Bayesian inferences suggest that Amazon Yunga Natives diverged from Andeans less than 5000 ybp: implications for South American prehistory
- Marilia O Scliar1,
- Mateus H Gouveia1,
- Andrea Benazzo2,
- Silvia Ghirotto2,
- Nelson JR Fagundes3,
- Thiago P Leal1,
- Wagner CS Magalhães1,
- Latife Pereira1,
- Maira R Rodrigues1,
- Giordano B Soares-Souza1,
- Lilia Cabrera4,
- Douglas E Berg5, 6,
- Robert H Gilman4, 7, 8,
- Giorgio Bertorelle2 and
- Eduardo Tarazona-Santos1Email author
© Scliar et al.; licensee BioMed Central Ltd. 2014
Received: 8 April 2014
Accepted: 24 July 2014
Published: 30 September 2014
Archaeology reports millenary cultural contacts between Peruvian Coast-Andes and the Amazon Yunga, a rainforest transitional region between Andes and Lower Amazonia. To clarify the relationships between cultural and biological evolution of these populations, in particular between Amazon Yungas and Andeans, we used DNA-sequence data, a model-based Bayesian approach and several statistical validations to infer a set of demographic parameters.
We found that the genetic diversity of the Shimaa (an Amazon Yunga population) is a subset of that of Quechuas from Central-Andes. Using the Isolation-with-Migration population genetics model, we inferred that the Shimaa ancestors were a small subgroup that split less than 5300 years ago (after the development of complex societies) from an ancestral Andean population. After the split, the most plausible scenario compatible with our results is that the ancestors of Shimaas moved toward the Peruvian Amazon Yunga and incorporated the culture and language of some of their neighbors, but not a substantial amount of their genes. We validated our results using Approximate Bayesian Computations, posterior predictive tests and the analysis of pseudo-observed datasets.
We presented a case study in which model-based Bayesian approaches, combined with necessary statistical validations, shed light into the prehistoric demographic relationship between Andeans and a population from the Amazon Yunga. Our results offer a testable model for the peopling of this large transitional environmental region between the Andes and the Lower Amazonia. However, studies on larger samples and involving more populations of these regions are necessary to confirm if the predominant Andean biological origin of the Shimaas is the rule, and not the exception.
KeywordsPopulation genetics inferences Human evolution Native American
Knowing how Native Americans dispersed along the American continent is still a major challenge faced by researchers studying the biological and cultural evolution of the region -. Also, how natives adapted to diverse environmental challenges such as hypoxia and cold weather in the Andes  and the tropical forest  remain poorly understood.
When Europeans arrived in South America in the 16th century, the Pan-Andean Inca Empire dominated the Andean region and had a population density and levels of socioeconomic development unmatched elsewhere in South America. But the Inca Empire is just the tip of the iceberg of a long-term cultural and biological evolutionary process that involved the entire Andean region and its adjacent Pacific Coast (hereafter western South America). This process began 14–11 thousand years BP, with the peopling of this region in the Late Pleistocene , involving continuous cultural exchanges and gene flow along time, and leading to a relative genetic, cultural, and linguistic homogeneity between the populations of western South America when compared with eastern South America (a term that hereafter refers to the eastern region of the Andes, including the low Amazon Basin), where populations remained relatively more isolated  than those in western South America. For example, only two languages still predominate across the entire Andean region (Quechua and Aymara), whereas in eastern South America natives speak a wider spectrum of languages belonging to four different linguistic families . Also, despite some controversy about definitions and chronology, archeologists consensually recognize three temporal Horizons in the Andes and the Pacific Coast (Early, Middle, and Late). Each of the Horizons corresponds to periods of material cultural dispersion involving a wide geographic area and, in the case of Middle and Late Horizons, to the expansion of the Wari-Tiwanaku and Inca States, respectively .
The current knowledge about western South American prehistory derives mainly from a plethora of archeological studies , most of which have focused on the Pacific Coast and Andean people. However, the relationships between Andeans and their culturally, linguistically, and environmentally different eastern neighbors living in the Amazon Yunga remain relatively neglected by archeologists, despite early investigations by Lathrap  and some subsequent studies that have been done on the subject . Notwithstanding this knowledge gap, the Amazon Yunga, a region hosting at least six ethnic groups, is particularly interesting because it is a transitional environment between the Andes highlands and the lowland tropical forest of the Amazonia. Moreover, archeological research in the lowland Amazonia during the last decades has changed the traditional view of the Amazonian environment as incompatible with complex pre-Columbian societies . The emerging view, that has gained growing support, recognizes that the Amazonian basin has hosted the earliest ceramics of South America, that endogenous agricultural societies with complex organization have developed there, and that population sizes were larger than previously thought ,. In contrast, information derived from anthropological genetics is scantier, especially for Amazonian and Amazon Yunga populations -. Contributing to our poor understating of how these populations evolved is the fact that Native Americans are under-represented in modern genetic studies  due to cultural issues and logistic difficulties in reaching them in the tropical forest.
Cultural and commercial interactions occurring along the last millennia among the people living in the Peruvian Coast, the Andes and the Amazon Yunga regions are archaeologically documented. For example, cultivated plants such as sweet potato and manioc, ceramic iconography and styles (Tutishcanyo, Kotosh, and Valdivia) and traditional coca chewing  have been shared among Coast, Andean and Amazon Yunga populations. However, we ignore how the demographic evolutionary history of these populations accompanied their cultural and socioeconomic interactions. Specifically, and this is the goal of this study, we aimed to investigate the demographic relationships between the Shimaa population and their western Andean neighbors. Our study is the first to analyze a sample of the Amazon Yunga population at a multilocus level. Here we show that the genetic diversity of the Shimaa, a Matsiguenga Arawak-speaking population settled in and with an Amazon Yunga lifestyle, is a subset of the Andean Quechua diversity. We used a Bayesian inference framework and several statistical validation tools to infer that the Shimaa likely originated from an ancestral Andean population less than 5300 years ago, around or after the time when complex societies in the Andean region emerged.
Results and discussion
Estimates of demographic parameters
Time split (t)
N Ancestral (NA)
N Shimaa (N2)
We validated our MCMC results by assessing the statistical convergence of multiple MCMC runs and by performing posterior predictive tests , and both procedures confirmed our inferences (Additional file 1: Section 2.1). To further validate our results, and because the IM model implemented in the MCMC framework  does not consider genetic intra-locus recombination, we also inferred the model parameters (Figure 1) by Approximate Bayesian Computations (ABC) , although our analyzed dataset shows almost no intra-locus recombination (see Methods). While the MCMC-approach uses the complete dataset at a cost of less flexibility of the model (for example, not allowing recombination), ABC allows more flexibility (in this case, the inclusion of recombination), but it only uses summary statistics to compare simulated and real data. This suboptimal use by ABC of the available information in analyzed datasets comes at a cost of larger credible intervals . To analyze the consequences of considering recombination in the ABC, we did one analysis with recombination (ABC_rec) and one without recombination (ABC). The ABC estimates of the ancestral population size and the relative ratio of the two population sizes at the time of the split are similar to the IM estimates (Table 1). We inferred an earlier population divergence, though the point estimates with ABC is still within the 90% interval obtained with the MCMC approach. We found, however, that ABC tended to overestimate the divergence time with our data (see pseudo-observed datasets validation in Additional file 1: Section 2.2). Similar results are obtained regardless the inclusion of recombination in the ABC. Overall, our ABC estimates confirm our MCMC results (Figures 2 and 3, Table 1, and Additional file 1: Section 2.2).
