Integration of polygenic risk with single cell methylation data using met-scDRS.

Authors

Xinzhe Li, Zhuozheng Shi, Kangcheng Hou, Katherine W Eyring, Cuining Liu, Matthew G Heffel, Chongyuan Luo, Daniel H Geschwind, Bogdan Pasaniuc

Published in

Bioinformatics (Oxford, England). Oct 20, 2026. Epub Oct 20, 2026.

Abstract

Although large scale genome-wide association studies (GWAS) have identified many risk variants for psychiatric traits their functional implication remain largely unknown. One potential mechanism for their risk is through cellular context-dependent epigenetics such as methylation. Previous methodologies have focused on integrating GWAS with single cell transcriptomics, which allowed identification of within-cell-type heterogeneity in disease risk association. We extended this framework to provide a method called met-scDRS that integrates GWAS with single cell epigenomics and investigates how psychiatric disease risk can manifest through methylomics at single cell resolution.
We propose an approach that integrates single cell methylomes with disease associated genes identified from GWAS at single-cell resolution. We found our method to be well calibrated and powered in different scenarios. We then analyzed a publicly available single cell methylome dataset of the human brain for 75 different traits and generated disease relevance scores for each cell for each trait. We find high brain specificity with major cell type enrichments congruent with known findings. We identify within-cell-type heterogeneity for psychiatric disease risk along the brain spatial axis as well as subpopulations that are differentially associated with diseases. Finally, we showed that genes with high correlation between methylation levels and disease relevance scores to be highly enriched in specific functional pathways such as synaptic formation and transmission.
met-scDRS is freely available in GitHub as a python package under MIT license: https://github.com/bogdanlab/met-scDRS. Source code is also available from Zenodo with DOI: 10.5281/zenodo.21841506. All dataset used in this work are publicly available.
Supplementary data are available at Bioinformatics online.

PMID:
42899409
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.

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