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Genetic signal maximization using environmental regression

journal contribution
posted on 2023-05-22, 03:56 authored by Phillip MeltonPhillip Melton, Kent Jr, JW, Dyer, TD, Almasy, L, Blangero, J
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.

History

Publication title

BMC Proceedings

Volume

5

Issue

Suppl 1

Pagination

1-6

ISSN

1753-6561

Department/School

Menzies Institute for Medical Research

Publisher

BioMed Central

Place of publication

United Kingdom

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the biological sciences; Expanding knowledge in the mathematical sciences

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