Diptavo Dutta

This paper identifies 116 risk loci for melanoma through GWAS, links susceptibility to immunity and Wnt signaling, and develops a PRS improving risk prediction.

This study analyzes breast cancer GWAS across multiple ancestries, finding similar heritability and genetic correlations, and identifies shared regulatory and cellular contexts.

This study identifies 14 susceptibility loci for Ewing sarcoma through a genome-wide association meta-analysis, revealing genetic interactions with somatic EWSR1::FLI1 fusions.

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

The paper identifies key transcription factors and their target proteins in renal cell carcinoma using multi-omic integration of ChIP-seq and GWAS data.

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

The paper introduces a de-biased sparse canonical correlation analysis pipeline to identify cancer-related trans-regulated genes from multi-omic data, finding 256 genes associated with estrogen receptor-related CNAs in breast cancer.

This study identifies pleiotropic variants and trait-specific susceptibility variants using GWAS data from 116 traits, revealing widespread pleiotropy and unique genetic contributions to specific traits.

The paper introduces CSTWAS, a subset-based meta-analysis method for cross-tissue transcriptome-wide association studies that improves power and identifies active tissues for gene-trait associations.

This study introduces ARCHIE, a method to identify trait-specific gene sets through trans-eQTL analysis, finding 71 gene networks with significant trans-associations across 29 traits.

The paper identifies cis-pQTLs and develops models for proteome-wide association studies using plasma proteome data from European and African ancestry populations.

This study identifies 5 novel genetic loci associated with physical activity using 27 accelerometry-derived phenotypes from 88,411 UK Biobank participants.

GAUSS identifies gene-set-phenotype associations using GWAS summary statistics, improving power and interpretability in UK Biobank data.

This study identifies one known and two novel genetic variants associated with heart failure through GWAS and multi-omic analyses, revealing potential causal mechanisms and shared heritability with musculoskeletal traits.

Meta-MultiSKAT improves power for region-based genetic association tests by meta-analyzing multiple correlated phenotypes using summary statistics and variance component tests.

Multi-SKAT improves power to detect rare variant associations with multiple phenotypes while maintaining type I error rate using a general framework with kernel matrices and random effects.