Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

Wellcome Open Research

article

Paul A. Thompson¹  Dorothy V. M. Bishop¹  Else Eising²  Simon E. Fisher²  Dianne F. Newbury⁴ 

[1] Experimental Psychology, University of Oxford;Max Planck Institute for Psycholinguistics;Donders Institute for Brain, Cognition and Behaviour, Radboud University;Department of Biological and Medical Sciences, Oxford Brookes University, Headington Campus

关键词: genetics;    GSCA;    Structural equation modelling;    simulation;    power analysis;    developmental language disorder;   

DOI  :  10.12688/wellcomeopenres.15396.2

学科分类：内科医学

来源: Wellcome

【 摘 要 】

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.

【 授权许可】

CC BY