Overview of Admixture Mapping
Open accessAbstract
Admixture mapping is a powerful method of gene mapping for diseases or traits that show differential risk by ancestry. Admixture mapping has been applied most often to African Americans who trace ancestry to Europeans and West Africans. Recent developments in admixture mapping include improvements in methods to take advantage of higher densities of genetic variants, as well as extensions to admixed populations with three or more ancestral populations, such as Latino Americans. In this unit, the author outlines the key concepts of admixture mapping. The author describes several approaches for inferring local ancestry and provides strategies for performing admixture mapping depending on the study design. Finally, the author compares and contrasts linkage analysis, association analysis, and admixture mapping, with an emphasis on integrating admixture mapping and association testing. Curr. Protoc. Hum. Genet. 76:1.23.1‐1.23.8. © 2013 by John Wiley & Sons, Inc.
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European Americans are often used as proxies for Europeans in genetic studies.
“European Americans are often used as proxies for Europeans in genetic studies.26”
From The genetic ancestry of African Americans, Latinos, and European Americans across the United States (2015) · cites this paper as [26]
In “Inferring Local Ancestry” (page unavailable), the paper says reference samples typically serve as proxies for ancestral populations and that CEU samples work well for estimating European ancestry in admixed African Americans. It does not establish that European Americans generally are often used as proxies for Europeans across genetic studies. (LLM, conf=0.78)
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“We adopted a similar approach as Shriner and colleagues and our previous studies to determine the effective number of tests (n= 595) by fitting an autoregression model to the summary statistics generated from the admixturemapping analysis using the coda package in R (8, 12, 27).”
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“Briefly, we used the LAMP-LD program37 to estimates local ancestry per individual using a hidden Markov model (HMM) algorithm comparing observed genotypes and haplotypes from reference ancestral populations.”
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Oluwole, O. G., Oosterwyk, C., Anderson, D. E., Adadey, S. M., Mnika, K., Manyisa, N. R., Yalcouyé, A., Wonkam‐Tingang, E., Aboagye, E. T., Dia, Y., et al. (2022) · Journal of Molecular Pathology · doi:10.3390/jmp3040022 · Cited by 12 · see citing works
Admixture mapping of asthma in southwestern Europeans with North African ancestry influences
Guillén‐Guío, B., Hernández-Beeftink, T., Marcelino-Rodríguez, I., Rodríguez‐Pérez, H., Lorenzo-Salazar, J. M., Espinilla-Peña, M., Corrales, A., Pino-Yanes, M. D. M., Callero, A., Perez-Rodriguez, E., et al. (2020) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00344.2019 · Cited by 11 · see citing works
Chiang, C. W. K. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.643883 · Cited by 9 · see citing works
Corvalán, A. H., Jenny Ruedlinger, S., de Mayo, T., Polakovičová, I., González‐Hormazábal, P., & Aguayo, F. (2019) · Cancers · doi:10.3390/cancers11020217 · Cited by 11 · see citing works
Xue, D., Blue, E., Conomos, M. P., & Fohner, A. E. (2024) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12462 · Cited by 10 · see citing works
“54 57 Methods have been developed that allow meta-analysis of GWAS across ances-tries or inclusion of participants with diverse ancestries in the same GWAS.”
