Geographic population structure analysis of worldwide human populations infers their biogeographical origins

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Abstract

The search for a method that utilizes biological information to predict humans’ place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000–130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS’s accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing.

Abstract

Current methods to identify the geographical origin of humans based on DNA data present limited accuracy. Here, the authors develop a new algorithm, the Genographic Population Structure (GPS), and demonstrate its ability to place worldwide individuals within their country or, in some cases, village of origin.

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Most cited references 33

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Ascertainment biases in SNP chips affect measures of population divergence.

Anders Albrechtsen, Anders Albrechtsen, Finn Nielsen (2010)

Chip-based high-throughput genotyping has facilitated genome-wide studies of genetic diversity. Many studies have utilized these large data sets to make inferences about the demographic history of human populations using measures of genetic differentiation such as F(ST) or principal component analyses. However, the single nucleotide polymorphism (SNP) chip data suffer from ascertainment biases caused by the SNP discovery process in which a small number of individuals from selected populations are used as discovery panels. In this study, we investigate the effect of the ascertainment bias on inferences regarding genetic differentiation among populations in one of the common genome-wide genotyping platforms. We generate SNP genotyping data for individuals that previously have been subject to partial genome-wide Sanger sequencing and compare inferences based on genotyping data to inferences based on direct sequencing. In addition, we also analyze publicly available genome-wide data. We demonstrate that the ascertainment biases will distort measures of human diversity and possibly change conclusions drawn from these measures in some times unexpected ways. We also show that details of the genotyping calling algorithms can have a surprisingly large effect on population genetic inferences. We not only present a correction of the spectrum for the widely used Affymetrix SNP chips but also show that such corrections are difficult to generalize among studies.

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Genomic variation in seven Khoe-San groups reveals adaptation and complex African history.

Dena Hernandez, Himla Soodyall, Pontus Skoglund … (2012)

The history of click-speaking Khoe-San, and African populations in general, remains poorly understood. We genotyped ~2.3 million single-nucleotide polymorphisms in 220 southern Africans and found that the Khoe-San diverged from other populations ≥100,000 years ago, but population structure within the Khoe-San dated back to about 35,000 years ago. Genetic variation in various sub-Saharan populations did not localize the origin of modern humans to a single geographic region within Africa; instead, it indicated a history of admixture and stratification. We found evidence of adaptation targeting muscle function and immune response; potential adaptive introgression of protection from ultraviolet light; and selection predating modern human diversification, involving skeletal and neurological development. These new findings illustrate the importance of African genomic diversity in understanding human evolutionary history.

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Skin pigmentation, biogeographical ancestry and admixture mapping.

Li Jin, Alison Jones, Sonia Dios … (2003)

Ancestry informative markers (AIMs) are genetic loci showing alleles with large frequency differences between populations. AIMs can be used to estimate biogeographical ancestry at the level of the population, subgroup (e.g. cases and controls) and individual. Ancestry estimates at both the subgroup and individual level can be directly instructive regarding the genetics of the phenotypes that differ qualitatively or in frequency between populations. These estimates can provide a compelling foundation for the use of admixture mapping (AM) methods to identify the genes underlying these traits. We present details of a panel of 34 AIMs and demonstrate how such studies can proceed, by using skin pigmentation as a model phenotype. We have genotyped these markers in two population samples with primarily African ancestry, viz. African Americans from Washington D.C. and an African Caribbean sample from Britain, and in a sample of European Americans from Pennsylvania. In the two African population samples, we observed significant correlations between estimates of individual ancestry and skin pigmentation as measured by reflectometry (R(2)=0.21, P<0.0001 for the African-American sample and R(2)=0.16, P<0.0001 for the British African-Caribbean sample). These correlations confirm the validity of the ancestry estimates and also indicate the high level of population structure related to admixture, a level that characterizes these populations and that is detectable by using other tests to identify genetic structure. We have also applied two methods of admixture mapping to test for the effects of three candidate genes (TYR, OCA2, MC1R) on pigmentation. We show that TYR and OCA2 have measurable effects on skin pigmentation differences between the west African and west European parental populations. This work indicates that it is possible to estimate the individual ancestry of a person based on DNA analysis with a reasonable number of well-defined genetic markers. The implications and applications of ancestry estimates in biomedical research are discussed.

