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

Authors
  • Yan, Kang K1
  • Zhao, Hongyu2
  • Wu, Joseph T1
  • Pang, Herbert1
  • 1 School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. , (China)
  • 2 Department of Biostatistics, Yale University, New Haven, Connecticut.
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Nov 01, 2020
Volume
44
Issue
8
Pages
798–810
Identifiers
DOI: 10.1002/gepi.22341
PMID: 32700329
Source
Medline
Keywords
Language
English
License
Unknown

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 accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction. © 2020 Wiley Periodicals LLC.

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