Association mapping in structured populations

The use, in association studies, of the forthcoming dense genomewide collec tion of single-nucleotide polymorphism (SNPs) has been heralded as a potent ial breakthrough in the study of the genetic basis of common complex disord ers. A serious problem with association mapping is that population structur e can lead to spurious associations between a candidate marker and a phenot ype. One common solution has been to abandon case-control studies in favor of family-based tests of association, such as the transmission/disequilibri um test (TDT), but this comes at a considerable cost in the need to collect DNA from close relatives of affected individuals. In this article we descr ibe a novel, statistically valid, method for ease-control association studi es in structured populations. Our method uses a set of unlinked genetic mar kers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associat ions within subpopulations. It provides power comparable with the TDT in ma ny settings and may substantially outperform it if there are conflicting as sociations in different subpopulations.