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Journal Article•DOI•

Ancient human genomes suggest three ancestral populations for present-day Europeans

Iosif Lazaridis1, Iosif Lazaridis2, Nick Patterson1, Alissa Mittnik3, Gabriel Renaud4, Swapan Mallick2, Swapan Mallick1, Karola Kirsanow5, Peter H. Sudmant6, Joshua G. Schraiber7, Joshua G. Schraiber6, Sergi Castellano4, Mark Lipson8, Bonnie Berger8, Bonnie Berger1, Christos Economou9, Ruth Bollongino5, Qiaomei Fu4, Kirsten I. Bos3, Susanne Nordenfelt1, Susanne Nordenfelt2, Heng Li1, Heng Li2, Cesare de Filippo4, Kay Prüfer4, Susanna Sawyer4, Cosimo Posth3, Wolfgang Haak10, Fredrik Hallgren11, Elin Fornander11, Nadin Rohland1, Nadin Rohland2, Dominique Delsate12, Michael Francken3, Jean-Michel Guinet12, Joachim Wahl, George Ayodo, Hamza A. Babiker13, Hamza A. Babiker14, Graciela Bailliet, Elena Balanovska, Oleg Balanovsky, Ramiro Barrantes15, Gabriel Bedoya16, Haim Ben-Ami17, Judit Bene18, Fouad Berrada19, Claudio M. Bravi, Francesca Brisighelli20, George B.J. Busby21, Francesco Calì, Mikhail Churnosov22, David E. C. Cole23, Daniel Corach24, Larissa Damba, George van Driem25, Stanislav Dryomov26, Jean-Michel Dugoujon27, Sardana A. Fedorova28, Irene Gallego Romero29, Marina Gubina, Michael F. Hammer30, Brenna M. Henn31, Tor Hervig32, Ugur Hodoglugil33, Aashish R. Jha29, Sena Karachanak-Yankova34, Rita Khusainova35, Elza Khusnutdinova35, Rick A. Kittles30, Toomas Kivisild36, William Klitz7, Vaidutis Kučinskas37, Alena Kushniarevich38, Leila Laredj39, Sergey Litvinov38, Theologos Loukidis40, Theologos Loukidis41, Robert W. Mahley42, Béla Melegh18, Ene Metspalu43, Julio Molina, Joanna L. Mountain, Klemetti Näkkäläjärvi44, Desislava Nesheva34, Thomas B. Nyambo45, Ludmila P. Osipova, Jüri Parik43, Fedor Platonov28, Olga L. Posukh, Valentino Romano46, Francisco Rothhammer47, Francisco Rothhammer48, Igor Rudan14, Ruslan Ruizbakiev49, Hovhannes Sahakyan50, Hovhannes Sahakyan38, Antti Sajantila51, Antonio Salas52, Elena B. Starikovskaya26, Ayele Tarekegn, Draga Toncheva34, Shahlo Turdikulova49, Ingrida Uktveryte37, Olga Utevska53, René Vasquez54, Mercedes Villena54, Mikhail Voevoda55, Cheryl A. Winkler56, Levon Yepiskoposyan50, Pierre Zalloua2, Pierre Zalloua57, Tatijana Zemunik58, Alan Cooper10, Cristian Capelli21, Mark G. Thomas40, Andres Ruiz-Linares40, Sarah A. Tishkoff59, Lalji Singh60, Kumarasamy Thangaraj61, Richard Villems62, Richard Villems43, Richard Villems38, David Comas63, Rem I. Sukernik26, Mait Metspalu38, Matthias Meyer4, Evan E. Eichler6, Joachim Burger5, Montgomery Slatkin7, Svante Pääbo4, Janet Kelso4, David Reich1, David Reich2, David Reich64, Johannes Krause4, Johannes Krause3 •
Broad Institute1, Harvard University2, University of Tübingen3, Max Planck Society4, University of Mainz5, University of Washington6, University of California, Berkeley7, Massachusetts Institute of Technology8, Stockholm University9, University of Adelaide10, The Heritage Foundation11, National Museum of Natural History12, Sultan Qaboos University13, University of Edinburgh14, University of Costa Rica15, University of Antioquia16, Rambam Health Care Campus17, University of Pécs18, Al Akhawayn University19, Catholic University of the Sacred Heart20, University of Oxford21, Belgorod State University22, University of Toronto23, University of Buenos Aires24, University of Bern25, Russian Academy of Sciences26, Paul Sabatier University27, North-Eastern Federal University28, University of Chicago29, University of Arizona30, Stony Brook University31, University of Bergen32, Illumina33, Sofia Medical University34, Bashkir State University35, University of Cambridge36, Vilnius University37, Estonian Biocentre38, University of Strasbourg39, University College London40, Amgen41, Gladstone Institutes42, University of Tartu43, University of Oulu44, Muhimbili University of Health and Allied Sciences45, University of Palermo46, University of Chile47, University of Tarapacá48, Academy of Sciences of Uzbekistan49, Armenian National Academy of Sciences50, University of North Texas51, University of Santiago de Compostela52, University of Kharkiv53, Higher University of San Andrés54, Novosibirsk State University55, Leidos56, Lebanese American University57, University of Split58, University of Pennsylvania59, Banaras Hindu University60, Centre for Cellular and Molecular Biology61, Estonian Academy of Sciences62, Pompeu Fabra University63, Howard Hughes Medical Institute64
18 Sep 2014-Nature (Nature Publishing Group)-Vol. 513, Iss: 7518, pp 409-413
TL;DR: It is shown that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians; and early European farmers, who were mainly of Near Eastern origin but also harboured west Europeanhunter-gatherer related ancestry.
Abstract: We sequenced the genomes of a ∼7,000-year-old farmer from Germany and eight ∼8,000-year-old hunter-gatherers from Luxembourg and Sweden. We analysed these and other ancient genomes with 2,345 contemporary humans to show that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians, who contributed to both Europeans and Near Easterners; and early European farmers, who were mainly of Near Eastern origin but also harboured west European hunter-gatherer related ancestry. We model these populations' deep relationships and show that early European farmers had ∼44% ancestry from a 'basal Eurasian' population that split before the diversification of other non-African lineages.
Citations
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Journal Article•DOI•
Ancient DNA and the rewriting of human history: be sparing with Occam's razor.

