Title |
Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits
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Published in |
Nature Genetics, June 2018
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DOI | 10.1038/s41588-018-0148-2 |
Pubmed ID | |
Authors |
Farhad Hormozdiari, Steven Gazal, Bryce van de Geijn, Hilary K. Finucane, Chelsea J.-T. Ju, Po-Ru Loh, Armin Schoech, Yakir Reshef, Xuanyao Liu, Luke O’Connor, Alexander Gusev, Eleazar Eskin, Alkes L. Price |
Abstract |
There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10-31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10-35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 25 | 34% |
United Kingdom | 6 | 8% |
Spain | 2 | 3% |
Australia | 1 | 1% |
France | 1 | 1% |
Netherlands | 1 | 1% |
Malaysia | 1 | 1% |
India | 1 | 1% |
Argentina | 1 | 1% |
Other | 4 | 5% |
Unknown | 31 | 42% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 39 | 53% |
Scientists | 33 | 45% |
Practitioners (doctors, other healthcare professionals) | 2 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 234 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 58 | 25% |
Researcher | 55 | 24% |
Student > Doctoral Student | 16 | 7% |
Student > Bachelor | 12 | 5% |
Student > Master | 11 | 5% |
Other | 34 | 15% |
Unknown | 48 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 91 | 39% |
Agricultural and Biological Sciences | 46 | 20% |
Medicine and Dentistry | 10 | 4% |
Computer Science | 6 | 3% |
Neuroscience | 6 | 3% |
Other | 21 | 9% |
Unknown | 54 | 23% |