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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doaj-9fe0b598362b46fcad9e584b7bc95b092021-01-25T16:34:13ZengWellcomeWellcome Open Research2398-502X2020-10-01410.12688/wellcomeopenres.15396.217983Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved]Paul A. Thompson0Dorothy V. M. Bishop1Else Eising2Simon E. Fisher3Dianne F. Newbury4Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UKExperimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UKMax Planck Institute for Psycholinguistics, Wundtlaan 1, Nijmegen, 6525 XD, The NetherlandsMax Planck Institute for Psycholinguistics, Wundtlaan 1, Nijmegen, 6525 XD, The NetherlandsDepartment of Biological and Medical Sciences, Oxford Brookes University, Headington Campus, Oxford, OX3 0BP, UKBackground: 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.https://wellcomeopenresearch.org/articles/4-142/v2 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paul A. Thompson Dorothy V. M. Bishop Else Eising Simon E. Fisher Dianne F. Newbury |
spellingShingle |
Paul A. Thompson Dorothy V. M. Bishop Else Eising Simon E. Fisher Dianne F. Newbury Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] Wellcome Open Research |
author_facet |
Paul A. Thompson Dorothy V. M. Bishop Else Eising Simon E. Fisher Dianne F. Newbury |
author_sort |
Paul A. Thompson |
title |
Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] |
title_short |
Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] |
title_full |
Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] |
title_fullStr |
Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] |
title_full_unstemmed |
Generalized Structured Component Analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] |
title_sort |
generalized structured component analysis in candidate gene association studies: applications and limitations [version 2; peer review: 2 approved] |
publisher |
Wellcome |
series |
Wellcome Open Research |
issn |
2398-502X |
publishDate |
2020-10-01 |
description |
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. |
url |
https://wellcomeopenresearch.org/articles/4-142/v2 |
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