GWAS project

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Project description

We offer projects on Genome Wide Association Studies (GWAS). These studies search for correlations between genetic markers (usually Single Nucleotide Polymorphisms, short SNPs) and any measurable trait in a population of individuals. The motivation is that such associations could provide new candidates for causal variants in genes (or their regulatory elements) that play a role for the phenotype of interest. In the clinical context this may eventually lead to a better understanding of the genetic components of diseases and their risk factors.

We concentrate on the data generated for the Cohorte Lausanne (CoLaus). The CoLaus phenotypic dataset includes a large range of measurements, including extensive blood chemistry, anatomic and physiological measures, as well as parameters related to life style and history. Genotypes have been measured for ~500`000 SNPs using Affymetrix 500k SNP arrays. Regressing the various phenotypes onto these SNPs has already revealed a number of highly significant associations (see for our publications).

In this GWAS project students will gain first experience with the “standard” protocols for GWAS:

  • genotype calling from the raw chip-data and basic quality control
  • principle component analysis (PCA) to detect and possibly correct for population stratification
  • genotype imputation (using linkage disequilibrium information from HapMap)
  • testing for association between a single SNP and continuous or categorical phenotypes
  • global significance analysis and correction for multiple testing
  • data presentation (e.g. using quantile-quantile and Manhattan plots)
  • cross-replication and meta-analysis for integration of association data from multiple studies

From the many GWAS that were performed in the last years it became apparent that even well-powered (meta-)studies with many thousands (and even ten-thousands) of samples could at best identify a few (dozen) candidate loci with highly significant associations. While many of these associations have been replicated in independent studies, each locus explains but a tiny (<1%) fraction of the genetic variance of the phenotype (as predicted from twin-studies). Remarkably, models that pool all significant loci into a single predictive scheme still miss out by at least one order of magnitude in explained variance. Thus, while GWAS already today provide new candidates for disease-associated genes and potential drug targets, very few – if any – of the currently identified (sets of) genotypic markers are of any practical use for accessing risk for predisposition to any of the complex diseases that have been studied. Different solutions to this apparent enigma have been proposed:

  • other variants like Copy Number Variations (CNVs) or epigenetics may play an important role
  • interactions between genetic variants (GxG) or with the environment (GxE)
  • many causal variants may be rare and/or poorly tagged by the measured SNPs
  • many causal variants may have very small effect sizes
  • overestimation of heritabilities from twin-studies

If time permits the student may develop his or her own research towards any of these new directions.

Further reading

For an introduction to GWAS, with an emphasis on human studies, you could start with a nice tutorial article [1], and a review of more recent issues [2]. There is also a nice review about approaches for rodent studies [3].

More Advanced Statistical Methodology

An important and widely used approach to dealing with cryptic population structure [4], and key references on genotype imputation [5][6].

A powerful approach to deal with strain structure or relatedness between individuals [7].


PLINK is an excellent data handling tool, and implements many useful statistical methods. It's the Swiss Army Knife for GWAS.

EIGENSOFT is widely used for population structure analysis and correction.

IMPUTE and SNPTEST, or MACH and ProbABEL, or BimBam, and all be used to perform more sophisticated model based genotype imputation and association testing.

QUICKTEST is our own software for association testing using uncertain genotypes. For quantitative trait analysis, we think it is faster and better than SNPTEST.


  1. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet 2006 Oct; 7(10) 781-91. doi:10.1038/nrg1916 pmid:16983374. PubMed HubMed [BaldingTutorial]
  2. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, and Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008 May; 9(5) 356-69. doi:10.1038/nrg2344 pmid:18398418. PubMed HubMed [McCarthyReview]
  3. Flint J, Valdar W, Shifman S, and Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet 2005 Apr; 6(4) 271-86. doi:10.1038/nrg1576 pmid:15803197. PubMed HubMed [FlintReview]
  4. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, and Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006 Aug; 38(8) 904-9. doi:10.1038/ng1847 pmid:16862161. PubMed HubMed [PricePC]
  5. Servin B and Stephens M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet 2007 Jul; 3(7) e114. doi:10.1371/journal.pgen.0030114 pmid:17676998. PubMed HubMed [ServinImputation]
  6. Marchini J, Howie B, Myers S, McVean G, and Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007 Jul; 39(7) 906-13. doi:10.1038/ng2088 pmid:17572673. PubMed HubMed [MarchiniImputation]
  7. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, and Eskin E. Efficient control of population structure in model organism association mapping. Genetics 2008 Mar; 178(3) 1709-23. doi:10.1534/genetics.107.080101 pmid:18385116. PubMed HubMed [KangEMMA]
All Medline abstracts: PubMed HubMed