title: A statistical approach for rare-variant association testing in affected sibships.
abstract: Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical by descent. We propose both burden and variance-component tests of rare variation that are applicable to affected sibships of arbitrary size and that do not require genotype information from unaffected siblings or independent controls. Our approaches are robust to population stratification and produce analytic p values, thereby enabling our approach to scale easily to genome-wide studies of rare variation. We illustrate our methods by using simulated data and exome chip data from sibships ascertained for hypertension collected as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) study.
authors: Department of Human Genetics, Emory University, Atlanta, GA 30322, USA. Electronic address: mpepste@emory.edu.
authors: Michael P
authors: MP
authors: Epstein
authors: Department of Human Genetics, Emory University, Atlanta, GA 30322, USA.
authors: Richard
authors: R
authors: Duncan
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Erin B
authors: EB
authors: Ware
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Min A
authors: MA
authors: Jhun
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Lawrence F
authors: LF
authors: Bielak
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Wei
authors: W
authors: Zhao
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Jennifer A
authors: JA
authors: Smith
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Patricia A
authors: PA
authors: Peyser
authors: Department of Epidemiology, University of Michigan, Ann Arbor, MI 48103, USA.
authors: Sharon L R
authors: SL
authors: Kardia
authors: Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.
authors: Glen A
authors: GA
authors: Satten
mesh_terms: D001741:Black or African American / Q000235:genetics; D003198:Computer Simulation; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D006973:Hypertension / Q000235:genetics; D015233:Models, Statistical; D059645:Mutation Rate; D035583:Rare Diseases / Q000235:genetics*; D035781:Siblings