Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.

ABSTRACT: Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized

SUBMITTER: Bhatnagar SR 

PROVIDER: S-EPMC7224575 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature