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Geographic Population Structure Analysis of Worldwide Human Populations Infers Their Biogeographical Origins

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Geographic Population Structure Analysis of Worldwide Human Populations Infers Their Biogeographical Origins ARTICLE Received 17 Apr 2013 | Accepted 26 Feb 2014 | Published 29 Apr 2014 DOI: 10.1038/ncomms4513 OPEN Geographic population structure analysis of worldwide human populations infers their biogeographical origins Eran Elhaik1,2,*, Tatiana Tatarinova3,*, Dmitri Chebotarev4, Ignazio S. Piras5, Carla Maria Calo`5, Antonella De Montis6, Manuela Atzori6, Monica Marini6, Sergio Tofanelli7, Paolo Francalacci8, Luca Pagani9, Chris Tyler-Smith9, Yali Xue9, Francesco Cucca5, Theodore G. Schurr10, Jill B. Gaieski10, Carlalynne Melendez10, Miguel G. Vilar10, Amanda C. Owings10, Rocı´oGo´mez11, Ricardo Fujita12, Fabrı´cio R. Santos13, David Comas14, Oleg Balanovsky15,16, Elena Balanovska16, Pierre Zalloua17, Himla Soodyall18, Ramasamy Pitchappan19, ArunKumar GaneshPrasad19, Michael Hammer20, Lisa Matisoo-Smith21, R. Spencer Wells22 & The Genographic Consortium23 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. 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 Ban˜os, 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, Localita` 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 Toxicologı´a, Cinvestav, San Pedro Zacatenco, CP 07360, Mexico. 12 Instituto de Gene´tica y Biologı´a 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. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to E.E. (email: e.elhaik@sheffield.ac.uk). 23Lists of participants and their affiliations appear at the end of the paper. NATURE COMMUNICATIONS | 5:3513 | DOI: 10.1038/ncomms4513 | www.nature.com/naturecommunications 1 & 2014 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms4513 he ability to identify the geographic origin of an individual K hypothetical populations whose genotypes can be simulated to using biological data, poses a formidable challenge in form putative ancestral populations. Next, from the data set of Tanthropology and genetics because of its complexity and worldwide population, we constructed a smaller data set of potentially dangerous misinterpretations1. For instance, owing to reference populations that are both genetically diverse and have the intrinsic nature of biological variation, it is difficult to say resided in their current geographical region for at least few where one population stops and another starts by looking at the centuries. These populations were next analysed in a supervised spatial distribution of a trait (for example, hair colour2). Darwin ADMIXTURE analysis that calculated their admixture acknowledged this problem, stating that ‘it may be doubted proportions in relation to the putative ancestral populations whether any character can be named which is distinctive of a race (Fig. 1). and is constant’3. Yet, questions of biogeography and genetic Looking at the resulting graph, we found that all populations diversity such as ‘why we are what we are where we are?’ have exhibit a certain amount of admixture, with Puerto Ricans and piqued human curiosity as far back as Herodotus of Bermudians exhibiting the highest diversity and Yoruba the least. Halicarnassus, who has been called ‘the first anthropologist’4. We further found distinct substructure among geographically However, only in the past decade have researchers begun adjacent populations that decreased in similarity with distance, harnessing high-throughput genetic data to address them. suggesting that populations can be localized based on their The work of Cavalli-Sforza and other investigators5–7 admixture patterns. To correlate the admixture patterns with established a strong relationship between genetic and geo- geography, we calculated two distance matrices between all graphic distances in worldwide human populations, with major reference populations based on their mean admixture fractions deviations explicable by admixture or extreme isolation8. These (GEN) and their mean geographic distances (GEO) from each observations, stimulated the development of biogeographical other. Using these distance matrices, we calculated the relation- methods. Accurate measurements of biogeography began at the ship between GEN and GEO (Equation 1). cusp of the DNA sequencing era and have since been applied In the second step, GPS inferred the geographical coordinates extensively to study genetic diversity and anthropology9,10, of a sample of unknown origin by performing a supervised infer origin and ancestry11,12, map genes13, identify disease ADMIXTURE analysis for that sample with the putative ancestral susceptibility markers2 and correct for population stratification in populations. It then calculated the Euclidean distance between the disease studies14. sample’s admixture proportions and GEN. The shortest distance, Biogeographic applications currently employ principal compo- representing the test sample’s deviation from its nearest reference nent (PC)-based methods such as PC analysis (PCA), which was population, was subsequently converted into geographical shown to be accurate within 700 km in Europe11, and the most distance using the inferred relationships (Equation 1). The final recent Spatial Ancestry Analysis (SPA)12 that explicitly models position of the sample on the map was calculated by a linear allele frequencies. Yet, estimated by the percentage of individuals combination of vectors, with the origin at the geographic centre correctly assigned to their country of origin, the accuracy of PCA of the best matching population weighted by the distances to and SPA is relatively low for Europeans (40±5 and 45±5%, M nearest reference population and further scaled to fit on a respectively) and much lower for non-Europeans12. Such results circle with a radius proportional to the geographical distance suggest limitations with these approaches for biogeographical obtained by Equation 1 (see Calculating the bio-origin of a test inference12,15,16, coupled with a possible limitation of commonly sample in the Methods). used genotyping arrays in capturing fine substructure17,18.As patterns of genetic diversity in human populations or admixture are frequently described as genome-based estimates of several Biogeographical prediction for worldwide individuals.We ancestries that sum to 100% (refs 19–21), we speculated that an applied GPS to approximately 600 worldwide individuals admixture-based approach may yield better results. collected as part of the Genographic Project and the To overcome the possible limitations of markers included on 1000 Genomes Project and genotyped on the GenoChip commonly used microarrays, we used the GenoChip, a dedicated (Supplementary Table 1). We included the highly heterogeneous genotyping array for population genetics17, which includes over populations of Kuwait22,
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