Asthma exacerbations: the genes behind the scenes
Herrera-Luis, E., Forno, E., Celedon, J., & Pino-Yanes, M. (2022) · Journal of investigational allergology & clinical immunology · doi:10.18176/jiaci.0878 · Cited by 8 · see citing works
LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations
Wathen, M. J., Gautam, Y., Ghandikota, S. K., Rao, M. B., & Baye, T. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-47012-y · Cited by 9 · see citing works
GWAS in the southern African context
Swart, Y., van Eeden, G., Uren, C., van der Spuy, G. D., Tromp, G. C., & Möller, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264657 · Cited by 9 · see citing works
Casares‐Marfil, D., Guillén‐Guío, B., Lorenzo-Salazar, J. M., Rodríguez‐Pérez, H., Kerick, M., Campos, M. A. J., Díaz, M. L., Estupiñán‐Moreno, E., Echeverría, L. E., González, C. I., et al. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab213 · Cited by 9 · see citing works
Chan, T. F., Rui, X., Conti, D. V., Fornage, M., Graff, M., Haessler, J., Haiman, C., Highland, H. M., Jung, S. Y., Kenny, E. E., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.09.012 · Cited by 9 · see citing works
Wang, S., Rohwer, S. A., de Zwaan, D. R., Toews, D. P. L., Lovette, I. J., Mackenzie, J., & Irwin, D. E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/853390 · Cited by 8 · see citing works
Population genomics of Marchantia polymorpha subsp. ruderalis reveals evidence of climate adaptation
Wu, S., Jandrasits, K., Swarts, K., Rötzer, J., Akimcheva, S., Shimamura, M., Hisanaga, T., Berger, F., & Dolan, L. (2025) · Current Biology · doi:10.1016/j.cub.2025.01.008 · Cited by 7 · see citing works
Goddard, P. C., Keys, K. L., Mak, A. C. Y., Lee, E. Y., Liu, A. K., Samedy-Bates, L.-A., Risse-Adams, O. S., Contreras, M. G., Elhawary, J. R., Hu, D., et al. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22365 · Cited by 7 · see citing works
“performed admixture mapping, a technique designed to identify regions of the genome where locus‐ specific ancestry drives variation in a disease trait (Shriner, 2013) that has been helpful in studies of complex diseases, including asthma and breast cancer (Féjerman et al.”
MethodAsthma—Genomic Advances Toward Risk Prediction
Luis, E. H., Martin‐Almeida, M., & Pino-Yanes, M. D. M. (2024) · Clinics in Chest Medicine · doi:10.1016/j.ccm.2024.03.002 · Cited by 9 · see citing works
Saini, G., Gogineni, K., Kittles, R., & Aneja, R. (2021) · Breast Cancer Research and Treatment · doi:10.1007/s10549-021-06264-x · Cited by 6 · see citing works
A map of climate change-driven natural selection in Arabidopsis thalianaPreprint
Expósito‐Alonso, M., 500 Genomes Field Experiment Team, Burbano, H. A., Bossdorf, O., Nielsen, R., & Weigel, D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/321133 · Cited by 7 · see citing works
Admixture mapping reveals loci for carcass mass in red deer x sika hybrids in Kintyre, Scotland
McFarlane, S. E., & Pemberton, J. M. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab274 · Cited by 6 · see citing works
Population structure and selective signature of Kirghiz sheep by Illumina Ovine SNP50 BeadChip
Yang, R., Han, Z., Zhou, W., Li, X., Zhang, X., Zhu, L., Wang, J., Li, X., Zhang, C.-L., Han, Y., et al. (2024) · PeerJ · doi:10.7717/peerj.17980 · Cited by 6 · see citing works
“We conducted an analysis of K values (representing the number of ancestral populations) from two to six using the admixture software (version 1.3.0) ( Shriner, 2023 ; Montana & Hoggart, 2007 ; Tang et al., 2005 ).”
MethodInfluentialSocial and scientific motivations to move beyond groups in allele frequencies: The TOPMed experience
Nelson, S. C., Gogarten, S. M., Fullerton, S. M., Isasi, C. R., Mitchell, B. D., North, K. E., Rich, S. S., Taylor, M. R., Zöllner, S. K., & Sofer, T. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.07.008 · Cited by 6 · see citing works
Giri, A., Hartmann, K. E., Aldrich, M. C., Ward, R. M., Wu, J. M., Park, A. J., Graff, M., Qi, L., Nassir, R., Wallace, R. B., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178839 · Cited by 6 · see citing works
Marttinen, T.-L. M. (2022) · Journal of Critical Mixed Race Studies · doi:10.5070/c81258341 · Cited by 6 · see citing works
Malta, M. C. F. D. S., Rodrigues, P. S., Zuccherato, L. W., de Souza, F. C. B., Domingues, E. M. F. L., de Souza, V. R., Tarazona‐Santos, E., & Martins, M. L. (2017) · HLA · doi:10.1111/tan.13102 · Cited by 6 · see citing works
Butković, A., & Elena, S. F. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.1005758 · Cited by 5 · see citing works
“Another good measure that can be used is admixture mapping, a method that tries to associate an allele with a disease based on ancestry information between different populations (Shriner, 2013).”
