title: Adjustment of familial relatedness in association test for rare variants.
abstract: High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Cong
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Mengjie
authors.initials: M
authors.lastname: Chen
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Hou
authors.affiliation: Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao