Automated quantitative trait locus analysis (AutoQTL).

ABSTRACT:

Background

Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic dat

SUBMITTER: Freda PJ 

PROVIDER: S-EPMC10088184 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature