title: Robust and Powerful Affected Sibpair Test for Rare Variant Association.
abstract: Advances in DNA sequencing technology facilitate investigating the impact of rare variants on complex diseases. However, using a conventional case-control design, large samples are needed to capture enough rare variants to achieve sufficient power for testing the association between suspected loci and complex diseases. In such large samples, population stratification may easily cause spurious signals. One approach to overcome stratification is to use a family-based design. For rare variants, this strategy is especially appropriate, as power can be increased considerably by analyzing cases with affected relatives. We propose a novel framework for association testing in affected sibpairs by comparing the allele count of rare variants on chromosome regions shared identical by descent to the allele count of rare variants on nonshared chromosome regions, referred to as test for rare variant association with family-based internal control (TRAFIC). This design is generally robust to population stratification as cases and controls are matched within each sibpair. We evaluate the power analytically using general model for effect size of rare variants. For the same number of genotyped people, TRAFIC shows superior power over the conventional case-control study for variants with summed risk allele frequency f < 0.05; this power advantage is even more substantial when considering allelic heterogeneity. For complex models of gene-gene interaction, this power advantage depends on the direction of interaction and overall heritability. In sum, we introduce a new method for analyzing rare variants in affected sibpairs that is robust to population stratification, and provide freely available software.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.|Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Keng-Han
authors.initials: KH
authors.lastname: Lin
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.|Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Zöllner
mesh_terms: D016022:Case-Control Studies; D003198:Computer Simulation; D005787:Gene Frequency; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation / Q000235:genetics*; D005838:Genotype; D006801:Humans; D016555:Matched-Pair Analysis; D008957:Models, Genetic; D015233:Models, Statistical; D009679:Nuclear Family; D019655:Quantitative Trait, Heritable; D012984:Software
keywords: association test; dichotomous traits; family studies; rare variants; sequencing