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Peer-reviewedCC BYBioinformatics (Oxford, England)

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

  • Xinzhe Li1
  • Zhuozheng Shi1
  • Kangcheng Hou2
  • Katherine W Eyring3
  • Cuining Liu4,5
  • Matthew G Heffel5
  • Chongyuan Luo5
  • Daniel H Geschwind3,4,5,6,7,8
  • Bogdan Pasaniuc9
9 affiliations
  1. 1Genomics and Computational Biology Program, University of Pennsylvania, Philadelphia, PA 19104, United States.
  2. 2Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States.
  3. 3Department of Neurology, David Geffen School of Medicine, Neurogenetics Program, University of California, Los Angeles, Los Angeles, CA 90095, United States.
  4. 4Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90024, United States.
  5. 5Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States.
  6. 6Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States.
  7. 7Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States.
  8. 8Institute for Precision Health, University of California, Los Angeles, CA 90095, United States.
  9. 9Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
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Abstract

Motivation

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.Results

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 analysed 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.Availability

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.

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Cite

BibTeXRIS
BibTeX
@article{li2026integration,
  title = {{Integration of polygenic risk with single cell methylation data using met-scDRS.}},
  author = {Xinzhe Li and Zhuozheng Shi and Kangcheng Hou and Katherine W Eyring and Cuining Liu and Matthew G Heffel and Chongyuan Luo and Daniel H Geschwind and Bogdan Pasaniuc},
  journal = {Bioinformatics (Oxford, England)},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag751},
  url = {https://europepmc.org/article/MED/42899409},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Integration of polygenic risk with single cell methylation data using met-scDRS.
AU  - Xinzhe Li
AU  - Zhuozheng Shi
AU  - Kangcheng Hou
AU  - Katherine W Eyring
AU  - Cuining Liu
AU  - Matthew G Heffel
AU  - Chongyuan Luo
AU  - Daniel H Geschwind
AU  - Bogdan Pasaniuc
PY  - 2026
DA  - 2026/10/01
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag751
UR  - https://europepmc.org/article/MED/42899409
AB  - Motivation  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.Results  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 analysed 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.Availability  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.
SN  - 1367-4803
SN  - 1367-4811
ER  -