Genome-wide association studies have identified many common genetic variants which are associated with certain diseases. The identified common variants, however, explain only a small portion of the heritability of a complex disease phenotype. The missing heritability motivated researchers to test the hypothesis that rare variants influence common diseases. Next-generation sequencing technologies have made the studies of rare variants practicable. Quite a few statistical tests have been developed for exploiting the cumulative effect of a set of rare variants on a phenotype. The best-known sequence kernel association tests (SKATs) were developed for rare variants analysis of homogeneous genomes. In this chapter, we illustrate applications of the SKATs and offer several caveats regarding them. In particular, we address how to modify the SKATs to integrate local allele ancestries and calibrate the cryptic relatedness and population structure of admixed genomes.
Next-generation sequencing Common disease–rare variants hypothesis Linear mixed-effect models Unrelated individuals Sib pair designs Family designs Homogeneous population Admixed population Global ancestry Local ancestry Cryptic relatedness Population structure
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This work was funded in part by NIH grant HG003054 to X.Z. and by Tulane’s Committee on Research fellowship (600890) and Carol Lavin Bernick Faculty Grant (632119) to H.Q.
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