Our results indicate that the Shimaa diverged from the Quechua Andean population after the late Pleistocene peopling of South America, and very likely, less than 5300 years ago. The 5000–3000 years period BP was characterized by the development of complex societies in the Andean Region and the Pacific Coast, being a period of major cultural development, when large permanent communities settled, monumental architecture appeared, pottery came into use, and agriculture became the predominant source of food supply (see Figure 2 and its references in Additional file 2). Representative settlements of that time were Kotosh in the Amazon Yunga (Huanuco Region), La Galgada in the Central Andes, and Caral and El Paraiso in the Central Pacific Coast (Figure 2). The initial dispersal of the Arawak, which is the language currently spoken by the Shimaa, also seems to have occurred during this time , and some authors (see Figure 2), suggest this linguistic family has originated in the Peruvian Amazon Yunga -. The most plausible scenario compatible with our results is that a small subpopulation (the ancestors of the Shimaa) split from a larger Andean population, moved toward the Peruvian Amazon Yunga and then adapted to the different lifestyle of the Amazon Yunga, incorporating the culture of some of their neighbors and their language (Arawak), but not a substantial amount of their genes.
The question about the evolutionary relationship between Andean and the Lower Amazonian populations (i.e. if they derived from different migration routes into South America) is still open in American anthropology. We contributed to clarify -for the first time using multilocus data; the prehistoric demographic relationship between Andeans and a population from the Amazon Yunga. Moreover, our results offer a testable model for the peopling of this large transitional environmental region between the Andes and the Lower Amazonia. Andean populations are highly homogeneous ,- which supports our assumption that the Quechua sample used in this study is a fair representative of Andean populations. Nevertheless, further studies on the populations of the Amazon Yunga and the Lower Amazonia are necessary to show if the predominant Andean biological origin of the Shimaas and its pattern of adaptation to the new environment is the rule, and not the exception.
We studied Native Americans from two Peruvian populations: (i) 11 Andean Quechua individuals reported in Scliar et al.  and (ii) 10 Matsiguenga individuals from the Shimaa population, randomly selected from a total sample of 180 individuals available at our laboratory. The Matsiguenga are settled in this area of the Amazon Yunga since the 16th century . This study was conducted under approval of the Institutional Reviews Boards from the Universidad Peruana Cayetano Heredia, Asociación Benéfica PRISMA, Universidade Federal de Minas Gerais and Johns Hopkins University. Detailed information about the re-sequencing of the 10 autosomal non-coding unlinked loci  used in this study is available in Scliar et al.  and in Additional file 1: Section 1.1. Sequence analysis were performed following the pipeline specified in Machado et al. . We used 106 Ancestry Informative Markers  to perform admixture analyses of these individuals, using the software Structure , as detailed in Additional file 1: Section 1.2. The Shimaa dataset is available in GenBank [GenBank: KF690381-KF690580]. Additional file 3 presents the individual genotypes of the dataset used for the analysis that contains the 52 segregating sites identified in this study.
We used the IM program, that uses MCMC simulations of genealogies to estimate seven parameters of the Isolation-with-Migration model depicted in Figure 1,: three population mutation rate parameters for the ancestral and the two descendant populations (θA, θ1, and θ2, respectively, where θ = 4 Neμ); the splitting time parameter (T = tμ); the ratio of migration rate per mutation rate, in both directions (M1 = m1/μ and M2 = m2/μ), and the proportion of the ancestral population that founded population 1 (s) . We assumed the infinite-site mutation model  for all loci. Except for s, the other model parameters are scaled by the neutral mutation rate μ. Therefore, to obtain the demographic estimates Ne, t and m, a mutation rate needs to be assumed. Mutation rates for each locus were estimated using the BEAST software  assuming a divergence time of 6 million years between humans and chimpanzee. Under the multilocus model, the mutation rate is the geometric mean of the individual locus-specific mutation rates . We used the geometric mean per year (1.47 × 10–6) to obtain the estimated time since splitting, t, in years and migration rate, m, per year. To obtain Ne, a measure of mutation rate on a scale of generations is needed. We assumed 25 years/generation, which yield a geometric mean value of 3.68 × 10–5 mutations per generation.
