Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

English version

Autoři: Haoran Xue ^aff001; Wei Pan ^aff002
Působiště autorů: School of Statistics, University of Minnesota, Minneapolis, Minnesota, United States of America ^aff001; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America ^aff002
Vyšlo v časopise: Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data. PLoS Genet 16(11): e32767. doi:10.1371/journal.pgen.1009105
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009105

Souhrn

Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger’s method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger’s method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose several methods, including an Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger’s method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger’s method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, might fail.

Klíčová slova:

Coronary heart disease – Covariance – Gene expression – Genome-wide association studies – Cholesterol – Quantitative trait loci – Simulation and modeling – Single nucleotide polymorphisms

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