Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.

TitleStudying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.
Publication TypeJournal Article
Year of Publication2011
AuthorsTzeng, Jung-Ying, Daowen Zhang, Monnat Pongpanich, Chris Smith, Mark I. McCarthy, Michèle M. Sale, Bradford B. Worrall, Fang-Chi Hsu, Duncan C. Thomas, and Patrick F. Sullivan
JournalAm J Hum Genet
Volume89
Issue2
Pagination277-88
Date Published2011 Aug 12
ISSN1537-6605
KeywordsChromosomes, Human, Pair 21, Computer Simulation, Databases, Genetic, Environment, Genes, Genetic Markers, Humans, Models, Genetic, Mutation, Quantitative Trait, Heritable, Regression Analysis
Abstract

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis.

DOI10.1016/j.ajhg.2011.07.007
Alternate JournalAm J Hum Genet
Original PublicationStudying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.
PubMed ID21835306
PubMed Central IDPMC3155192
Grant ListR01 CA85848 / CA / NCI NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
R01 MH084022-02 / MH / NIMH NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 ES019876 / ES / NIEHS NIH HHS / United States
M01 RR007122 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 MH084022-01A1 / MH / NIMH NIH HHS / United States
R01 MH074027 / MH / NIMH NIH HHS / United States
U01 HG005160 / HG / NHGRI NIH HHS / United States
090532 / / Wellcome Trust / United Kingdom
R01 MH084022-03 / MH / NIMH NIH HHS / United States
R37 AI031789-20 / AI / NIAID NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States
M01 RR07122 / RR / NCRR NIH HHS / United States
Project: