Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci

• Published in: Trends in molecular medicine 2020-02, Vol.26 (2), p.232-241
• Main Authors: Neumeyer, Sonja, Hemani, Gibran, Zeggini, Eleftheria
• Format: Article
• Language: English
• Subjects: complex trait; gene expression; Gene Expression - genetics; Genetic Variation - genetics; genome-wide association study; Genome-Wide Association Study - methods; GWAS; Humans; Mendelian randomization; Phenotype; QTL; Quantitative Trait Loci - genetics
• ISSN: 1471-4914; 1471-499X
• DOI: 10.1016/j.molmed.2019.10.004

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. GWAS using large sample sizes have allowed the identification of many DNA sequence variants associated with molecular traits such as gene expression, DNA methylation, and protein levels that could be mediators between disease-associated genetic variants and the disease.Quantitative trait loci (QTLs), genetic variants influencing molecular traits, are increasingly used to identify causal features of complex traits.MR, a method using genetic variants as instrumental variables for a modifiable exposure, is employed to evaluate whether a molecular trait has an influence on a complex trait.Many challenges remain, such as linkage disequilibrium between causal variants of different complex traits, pleiotropy, and the non-specificity of molQTLs, or molQTLs being reverse causally influenced by a complex trait; methods to address them are being developed.