A comparison of principal component methods between multiple phenotype regression and multiple SNP regression in genetic association studies

DOI10.1214/19-AOAS1312zbMath1441.62171OpenAlexW3017329137MaRDI QIDQ2179976

Xihong Lin, Ian Barnett, Zhong-Hua Liu

Publication date: 13 May 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1587002681

zbMATH Keywords

eigenvaluesprincipal component analysishypothesis testingdimension reductionminimum \(p\)-value testmultiple phenotypesSNP-setvariance-component test

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Uses Software

• PLINK
• MPAT
• Eigenstrat

Cites Work

• Multiple phenotype association tests using summary statistics in genome-wide association studies
• Variance component testing in generalised linear models with random effects
• A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies

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