Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization.

Abstract

Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to either correlation among instrumental variables or noises in approximated linkage disequilibrium (LD). EMIC can further perform a pleiotropy fine-mapping analysis to remove possible false-positive estimates. With the usage of multiple cis-expression quantitative trait loci (eQTLs), EMIC was also more powerful than the alternative methods for the causal gene inference in the simulated datasets. Furthermore, EMIC rediscovered many known causal genes of complex phenotypes (schizophrenia, bipolar disorder, and total cholesterol) and reported many new and promising candidate causal genes. In sum, this study provided an efficient solution to discriminate the candidate causal genes from vast amounts of GWAS signals with eQTLs. EMIC has been implemented in our integrative software platform KGGSEE.

• Authors: Lin Jiang, Lin Miao, Guorong Yi, Xiangyi Li, Chao Xue, Mulin Jun Li, Hailiang Huang, Miaoxin Li
• Journal: American journal of human genetics (Am J Hum Genet) Vol. 109 Issue 5 Pg. 838-856 (05 05 2022) ISSN: 1537-6605 [Electronic] United States
• PMID: 35460606 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Topics

• Genome-Wide Association Study (methods)
• Humans
• Linkage Disequilibrium
• Mendelian Randomization Analysis (methods)
• Phenotype
• Polymorphism, Single Nucleotide (genetics)
• Quantitative Trait Loci (genetics)