Flexible and Robust Methods for Rare-Variant Testing of Quantitative Traits in Trios and Nuclear Families

• Type of Material: Article
• Authors: Yunxuan Jiang, Emory University; Karen Conneely, Emory University; Michael Epstein, Emory University
• Language: English
• Date: 2014-09-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 WILEY PERIODICALS, INC.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21839
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 38
• Issue: 6
• Start Page: 542
• End Page: 551
• Grant/Funding Information: This work was supported by National Institutes of Health grant HG007508.
• Keywords: SET; rare variants; Genetics & Heredity; KERNEL ASSOCIATION TEST; Life Sciences & Biomedicine; Mathematical & Computational Biology; STRATEGIES; Science & Technology; SEQUENCE; family studies; quantitative traits; GENE; PEDIGREE; TRANSMISSION DISEQUILIBRIUM; sequencing; DESIGNS
• Research Categories: Biology, Genetics

Abstract

Most rare-variant association tests for complex traits are applicable only to population-based or case-control resequencing studies. There are fewer rare-variant association tests for family-based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family-based studies can be more powerful than their population-based counterparts due to increased genetic load and further enable the implementation of rare-variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare-variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis et al., ] into the kernel-based SNP association test KMFAM of Schifano et al. [Schifano et al., ] . The resulting within-family test enjoys the many benefits of the kernel framework for rare-variant association testing, including rapid evaluation of P-values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within-family test is robust to confounding due to population stratification. Although within-family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.