  Large case/control Genome-Wide Association Studies (GWAS) often include
groups of related individuals with known relationships. When testing for
associations at a given locus, current methods incorporate only the familial
relationships between individuals. Here, we introduce the chromosome-based
Quasi Likelihood Score (cQLS) statistic that incorporates local
Identity-By-Descent (IBD) to increase the power to detect associations. In
studies robust to population stratification, such as those with case/control
sibling pairs, simulations show that the study power can be increased by over
50%. 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 to
decrease, 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 reevaluate
our expectations about the use of families in GWAS. We show that, although
adding only half as many unique chromosomes, genotyping affected siblings is
more efficient than genotyping randomly ascertained cases. We also show that
genotyping cases with a family history of disease will be less beneficial when
searching for SNPs with smaller effect sizes.