Because the IM model originally assumes no recombination within loci, we used the program IMgc to find the largest subset of the data containing no signs of recombination. IMgc uses the four-gametes criteria to remove either sequences or variable sites containing evidence of recombination . This procedure resulted in the removal of sequences QT80 and QT135 for locus 4, of sequence QA38 for locus 5, and of the second half segment of locus 3.
Three independent MCMC runs were performed, each with 30 Metropolis-coupled chains of 10 million steps using a geometric heating scheme and a burn-in period. Prior uniform distributions were defined as follows: θi ∈ (0, 5), T∈ (0, 0.05), Mi ∈ (0, 10), and s ∈ (0, 1). These scaled values correspond to the values specified in Additional file 1: Table S2. We also performed three independent runs without migration (Mi = 0 as prior), with 30 Metropolis-coupled chains of 8 million steps. We compared the results from the runs with and without migration, because Kitchen et al.  identified important changes in the estimates when using different migration rates as priors. Additional file 1: Section 1.3 details the criteria used to check convergence and MCMC results validation by posterior predictive test ,-.
Inferences by ABC
To validate the results of the MCMC method by incorporating genetic intra-locus recombination (not allowed in the MCMC-IM model) in our analyses, we used ABC , as a more general model framework, to infer the same seven demographic parameters of the MCMC model. We performed one analysis with recombination (ABC_rec) and one without recombination (ABC). The ABC approach approximates the posterior distribution by performing a large number of simulations under a specific model and calculating the distance between Summary Statistics (SuSt) estimated from the simulated data and SuSt estimated from the observed data. We used the program fastsimcoal within the ABCToolBox for simulations -. Used prior distributions are given in Additional file 1: Table S2 and its rationale explained in the Additional file 1: Section 1.4 when necessary. SuSt used in the ABC analyses are explained in detail in the Additional file 1: Section 1.4. Additional file 1: Table S3 presents the SuSt estimates for the observed data. For parameter estimation, we calculated the Euclidian distance between the simulated and observed SuSt and retained the 1% of the total simulations corresponding to the shortest distances. Posterior probability for each parameter was estimated using a weighted local regression . We assessed the quality of the parameters estimated by ABC by assessing the determination coefficient R2 (the proportion of parameter variance explained by the summary statistics), by analyzing pseudo-observed datasets and by posterior predictive tests , as detailed in the Additional file 1: Section 1.5.
Availability of supporting data section
New sequence data generated for this study have been deposited in GenBank (http://www.ncbi.nlm.nih.gov/genbank) under accession numbers KF690381 to KF690580.
We thank Fernando L Soares for preparation of figures, Silvia Fuselli, Guido Barbujani, Rosangela Loschi, Renato Assunção,, Ricardo Santos, Celso Teixeira Mendes Junior and Juan C Riveros for discussions. Funding agencies: Fogarty International Center and National Cancer Institute-US (5R01TW007894), Brazilian National Research Council (CNPq), Brazilian CAPES Agency, Brazilian Ministry of Health (PNPD-Saúde Program), and the Minas Gerais State Research Agency (FAPEMIG) to E.T-S group. National Institutes of Health-US R21AI078237 and R21AI088337 to D.E.B.
