A comparison of principal component methods between multiple phenotype regression and multiple SNP regression in genetic association studies
Abstract
Principal component analysis (PCA) is a popular method for dimension reduction in unsupervised multivariate analysis. However, existing ad hoc uses of PCA in both multivariate regression (multiple outcomes) and multiple regression (multiple predictors) lack theoretical justification. The differences in the statistical properties of PCAs in these two regression settings are not well understood. In this paper we provide theoretical results on the...
Paper Details
Title
A comparison of principal component methods between multiple phenotype regression and multiple SNP regression in genetic association studies
Published Date
Mar 1, 2020
Volume
14
Issue
1
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