Case‐control association studies with matching and genomic controlling

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Abstract
Family-based association studies have gained in popularity for mapping disease-susceptibility gene(s) of complex diseases. However, recruiting family controls is often more difficult than recruiting unrelated controls. The author proposes a case-control study, where the possible biases due to population stratification are controlled by matching in the design stage and by genomic controlling in the data-analytic stage. The matching is based on a set of “stratum-delineating variables,” such as, race, ethnicity, nationality, ancestry, and birthplace; and the genomic controlling is based on typing a number of null markers across the genome and applying the principle of multiplicative scaling of chi-square distribution. It pays to match carefully to have a higher proportion of correctly matched sets, as computer simulation showed that this would increase the power of the study. If matching is crude, one loses power but still has the correct type I error rate after genomic controlling. Power studies showed that the numbers of affected subjects required for the pair-matched study are comparable to those required by the case-parents design, if the study was conducted in a homogeneous population. As the (control-to-case) matching ratio increases, the number of affected subjects required decreases. With matching ratio tending toward infinity, the number required shrinks roughly by half. The case-control study with matching and genomic controlling frees us from family bondage, and the genetic problem as complicated as mapping genes can now be studied using simple epidemiologic methods.