GEMS (GWAS Effect Mechanisms via Sequence-to-Function): Unveiling cell type-specific mechanisms for GWAS variants
- 1Genentech Inc
Abstract
Genome-wide association studies (GWAS) have linked thousands of loci to common diseases, yet for non-coding variants, which constitute the vast majority of the signal, the cell types and states through which individual risk variants act remain largely unknown. Here, we develop GEMS (GWAS Effect Mechanisms via Sequence-to-Function), a framework that trains supervised classifiers on multimodal variant effect predictions (ATAC-seq and RNA-seq) from the sequence-to-function model Borzoi and applies SHAP-based attribution to associate individual GWAS variants with the cell types most relevant to their disease association. Applied to asthma, LDL cholesterol, multiple sclerosis, and type 2 diabetes, GEMS prioritizes known disease-relevant cell types concordant with stratified LD-score regression. At the variant level, GEMS associates a median of 58.7% of fine-mapped variants with at least one cell type via ATAC-seq and 65.9% via RNA-seq, extending cellular annotation beyond the chromatin accessibility peaks in the single-cell atlas used for model training. ATAC-seq variant-cell-type associations are significantly enriched in cis-regulatory elements from independent experimental studies not used in model training, and enrichment persists after excluding variants overlapping training atlas peaks. For multiple sclerosis, hierarchical biclustering of variant--cell-type associations partitions risk variants into immune and CNS modules driven by distinct transcription factor families, recapitulating the immune-mediated and neurodegenerative components of the disease and demonstrating that integrative analysis of sequence-to-function predictions can stratify phenotype-associated variants into mechanistically distinct, cell-type-specific regulatory programs.
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BibTeX
@misc{xie2026gems,
title = {{GEMS (GWAS Effect Mechanisms via Sequence-to-Function): Unveiling cell type-specific mechanisms for GWAS variants}},
author = {X. Xie and S. Qian and D. Chang and T. Bhangale and M. I. McCarthy and G. Eraslan and K. Fletez-Brant},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.759213},
url = {https://www.biorxiv.org/content/10.64898/2026.10.14.759213v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - GEMS (GWAS Effect Mechanisms via Sequence-to-Function): Unveiling cell type-specific mechanisms for GWAS variants
AU - X. Xie
AU - S. Qian
AU - D. Chang
AU - T. Bhangale
AU - M. I. McCarthy
AU - G. Eraslan
AU - K. Fletez-Brant
PY - 2026
DA - 2026/10/14
PB - bioRxiv
DO - 10.64898/2026.10.14.759213
UR - https://www.biorxiv.org/content/10.64898/2026.10.14.759213v1
AB - Genome-wide association studies (GWAS) have linked thousands of loci to common diseases, yet for non-coding variants, which constitute the vast majority of the signal, the cell types and states through which individual risk variants act remain largely unknown. Here, we develop GEMS (GWAS Effect Mechanisms via Sequence-to-Function), a framework that trains supervised classifiers on multimodal variant effect predictions (ATAC-seq and RNA-seq) from the sequence-to-function model Borzoi and applies SHAP-based attribution to associate individual GWAS variants with the cell types most relevant to their disease association. Applied to asthma, LDL cholesterol, multiple sclerosis, and type 2 diabetes, GEMS prioritizes known disease-relevant cell types concordant with stratified LD-score regression. At the variant level, GEMS associates a median of 58.7% of fine-mapped variants with at least one cell type via ATAC-seq and 65.9% via RNA-seq, extending cellular annotation beyond the chromatin accessibility peaks in the single-cell atlas used for model training. ATAC-seq variant-cell-type associations are significantly enriched in cis-regulatory elements from independent experimental studies not used in model training, and enrichment persists after excluding variants overlapping training atlas peaks. For multiple sclerosis, hierarchical biclustering of variant--cell-type associations partitions risk variants into immune and CNS modules driven by distinct transcription factor families, recapitulating the immune-mediated and neurodegenerative components of the disease and demonstrating that integrative analysis of sequence-to-function predictions can stratify phenotype-associated variants into mechanistically distinct, cell-type-specific regulatory programs.
ER -