MGAS: a powerful tool for multivariate gene-based genome-wide association analysis | Zendy

Sophie van der Sluis | Zendy; Conor V. Dolan | Zendy; Jiang Li | Zendy; YouQiang Song | Zendy; Pak C. Sham | Zendy; Daniëlle Posthuma | Zendy; Miaoxin Li | Zendy

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MGAS: a powerful tool for multivariate gene-based genome-wide association analysis

Author(s) -

Sophie van der Sluis,

Conor V. Dolan,

Jiang Li,

YouQiang Song,

Pak C. Sham,

Daniëlle Posthuma,

Miaoxin Li

Publication year - 2014

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btu783

Subject(s) - multivariate statistics , univariate , single nucleotide polymorphism , multivariate analysis , genome wide association study , multivariate analysis of variance , multiple comparisons problem , computational biology , biology , phenotype , genetics , genetic association , false discovery rate , gene , genotype , computer science , statistics , mathematics , machine learning

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.

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