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Journal Article
Fine-mapping causal tissues and genes at disease-associated loci
2025
Overview
Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue–gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene–tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in
cis
-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype–Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease or trait, of which 11% were gene–tissue pairs. Causal gene–tissue pairs identified by TGFM reflected both known biology (for example,
TPO
–thyroid for hypothyroidism) and biologically plausible findings (for example,
SLC20A2
–artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene–PBMC cell type pairs.
Tissue–gene fine-mapping (TGFM) generalizes the SuSiE method to fine-map causal tissues and genes at disease loci using external eQTL data, offering improved calibration owing to modeling of
cis
-predicted expression uncertainty.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ Animal Genetics and Genomics
/ Aorta
/ Biobanks
/ Biomedical and Life Sciences
/ Chromosome Mapping - methods
/ Datasets
/ Disease
/ Genes
/ Genetic Predisposition to Disease - genetics
/ Genome-Wide Association Study
/ Genomes
/ Genotype
/ Humans
/ Mapping
/ Organ Specificity - genetics
/ Peripheral blood mononuclear cells
/ Polymorphism, Single Nucleotide
/ Quantitative Trait Loci - genetics
/ Thyroid
/ Tissues
