Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.

Jingshu Wang,Nancy R Zhang,Qingyuan Zhao,George Davey Smith,Dylan S Small,Gibran Hemani,Jack Bowden,Xiaofeng Zhu

doi:10.1371/journal.pgen.1009575

Abstract

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.

Highlights

Understanding the pathogenic mechanism of common diseases is a fundamental goal in clinical research
Mendelian randomization uses genetic variants related to a modifiable risk factor to obtain evidence regarding its causal influence on disease from observational studies
The highly polygenic nature of complex traits where almost all genes contribute to every complex trait challenges the reliability of the causal inference from these genetic variants

Summary

Introduction

Understanding the pathogenic mechanism of common diseases is a fundamental goal in clinical research. As randomized controlled experiments are not always feasible, researchers are looking towards Mendelian Randomization (MR) as an alternative method for probing the causal mechanisms of common diseases [1]. MR uses inherited genetic variations as instrumental variables (IV) to interrogate the causal effect of heritable risk factor(s) on the disease of interest. The genotypes are independent from non-heritable confounding variables which may obfuscate causal estimation in parent-offspring studies. Such independence approximately holds for population data such as those collected in genome-wide association studies (GWAS) when individuals share the same ancestry [2]. With the accumulation of data from GWAS, there is increasing interest in MR approaches, especially in approaches that only rely on GWAS summary statistics that are publicly available [2, 3]

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