A generalized sequential bonferroni procedure for gwas in admixed populations incorporating admixture mapping information into association tests

Abstract

Objective: To develop effective methods for GWAS in admixed populations such as African Americans. Methods: We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. Results: Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry. Conclusion: The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.