Andrew Brown

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

The paper introduces a heuristic method to adaptively select a genetic distance threshold for HIV transmission network inference, demonstrating its utility in identifying clusters with different risk factors.

iPheGWAS integrates and visualizes GWAS results using hierarchical clustering, offering faster genetic correlation analysis and 3D visualization compared to LDSC.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

The paper analyzes HIV-1 full-genome sequences from London to reveal high strain variability, including unique recombinants and transmission clusters not detectable with partial sequencing.

FastQTL efficiently maps QTLs by correlating molecular phenotypes with genetic variants using permutation-based p-value estimation, improving speed over previous methods.