BSc Course Experimental design Genomewide Association Studies Sven

BSc Course: "Experimental design“ Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue de Bugnon 27 - DGM 328 CH-1005 Lausanne Switzerland work: ++41 -21 -692 -5452 cell: ++41 -78 -663 -4980 http: //serverdgm. unil. ch/bergmann

Overview • • • Population stratification Associations: Basics Whole genome associations Genotype imputation Uncertain genotypes New Methods

6’ 189 individuals Co. Laus = Cohort Lausanne Genotypes 500. 000 SNPs Phenotypes 159 measurement 144 questions Collaboration with: Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)

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

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

Example: 2 PCs for 3 d-data Raw data points: {a, …, z} http: //ordination. okstate. edu/PCA. htm

Example: 2 PCs for 3 d-data Normalized data points: zero mean (& unit std)! http: //ordination. okstate. edu/PCA. htm

Example: 2 PCs for 3 d-data The direction of most variance perpendicular to PCA 1 defines PCA 2 Most variance is along PCA 1 Identification of axes with the most variance http: //ordination. okstate. edu/PCA. htm

PC 2 Ethnic groups cluster according to geographic distances PC 1

PCA of POPRES cohort

Overview • • • Population stratification Associations: Basics Whole genome associations Genotype imputation Uncertain genotypes New Methods

Phenotypic variation:

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

Quantifying Significance

T-test t-value (significance) can be translated into p-value (probability)

phenotype Association using regression genotype Coded genotype

Regression analysis “residuals” “intercept” “coefficients” Y “response” X “feature(s)”

Regression formalism (monotonic) transformation effect size (regression coefficient) error (residual) phenotype (response variable) of individual i p(β=0) coded genotype (feature) of individual i Goal: Find effect size that explains best all (potentially transformed) phenotypes as 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)

Overview • • • Population stratification Associations: Basics Whole genome associations Genotype imputation Uncertain genotypes New Methods

Whole Genome Association

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

Whole Genome Association Quantile-quantile plot significance observed significance Manhattan plot 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)

GWAS: >20 publications in 2006/2007 Massive!

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 Di. Iorio, Dawn Waterworth, Andrew Singleton, Unnur Steina Bjornsdottir, Gunnar Sigurdsson, Dena Hernandez, Ranil De. Silva, 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

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

$Current insights from GWAS: • Each locus explains but a tiny (<1%) fraction of$

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 David Goldstein: “~93, 000 SNPs would be required to explain 80% of the population variation in height. ” Common Genetic Variation and Human Traits, NEJM 360; 17

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 Genomewide Association Studies are meant to as an undirected approach to elucidate new candidate loci that impact the trait!

So what do we miss? 1. Other variants like Copy Number Variations or epigenetics may play an important role 2. Interactions between genetic variants (Gx. G) or with the environment (Gx. E) 3. Many causal variants may be rare and/or poorly tagged by the measured SNPs 4. Many causal variants may have very small effect sizes 5. Overestimation of heritabilities from twin-studies?

Overview • • • Population stratification Associations: Basics Whole genome associations Genotype imputation Uncertain genotypes New Methods

Intensity of Allele A Genotypes are called with varying uncertainty Intensity of Allele G

Some Genotypes are missing at all …

… but are imputed with different uncertainties

… using Linkage Disequilibrium! Marker 1 2 3 D n LD Markers close together on chromosomes are often transmitted together, yielding a non-zero correlation between the alleles.

Conclusion • Genotypic markers are always measured or inferred with some degree of uncertainty • Association methods should take into account this uncertainty

Two easy ways dealing with uncertain genotypes 1. Genotype Calling: Choose the most likely genotype and continue as if it is true (p 11=10%, p 12=20% p 22=70% => G=2) 2. Mean genotype: Use the weighted average genotype (p 11=10%, p 12=20% p 22=70% => G=1. 6)

Overview • • • Associations: Basics Whole genome associations Population stratification Genotype imputation Uncertain genotypes New Methods

How could our models become more predictive? 1. Improve measurements: - measure more variants (e. g. by UHS) - measure other variants (e. g. CNVs) - measure “molecular phenotypes” 2. Improve models: - proper integration of uncertainties - include interactions - multi-layer models

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

The challenge of many datasets: How to integrate all the information? Organisms – – – Genotypic data Gene expression data Metabolomics data Interaction data Pathways De Ph y ve lo pm En en sio ta vir l l on ogi c m Ex en al pe ta C rim l lin en ica ta l l Conditions Data types

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

Using expression data from Hapmap panel we computed all 1012 pairwise interactions for several expression values: The strongest interactions do not occur for SNPs with high (marginal) significance! Bioinformatics Advance Access published online on April 7, 2010 Bioinformatics, doi: 10. 1093/bioinformatics/btq 147

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

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

Overview • • • Associations: Basics Whole genome associations Population stratification Genotype imputation Uncertain genotypes New Methods

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)