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      Ancient DNA from European Early Neolithic Farmers Reveals Their Near Eastern Affinities

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          Abstract

          The first farmers from Central Europe reveal a genetic affinity to modern-day populations from the Near East and Anatolia, which suggests a significant demographic input from this area during the early Neolithic.

          Abstract

          In Europe, the Neolithic transition (8,000–4,000 b. c.) from hunting and gathering to agricultural communities was one of the most important demographic events since the initial peopling of Europe by anatomically modern humans in the Upper Paleolithic (40,000 b. c.). However, the nature and speed of this transition is a matter of continuing scientific debate in archaeology, anthropology, and human population genetics. To date, inferences about the genetic make up of past populations have mostly been drawn from studies of modern-day Eurasian populations, but increasingly ancient DNA studies offer a direct view of the genetic past. We genetically characterized a population of the earliest farming culture in Central Europe, the Linear Pottery Culture (LBK; 5,500–4,900 calibrated b. c.) and used comprehensive phylogeographic and population genetic analyses to locate its origins within the broader Eurasian region, and to trace potential dispersal routes into Europe. We cloned and sequenced the mitochondrial hypervariable segment I and designed two powerful SNP multiplex PCR systems to generate new mitochondrial and Y-chromosomal data from 21 individuals from a complete LBK graveyard at Derenburg Meerenstieg II in Germany. These results considerably extend the available genetic dataset for the LBK ( n = 42) and permit the first detailed genetic analysis of the earliest Neolithic culture in Central Europe (5,500–4,900 calibrated b. c.). We characterized the Neolithic mitochondrial DNA sequence diversity and geographical affinities of the early farmers using a large database of extant Western Eurasian populations ( n = 23,394) and a wide range of population genetic analyses including shared haplotype analyses, principal component analyses, multidimensional scaling, geographic mapping of genetic distances, and Bayesian Serial Simcoal analyses. The results reveal that the LBK population shared an affinity with the modern-day Near East and Anatolia, supporting a major genetic input from this area during the advent of farming in Europe. However, the LBK population also showed unique genetic features including a clearly distinct distribution of mitochondrial haplogroup frequencies, confirming that major demographic events continued to take place in Europe after the early Neolithic.

          Author Summary

          The transition from a hunter–gatherer existence to a sedentary farming-based lifestyle has had key consequences for human groups around the world and has profoundly shaped human societies. Originating in the Near East around 11,000 y ago, an agricultural lifestyle subsequently spread across Europe during the New Stone Age (Neolithic). Whether it was mediated by incoming farmers or driven by the transmission of innovative ideas and techniques remains a subject of continuing debate in archaeology, anthropology, and human population genetics. Ancient DNA from the earliest farmers can provide a direct view of the genetic diversity of these populations in the earliest Neolithic. Here, we compare Neolithic haplogroups and their diversity to a large database of extant European and Eurasian populations. We identified Neolithic haplotypes that left clear traces in modern populations, and the data suggest a route for the migrating farmers that extends from the Near East and Anatolia into Central Europe. When compared to indigenous hunter–gatherer populations, the unique and characteristic genetic signature of the early farmers suggests a significant demographic input from the Near East during the onset of farming in Europe.

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          Most cited references54

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          Arlequin (version 3.0): An integrated software package for population genetics data analysis

          Arlequin ver 3.0 is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. Arlequin 3 introduces a completely new graphical interface written in C++, a more robust semantic analysis of input files, and two new methods: a Bayesian estimation of gametic phase from multi-locus genotypes, and an estimation of the parameters of an instantaneous spatial expansion from DNA sequence polymorphism. Arlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on http://cmpg.unibe.ch/software/arlequin3.
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            Assessing ancient DNA studies.

            The study of ancient DNA has the potential to make significant and unique contributions to ecology and evolution. However, the techniques used contain inherent problems, particularly with regards to the generation of authentic and useful data. The solution currently advocated to reduce contamination and artefactual results is to adopt criteria for authentication. Nevertheless, these criteria are not foolproof, and we believe that they have, in practice, replaced the use of thought and prudence when designing and executing ancient DNA studies. We argue here that researchers in this field must take a more cognitive and self-critical approach. Specifically, in place of checking criteria off lists, researchers must explain, in sufficient enough detail to dispel doubt, how the data were obtained, and why they should be believed to be authentic.
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              The fate of mutations surfing on the wave of a range expansion.

              Many species, including humans, have dramatically expanded their range in the past, and such range expansions had certainly an impact on their genetic diversity. For example, mutations arising in populations at the edge of a range expansion can sometimes surf on the wave of advance and thus reach a larger spatial distribution and a much higher frequency than would be expected in stationary populations. We study here this surfing phenomenon in more detail, by performing extensive computer simulations under a two-dimensional stepping-stone model. We find that the probability of survival of a new mutation depends to a large degree on its proximity to the edge of the wave. Demographic factors such as deme size, migration rate, and local growth rate also influence the fate of these new mutations. We also find that the final spatial and frequency distributions depend on the local deme size of a subdivided population. This latter result is discussed in the light of human expansions in Europe as it should allow one to distinguish between mutations having spread with Paleolithic or Neolithic expansions. By favoring the spread of new mutations, a consequence of the surfing phenomenon is to increase the rate of evolution of spatially expanding populations.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                November 2010
                November 2010
                9 November 2010
                : 8
                : 11
                : e1000536
                Affiliations
                [1 ]Australian Centre for Ancient DNA, School of Earth and Environmental Sciences, University of Adelaide, Adelaide, Australia
                [2 ]Research Centre for Medical Genetics, Russian Academy of Medical Sciences, Moscow, Russia
                [3 ]National Institute of Toxicology and Forensic Sciences, Canary Islands Delegation, Campus de Ciencias de la Salud, La Laguna, Tenerife, Spain
                [4 ]Faculty of Geography, Moscow State University, Moscow, Russia
                [5 ]Research Centre for Drug Evaluation, Ministry of Public Health of the Russian Federation, Moscow, Russia
                [6 ]Institute for Anthropology, Johannes Gutenberg University of Mainz, Mainz, Germany
                [7 ]Landesamt für Denkmalpflege und Archaeologie und Landesmuseum für Vorgeschichte, Halle (Saale), Germany
                [8 ]Department of Evolutionary Biology, Institute of Molecular and Cell Biology, University of Tartu and Estonian Biocentre, Tartu, Estonia
                Massey University, New Zealand
                Author notes

                The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: WH OB JJS KWA AC. Performed the experiments: WH OB CJA. Analyzed the data: WH OB SK CJA CSIDS CS NN. Contributed reagents/materials/analysis tools: WH JJS SK VZ GB VD BF EB RV HM KWA. Wrote the paper: WH OB JJS CJA BF AC.

                ¶ Membership for the Genographic Consortium is listed in the Acknowledgments section.

                Article
                10-PLBI-RA-6634R3
                10.1371/journal.pbio.1000536
                2976717
                21085689
                3aa2bf59-3087-4de7-8bc0-0ba161c3c2e9
                Haak et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 18 March 2010
                : 27 September 2010
                Page count
                Pages: 16
                Categories
                Research Article
                Evolutionary Biology
                Evolutionary Biology/Human Evolution
                Genetics and Genomics/Population Genetics

                Life sciences
                Life sciences

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