Profile
Our group is primarily involved in Statistical Genomics with applications to identification of genetic variants which confer susceptibility to complex diseases in humans. We use data from GWAS, transcriptomics, epigenomics, e-QTL studies, and develop techniques to accelerate discovery of variants, genes and pathways from high-throughput genomics data. For this, we apply analytic approaches such as multiple-testing, meta-analysis, pathway/enrichment analysis and integrative genomics. We are also interested in understanding the causal mechanisms underlying these variants driving disease pathogenesis and in developing ways to understand gene-gene and gene-environment interactions that is crucial for effective genomics driven personalized medicine.
Current Focus Areas
Statistical Methods and Computational Tools for Integrative Genomics in Complex Traits. We use both statistical methods and computational techniques such as machine-learning to integrate GWAS data with various databases to identify variants, genes and pathways causally involved in complex diseases.
Modelling the combined effect of the genome and environmental risk factors in predicting an individual's risk of complex disease. We use statistical methods to understand interactions among various genomic and non-genomic risk factors in complex traits and also machine-learning models for disease risk prediction.
Discovering genomic risk factors and biomarkers for complex diseases in Indian patient samples in collaboration with biologists and clinicians.
Selected Publications
Sengupta D, Banerjee S, Mukhopadhyay P, Mitra R, Chaudhuri T, Sarkar A, Bhattacharjee G, Nath S, Roychoudhury S, Bhattacharjee S, Sengupta M. (2021) A comprehensive meta-analysis and a case-control study give insights into genetic susceptibility of lung cancer and subgroups. Sci Rep. 2021 Jul 16;11(1):14572.
Biswas, S., Pal, S., Majumder, P. P., & Bhattacharjee, S. (2020). A framework for pathway knowledge driven prioritization in genome-wide association studies. Genetic epidemiology, 44(8), 841–853.
Bhattacharjee, S., Rajaraman, P., Jacobs, K. B., Wheeler, W. A., Melin, B. S., Hartge, P., GliomaScan Consortium, Yeager, M., Chung, C. C., Chanock, S. J., & Chatterjee, N. (2012). A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. American journal of human genetics, 90(5), 821–835.