Tractor workflow: a scalable Nextflow framework for local ancestry-aware genome-wide association studies

Area of Science:

• Genetics
• Bioinformatics
• Population Genetics

Background:

• Traditional Genome-Wide Association Studies (GWAS) often exclude admixed individuals, limiting genetic discovery.
• Existing methods for local ancestry analysis are complex and require significant bioinformatics expertise.
• Tractor enables local ancestry incorporation into association testing for ancestry-specific signals.

Purpose of the Study:

• To develop a scalable, automated workflow for local ancestry-aware GWAS.
• To simplify the integration of phasing, local ancestry inference, and association testing.
• To facilitate broader adoption of advanced genetic analysis methods in admixed populations.

Main Methods:

• Developed a Nextflow workflow integrating phasing, local ancestry inference, and Tractor association testing.
• Applied the pipeline to 32 blood biomarkers in 6,245 UK Biobank participants (African-European ancestry).
• Workflow is modular, customizable, and compatible with standard bioinformatics tools.

Main Results:

• The automated pipeline efficiently processed large-scale data, replicating known associations.
• Identified key ancestry-specific loci, primarily driven by variants on African ancestral tracts.
• Demonstrated the value of local ancestry-aware methods in uncovering masked genetic signals.

Conclusions:

• The developed workflow lowers technical barriers for local ancestry-aware GWAS.
• Facilitates expanded genetic discovery in diverse, admixed populations.
• Enables more comprehensive understanding of genotype-phenotype relationships across ancestries.