Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci

作者：Sonja Neumeyer, Gibran Hemani, Eleftheria Zeggini · 发表于：Trends in Molecular Medicine · 年份：2019 · DOI：10.1016/j.molmed.2019.10.004 · 被引用次数：62 · 研究领域：Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks、Advanced Causal Inference Techniques

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.