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



Our proposed sparse functional SEMs can incorporate both common and rare variants into the analysis and the ADMM algorithm can efficiently solve the penalized SEMs. Using this model we can jointly infer genetic architecture and casual phenotype network structure, and decompose the genetic effect into direct, indirect and total effect. Using large scale simulations we showed that the proposed methods have higher power to detect true causal genetic pleiotropic structure than other existing methods.
Human Genetics Center, Department of Biostatistics, University of Texas School of Public Health, Houston, TX, 77030, USA.|State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200433, China.
Panpan
P
Wang
Human Genetics Center, Department of Biostatistics, University of Texas School of Public Health, Houston, TX, 77030, USA.
Mohammad
M
Rahman
State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200433, China. lijin.fudan@gmail.com.
Li
L
Jin
Human Genetics Center, Department of Biostatistics, University of Texas School of Public Health, Houston, TX, 77030, USA. Momiao.Xiong@uth.tmc.edu.|Human Genetics Center, The University of Texas Health Science Center at Houston, P.O. Box 20186, Houston, TX, 77225, USA. Momiao.Xiong@uth.tmc.edu.
Momiao
M
Xiong
D000465:Algorithms; D003198:Computer Simulation; D056726:Genetic Association Studies / Q000379:methods; D058685:Genetic Pleiotropy; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable
Causal inference; Multiple phenotypes; Next-generation sequencing; Pleiotropic analysis; Quantitative trait; Structural equations