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Neumeyer; S. ; Hemani, G.* ; Zeggini, E.

Strengthening causal inference for complex disease using molecular quantitative trait loci.

Trends Mol. Med. 26, 232-241 (2020)
Verlagsversion Postprint DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Complex Trait ; Gene Expression ; Genome-wide Association Study ; Gwas ; Mendelian Randomization ; Qtl; Mendelian Randomization; Gene-expression; Association; Identification; Epidemiology; Variants; Atlas; Gwas; Help; Bias
ISSN (print) / ISBN 1471-4914
e-ISSN 1471-499X
Quellenangaben Band: 26, Heft: 2, Seiten: 232-241 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, Oxon, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Translational Genomics (ITG)