Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional g...
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Autor*in:	Paul A. Thompson [verfasserIn] Dorothy V. M. Bishop [verfasserIn] Else Eising [verfasserIn] Simon E. Fisher [verfasserIn] Dianne F. Newbury [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	In: Wellcome Open Research - Wellcome, 2017, 4(2020)
Übergeordnetes Werk:	volume:4 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.12688/wellcomeopenres.15396.2

Katalog-ID:	DOAJ061458309

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allfields	10.12688/wellcomeopenres.15396.2 doi (DE-627)DOAJ061458309 (DE-599)DOAJ9fe0b598362b46fcad9e584b7bc95b09 DE-627 ger DE-627 rakwb eng Paul A. Thompson verfasserin aut Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis. Medicine R Science Q Dorothy V. M. Bishop verfasserin aut Else Eising verfasserin aut Simon E. Fisher verfasserin aut Dianne F. Newbury verfasserin aut In Wellcome Open Research Wellcome, 2017 4(2020) (DE-627)872620239 (DE-600)2874778-1 2398502X nnns volume:4 year:2020 https://doi.org/10.12688/wellcomeopenres.15396.2 kostenfrei https://doaj.org/article/9fe0b598362b46fcad9e584b7bc95b09 kostenfrei https://wellcomeopenresearch.org/articles/4-142/v2 kostenfrei https://doaj.org/toc/2398-502X 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_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 4 2020
spelling	10.12688/wellcomeopenres.15396.2 doi (DE-627)DOAJ061458309 (DE-599)DOAJ9fe0b598362b46fcad9e584b7bc95b09 DE-627 ger DE-627 rakwb eng Paul A. Thompson verfasserin aut Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis. Medicine R Science Q Dorothy V. M. Bishop verfasserin aut Else Eising verfasserin aut Simon E. Fisher verfasserin aut Dianne F. Newbury verfasserin aut In Wellcome Open Research Wellcome, 2017 4(2020) (DE-627)872620239 (DE-600)2874778-1 2398502X nnns volume:4 year:2020 https://doi.org/10.12688/wellcomeopenres.15396.2 kostenfrei https://doaj.org/article/9fe0b598362b46fcad9e584b7bc95b09 kostenfrei https://wellcomeopenresearch.org/articles/4-142/v2 kostenfrei https://doaj.org/toc/2398-502X 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_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 4 2020
allfields_unstemmed	10.12688/wellcomeopenres.15396.2 doi (DE-627)DOAJ061458309 (DE-599)DOAJ9fe0b598362b46fcad9e584b7bc95b09 DE-627 ger DE-627 rakwb eng Paul A. Thompson verfasserin aut Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis. Medicine R Science Q Dorothy V. M. Bishop verfasserin aut Else Eising verfasserin aut Simon E. Fisher verfasserin aut Dianne F. Newbury verfasserin aut In Wellcome Open Research Wellcome, 2017 4(2020) (DE-627)872620239 (DE-600)2874778-1 2398502X nnns volume:4 year:2020 https://doi.org/10.12688/wellcomeopenres.15396.2 kostenfrei https://doaj.org/article/9fe0b598362b46fcad9e584b7bc95b09 kostenfrei https://wellcomeopenresearch.org/articles/4-142/v2 kostenfrei https://doaj.org/toc/2398-502X 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_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 4 2020
allfieldsGer	10.12688/wellcomeopenres.15396.2 doi (DE-627)DOAJ061458309 (DE-599)DOAJ9fe0b598362b46fcad9e584b7bc95b09 DE-627 ger DE-627 rakwb eng Paul A. Thompson verfasserin aut Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis. Medicine R Science Q Dorothy V. M. Bishop verfasserin aut Else Eising verfasserin aut Simon E. Fisher verfasserin aut Dianne F. Newbury verfasserin aut In Wellcome Open Research Wellcome, 2017 4(2020) (DE-627)872620239 (DE-600)2874778-1 2398502X nnns volume:4 year:2020 https://doi.org/10.12688/wellcomeopenres.15396.2 kostenfrei https://doaj.org/article/9fe0b598362b46fcad9e584b7bc95b09 kostenfrei https://wellcomeopenresearch.org/articles/4-142/v2 kostenfrei https://doaj.org/toc/2398-502X 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_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 4 2020
allfieldsSound	10.12688/wellcomeopenres.15396.2 doi (DE-627)DOAJ061458309 (DE-599)DOAJ9fe0b598362b46fcad9e584b7bc95b09 DE-627 ger DE-627 rakwb eng Paul A. Thompson verfasserin aut Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis. Medicine R Science Q Dorothy V. M. Bishop verfasserin aut Else Eising verfasserin aut Simon E. Fisher verfasserin aut Dianne F. Newbury verfasserin aut In Wellcome Open Research Wellcome, 2017 4(2020) (DE-627)872620239 (DE-600)2874778-1 2398502X nnns volume:4 year:2020 https://doi.org/10.12688/wellcomeopenres.15396.2 kostenfrei https://doaj.org/article/9fe0b598362b46fcad9e584b7bc95b09 kostenfrei https://wellcomeopenresearch.org/articles/4-142/v2 kostenfrei https://doaj.org/toc/2398-502X 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_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 4 2020
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author	Paul A. Thompson
spellingShingle	Paul A. Thompson misc Medicine misc R misc Science misc Q Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
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topic_title	Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
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title	Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
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title_full	Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
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title_sort	generalized structured component analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
title_auth	Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
abstract	Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.
abstractGer	Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.
abstract_unstemmed	Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.
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title_short	Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]
url	https://doi.org/10.12688/wellcomeopenres.15396.2 https://doaj.org/article/9fe0b598362b46fcad9e584b7bc95b09 https://wellcomeopenresearch.org/articles/4-142/v2 https://doaj.org/toc/2398-502X
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Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]

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