Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci
| Autor: | Gibran Hemani, Eleftheria Zeggini, Sonja Neumeyer |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Quantitative Trait Loci Instrumental variable Gene Expression Genetic Variation Genome-wide association study Computational biology Biology Quantitative trait locus Genome Complex Trait Genome-wide Association Study Gwas Mendelian Randomization Qtl 03 medical and health sciences Phenotype 030104 developmental biology 0302 clinical medicine Pleiotropy Causal inference Mendelian randomization Humans Molecular Medicine Molecular Biology 030217 neurology & neurosurgery Genome-Wide Association Study Genetic association |
| Zdroj: | Trends Mol. Med. 26, 232-241 (2020) |
| ISSN: | 1471-4914 |
| Popis: | 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, in termediate 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), car 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. |
| Databáze: | OpenAIRE |
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