- Salzano FM, Callegari-Jacques SM: South American Indians - A Case Study in Evolution. 1988, Clarendon Press, OxfordGoogle Scholar
- Dillehay TD: The Settlement of the Americas: A New Prehistory. 2000, Basic Books, New YorkGoogle Scholar
- Heggarty P, Beresford-Jones DG: Archaeology and Language in the Andes. 2012, Oxford University Press, OxfordView ArticleGoogle Scholar
- Tarazona–Santos E, Lavine M, Pastor S, Fiori G, Pettener D: Hematological and pulmonary responses to high altitude in Quechuas: a multivariate approach. Am J Phys Anthropol. 2000, 176: 165-176. 10.1002/(SICI)1096-8644(200002)111:2<165::AID-AJPA3>3.3.CO;2-7.View ArticleGoogle Scholar
- Erickson CL: Amazonia: the historical ecology of a domesticated landscape. The Handbook of South American Archaeology. Edited by: Silverman H, Isbell WH. 2008, Springer, New York, 157-183. 10.1007/978-0-387-74907-5_11.View ArticleGoogle Scholar
- Tarazona-Santos E, Carvalho-Silva DR, Pettener D, Luiselli D, De Stefano GF, Labarga CM, Rickards O, Tyler-Smith C, Pena SDJ, Santos FR: Genetic differentiation in South Amerindians is related to environmental and cultural diversity: evidence from the Y chromosome. Am J Hum Genet. 2001, 68: 1485-1496. 10.1086/320601.PubMedPubMed CentralView ArticleGoogle Scholar
- Campbell L: American Indian Languages, The Historical Linguistics of Native American. 1997, Oxford University Press, OxfordGoogle Scholar
- Pozorski S, Pozorski T: Chronology. The Origins and Development of the Andean State. Edited by: Haas J, Pozorski S, Pozorski T. 1987, Cambridge University Press, Cambridge, 5-8.Google Scholar
- Silverman H: Andean Archaeology. 2004, Wiley Blackwell, HobokenGoogle Scholar
- Lathrap DW: The Upper Amazon. 1970, Praeger, New YorkGoogle Scholar
- Raymond JS: A view from the tropical forest. Peruvian Prehistory. Edited by: Keatinge RW. 1988, Cambridge University Press, Cambridge, 279-302.Google Scholar
- Roosevelt AC, Lima da Costa M, Lopes Machado C, Michab M, Mercier N, Valladas H, Feathers J, Barnett W, Imazio da Silveira M, Henderson A, Silva J, Chernoff B, Reese DS, Holman JÁ, Toth N, Schick K: Paleoindian cave dwellers in the Amazon: the peopling of the Americas. Science. 1996, 272: 373-384. 10.1126/science.272.5260.373.View ArticleGoogle Scholar
- Roosevelt AC: The maritime, highland, forest dynamic and the origins of complex culture. The Cambridge History of the Native Peoples of the Americas, Volume 3. Edited by: Salomon F, Schwartz SB. 1999, Cambridge University Press, Cambridge, 264-349. 10.1017/CHOL9780521630757.006.Google Scholar
- Silverman H, Isbell WH: The Handbook of South American Archaeology. 2008, Springer, New YorkView ArticleGoogle Scholar
- Fuselli S, Tarazona-Santos E, Dupanloup I, Soto A, Luiselli D, Pettener D: Mitochondrial DNA diversity in South America and the genetic history of Andean highlanders. Mol Biol Evol. 2003, 20: 1682-1691. 10.1093/molbev/msg188.PubMedView ArticleGoogle Scholar
- Wang S, Lewis CM, Jakobsson M, Ramachandran S, Ray N, Bedoya G, Rojas W, Parra MV, Molina JA, Gallo C, Mazzotti G, Poletti G, Hill K, Hurtado AM, Labuda D, Klitz W, Barrantes R, Bortolini MC, Salzano FM, Petzl-Erler ML, Tsuneto LT, Llop E, Rothhammer F, Excoffier L, Feldman MW, Rosenberg NA, Ruiz-Linares A: Genetic variation and population structure in native Americans. PLoS Genet. 2007, 3: e185-10.1371/journal.pgen.0030185.PubMedPubMed CentralView ArticleGoogle Scholar
