Selection of the most informative individuals from families with multiple siblings for association studies

Association analyses may follow an initial linkage analysis for mapping and identifying genes underlying complex quantitative traits and may be conducted on unrelated subsets of individuals where only one member of a family is included. We evaluate two methods to select one sibling per sibship when multiple siblings are available: (1) one sibling with the most extreme trait value; and (2) one sibling using a combination score statistic based on extreme trait values and identity-by-descent sharing information. We compare the type I error and power. Furthermore, we compare these selection strategies with a strategy that randomly selects one sibling per sibship and with an approach that includes all siblings, using both simulation study and an application to fasting blood glucose in the Framingham Heart Study. When genetic effect is homogeneous, we find that using the combination score can increase power by 30-40% compared to a random selection strategy, and loses only 8-13% of power compared to the full sibship analysis, across all additive models considered, but offers at least 50% genotyping cost saving. In the presence of genetic heterogeneity, the score offers a 50% increase in power over a random selection strategy, but there is substantial loss compared to the full sibship analysis. In application to fasting blood sample, two SNPs are found in common for the selection strategies and the full sample among the 10 highest ranked single nucleotide polymorphisms. The EV strategy tends to agree with the IBD-EV strategy and the analysis of the full sample.