Statistical Approaches for Rare-Variant Association Testing in Affected Sibships. M. P. Epstein1, E. Ware2, M. A. Jhun2, L. F. Bielak2, W. Zhao2, J. Smith2, P. A. Peyser2, S. L. R. Kardia2, G. A. Satten3 1) Dept Human Gen, Emory Univ, Atlanta, GA; 2) Dept Epidemiology, University of Michigan, Ann Arbor, MI; 3) Centers for Disease Control and Prevention, Atlanta, GA.
The emergence of sequencing and exome-chip technologies has propelled the development of novel statistical tests to identify rare genetic variation that influence complex diseases. While many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for association testing in families. This is unfortunate, as families possess many valuable features for rare-variant mapping that population-based studies lack. Many projects have begun sequencing or exome-chipping relatives from families; either recently sampled or previously collected as part of linkage studies. As many past linkage studies employed an affected-sibship design, we propose a novel test of rare-variant association for use in such studies. The logic behind our approach is that, for relative pairs concordant for phenotype, rare susceptibility variation should be found more often on regions shared identical by descent. Our approach is applicable to affected sibships of arbitrary size and does not require genotype information from either unaffected siblings (although this information is helpful, if available) or independent controls. The method is robust to population stratification, can adjust for other covariates, and produces analytic p-values, thereby enabling the approach to scale to genome wide studies of rare variation. We illustrate the approach using exome chip data from sibships ascertained for hypertension collected as part of the GENOA study.