The benefits of selecting phenotype-specific variants for applications of mixed models in genomics

The benefits of selecting phenotype-specific variants for applications of mixed models in genomics

Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications,...

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Main Authors: Lippert, Christoph, Quon, Gerald, Kang, Eun Yong, Kadie, Carl M., Listgarten, Jennifer, Heckerman, David
Format: Online
Language:English
Published: Nature Publishing Group 2013
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648840/
id pubmed-3648840
recordtype oai_dc
spelling pubmed-36488402013-05-20 The benefits of selecting phenotype-specific variants for applications of mixed models in genomics Lippert, Christoph Quon, Gerald Kang, Eun Yong Kadie, Carl M. Listgarten, Jennifer Heckerman, David Article Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them. Nature Publishing Group 2013-05-09 /pmc/articles/PMC3648840/ /pubmed/23657357 http://dx.doi.org/10.1038/srep01815 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Lippert, Christoph
Quon, Gerald
Kang, Eun Yong
Kadie, Carl M.
Listgarten, Jennifer
Heckerman, David
spellingShingle Lippert, Christoph
Quon, Gerald
Kang, Eun Yong
Kadie, Carl M.
Listgarten, Jennifer
Heckerman, David
The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
author_facet Lippert, Christoph
Quon, Gerald
Kang, Eun Yong
Kadie, Carl M.
Listgarten, Jennifer
Heckerman, David
author_sort Lippert, Christoph
title The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
title_short The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
title_full The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
title_fullStr The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
title_full_unstemmed The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
title_sort benefits of selecting phenotype-specific variants for applications of mixed models in genomics
description Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them.
publisher Nature Publishing Group
publishDate 2013
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648840/
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