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Fine-mapping causal tissues and genes at disease-associated loci.

作者信息

Strober Benjamin J, Zhang Martin Jinye, Amariuta Tiffany, Rossen Jordan, Price Alkes L

机构信息

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

出版信息

medRxiv. 2024 Jun 17:2023.11.01.23297909. doi: 10.1101/2023.11.01.23297909.


DOI:10.1101/2023.11.01.23297909
PMID:37961337
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10635248/
Abstract

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., -thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., -artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/9a7fe1bbd202/nihpp-2023.11.01.23297909v3-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/f09542a0c78b/nihpp-2023.11.01.23297909v3-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/6fb8fe563c66/nihpp-2023.11.01.23297909v3-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/c48f6d3fd432/nihpp-2023.11.01.23297909v3-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/d81d0d26b710/nihpp-2023.11.01.23297909v3-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/57cdf35c1a8c/nihpp-2023.11.01.23297909v3-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/7e28a77a37c7/nihpp-2023.11.01.23297909v3-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/959dae7f641e/nihpp-2023.11.01.23297909v3-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/9a7fe1bbd202/nihpp-2023.11.01.23297909v3-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/f09542a0c78b/nihpp-2023.11.01.23297909v3-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/6fb8fe563c66/nihpp-2023.11.01.23297909v3-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/c48f6d3fd432/nihpp-2023.11.01.23297909v3-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/d81d0d26b710/nihpp-2023.11.01.23297909v3-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/57cdf35c1a8c/nihpp-2023.11.01.23297909v3-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/7e28a77a37c7/nihpp-2023.11.01.23297909v3-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/959dae7f641e/nihpp-2023.11.01.23297909v3-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4006/11195345/9a7fe1bbd202/nihpp-2023.11.01.23297909v3-f0008.jpg

相似文献

[1]
Fine-mapping causal tissues and genes at disease-associated loci.

medRxiv. 2024-6-17

[2]
Fine-mapping causal tissues and genes at disease-associated loci.

Nat Genet. 2025-1

[3]
Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

PLoS Genet. 2019-12-13

[4]
Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia.

Alzheimers Res Ther. 2024-6-1

[5]
Estimating colocalization probability from limited summary statistics.

BMC Bioinformatics. 2021-5-17

[6]
Modeling tissue co-regulation estimates tissue-specific contributions to disease.

Nat Genet. 2023-9

[7]
Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.

NAR Genom Bioinform. 2023-10-30

[8]
Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables.

PLoS Genet. 2024-11

[9]
An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer's disease.

Alzheimers Res Ther. 2020-4-16

[10]
Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables.

ArXiv. 2024-9-20

本文引用的文献

[1]
Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases.

Nat Genet. 2024-9

[2]
Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.

Nat Genet. 2024-2

[3]
Conditional transcriptome-wide association study for fine-mapping candidate causal genes.

Nat Genet. 2024-2

[4]
Analysis of Rare Variants in 470,000 Exome-Sequenced UK Biobank Participants Implicates Novel Genes Affecting Risk of Hypertension.

Pulse (Basel). 2023-11-13

[5]
Improving fine-mapping by modeling infinitesimal effects.

Nat Genet. 2024-1

[6]
Systematic differences in discovery of genetic effects on gene expression and complex traits.

Nat Genet. 2023-11

[7]
Plasma proteomic associations with genetics and health in the UK Biobank.

Nature. 2023-10

[8]
Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease.

Nat Genet. 2023-10

[9]
Modeling tissue co-regulation estimates tissue-specific contributions to disease.

Nat Genet. 2023-9

[10]
Current understanding of CTLA-4: from mechanism to autoimmune diseases.

Front Immunol. 2023

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