Duy T. Pham

The paper introduces two tools, REGEM and METAGEM, to analyze gene-environment interaction summary statistics from GWIS studies, enabling efficient re-analysis and meta-analysis.

This paper evaluates imputation and polygenic score performance of 23 human genotyping arrays across 6 ancestry groups using public and in-house datasets, finding that array selection significantly impacts results.

The paper introduces a method to generate summary statistics for gene-environment interaction tests and a meta-analysis approach to combine results across studies, demonstrating its effectiveness in large-scale sequencing data.

GEM enables scalable gene-environment interaction analysis in large samples, identifying novel loci in a gene-sex interaction study.

This study uses spatial transcriptomics to analyze human and mouse kidney tissues, identifying gene expression patterns, nephron segments, cell interactions, and genetic variants while preserving spatial context.