Detecting Genetic Association in Case-control Studies Using Similarity-based Association Tests

Although traditional case-control studies may be subject to bias caused by population stratification, alternative methods that are robust to population stratification such as family-based association designs may be less powerful due to overmatching between cases and controls. Furthermore, case-control studies have the advantages of easy sample collection. Recently, several statistical methods have been proposed for association tests in structured populations using case-control designs that may be robust to population stratification. In this article, we propose a similarity-based association test (SAT) to identify association between a candidate marker and a disease of interest using case-control designs. We first determine whether two individuals are from the same subpopulation or from different subpopulations using genotype data at a set of independent markers. We then perform an association test by comparing within-subpopulation allele-frequency differences between cases and controls. Simulation results show that the SAT has correct type-I error rate in the presence of population stratification. The power of the SAT is higher than that using family-based association designs and is also higher than other robust association methods when the high-risk allele is the same across all subpopulations.