- Reich D, Patterson N, Campbell D, Tandon A, Mazieres S, Ray N, Parra MV, Rojas W, Duque C, Mesa N, García LF, Triana O, Blair S, Maestre A, Dib JC, Bravi CM, Bailliet G, Corach D, Hünemeier T, Bortolini MC, Salzano FM, Petzl-Erler ML, Acuña-Alonzo V, Aguilar-Salinas C, Canizales-Quinteros S, Tusié-Luna T, Riba L, Rodríguez-Cruz M, Lopez-Alarcón M, Coral-Vazquez R, et al: Reconstructing Native American population history. Nature. 2012, 488: 370-374. 10.1038/nature11258.PubMedPubMed CentralView ArticleGoogle Scholar
- Sandoval JR, Lacerda DR, Jota MS, Salazar-Granara A, Vieira PP, Acosta O, Cuellar C, Revollo S, Fujita R, Santos FR: The genetic history of indigenous populations of the Peruvian and Bolivian Altiplano: the legacy of the Uros. PLoS One. 2013, 8: e73006-10.1371/journal.pone.0073006.PubMedPubMed CentralView ArticleGoogle Scholar
- Bustamante CD, Burchard EG, De la Vega FM: Genomics for the world. Nature. 2011, 475: 163-165. 10.1038/475163a.PubMedPubMed CentralView ArticleGoogle Scholar
- Frisse L, Hudson RR, Bartoszewicz A, Wall JD, Donfack J, Di Rienzo A: Gene conversion and different population histories may explain the contrast between polymorphism and linkage disequilibrium levels. Am J Hum Genet. 2001, 69: 831-843. 10.1086/323612.PubMedPubMed CentralView ArticleGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.PubMedPubMed CentralGoogle Scholar
- Scliar MO, Soares-Souza GB, Chevitarese J, Lemos L, Magalhães WCS, Fagundes NJ, Bonatto SL, Yeager M, Chanock SJ, Tarazona-Santos E: The population genetics of Quechuas, the largest native South American group: autosomal sequences, SNPs, and microsatellites evidence high level of diversity. Am J Phys Anthropol. 2012, 147: 443-451. 10.1002/ajpa.22013.PubMedView ArticleGoogle Scholar
- Pereira L, Zamudio R, Soares-Souza G, Herrera P, Cabrera L, Hooper CC, Cok J, Combe JM, Vargas G, Prado WA, Schneider S, Kehdy F, Rodrigues MR, Chanock SJ, Berg DE, Gilman RH, Tarazona-Santos E: Socioeconomic and nutritional factors account for the association of gastric cancer with Amerindian ancestry in a Latin American admixed population. PLoS One. 2012, 7: e41200-10.1371/journal.pone.0041200.PubMedPubMed CentralView ArticleGoogle Scholar
- Hey J: On the number of New World founders: a population genetic portrait of the peopling of the Americas. PLoS Biol. 2005, 3: e193-10.1371/journal.pbio.0030193.PubMedPubMed CentralView ArticleGoogle Scholar
- Strasburg JL, Rieseberg LH: How robust are “isolation with migration” analyses to violations of the im model? A simulation study. Mol Biol Evol. 2010, 27: 297-310. 10.1093/molbev/msp233.PubMedPubMed CentralView ArticleGoogle Scholar
- Hedrick PW: Genetics of Populations. 2009, Jones & Bartlett Publishers, FalmouthGoogle Scholar
- Gelman A, Carlin JB, Stern HS, Rubin DB: Bayesian Data Analysis. 2004, Chapman & Hall/CRC, LondonGoogle Scholar
- Beaumont MA, Zhang W, Balding DJ: Approximate Bayesian computation in population genetics. Genetics. 2002, 162: 2025-2035.PubMedPubMed CentralGoogle Scholar
- Zucchi A: The Arawakan matrix: ethos, language, and history in Native South America. Comparative Arawakan Histories: Rethinking Language and Culture Area in Amazonia. Edited by: Hill JD, Santos-Granero F. 2002, University of Illinois Press, Urbana, 199-222.Google Scholar