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Author and article information

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Pub. Group

ISSN (Electronic): 2041-1723

Publication date (Electronic): 29 April 2014

Volume: 5

Electronic Location Identifier: 3513

Affiliations

[1 ]Department of Animal and Plant Sciences, University of Sheffield , Western Bank, Sheffield, S10 2TN, UK

[2 ]Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health , 615 N. Wolfe Street, Baltimore, Maryland 21205, USA

[3 ]Department of Pediatrics, Keck School of Medicine and Children’s Hospital Los Angeles, University of Southern California , 4650 Sunset Blvd, Los Angeles, California 90027, USA

[4 ]T.T. Chang Genetic Resources Center, International Rice Research Institute , Los Baños, Laguna , Philippines

[5 ]Department of Sciences of Life and Environment, University of Cagliari , SS 554, Monserrato 09042, Italy

[6 ]Research Laboratories, bcs Biotech S.r.l. , Viale Monastir 112, Cagliari 09122, Italy

[7 ]Department of Biology, University of Pisa , Via Ghini 13, Pisa 56126, Italy

[8 ]Department of Science of Nature and Territory, University of Sassari , Località Piandanna 07100, Italy

[9 ]The Wellcome Trust Sanger Institute , Hinxton CB10 1SA, UK

[10 ]Department of Anthropology, University of Pennsylvania , Philadelphia, Pennsylvania, 19104, USA

[11 ]Departamento de Toxicología, Cinvestav, San Pedro Zacatenco , CP 07360, Mexico

[12 ]Instituto de Genética y Biología Molecular, University of San Martin de Porres , Lima, Peru

[13 ]Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais , CEP 31270-901, Brazil

[14 ]Institut de Biologia Evolutiva (CSIC-UPF), Departament de Ciences de la Salut i de la Vida, Universitat Pompeu Fabra , 08003 Barcelona, Spain

[15 ]Vavilov Institute for General Genetics : 119991, Moscow, Russia

[16 ]Research Centre for Medical Genetics : 115478, Moscow, Russia

[17 ]The Lebanese American University , Chouran, Beirut 1102 2801, Lebanon

[18 ]National Health Laboratory Service, Sandringham 2131 , Johannesburg, South Africa

[19 ]The Genographic Laboratory, School of Biological Sciences, Madurai Kamaraj University , Madurai 625 021, Tamil Nadu, India

[20 ]Department of ecology and evolutionary biology, University of Arizona , Tucson, Arizona 85721, USA

[21 ]Department of Anatomy, University of Otago , Dunedin 9054, New Zealand

[22 ]National Geographic Society , Washington, District of Columbia 20036, USA

[23 ]These authors contributed equally to this work

[24 ]Applied Biosystems, Foster City, California 94494, USA.

[25 ]The Australian Centre for Ancient DNA, School of Earth and Environmental Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia

[26 ]Department of Genetics, School of Molecular Sciences, La Trobe University, Melbourne, Victoria 3086, Australia

[27 ]Department of Pathology, Fudan University, Shanghai 200433, China

[28 ]IBM, Somers, New York 10589, USA

[29 ]Institut Pasteur, Unit of Evolutionary Genetics, 75015 Paris, France

[30 ]McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, CB2 3ER, UK

Author notes

[a ] e.elhaik@ 123456sheffield.ac.uk

[*]

Lists of participants and their affiliations appear at the end of the paper.

[*]

(Members are listed alphabetically by surname)

Article

Publisher Item ID: ncomms4513

DOI: 10.1038/ncomms4513

PMC ID: 4007635

PubMed ID: 24781250

SO-VID: bc98c6c5-dec8-49f4-b3bf-02d637f22695

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/

History

Date received : 17 April 2013

Date accepted : 26 February 2014

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Geographic population structure analysis of worldwide human populations infers their biogeographical origins

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Most cited references 33

Ascertainment biases in SNP chips affect measures of population divergence.

Genomic variation in seven Khoe-San groups reveals adaptation and complex African history.

Skin pigmentation, biogeographical ancestry and admixture mapping.

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