TLS-Tractor: A transfer learning framework for incorporating summary-statistics into local ancestry-aware GWAS in admixed populations

Abstract

Including recently admixed populations in genome-wide association studies (GWAS) is important for equitable and ancestry-resolved genetic discovery. The existing popular method, Tractor, estimates ancestry-specific effects from individual-level data but cannot leverage external GWAS summary statistics due to mismatches in underlying model parameters. We introduce TLS-Tractor, a transfer-learning method that uses the generalized method of moments to integrate external GWAS summary statistics with internal individual-level data for local ancestry-aware association analysis. In simulations, TLS-Tractor controlled type I error, accurately estimated ancestry-specific effects, and increased power relative to the internal-only Tractor. Analyses integrating African-European admixed participants from All of Us with Million Veteran Program summary statistics corroborated these gains and showed that local ancestry adjustment can improve calibration, localization, and interpretation, whereas standard GWAS meta-analysis often provides greater power. We introduce an efficient tlstractor R package that achieves over 200x faster local ancestry tract extraction and 4-32x faster association testing than the original Tractor implementation.