A New Statistical Framework for Genetic Pleiotropic Analysis of High Dimensional Phenotype Data

TL;DR

This paper introduces a novel sparse structural equation modeling framework for high-dimensional genetic pleiotropic analysis, effectively integrating common and rare variants to identify gene-phenotype networks with improved power and computational efficiency.

Contribution

It extends traditional SEMs to sparse functional SEMs, enabling analysis of high-dimensional genotype and phenotype data with enhanced detection power and efficiency.

Findings

Proposed methods outperform existing approaches in simulation studies.

Gene-based pleiotropic analysis shows higher power than single variant analysis.

Application to exome data reveals a network of 137 genes linked to 11 phenotypes.

Abstract

The widely used genetic pleiotropic analysis 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 these limitations, 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 methods of multiplier (ADMM) techniques to reduce data dimension and improve computational efficiency. Using large scale simulations we showed that the proposed methods have higher power to detect true causal genetic pleiotropic structure than other existing methods. Simulations also demonstrate that the gene-based pleiotropic analysis has higher power than the single variant-based pleiotropic analysis. The proposed method is applied to exome sequence data from the NHLBI Exome Sequencing Project (ESP) with 11 phenotypes, which identifies a network with 137 genes connected to 11 phenotypes and 341 edges. Among them, 114 genes showed pleiotropic genetic effects and 45 genes were reported to be associated with phenotypes in the analysis or other cardiovascular disease (CVD) related phenotypes in the literature.