Genome-wide association studies have been proposed as an efficient and powerful method of uncovering genetic variants that contribute to complex disease [Hirschhorn and Daly, 2005; The Wellcome Trust Case Control Consortium, 2007]. Especially when genes have modest effects on disease risk and have common risk allele frequencies, association studies are believed to be more powerful than linkage studies, which are widely used for detecting genes of a major effect [Risch and Merikangas, 1996; Cardon and Bell, 2001; Carlson et al., 2004]. However association testing can lead to spurious positive results when unrecognized population structure exists [Hirschhorn and Daly, 2005; Cardon and Bell, 2001]. This has motivated the development and use of robust association methods to correct for effects of population structure caused by stratification, including the Transmission Disequilibrium Test (TDT) [Spielman et al., 1993; Ewans and Spielman, 1995], and generalizations implemented in the Family Based Association Test (FBAT) [Laid et al., 2000], even though such family-based designs diminish statistical efficiency. In population-based association studies, several statistical methods have also been developed to detect population stratification and to account for its