MethodThe genetic ancestry of African, Latino, and European Americans across the United StatesPreprint
Bryc, K., Durand, E., Macpherson, J. M., Reich, D., & Mountain, J. L. (2014) · bioRxiv · doi:10.1101/009340 · Cited by 7 · see citing works
Leal, T. P., Waldo, E., Duarte-Zambrano, F., Inca‐Martinez, M., Ramchandra, J., Chaparro-Solano, H. M., Anello, A. E., Borda, V., Gouveia, M. H., dos Santos, D. T., et al. (2025) · medRxiv · doi:10.1101/2025.07.18.25331793 · Cited by 5 · see citing works
PRECISION MEDICINE: FROM DIPLOTYPES TO DISPARITIES TOWARDS IMPROVED HEALTH AND THERAPIES
Crawford, D. C., Morgan, A. A., Denny, J. C., Aronow, B. J., & Brenner, S. E. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0036 · Cited by 4 · see citing works
Spotting genome-wide pigmentation variation in a brown trout admixture contextPreprint
Valette, T., Leitwein, M., LASCAUX, J. M., Desmarais, É., Berrebi, P., & Guinand, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.23.217109 · Cited by 4 · see citing works
Cole, S. A., Lyke, M. M., Christensen, C., Newman, D. E., Bagwell, A., Galindo, S., Glenn, J. P., Layne‐Colon, D., Sayers, K., Tardif, S. D., et al. (2024) · American Journal of Primatology · doi:10.1002/ajp.23630 · Cited by 3 · see citing works
O'Toole, B., Simmons, N. B., & Hekkala, E. R. (2020) · Acta Chiropterologica · doi:10.3161/15081109acc2020.22.2.001 · Cited by 3 · see citing works
Old vs. new local ancestry inference in HCHS/SOL: a comparative study
Chen, X., Wang, H., Broce, I., Dale, A. M., Yu, B., Zhou, L. Y., Li, X., Argos, M., Daviglus, M. L., Cai, J., et al. (2025) · Human Molecular Genetics · doi:10.1093/hmg/ddaf093 · Cited by 2 · see citing works
de Aquino, M. M., Xu, C. Y., Engchuan, W., Trost, B., Zhou, X., Miwa, B., Salazar, N. B., Iglar, J., Thiruvahindrapuram, B., Wallich, L., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03886-9 · Cited by 2 · see citing works
Simplified detection of genetic background admixture using artificial intelligence
Pauly, R., & Feltus, F. A. (2024) · Clinical Genetics · doi:10.1111/cge.14527 · Cited by 1 · see citing works
Race Differences: A Very Brief Review
Kirkegaard, E. O. W. (2019) · Mankind Quarterly · doi:10.46469/mq.2019.60.2.2 · Cited by 2 · see citing works
Ruiz-Narváez, E. (2021) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0000000000000757 · Cited by 2 · see citing works
Local ancestry inference in heterogeneous populations—Are recent recombination events more relevant?
van Eeden, G., Uren, C., van der Spuy, G. D., Tromp, G. C., & Möller, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab300 · Cited by 2 · see citing works
Rivera, W. G. G., Liu, Y., Mirmira, T., Ma, N., Kim, J., Antonio, M. D., Dube, U., Frazer, K. A., & Gymrek, M. (2025) · medRxiv · doi:10.64898/2025.12.29.25343152 · Cited by 2 · see citing works
A comprehensive overview of the biological foundations of ADHD
Folego-Temoteo, I., Lima, Y. C., Grevet, E. H., Vidor, M. V., Tavares, M. E., da Silva, B. S., Bau, C. H. D., & Rovaris, D. L. (2026) · Bioscience Reports · doi:10.1042/bsr20254061 · Cited by 1 · see citing works
“Recognizing this statistical reality is fundamental for a balanced and mature interpretation of biological findings.”
