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

Abstract 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...
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Autor*in:	Xin Li [verfasserIn] Dongya Wu [verfasserIn] Yue Cui [verfasserIn] Bing Liu [verfasserIn] Henrik Walter [verfasserIn] Gunter Schumann [verfasserIn] Chong Li [verfasserIn] Tianzi Jiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Heritability Reliable estimation Sparse regularization Standard error Simulation

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 20(2019), 1, Seite 11
Übergeordnetes Werk:	volume:20 ; year:2019 ; number:1 ; pages:11

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12859-019-2792-7

Katalog-ID:	DOAJ044043279

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520			\|a Abstract 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.
650		4	\|a Heritability
650		4	\|a Reliable estimation
650		4	\|a Sparse regularization
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700	0		\|a Tianzi Jiang \|e verfasserin \|4 aut
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allfields	10.1186/s12859-019-2792-7 doi (DE-627)DOAJ044043279 (DE-599)DOAJ08a6fedca2424218a9649d009f0d07bc DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Xin Li verfasserin aut Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Heritability Reliable estimation Sparse regularization Standard error Simulation Computer applications to medicine. Medical informatics Biology (General) Dongya Wu verfasserin aut Yue Cui verfasserin aut Bing Liu verfasserin aut Henrik Walter verfasserin aut Gunter Schumann verfasserin aut Chong Li verfasserin aut Tianzi Jiang verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 11 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:11 https://doi.org/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/article/08a6fedca2424218a9649d009f0d07bc kostenfrei http://link.springer.com/article/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 11
spelling	10.1186/s12859-019-2792-7 doi (DE-627)DOAJ044043279 (DE-599)DOAJ08a6fedca2424218a9649d009f0d07bc DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Xin Li verfasserin aut Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Heritability Reliable estimation Sparse regularization Standard error Simulation Computer applications to medicine. Medical informatics Biology (General) Dongya Wu verfasserin aut Yue Cui verfasserin aut Bing Liu verfasserin aut Henrik Walter verfasserin aut Gunter Schumann verfasserin aut Chong Li verfasserin aut Tianzi Jiang verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 11 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:11 https://doi.org/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/article/08a6fedca2424218a9649d009f0d07bc kostenfrei http://link.springer.com/article/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 11
allfields_unstemmed	10.1186/s12859-019-2792-7 doi (DE-627)DOAJ044043279 (DE-599)DOAJ08a6fedca2424218a9649d009f0d07bc DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Xin Li verfasserin aut Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Heritability Reliable estimation Sparse regularization Standard error Simulation Computer applications to medicine. Medical informatics Biology (General) Dongya Wu verfasserin aut Yue Cui verfasserin aut Bing Liu verfasserin aut Henrik Walter verfasserin aut Gunter Schumann verfasserin aut Chong Li verfasserin aut Tianzi Jiang verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 11 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:11 https://doi.org/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/article/08a6fedca2424218a9649d009f0d07bc kostenfrei http://link.springer.com/article/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 11
allfieldsGer	10.1186/s12859-019-2792-7 doi (DE-627)DOAJ044043279 (DE-599)DOAJ08a6fedca2424218a9649d009f0d07bc DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Xin Li verfasserin aut Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Heritability Reliable estimation Sparse regularization Standard error Simulation Computer applications to medicine. Medical informatics Biology (General) Dongya Wu verfasserin aut Yue Cui verfasserin aut Bing Liu verfasserin aut Henrik Walter verfasserin aut Gunter Schumann verfasserin aut Chong Li verfasserin aut Tianzi Jiang verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 11 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:11 https://doi.org/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/article/08a6fedca2424218a9649d009f0d07bc kostenfrei http://link.springer.com/article/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 11
allfieldsSound	10.1186/s12859-019-2792-7 doi (DE-627)DOAJ044043279 (DE-599)DOAJ08a6fedca2424218a9649d009f0d07bc DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Xin Li verfasserin aut Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract 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. Heritability Reliable estimation Sparse regularization Standard error Simulation Computer applications to medicine. Medical informatics Biology (General) Dongya Wu verfasserin aut Yue Cui verfasserin aut Bing Liu verfasserin aut Henrik Walter verfasserin aut Gunter Schumann verfasserin aut Chong Li verfasserin aut Tianzi Jiang verfasserin aut In BMC Bioinformatics BMC, 2003 20(2019), 1, Seite 11 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:20 year:2019 number:1 pages:11 https://doi.org/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/article/08a6fedca2424218a9649d009f0d07bc kostenfrei http://link.springer.com/article/10.1186/s12859-019-2792-7 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 11
language	English
source	In BMC Bioinformatics 20(2019), 1, Seite 11 volume:20 year:2019 number:1 pages:11
sourceStr	In BMC Bioinformatics 20(2019), 1, Seite 11 volume:20 year:2019 number:1 pages:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Heritability Reliable estimation Sparse regularization Standard error Simulation Computer applications to medicine. Medical informatics Biology (General)
isfreeaccess_bool	true
container_title	BMC Bioinformatics
authorswithroles_txt_mv	Xin Li @@aut@@ Dongya Wu @@aut@@ Yue Cui @@aut@@ Bing Liu @@aut@@ Henrik Walter @@aut@@ Gunter Schumann @@aut@@ Chong Li @@aut@@ Tianzi Jiang @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ044043279
language_de	englisch
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callnumber-first	R - Medicine
author	Xin Li
spellingShingle	Xin Li misc R858-859.7 misc QH301-705.5 misc Heritability misc Reliable estimation misc Sparse regularization misc Standard error misc Simulation misc Computer applications to medicine. Medical informatics misc Biology (General) Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
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topic_title	R858-859.7 QH301-705.5 Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies Heritability Reliable estimation Sparse regularization Standard error Simulation
topic	misc R858-859.7 misc QH301-705.5 misc Heritability misc Reliable estimation misc Sparse regularization misc Standard error misc Simulation misc Computer applications to medicine. Medical informatics misc Biology (General)
topic_unstemmed	misc R858-859.7 misc QH301-705.5 misc Heritability misc Reliable estimation misc Sparse regularization misc Standard error misc Simulation misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
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title_full	Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
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author_browse	Xin Li Dongya Wu Yue Cui Bing Liu Henrik Walter Gunter Schumann Chong Li Tianzi Jiang
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title_sort	reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
callnumber	R858-859.7
title_auth	Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
abstract	Abstract 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.
abstractGer	Abstract 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.
abstract_unstemmed	Abstract 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.
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title_short	Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies
url	https://doi.org/10.1186/s12859-019-2792-7 https://doaj.org/article/08a6fedca2424218a9649d009f0d07bc http://link.springer.com/article/10.1186/s12859-019-2792-7 https://doaj.org/toc/1471-2105
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up_date	2024-07-03T20:53:32.865Z
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Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies

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