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Large case/control Genome-Wide Association Studies (GWAS) often includegroups of related individuals with known relationships. When testing forassociations at a given locus, current methods incorporate only the familialrelationships between individuals. Here, we introduce the chromosome-basedQuasi Likelihood Score (cQLS) statistic that incorporates localIdentity-By-Descent (IBD) to increase the power to detect associations. Instudies robust to population stratification, such as those with case/controlsibling pairs, simulations show that the study power can be increased by over50%. In our example, a GWAS examining late-onset Alzheimer's disease, the$p$-values among the most strongly associated SNPs in the APOE gene tend todecrease, with the smallest $p$-value decreasing from $1.23\times10^{-8}$ to$7.70\times 10^{-9}$. Furthermore, as a part of our simulations, we reevaluateour expectations about the use of families in GWAS. We show that, althoughadding only half as many unique chromosomes, genotyping affected siblings ismore efficient than genotyping randomly ascertained cases. We also show thatgenotyping cases with a family history of disease will be less beneficial whensearching for SNPs with smaller effect sizes.

Leveraging local identity-by-descent increases the power of case/control GWAS with related individuals