Title: "Model selection approach for genome wide association studies in admixed populations*"
Speaker: Malgorzata Bogdan, Departments of Mathematics and Computer Science, Wroclaw University of Technology and Jan Dlugosz University, Czestochowa, Poland; and Fulbright Visiting Scholar at the Department of Statistics, Stanford University

Place: LILY G126
Date: Novemeber 27, 2012; Tuesday

Genome Wide Association Studies (GWAS) are used to identify regions of the genome hosting genes influencing traits of interest. In such studies scientists test a large number of genetic markers for the association between their genotypes and a given trait. This creates a huge multiple testing problem and results in a relatively low power of detection of influential genes. In admixed populations, which originate from a recent interbreeding between two previously isolated populations, one can locate influential genes by using admixture mapping, where the information on the genotypes of genetic markers is replaced with the information on the ancestry of a given region of the genome. In recent articles some methods for genome wide association studies which combine the information on the genotypes and admixture were proposed. These methods rely mainly on single marker tests. In this talk we will show how the model selection approach to GWAS, based on modified versions of Bayesian Information Criterion, can be extended supplementing the design matrix by ancestry information. Our simulation studies show that ancestry information can help detect influential genes in the regions of low linkage disequilibrium and, due to elimination of these genes from the residual error, can increase the overall power of detection of other genes.

*This is a joint work with Hua Tang from Stanford University, Florian Frommlet from Medical University of Vienna and Piotr Szulc from Wroclaw University of Technology.

Associated Reading:

1. Bogdan M, Frommlet F, Biecek P, Cheng R, Ghosh JK, Doerge RW (2008). Extending the Modified Bayesian Information Criterion (mBIC) to dense markers and multiple interval mapping. Biometrics 64: 1162--1169.

2. Frommlet F, Ruhaltinger F, Twarˇg P, Bogdan M (2012) A model selection approach to genome wide association studies, Computational Statistics and Data Analysis, 56: 1038-1051.

3. Redden DT, Divers J, Vaughan LK, Tiwari HK, Beasley TM, Fernandez JR, Kimberly RP, Feng R, Padilla MA, Liu N, Miller MB, Allison DB (2006) Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model. PLoS Genet 2:e137.

4. Tang H, Siegmund DO, Johnson NA, Romieu I, London SJ (2010) Joint testing of genotype and ancestry association in admixed families. Genet Epidemiol 34: 783-791.

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