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Genetic signal maximization using environmental regression


Melton, PE and Kent Jr, JW and Dyer, TD and Almasy, L and Blangero, J, Genetic signal maximization using environmental regression, BMC Proceedings, 5, (Suppl 1) pp. 1-6. ISSN 1753-6561 (2011) [Contribution to Refereed Journal]

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DOI: doi:10.1186/1753-6561-5-S9-S72


Joint analyses of correlated phenotypes in genetic epidemiology studies are common. However, these analyses primarily focus on genetic correlation between traits and do not take into account environmental correlation. We describe a method that optimizes the genetic signal by accounting for stochastic environmental noise through joint analysis of a discrete trait and a correlated quantitative marker. We conducted bivariate analyses where heritability and the environmental correlation between the discrete and quantitative traits were calculated using Genetic Analysis Workshop 17 (GAW17) family data. The resulting inverse value of the environmental correlation between these traits was then used to determine a new β coefficient for each quantitative trait and was constrained in a univariate model. We conducted genetic association tests on 7,087 nonsynonymous SNPs in three GAW17 family replicates for Affected status with the β coefficient fixed for three quantitative phenotypes and compared these to an association model where the β coefficient was allowed to vary. Bivariate environmental correlations were 0.64 ( 0.09) for Q1, 0.798 ( 0.076) for Q2, and -0.169 ( 0.18) for Q4. Heritability of Affected status improved in each univariate model where a constrained β coefficient was used to account for stochastic environmental effects. No genome-wide significant associations were identified for either method but we demonstrated that constraining β for covariates slightly improved the genetic signal for Affected status. This environmental regression approach allows for increased heritability when the β coefficient for a highly correlated quantitative covariate is constrained and increases the genetic signal for the discrete trait.

Item Details

Item Type:Contribution to Refereed Journal
Keywords:bivariate, hertiablilty, exome sequencing, biostatistics, gene by environment
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Gene mapping
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Melton, PE (Dr Phillip Melton)
ID Code:141471
Year Published:2011
Deposited By:Menzies Institute for Medical Research
Deposited On:2020-10-22
Last Modified:2021-05-17

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