Taotao Tan

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

The paper introduces Tractor-Mix, a mixed model method for GWAS in admixed cohorts with high relatedness, improving ancestry-specific association detection and controlling false positives.

Tractor-Burden improves gene discovery in admixed populations by incorporating local ancestry in rare variant burden testing, identifying additional genes and pathways for type 2 diabetes.

K-PoPS improves gene prioritization for complex traits by providing explainable insights into GWAS results, identifying plausible effector genes with supporting evidence.

The paper introduces a Nextflow workflow for local ancestry-aware GWAS, demonstrating its utility in analyzing admixed individuals and identifying ancestry-specific genetic associations.

The paper introduces the Causal Pivot (CP) method to detect causal genetic variants in complex diseases using structural causal models and applies it to UK Biobank data for three diseases.

This study compares polygenic scores from genotyping arrays and whole-genome sequencing across traits and populations, finding that WGS generally improves prediction accuracy but with context-dependent advantages.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.