Tractor Workflow Pipeline: A Scalable Nextflow Framework for Local Ancestry-Aware Genome-Wide Association Studies

Area of Science:

• Population Genetics
• Bioinformatics
• Genomic Association Studies

Background:

• Traditional Genome-Wide Association Studies (GWAS) often exclude admixed individuals, limiting genetic discovery in diverse populations.
• Existing methods like Tractor GWAS require complex bioinformatics pipelines for local ancestry inference and phasing.
• There is a need for streamlined, automated workflows to facilitate genetic analysis in admixed ancestries.

Purpose of the Study:

• To develop and validate a scalable Nextflow workflow for automating Tractor GWAS analysis in admixed populations.
• To enable efficient identification of ancestry-specific genetic associations by integrating local ancestry information.
• To reduce the bioinformatics expertise required for analyzing individuals with multiple ancestries.

Main Methods:

• Developed a modular and customizable Nextflow workflow integrating genomic phasing, local ancestry inference, and Tractor GWAS.
• Applied the pipeline to analyze 32 blood biomarkers in 6,245 admixed individuals (AFR-EUR) from the UK Biobank.
• Supported multiple commonly used bioinformatics tools for flexibility and harmonization.

Main Results:

• The workflow demonstrated efficient scalability and successful replication of known genetic associations.
• Identified novel ancestry-specific loci, primarily driven by variants on African ancestral tracts.
• Highlighted the value of local ancestry-aware methods in uncovering previously missed genetic signals in admixed cohorts.

Conclusions:

• The developed Nextflow workflow effectively automates and streamlines genetic analysis for admixed individuals using Tractor GWAS.
• This approach facilitates broader genetic discovery by enabling the efficient analysis of complex ancestries.
• The findings underscore the importance of incorporating local ancestry information to maximize insights from diverse populations.