CaltechAUTHORS
  A Caltech Library Service

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

Lu, Andrew and Thompson, Mike and Gordon, M. Grace and Dahl, Andy and Ye, Chun Jimmie and Zaitlen, Noah and Balliu, Brunilda (2021) Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210621-155056493

[img] PDF - Submitted Version
Creative Commons Attribution Non-commercial No Derivatives.

17MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20210621-155056493

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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.06.17.448889DOIDiscussion Paper
https://github.com/BrunildaBalliu/FastGxCRelated ItemData/Code
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. This version posted June 18, 2021. N.Z. is supported by NIH grants R01HG006399, R01CA227237, R01CA227466, R01ES029929, R01MH122688, U01HG009080, R01HG011345, R35GM133531, R01HL155024, R01MH125252, DoD grant W81XWH-16-2-0018, and the Chan Zuckerberg Science Initiative. C.J.Y. is supported by the NIH grants R01AR071522, R01AI136972, R01HG011239, and the Chan Zucker berg Science Initiative, and is an investigator at the Chan Zuckerberg Biohub and a member of the Parker Institute for Cancer Immunotherapy (PICI). The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Author contributions: B.B. conceived of the project and developed the statistical methods. A.L. and B.B. implemented the comparisons with simulated data. A.L., B.B., M.T., and M.G.G. performed the analyses of the GTEx and CLUES data and additional analyses. B.B. and A.L. implemented the software. A.L. and B.B. wrote the manuscript, with significant input from N.Z., C.J.Y., A.D., M.G.G., and M.T. A.L. and B.B. prepared the online code and data resources. Software Availability: We provide free access to the software at https://github.com/BrunildaBalliu/FastGxC. Due to size limitations, the map of shared and context-specific eQTLs for all GTEx tissues and all CLUES PBMCs is available upon request. Competing interests: C.J.Y. is a Scientific Advisory Board member for and hold equity in Related Sciences and ImmunAI, a consultant for and hold equity in Maze Therapeutics, and a consultant for TReX Bio. C.J.Y. has received research support from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, and Genentech.
Funders:
Funding AgencyGrant Number
NIHR01HG006399
NIHR01CA227237
NIHR01CA227466
NIHR01ES029929
NIHR01MH122688
NIHU01HG009080
NIHR01HG011345
NIHR35GM133531
NIHR01HL155024
NIHR01MH125252
Department of DefenseW81XWH-16-2-0018
Chan Zuckerberg InitiativeUNSPECIFIED
NIHR01AR071522
NIHR01AI136972
NIHR01HG011239
Parker Institute for Cancer ImmunotherapyUNSPECIFIED
Record Number:CaltechAUTHORS:20210621-155056493
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210621-155056493
Official Citation:Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies. Andrew Lu, Mike Thompson, M Grace Gordon, Andy Dahl, Chun Jimmie Ye, Noah Zaitlen, Brunilda Balliu. bioRxiv 2021.06.17.448889; doi: https://doi.org/10.1101/2021.06.17.448889
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109504
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:21 Jun 2021 17:38
Last Modified:21 Jun 2021 17:38

Repository Staff Only: item control page