Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies

released
1{
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5        "Publication",
6        "Item"
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8    "abstract": "Recent studies suggest that context-specific eQTLs underlie genetic risk factors for complex diseases. However, methods for identifying them are still nascent, limiting their comprehensive characterization and downstream interpretation of disease-associated variants. Here, we introduce FastGxC, a method to efficiently and powerfully map context-specific eQTLs by leveraging the correlation structure of multi-context studies. We first show via simulations that FastGxC is orders of magnitude more powerful and computationally efficient than previous approaches, making previously year-long computations possible in minutes. We next apply FastGxC to bulk multi-tissue and single-cell RNA-seq data sets to produce the most comprehensive tissue- and cell-type-specific eQTL maps to date. We then validate these maps by establishing that context-specific eQTLs are enriched in corresponding functional genomic annotations. Finally, we examine the relationship between context-specific eQTLs and human disease and show that FastGxC context-specific eQTLs provide a three-fold increase in precision to identify relevant tissues and cell types for GWAS variants than standard eQTLs. In summary, FastGxC enables the construction of context-specific eQTL maps that can be used to understand the context-specific gene regulatory mechanisms underlying complex human diseases.",
9    "audit": {},
10    "authors": "Andrew Lu, Mike Thompson, M Grace Gordon, Andy Dahl, Chun Jimmie Ye, Noah Zaitlen, Brunilda Balliu",
11    "award": {
12        "@id": "/awards/Community/"
13    },
14    "creation_timestamp": "2025-05-15T21:24:17.615587+00:00",
15    "date_published": "2021-06-17",
16    "donors": [],
17    "file_sets": [],
18    "journal": "biorxiv",
19    "lab": {
20        "@id": "/labs/community/",
21        "title": "External Lab, Community"
22    },
23    "publication_identifiers": [
24        "doi:10.1101/2021.06.17.448889"
25    ],
26    "publication_year": 2021,
27    "published_by": [
28        "IGVF"
29    ],
30    "release_timestamp": "2025-05-14T22:51:37.615875+00:00",
31    "samples": [],
32    "schema_version": "6",
33    "software": [
34        "/software/fastgxc/"
35    ],
36    "software_versions": [],
37    "status": "released",
38    "submitted_by": {
39        "@id": "/users/17fd5606-081c-455b-8529-1958fc729e73/",
40        "title": "Jessica Van Hattem"
41    },
42    "summary": "Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies",
43    "title": "Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies",
44    "uuid": "eedca17d-f486-4d0e-91e0-e681d08db786",
45    "workflows": []
46}