Genetics and Genomics of Preterm Birth
Nadeem, L., Lye, S. J., & Shynlova, O. P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813570-9.00015-2 · Cited by 1 · see citing works
Fenech, A. G., Sayers, I., & Portelli, M. A. (2015) · Advances in predictive, preventive and personalised medicine · doi:10.1007/978-3-319-15344-5_9 · Cited by 1 · see citing works
Tardo, L. M., Salter, A. R., Truong-Le, M., Horton, L. A., Blackburn, K. M., & Sguigna, P. V. (2023) · Therapeutic Advances in Chronic Disease · doi:10.1177/20406223231202645 · Cited by 1 · see citing works
Estudio de la relación de la ancestría genética con el riesgo al cáncer colorrectal en Colombia
Criollo, Á., Bohórquez, M., Lott, P. C., Carracedo, Á., Tomlinson, I. P. M., Carvajal‐Carmona, L. G., & Echeverry, M. (2019) · Revista de la Asociación Colombiana de Ciencias Biológicas · doi:10.47499/revistaaccb.v1i31.183 · Cited by 1 · see citing works
Mito-nuclear effects uncovered in admixed populationsPreprint
Zaidi, A. A., & Makova, K. (2018) · bioRxiv · doi:10.1101/349126 · Cited by 1 · see citing works
Modeling of linkage disequilibrium in whole genome genetic association studies
Johnson, R. (2014) · HAL · doi:10.70675/077a2830z02f9z4591za4e5z254e19208d13
“Review (Shriner 2013) Review (Thompson 2013) Type Software Used Map Citation !”
BackgroundHsu, C., Yang, H., & McGrath, J. A. (2019) · Springer eBooks · doi:10.1007/978-981-32-9791-3_4
Discovery through Diversity: Insights into the Genetics of Lung Function in Latino Youth
Jones, T. K., & Christie, J. D. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202006-2404ed
“Admixture mapping, a methodology that leverages shared genetic variance and differing allele frequencies among ancestral populations, holds promise for uncovering associations between ancestry at specific genetic loci and disease states (10).”
Background
- Authors
- Shriner, D.
- Affiliations
- National Human Genome Research Institute (US)
- Corresponding author
- Shriner, Daniel
- ORCID
- Shriner, Daniel
- Venue
- Current Protocols in Human Genetics
- Publisher
- Wiley
- Published
- 2013-01
- Volume
- 76
- Issue
- 1
- Pages
- 1.23.1-1.23.8
- ISSN
- 1934-8258 (ISSN-L)
- 1934-8266
- PMC
- PMC3556814
- OpenAlex
- W1588484659
- Cited by
- 111
- References
- 24
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 8 · 2023: 9 · 2022: 11 · 2021: 16 · 2020: 10 · 2019: 13 · 2018: 8 · 2017: 11 · 2016: 2 · 2015: 3 · 2014: 7
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Protocols in Human Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 HL999999, Z99 MH999999, Z99 HG999999, 1ziahg200362-15, Z01HG200362))
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- admixture mapping, local ancestry inference, gene mapping, African Americans, Hispanic Americans, linkage analysis, association analysis, admixed populations, genetic variants
- MeSH terms
- Black or African American, Chromosome Mapping, Hispanic or Latino, Humans, Genetic Linkage, Phenotype, Genome, Human, Quantitative Trait Loci, Black People, White People, Genetic Association Studies
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