Robust Rare-Variant Association Tests for Quantitative Traits in General Pedigrees

• Type of Material: Article
• Authors: Yunxuan Jiang, Emory University; Karen Conneely, Emory University; Michael Epstein, Emory University
• Language: English
• Date: 2018-12-01
• Publisher: Springer
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017, International Chinese Statistical Association.
• Final Published Version (URL): http://dx.doi.org/10.1007/s12561-017-9197-9
• Title of Journal or Parent Work: Statistics in Biosciences
• Volume: 10
• Issue: 3
• Start Page: 491
• End Page: 505
• Grant/Funding Information: This work was supported by NIH grants GM117946 and HG007508.; The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482 and R01 DK053889; The Genetic Analysis Workshop 18 (GAW18) is supported by NIH grant R01 GM031575.; The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, U01 DK085545.
• Keywords: pedigree; quantitative trait; population stratification; rare variant
• Research Categories: Biology, Biostatistics; Biology, Genetics

Abstract

Next-generation sequencing technology has propelled the development of statistical methods to identify rare polygenetic variation associated with complex traits. The majority of these statistical methods are designed for case–control or population-based studies, with few methods that are applicable to family-based studies. Moreover, existing methods for family-based studies mainly focus on trios or nuclear families; there are far fewer existing methods available for analyzing larger pedigrees of arbitrary size and structure. To fill this gap, we propose a method for rare-variant analysis in large pedigree studies that can utilize information from all available relatives. Our approach is based on a kernel machine regression (KMR) framework, which has the advantages of high power, as well as fast and easy calculation of p-values using the asymptotic distribution. Our method is also robust to population stratification due to integration of a QTDT framework (Abecasis et al., Eur J Hum Genet 8(7):545–551, 2000b) with the KMR framework. In our method, we first calculate the expected genotype (between-family component) of a non-founder using all founders’ information and then calculate the deviates (within-family component) of observed genotype from the expectation, where the deviates are robust to population stratification by design. The test statistic, which is constructed using within-family component, is thus robust to population stratification. We illustrate and evaluate our method using simulated data and sequence data from Genetic Analysis Workshop 18.