

17. What is association? SNPs trait variant chromosome Genetic variation yields phenotypic variation Population with ‘ ’ allele Population with ‘ ’ allele Distributions of “trait”

18. Association using regression phenotype genotype Coded genotype

19. effect size (regression coefficient) (monotonic) transformation error (residual) p(β=0) phenotype (response variable) of individual i coded genotype(feature) of individual i Regression formalism Goal: Find effect size that explains best all (potentially transformed) phenotypesas a linear function of the genotypes and estimate the probability (p-value) for the data being consistent with the null hypothesis (i.e. no effect)

20. Whole Genome Association

21. Whole Genome Association Current microarrays probe ~1M SNPs! Standard approach: Evaluate significance for association of each SNP independently: significance

22. Whole Genome Association Manhattan plot Quantile-quantile plot observedsignificance significance Chromosome & position Expected significance • GWA screens include large number of statistical tests! • Huge burden of correcting for multiple testing! • Can detect only highly significant associations (p < α / #(tests) ~ 10-7)

27. Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing receptor (CASR) gene Karen Kapur, Toby Johnson, Noam D. Beckmann, Joban Sehmi, Toshiko Tanaka, Zoltán Kutalik, Unnur Styrkarsdottir, Weihua Zhang, Diana Marek, Daniel F. Gudbjartsson, Yuri Milaneschi, Hilma Holm, Angelo DiIorio, Dawn Waterworth, Andrew Singleton, Unnur Steina Bjornsdottir, Gunnar Sigurdsson, Dena Hernandez, Ranil DeSilva, Paul Elliott, Gudmundur Eyjolfsson, Jack M Guralnik, James Scott, Unnur Thorsteinsdotti, Stefania Bandinelli, John Chambers, Kari Stefansson, Gérard Waeber, Luigi Ferrucci, Jaspal S Kooner, Vincent Mooser, Peter Vollenweider, Jacques S. Beckmann, Murielle Bochud, Sven Bergmann

28. Current insights from GWAS: • Well-powered (meta-)studies with (ten-)thousands of samples have identified a few (dozen) candidate loci with highly significant associations • Many of these associations have been replicated in independent studies

29. Current insights from GWAS: • Each locus explains but a tiny (<1%) fraction of the phenotypic variance • All significant loci together explain only a small (<10%) of the variance

30. The “Missing variance” (Non-)Problem Why should a simplistic (additive) model using incomplete or approximate features possibly explain anything close to the genetic variance of a complex trait? … and it doesn’t have to as long as Genome-wide Association Studies are meant to as an undirected approach to elucidate new candidate loci that impact the trait!

31. How could our models become more predictive? • Improve measurements:- measure more variants (e.g. by UHS)- measure other variants (e.g. CNVs)- measure “molecular phenotypes” • Improve models:- proper integration of uncertainties- include interactions- multi-layer models

32. Towards a layered Systems Model We need intermediate (molecular) phenotypes to better understand organismal phenotypes

33. Network Approaches for Integrative Association Analysis Using knowledge on physical gene-interactions or pathways to prioritize the search for functional interactions

34. Transcription Modules reduce Complexity http://maya.unil.ch: 7575/ExpressionView SB, J Ihmels & N Barkai Physical Review E (2003)

35. Association of (average) module expression is often stronger than for any of its constituent genes

36. Take-home Messages: • Analysis of genome-wide SNP data reveals that population structure mirrors geography • Genome-wide association studies elucidate candidate loci for a multitude of traits, but have little predictive power so far • Future improvement will require • better genotyping (CGH, UHS, …) • New analysis approaches (interactions, networks, data integration)