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

Abstract

BackgroundThe 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.ResultsUsing 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3169-1) contains supplementary material, which is available to authorized users.

Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses

“…For conditional analysis with some traits as both responses and predictors (i.e., endogenous variables), in addition to extending the LRT of Schaid et al (2016), for robustness, we also propose using the Wald test in GEE with the working independence model and its corresponding sandwich covariance matrix estimate. Note that we assume a recursive system, for which the OLSE (i.e., GEE estimator with the working independence model) is unbiased (Hanushek and Jackson 1977, p. 229); more general structural equation models (Li et al 2006;P. Wang et al 2016) may require a two-stage least squares estimator, which is much more complex and, more importantly, it is unclear whether it can be applied to summary statistics only.…”

Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data

“…SEM is a powerful tool for multivariate causal inference and will be used as the framework for our work. With a one‐sample GWAS individual‐level data set, we consider a linear system for the $$n$$ individuals in the sample, and we follow the notations in Wang et al (2016). In the model the random errors are denoted as an $$n\times M$$ matrix $$E=[{e}_{1},{\rm{\ldots }},{e}_{M}]$$ with $${e}_{i}={({e}_{1i},{e}_{2i},{\rm{\ldots }},{e}_{ni})}^{T}$$ be the vector of the $$n$$ random errors for each trait $$i=1,2,{\rm{\ldots }},M$$.…”

“…SEM is a powerful tool for multivariate causal inference and will be used as the framework for our work. With a one-sample GWAS individual-level data set, we consider a linear system for the n individuals in the sample, and we follow the notations in Wang et al (2016). In the model the random errors are denoted as an…”

“…However, GWAS data can be applied for analyses of multiple related traits with improved power and new biological insights (Aschard et al, 2014; Knutson et al, 2020). In particular, network analysis of multiple traits is both of interest and challenging (Wang et al, 2016). Causal network analysis is essential to elucidating the relationships among multiple traits, such as in gene network and protein network analyses (Stelzl et al, 2005).…”

“…Chen and Pearl (2014) applied SEMs to infer complex causal relationships among variables. Wang et al (2016) later developed sparse functional SEMs that can incorporate common and rare variants. Chen et al (2018) proposed a two‐stage penalized least squares method for large‐scale SEMs under classical two‐stage least squares (2SLS) settings.…”