Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models

Abstract

Motivation: Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci are associated with multiple traits -a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide SNPs together.Results: We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization not peer-reviewed)

Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets

“…However, for the convenience of the discussion, we will assume n 1 = n 2 = n. The case of n 1 = n 2 can be easily generalized by weighing the corresponding cost function components with 1/n 1 and 1/n 2 , respectively. Following the similar logic, we introduce our method with the simplest linear models, but our method can be extended to the case of generalized linear models; for example, for case-control data, one can directly apply our method to binary trait data, as done by many previous examples (Moser et al, 2015;Speed and Balding, 2014;Weissbrod et al, 2016;Zhou et al, 2013;Zeng et al, 2018). Also, one can use our method with the residual phenotype after regressing other additional covariates (e.g, age or sex).…”

“…• iMAP: integrative MApping of Pleiotropic association, which is a method for joint analysis that models summary statistics from GWAS results by integrating SNP annotations in the model (Zeng et al, 2018). For a fair comparison of the methods, we do not use the SNP annotations with this method.…”

“…Existing methods for joint analysis on genetic data are mostly built on summary statistics (e.g., McGeachie et al, 2014;Giambartolomei et al, 2014;Kang et al, 2014;Zhu et al, 2015;Bulik-Sullivan et al, 2015;Nieuwboer et al, 2016;Hu et al, 2017;Wen et al, 2017;Liu et al, 2017;Sha et al, 2018;Guo and Wu, 2018) More recently, Turley et al (2018) introduced multi-trait analysis of GWAS (MTAG) that can perform joint analysis using the summary statistics calculated from cohorts with overlapping samples. Zeng et al (2018) proposed a regularized Gaussian mixture model called iMAP to infer the association between SNPs to correlated phenotypes. Qi and Chatterjee (2018) presented a heritability-informed power optimization method that finds an optimal linear combinations of association coefficients.…”

Birth weight is not causally associated with adult asthma: results from instrumental variable analyses

“…We further applied a recently developed method, iMAP, to complementally analyze the relationship between birth weight and adult asthma. iMAP is an integrative method for modeling pleiotropy and can be employed to investigate causality between pairs of complex traits using summary statistics from GWAS (Zeng et al 2018).…”

“…Unlike the genetic score or MR method, iMAP jointly analyzes all genome-wide SNPs and has the potential to provide additional evidence supporting or against causal relationship between pairs of traits. iMAP aims to estimate some proportional parameters that characterize the SNP causal effects on the two traits in order to better understand the relationship between the traits (Zeng et al 2018). In particular, iMAP estimates an important ratio quantity π 11 /(π 10 +π 11 ) (or π 11 /(π 01 +π 11 )), where π 11 represents the probability that a SNP is associated with both traits; π 10 represents the probability that a SNP is associated with the first trait but not the second; π 01 represents the probability that a SNP is associated with the second trait but not the first; and π 00 represents the probability that a SNP is not associated with any traits.…”