Current data repositories include samples of families large enough to generate intriguing results, but typically not large enough to yield genome wide significance for variants with small to moderate effect size. We propose a hybrid design we call mCLR that simultaneously utilizes the information from unrelated case–control samples, trio data, and freely available controls obtained from a generic database. The method builds on the principal of matching by ancestry to remove the potential confounding effects of population stratification. Thus trio probands are matched to unrelated controls based on ancestry, and pseudo-controls based on genetic transmission. Unrelated cases are matched to unrelated controls based on ancestry. Both family-based and case–control study designs produce genetically matched strata consisting of a single case and one or more controls. These data can be analyzed using the conditional logistic model. Simulations show that the resulting method is both powerful and robust to population stratification. Thus through careful matching, the mCLR approach has the advantages of family-based studies, but the enhanced power of a case–control study.
