Arunabha Majumdar

A TWAS method calibrated for the uncertainty of predicted expression

This paper introduces a TWAS method that adjusts for predicted expression uncertainty, improving type I error control and effect size estimation compared to traditional approaches.

Testing for gene-environment (GxE) interaction using p-value aggregation identifies many GxE loci

This paper introduces a p-value aggregation method to detect gene-environment interactions, identifying more loci than traditional additive models in UK Biobank data.

A unified hierarchical Bayesian approach to transcriptome-wide association study

This paper introduces a unified Bayesian approach for transcriptome-wide association studies (TWAS) that improves accuracy by jointly modeling transcriptome and GWAS data, showing better performance in gene-trait association detection.

A multivariate approach to joint testing of main genetic and gene-environment interaction effects

This paper introduces a multivariate joint test for genetic and gene-environment interaction effects, showing improved power in detecting associations compared to univariate methods.

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

This paper introduces a statistical method to identify tissue-specific genetic contributions to complex traits at the individual level using eQTLs and applies it to BMI and WHRadjBMI in the UK Biobank.

Leveraging expression from multiple tissues using sparse canonical correlation analysis (sCCA) and aggregate tests improves the power of transcriptome-wide association studies (TWAS)

This paper improves transcriptome-wide association study (TWAS) power by integrating multiple tissues using sparse canonical correlation analysis (sCCA) and aggregate tests, identifying more gene-trait associations.

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

The paper proposes a two-step method to detect gene-environment interactions across multiple phenotypes, showing improved power in simulations and identifying significant signals in lipid data from UK Biobank.

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.

Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

This paper introduces a method to accurately estimate SNP-heritability from large biobank data without assuming genetic architecture, showing existing methods can be off by up to 30%.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

This paper introduces CPBayes, a Bayesian method for detecting pleiotropic genetic associations across multiple traits, showing improved accuracy over existing methods in both simulations and real data.