Computational & Systems Biology Program
The Kushal Dey Lab
The Kushal Dey Lab builds statistical and machine learning models that integrate genetic and genomic data to prioritize variants, genes, and cell types, and to decode the causal functional architecture underlying heritable complex diseases — including immune-related diseases, like Alzheimer’s and inflammatory bowel disease, and heritable cancers, like breast cancer.
Nominating candidate risk genes and gene sets underlying disease-critical processes is of utmost importance for developing drug targets and informing CRISPR screening. The Kushal Dey Lab focuses on developing machine learning models and computational pipelines that integrate genomic and epigenomic data from RNA-seq, ChiP-seq, Perturb-seq experiments with genetic association studies (GWAS, WES) to enhance our understanding of the functional architecture of all heritable complex diseases, including immune-related diseases like Alzheimers’, IBD, Lupus and several heritable cancers like Breast and Prostate cancers.
Some of the research directions of interest include developing:
- Models to prioritize variants, genes and cell states for disease using a combination of genetic, genomic and perturbation data.
- Models to identify the causal directed graphs underlying gene and gene interaction models for disease.
- Benchmarking pipelines informed by disease genetics to validate and compare different genomic prediction models.
Jagadeesh, K.A.*, Dey, K.K.*, Montoro, D.T., Mohan, R., Gazal, S, Engreitz, J.M., Xavier, R.J., Price, A.L., and Regev, A., 2022. Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics. Nature Genetics, 54, pp. 1479-1492.
Dey, K.K., Gazal, S., van de Geijn, B., Kim, S.S., Nasser, J., Engreitz, J.M. and Price, A.L., 2022. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. Cell Genomics, 2(7), p.100145.
Delorey, T.M.*, .., Dey, K.K.* et al, 2021. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature, 595(7865), pp.107-113.
Dey, K.K., Van de Geijn, B., Kim, S.S., Hormozdiari, F., Kelley, D.R. and Price, A.L. 2020. Evaluating the informativeness of deep learning annotations for human complex diseases. Nature Communications, 11 (4703).
Dey, K.K., Hsiao, C.J. and Stephens, M., 2017. Visualizing the structure of RNA-seq expression data using grade of membership models. PLOS Genetics,13(3):e1006599
Kushal Dey, PhD
- The Kushal Dey lab focuses on developing machine learning models and computational pipelines that integrate genomic and epigenomic data.
- PhD, University of Chicago
- [email protected]
- Email Address
- Josie Robertson Investigator (2023–2028)
- K99/R00 Pathway to Independence Award (NIH/NHGRI) (2022–2026)
- University of Chicago Arts, Science + Culture Graduate Collaboration Grant (2017–2018)
- David Wallace Award for Applied Statistics, University of Chicago (2016)
To learn more about available postdoctoral opportunities, please visit our Career Center
To learn more about compensation and benefits for postdoctoral researchers at MSK, please visit Resources for Postdocs
Graduate and Undergraduate Students
The Dey Lab welcomes graduate and undergraduate students interested in developing statistical and machine learning models in the interface of disease genetics and genomics, as well as their applications to state-of-the-art data.
We are seeking to recruit a postdoctoral fellow to lead inter-disciplinary collaborative projects jointly supervised by Dr. Dey and Dr. Vierbuchen.
The Kushal Dey Lab is seeking a highly motivated and successful individual with a background in statistical genetics and genomics.
Get in Touch
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