Large-scale genetic studies are often composed of related participants, and utilizing familial relationships can be cumbersome and computationally challenging. We present an approach to efficiently handle sequencing data from complex pedigrees that incorporates information from rare variants as well as common variants. Our method employs a 2-step procedure that sequentially regresses out correlation from familial relatedness and then uses the resulting phenotypic residuals in a penalized regression framework to test for associations with variants within genetic units. The operating characteristics of this approach are detailed using simulation data based on a large, multigenerational cohort.
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The Genetic Analysis Workshop is supported by National Institutes of Health grant R01 GM031575. This work was supported by NIH grants 8P20GM103436-12 (DWF, KN) and K25AG043546 (DWF). 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, and U01 DK085545. 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 is supported by NIH grant R01 GM031575.
This proceeding is from Genetic Analysis Workshop 18: Human Sequence Data in Extended Pedigrees, held in Stevenson, Washington from October 13-17, 2012.
More proceedings from this conference are available at: http://bmcproc.biomedcentral.com/articles/supplements/volume-8-supplement-1
Ding, Xiuhua; Su, Shaoyong; Nandakumar, Kannabiran; Wang, Xiaoling; and Fardo, David W., "A 2-Step Penalized Regression Method for Family-Based Next-Generation Sequencing Association Studies" (2014). Biostatistics Faculty Publications. 13.