Nilanjan Chatterjee

This study uses Mendelian randomisation to show higher kidney function causally lowers blood pressure, but blood pressure does not significantly affect kidney function.

The paper shows oncologic therapy increases clonal hematopoiesis with DDR mutations, which predicts risk of therapy-related myeloid neoplasms.

The paper proposes a case-only method to analyze gene-environment interactions using polygenic risk scores in case-control studies, showing efficiency gains over logistic regression.

The paper introduces MRMix, a mixture model-based method for Mendelian randomization that provides robust causal effect estimates, showing improved performance in simulations and identifying several genetic risk factors for diseases.

HIPO enhances genetic association detection by optimizing pleiotropic analysis using heritability and trait overlaps, increasing significant loci across multiple traits.

The paper estimates effect-size distributions of SNPs using GWAS summary statistics across 32 traits, revealing polygenic diversity and sample size requirements for heritability explanation.

The paper develops a simplified power analysis framework for rare variant association studies and provides insights into genetic architecture and annotation quality.

The paper develops a framework for post-selection inference in genomic data to control false positive rates after class-level hypothesis testing, applied to eQTL analysis in 17 tissue types.

This paper introduces sARTP, a pathway-based meta-analysis method using summary statistics, identifying 43 T2D-associated pathways in European populations and 7 trans-ethnic pathways in Asian populations.

The paper introduces MEGSA, a framework for analyzing mutual exclusivity of tumor mutations, identifying novel gene sets and associations in cancer types like AML and breast cancer.

This paper improves polygenic risk scores by applying winners curse correction and variable thresholds to GWAS summary data, enhancing prediction accuracy for some diseases.