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Strengthening causal inference for complex disease using molecular quantitative trait loci.
Trends Mol. Med. 26, 232-241 (2020)
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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11.099
2.262
9
14
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Publication type
Article: Journal article
Document type
Review
Keywords
Complex Trait ; Gene Expression ; Genome-wide Association Study ; Gwas ; Mendelian Randomization ; Qtl; Mendelian Randomization; Gene-expression; Association; Identification; Epidemiology; Variants; Atlas; Gwas; Help; Bias
Language
english
Publication Year
2020
Prepublished in Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
1471-4914
e-ISSN
1471-499X
Journal
Trends in Molecular Medicine
Quellenangaben
Volume: 26,
Issue: 2,
Pages: 232-241
Publisher
Elsevier
Publishing Place
The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, Oxon, England
Reviewing status
Peer reviewed
Institute(s)
Institute of Translational Genomics (ITG)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Genetics and Epidemiology
PSP Element(s)
G-506700-001
WOS ID
WOS:000510616500009
Scopus ID
85075375834
PubMed ID
31718940
Erfassungsdatum
2019-11-26