MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.
Bioinformatics
; 31(7): 1007-15, 2015 Apr 01.
Article
em En
| MEDLINE
| ID: mdl-25431328
ABSTRACT
MOTIVATION Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. RESULTS:
Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis.CONCLUSION:
MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. AVAILABILITY AND IMPLEMENTATION MGAS is freely available in KGG v3.0 (http//statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https//dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http//ctglab.nl/people/sophie_van_der_sluis. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
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Genoma Humano
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Análise Multivariada
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Polimorfismo de Nucleotídeo Único
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Síndrome Metabólica
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Estudo de Associação Genômica Ampla
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article