Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression.

ABSTRACT:

Background

Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of

SUBMITTER: Walker JA 

PROVIDER: S-EPMC5068350 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature