CASIA OpenIR
Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
Li,Xin1; Wu,Dongya2,3,7; Cui,Yue2,3; Liu,Bing2,3; Walter,Henrik8; Schumann,Gunter9; Li,Chong1; Jiang,Tianzi2,3,4,5,6,7
Source PublicationBMC Bioinformatics
ISSN1471-2105
2019-04-30
Volume20Issue:1
Corresponding AuthorJiang,Tianzi(jiangtz@nlpr.ia.ac.cn)
AbstractAbstractBackgroundData 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.ResultsIn 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.ConclusionsThe 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.
KeywordHeritability Reliable estimation Sparse regularization Standard error Simulation
DOI10.1186/s12859-019-2792-7
Language英语
WOS IDBMC:10.1186/s12859-019-2792-7
PublisherBioMed Central
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24462
Collection中国科学院自动化研究所
Corresponding AuthorJiang,Tianzi
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Recommended Citation
GB/T 7714
Li,Xin,Wu,Dongya,Cui,Yue,et al. Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies[J]. BMC Bioinformatics,2019,20(1).
APA Li,Xin.,Wu,Dongya.,Cui,Yue.,Liu,Bing.,Walter,Henrik.,...&Jiang,Tianzi.(2019).Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies.BMC Bioinformatics,20(1).
MLA Li,Xin,et al."Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies".BMC Bioinformatics 20.1(2019).
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