Identifying independent causal cell types for human diseases and risk variants
5 affiliations
- 1Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: [email protected]
- 2Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- 3Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- 4Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- 5Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Electronic address: [email protected]
Abstract
The SNP-heritability of human diseases is extremely enriched in candidate regulatory elements (cREs) from disease-relevant cell types. Critical next steps are to understand whether these enrichments are driven by multiple causal cell types and whether individual variants impact disease risk via a single or multiple of cell types. Here, we propose CT-FM and CT-FM-SNP, 2 methods accounting for cREs shared across cell types to identify independent sets of causal cell types for a trait and its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using 924 cRE annotations, primarily from ENCODE4. CT-FM inferred 79 sets of causal cell types, with corresponding SNP-annotations explaining 39.0 ± 1.8% of trait SNP-heritability. It identified 14 traits with independent causal cell types, uncovering previously unexplored cellular mechanisms in height, schizophrenia and autoimmune diseases. We applied CT-FM-SNP to 39 UK Biobank traits and predicted high-confidence causal cell types for 3,091 candidate causal non-coding SNPs-trait pairs. Our results suggest that most SNPs affect a phenotype via a single set of cell types, whereas pleiotropic SNPs might target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to affect disease risk.
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BibTeX
@article{kim2026identifying,
title = {{Identifying independent causal cell types for human diseases and risk variants}},
author = {Artem Kim and Zixuan Zhang and Come Legros and Hongbo Wang and Zeyun Lu and Adam J. de Smith and Jill E. Moore and Arun Durvasula and Nicholas Mancuso and Steven Gazal},
journal = {Cell Genomics},
year = {2026},
month = aug,
doi = {10.1016/j.xgen.2026.101325},
url = {https://doi.org/10.1016/j.xgen.2026.101325},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Identifying independent causal cell types for human diseases and risk variants
AU - Artem Kim
AU - Zixuan Zhang
AU - Come Legros
AU - Hongbo Wang
AU - Zeyun Lu
AU - Adam J. de Smith
AU - Jill E. Moore
AU - Arun Durvasula
AU - Nicholas Mancuso
AU - Steven Gazal
PY - 2026
DA - 2026/08/14
JO - Cell Genomics
DO - 10.1016/j.xgen.2026.101325
UR - https://doi.org/10.1016/j.xgen.2026.101325
AB - The SNP-heritability of human diseases is extremely enriched in candidate regulatory elements (cREs) from disease-relevant cell types. Critical next steps are to understand whether these enrichments are driven by multiple causal cell types and whether individual variants impact disease risk via a single or multiple of cell types. Here, we propose CT-FM and CT-FM-SNP, 2 methods accounting for cREs shared across cell types to identify independent sets of causal cell types for a trait and its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using 924 cRE annotations, primarily from ENCODE4. CT-FM inferred 79 sets of causal cell types, with corresponding SNP-annotations explaining 39.0 ± 1.8% of trait SNP-heritability. It identified 14 traits with independent causal cell types, uncovering previously unexplored cellular mechanisms in height, schizophrenia and autoimmune diseases. We applied CT-FM-SNP to 39 UK Biobank traits and predicted high-confidence causal cell types for 3,091 candidate causal non-coding SNPs-trait pairs. Our results suggest that most SNPs affect a phenotype via a single set of cell types, whereas pleiotropic SNPs might target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to affect disease risk.
SN - 2666-979X
ER -