Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci

Sonja Neumeyer, Gibran Hemani, Eleftheria Zeggini

Research output: Contribution to journalReview article (Academic Journal)

Abstract

Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed.

Original languageEnglish
JournalTrends in Molecular Medicine
Early online date9 Nov 2019
DOIs
Publication statusPublished - 9 Nov 2019

Bibliographical note

Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Fingerprint Dive into the research topics of 'Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci'. Together they form a unique fingerprint.

Cite this