- Noble GK: Proto-Arawakan and its Descendants. 1965, Indiana University, BloomingtonGoogle Scholar
- Urban G: A história brasileira segundo as línguas nativas. história dos Índios no Brasil. Edited by: Cunha MC. 1992, FAPESP, São Paulo, 82-102.Google Scholar
- Walker RS, Ribeiro LA: Bayesian phylogeography of the Arawak expansion in lowland South America. Proc Biol Sci. 2011, 278: 2562-2567. 10.1098/rspb.2010.2579.PubMedPubMed CentralView ArticleGoogle Scholar
- Salomon F, Schwartz SB: The Cambridge History of the Native Peoples of the Americas, Volume 3, Part 2. 1999, Cambridge Univ. Press, CambridgeView ArticleGoogle Scholar
- Machado M, Magalhães WC, Sene A, Araújo B, Faria-Campos AC, Chanock SJ, Scott L, Oliveira G, Tarazona-Santos E, Rodrigues MR: Phred-Phrap package to analyses tools: a pipeline to facilitate population genetics re-sequencing studies. Investig Genet. 2011, 2: 3-10.1186/2041-2223-2-3.PubMedPubMed CentralView ArticleGoogle Scholar
- Hey J, Nielsen R: Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics. 2004, 167: 747-760. 10.1534/genetics.103.024182.PubMedPubMed CentralView ArticleGoogle Scholar
- Kimura M: The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics. 1969, 61: 893-903.PubMedPubMed CentralGoogle Scholar
- Drummond AJ, Rambaut A: BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007, 7: 214-10.1186/1471-2148-7-214.PubMedPubMed CentralView ArticleGoogle Scholar
- Woerner AE, Cox MP, Hammer MF: Recombination-filtered genomic datasets by information maximization. Bioinformatics. 2007, 23: 1851-1853. 10.1093/bioinformatics/btm253.PubMedView ArticleGoogle Scholar
- Kitchen A, Miyamoto MM, Mulligan CJ: A three-stage colonization model for the peopling of the Americas. PLoS One. 2008, 3: e1596-10.1371/journal.pone.0001596.PubMedPubMed CentralView ArticleGoogle Scholar
- Kuhner MK: Coalescent genealogy samplers: windows into population history. Trends Ecol Evol. 2009, 24: 86-93. 10.1016/j.tree.2008.09.007.PubMedView ArticleGoogle Scholar
- Ghirotto S, Mona S, Benazzo A, Paparazzo F, Caramelli D, Barbujani G: Inferring genealogical processes from patterns of Bronze-Age and modern DNA variation in Sardinia. Mol Biol Evol. 2010, 27: 875-886. 10.1093/molbev/msp292.PubMedView ArticleGoogle Scholar
- Voight BF, Adams AM, Frisse LA, Qian Y, Hudson RR, Di Rienzo A: Interrogating multiple aspects of variation in a full resequencing data set to infer human population size changes. Proc Natl Acad Sci U S A. 2005, 102: 18508-18513. 10.1073/pnas.0507325102.PubMedPubMed CentralView ArticleGoogle Scholar
- Bertorelle G, Benazzo A, Mona S: ABC as a flexible framework to estimate demography over space and time: some cons, many pros. Mol Ecol. 2010, 19: 2609-2625. 10.1111/j.1365-294X.2010.04690.x.PubMedView ArticleGoogle Scholar
- Excoffier L, Lischer HEL: Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010, 10: 564-567. 10.1111/j.1755-0998.2010.02847.x.PubMedView ArticleGoogle Scholar
- Excoffier L, Foll M: Fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios. Bioinformatics. 2011, 27: 1332-1334. 10.1093/bioinformatics/btr124.PubMedView ArticleGoogle Scholar
- Wegmann D, Leuenberger C, Neuenschwander S, Excoffier L: ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics. 2010, 11: 116-10.1186/1471-2105-11-116.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.