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Including Additional Controls from Public Databases Improves the Power of a Genome-Wide Association StudyMukherjee S.a, b · Simon J.c · Bayuga S.c · Ludwig E.d · Yoo S.c · Orlow I.c · Viale A.e · Offit K.b, d · Kurtz R.C.d · Olson S.H.c · Klein R.J.b
aGerstner Sloan-Kettering Graduate School of Biomedical Sciences, bProgram in Cancer Biology and Genetics, cDepartment of Epidemiology and Biostatistics, dDepartment of Medicine, and eGenomics Core Laboratory, Memorial Sloan-Kettering Cancer Center, New York, N.Y., USA Corresponding Author
Robert J. Klein
Program in Cancer Biology and Genetics
Memorial Sloan-Kettering Cancer Center
1275 York Ave., Box 337, New York, NY 10065 (USA)
Tel. +1 646 888 2525, E-Mail email@example.com
Though genome-wide association studies (GWAS) have identified numerous susceptibility loci for common diseases, their use is limited due to the expense of genotyping large cohorts of individuals. One potential solution is to use ‘additional controls’, or genotype data from control individuals deposited in public repositories. While this approach has been used by several groups, the genetically heterogeneous nature of the population of the United States makes this approach potentially problematic. We empirically investigated the utility of this approach in a US-based GWAS. In a small GWAS of pancreatic cancer in New York, we observed clear population structure differences relative to controls from the database of Genotypes and Phenotypes (dbGaP). When we conduct the GWAS using these additional controls, we find large inflation of the test statistic that is properly corrected by using eigenvectors from principal components analysis as covariates. To deal with errors introduced due to different sources, we propose simultaneously genotyping a small number of controls along with cases and then comparing this group to the additional controls. We show that removing SNPs that show differences between these control groups reduces false-positive findings. Thus, through an empirical approach, this report provides practical guidance for using additional controls from publicly available datasets.
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