An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.

ABSTRACT: Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accura

SUBMITTER: Yan KK 

PROVIDER: S-EPMC7875251 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature