A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data.

ABSTRACT:

Background

The widely used genetic pleiotropic analyses of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high dimensional genotype (next-generation sequencing) data. To overcome limitations of the traditional genetic pleiotropic analysis of multiple phenotypes, we develop sparse structural equation models (SEMs) as a general framework for a new paradigm of genetic analysis of multiple phenotypes. To incorporate both common and rare variants into the analysis, we extend the traditional multivariate SEMs to sparse functional SEMs. To deal with high dimensional phenotype and genotype data, we employ functional data analysis and the alternative direction me

SUBMITTER: Wang P 

PROVIDER: S-EPMC5100198 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature