Cargando…

Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies

BACKGROUND: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed e...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Xin, Wu, Dongya, Cui, Yue, Liu, Bing, Walter, Henrik, Schumann, Gunter, Li, Chong, Jiang, Tianzi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492418/
https://www.ncbi.nlm.nih.gov/pubmed/31039742
http://dx.doi.org/10.1186/s12859-019-2792-7
_version_ 1783415142195658752
author Li, Xin
Wu, Dongya
Cui, Yue
Liu, Bing
Walter, Henrik
Schumann, Gunter
Li, Chong
Jiang, Tianzi
author_facet Li, Xin
Wu, Dongya
Cui, Yue
Liu, Bing
Walter, Henrik
Schumann, Gunter
Li, Chong
Jiang, Tianzi
author_sort Li, Xin
collection PubMed
description BACKGROUND: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS: In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS: The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2792-7) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6492418
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-64924182019-05-08 Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies Li, Xin Wu, Dongya Cui, Yue Liu, Bing Walter, Henrik Schumann, Gunter Li, Chong Jiang, Tianzi BMC Bioinformatics Methodology Article BACKGROUND: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS: In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS: The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2792-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-30 /pmc/articles/PMC6492418/ /pubmed/31039742 http://dx.doi.org/10.1186/s12859-019-2792-7 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Li, Xin
Wu, Dongya
Cui, Yue
Liu, Bing
Walter, Henrik
Schumann, Gunter
Li, Chong
Jiang, Tianzi
Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
title Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
title_full Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
title_fullStr Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
title_full_unstemmed Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
title_short Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
title_sort reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492418/
https://www.ncbi.nlm.nih.gov/pubmed/31039742
http://dx.doi.org/10.1186/s12859-019-2792-7
work_keys_str_mv AT lixin reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT wudongya reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT cuiyue reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT liubing reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT walterhenrik reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT schumanngunter reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT lichong reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies
AT jiangtianzi reliableheritabilityestimationusingsparseregularizationinultrahighdimensionalgenomewideassociationstudies