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Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models

Expression quantitative trait loci (eQTLs) are important for understanding the genetic basis of cellular activities and complex phenotypes. Genome-wide eQTL analyses can be effectively conducted by employing a mixed model. The mixed model includes random polygenic effects with variability, which can...

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Autor principal: Lee, Chaeyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110903/
https://www.ncbi.nlm.nih.gov/pubmed/30186313
http://dx.doi.org/10.3389/fgene.2018.00341
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author Lee, Chaeyoung
author_facet Lee, Chaeyoung
author_sort Lee, Chaeyoung
collection PubMed
description Expression quantitative trait loci (eQTLs) are important for understanding the genetic basis of cellular activities and complex phenotypes. Genome-wide eQTL analyses can be effectively conducted by employing a mixed model. The mixed model includes random polygenic effects with variability, which can be estimated by the covariance structure of pairwise genomic similarity among individuals based on genotype information for nucleotide sequence variants. This increases the accuracy of identifying eQTLs by avoiding population stratification. Its extensive use will accelerate our understanding of the genetics of gene expression and complex phenotypes. An overview of genome-wide eQTL analyses using mixed model methodology is provided, including discussions of both theoretical and practical issues. The advantages of employing mixed models are also discussed in this review.
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spelling pubmed-61109032018-09-05 Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models Lee, Chaeyoung Front Genet Genetics Expression quantitative trait loci (eQTLs) are important for understanding the genetic basis of cellular activities and complex phenotypes. Genome-wide eQTL analyses can be effectively conducted by employing a mixed model. The mixed model includes random polygenic effects with variability, which can be estimated by the covariance structure of pairwise genomic similarity among individuals based on genotype information for nucleotide sequence variants. This increases the accuracy of identifying eQTLs by avoiding population stratification. Its extensive use will accelerate our understanding of the genetics of gene expression and complex phenotypes. An overview of genome-wide eQTL analyses using mixed model methodology is provided, including discussions of both theoretical and practical issues. The advantages of employing mixed models are also discussed in this review. Frontiers Media S.A. 2018-08-21 /pmc/articles/PMC6110903/ /pubmed/30186313 http://dx.doi.org/10.3389/fgene.2018.00341 Text en Copyright © 2018 Lee. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Lee, Chaeyoung
Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
title Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
title_full Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
title_fullStr Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
title_full_unstemmed Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
title_short Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models
title_sort genome-wide expression quantitative trait loci analysis using mixed models
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110903/
https://www.ncbi.nlm.nih.gov/pubmed/30186313
http://dx.doi.org/10.3389/fgene.2018.00341
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