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

Shah Nirav N, NN Tan, Taotao T et al.

41838407 PubMed ID
33 Authors
2026-05-03 Published
122 Views
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

SN
Shah Nirav N
NT
NN Tan
TT
Taotao T
HJ
Honorato-Mauer Jessica
JL
J Lin
YY
Yi-Sian YS
MA
Maihofer Adam X
AZ
AX Zai
CC
Clement C CC
SM
Santoro Marcos
MN
M Nievergelt
CM
Caroline M CM
AE
Atkinson Elizabeth G
EA
EG Atkinson
EE
Elizabeth E
SM
Santoro Marcos
MN
M Nievergelt
CC
Caroline C
MA
Maihofer Adam
AS
A Shah
NN
Nirav N
ZC
Zai Clement
CL
C Lin
YY
Yi-Sian YS
DP
du Plessis M
MH
M Honorato Mauer
JJ
Jessica J
DS
Dalvie Shareefa
SB
S Bruxel
EE
Estela E
HS
Hemmings S
SA
S Almodobar
LL
Liriel L
Chapter II

Abstract

Summary of the research findings

The routine exclusion of admixed individuals from traditional genome-wide association studies (GWAS) due to concerns about spurious associations has limited multi-ancestry genetic discovery. Tractor addresses this issue by incorporating local ancestry into association testing, enabling the identification of ancestry-enriched signals and generating ancestry-specific summary statistics. However, adoption has been constrained by the complexity of prerequisite steps, including phasing and local ancestry inference, which require substantial bioinformatics expertise and introduce key analytical decision points.We developed a scalable, automated Nextflow workflow that integrates phasing, local ancestry inference, and Tractor association testing into a reproducible end-to-end pipeline. To demonstrate its utility, we applied the workflow to 32 blood biomarkers in 6245 two-way African-European admixed individuals from the UK Biobank. This pipeline performed efficiently at scale, replicating known associations and uncovering key ancestry-specific loci. These associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously masked genetic signals.The workflow is modular, customizable, and compatible with commonly used phasing and local ancestry tools, minimizing manual intervention while preserving analytical flexibility. By lowering technical barriers to implementation, this framework facilitates broader adoption of local ancestry-aware GWAS, paving the way for expanded genetic discovery.

Chapter III

AI-Generated Summary

AI-generated by DNAGENICS

Independent AI summary of ancestry and genetic findings from the published study

Important: This summary is AI-generated by DNAGENICS for informational purposes only. It was not created by, affiliated with, or endorsed by the researchers behind the original publication, and is based solely on that published research. It may contain errors or omissions. DNAGENICS disclaims all liability for any inaccuracies or consequences arising from use of this information. Verify all information against the original publication. This is not professional scientific review or medical advice.

Summary

Key Findings

Ancestry Insights

Traits Analysis

Historical Context