Skip to content
DraconiticResearch
Search Sources API
186,123 studies31 sourcesUpdated 18 min ago
Peer-reviewedCC BY-NC-ND 4.0Nature Communications

Systematic comparison of phenome-wide admixture mapping and genome-wide association at biobank-scale

  • Sinead Cullina1,2
  • Ruhollah Shemirani1
  • Zhuozheng Shi3,4
  • Ravi Mandla3,4
  • Bogdan Pasaniuc4,5,6
  • Samira Asgari1,2
  • Eimear E. Kenny1,2,7
7 affiliations
  1. 1Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY USA
  2. 2Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
  3. 3Graduate Program in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA USA
  4. 4Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
  5. 5Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
  6. 6Department of Computer and Information Sciences, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA USA
  7. 7Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
Read full textPDFDOICite

Abstract

Opportunities remain to leverage population structure in large scale genomic studies for additional insight. Admixture mapping (AM) identifies loci where disease risk differs by ancestral background, offering a complementary strategy to genome-wide association studies (GWAS) in admixed populations. We performed a systematic comparison of AM and GWAS across over 800 clinical phenotypes in Hispanic/Latino (HL) and African American (AA) participants from the BioMe biobank. We developed a well-calibrated AM pipeline tailored to the local ancestry structure of HL (N = 14,876) and AA (N = 8,819). While GWAS identified a greater number of significant associations, AM revealed 77 signals, including 48 not detected by GWAS. AM-tagged variants had higher minor allele frequency and population differentiation (Fst), while GWAS demonstrated higher odds ratios, underscoring the distinct genetic architecture identified by each method. This study emphasizes the importance of applying both GWAS and AM approaches to uncover previously unreported loci in admixed populations.

Related

  1. FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies
    Travis Canida, Zhenyao Ye, Shao-Hsuan Wang +5
    PLOS GeneticsPeer-reviewedCC BY 4.0Full textMolecular biology & gene regulationGenetics & genomicsPLOS
  2. Exploring the Pleiotropic Genetic Architecture of Substance Use Disorders: Findings From Phenome-Wide Association Studies.
    Emily E Hartwell, Rachel L Kember
    Biological psychiatry global open sciencePeer-reviewedCC BY-NC-NDFull textGenetics & genomicsEurope PMC
  3. Accuracy, Precision, and Reproducibility of Four T1 Mapping Sequences: A Head-to-Head Comparison of MOLLI, ShMOLLI, SASHA, and SAPPHIRE
    Sébastien Roujol, Sebastian Weingärtner, Murilo Foppa +7
    RadiologyPeer-reviewedLicence not stated317 citedSystems & synthetic biologyHAL
  4. A multi‐race blast resistance locus identified and validated in US elite rice via association and biparental mapping
    J. M. Manangkil, B. Angira, E. Oliveira‐Garcia +7
    The plant genomePeer-reviewedCC BY-NC-NDFull textGenetics & genomicsPlant biologyEurope PMC
  5. SPLATCHE2: a spatially explicit simulation framework for complex demography, genetic admixture and recombination
    Nicolas Ray, Mathias Currat, Matthieu Foll +1
    BioinformaticsPeer-reviewedLicence not stated124 citedGenetics & genomicsHAL

Cite

BibTeXRIS
BibTeX
@article{cullina2026systematic,
  title = {{Systematic comparison of phenome-wide admixture mapping and genome-wide association at biobank-scale}},
  author = {Sinead Cullina and Ruhollah Shemirani and Zhuozheng Shi and Ravi Mandla and Bogdan Pasaniuc and Samira Asgari and Eimear E. Kenny},
  journal = {Nature Communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-76694-y},
  url = {https://doi.org/10.1038/s41467-026-76694-y},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Systematic comparison of phenome-wide admixture mapping and genome-wide association at biobank-scale
AU  - Sinead Cullina
AU  - Ruhollah Shemirani
AU  - Zhuozheng Shi
AU  - Ravi Mandla
AU  - Bogdan Pasaniuc
AU  - Samira Asgari
AU  - Eimear E. Kenny
PY  - 2026
DA  - 2026/09/24
JO  - Nature Communications
DO  - 10.1038/s41467-026-76694-y
UR  - https://doi.org/10.1038/s41467-026-76694-y
AB  - Opportunities remain to leverage population structure in large scale genomic studies for additional insight. Admixture mapping (AM) identifies loci where disease risk differs by ancestral background, offering a complementary strategy to genome-wide association studies (GWAS) in admixed populations. We performed a systematic comparison of AM and GWAS across over 800 clinical phenotypes in Hispanic/Latino (HL) and African American (AA) participants from the BioMe biobank. We developed a well-calibrated AM pipeline tailored to the local ancestry structure of HL (N = 14,876) and AA (N = 8,819). While GWAS identified a greater number of significant associations, AM revealed 77 signals, including 48 not detected by GWAS. AM-tagged variants had higher minor allele frequency and population differentiation (Fst), while GWAS demonstrated higher odds ratios, underscoring the distinct genetic architecture identified by each method. This study emphasizes the importance of applying both GWAS and AM approaches to uncover previously unreported loci in admixed populations.
SN  - 2041-1723
ER  -