Bhramar Mukherjee

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 paper introduces RareEffect, a method for estimating rare variant effects and improving polygenic risk prediction using UK Biobank whole exome sequencing data.

This paper introduces TL-PRS, a transfer learning method for constructing polygenic risk scores across ancestries, achieving up to 42% average relative improvement in South Asian and African ancestry groups.

The paper develops ExPRSweb, an online repository of polygenic risk scores for common exposures, showing improved prediction of chronic conditions when combined with traditional PRSs.

The paper introduces a method to improve genetic association studies by incorporating family disease history and addressing case-control imbalance, showing improved power and calibration.

This study evaluates the utility of breast and prostate cancer polygenic risk scores across different ancestry groups using UK Biobank data, showing significant enrichments in cancer cases within top PRS percentiles.

The paper introduces POLMM, a mixed model for ordinal categorical phenotypes in GWAS, showing it is computationally efficient, controls type I error, and identifies more significant variants than existing methods.

The paper introduces Cancer-PRSweb, an online repository of polygenic risk scores for cancer traits, evaluated in two biobanks with predictive performance metrics and phenome-wide association studies.

This study constructs and evaluates polygenic risk scores for skin cancer subtypes using the Michigan Genomics Initiative and UK Biobank data, and develops a visual catalog to compare PRS methods.

This study identifies 111 genetic loci and 163 risk variants associated with atrial fibrillation using a GWAS of over 1 million individuals.

This study uses polygenic risk scores (PRS) for cancers in a phenome-wide association study (PheWAS) to identify associations with multiple phenotypes, finding significant links to both cancer and non-cancer diagnoses.

This paper introduces a method to enhance genetic association discovery by incorporating gene-environment interactions across multiple studies or phenotypes.