Multiple phenotype association tests using summary statistics in genome-wide association studies

DOI10.1111/biom.12735zbMath1415.62116OpenAlexW2727414201WikidataQ38707659 ScholiaQ38707659MaRDI QIDQ3119821

Xihong Lin, Zhong-Hua Liu

Publication date: 13 March 2019

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5743780

zbMATH Keywords

summary statisticslinear mixed modelspleiotropyFisher methodcorrelated phenotypesvariance component test

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Genetics and epigenetics (92D10)

Related Items (3)

A comparison of principal component methods between multiple phenotype regression and multiple SNP regression in genetic association studies ⋮ A bootstrap method to calculate the p -value of Fisher’s combination for a large number of weakly dependent p -values ⋮ A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies

Uses Software

• PLINK
• AS 155

Cites Work

• Algorithm AS 155: The Distribution of a Linear Combination of χ 2 Random Variables
• Variance component testing in generalised linear models with random effects
• Asymptotic Optimality of Fisher's Method of Combining Independent Tests
• Combining Independent Tests of Significance

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