[...]

Marc Haber1, Massimo Mezzavilla1, Massimo Mezzavilla2, Yali Xue1, Chris Tyler-Smith1 •
Wellcome Trust Sanger Institute1, University of Trieste2
11 Jan 2016-Genome Biology
TL;DR: Some of the key events in the peopling of the world in the light of the findings of work on ancient DNA are reviewed.
Abstract: Ancient DNA research is revealing a human history far more complex than that inferred from parsimonious models based on modern DNA. Here, we review some of the key events in the peopling of the world in the light of the findings of work on ancient DNA.

1,365 citations

Cites background from "Ancient human genomes suggest three..."

  • ...A more substantial revision of the demic diffusion model was introduced when several 7000–8000-year-old individuals from Western Europe [29] and a 24,000-yearold individual from Siberia [30] were sequenced....

    [...]

  • ...Analysis showed that at least three different ancient populations contributed to the genetics of present-day Europeans: (1) West European hunter-gatherers, (2) ancient north Eurasians related to Upper Paleolithic Siberians, and (3) early European farmers, who were mainly of Near Eastern origin [29]....

    [...]

Journal Article•DOI•
Massive migration from the steppe was a source for Indo-European languages in Europe

[...]

Wolfgang Haak1, Iosif Lazaridis2, Nick Patterson3, Nadin Rohland2, Swapan Mallick4, Bastien Llamas1, Guido Brandt5, Susanne Nordenfelt2, Eadaoin Harney4, Kristin Stewardson4, Qiaomei Fu6, Alissa Mittnik7, Eszter Bánffy8, Christos Economou9, Michael Francken7, Susanne Friederich, Rafael Garrido Pena10, Fredrik Hallgren11, Valery Khartanovich, Aleksandr Khokhlov, Michael Kunst, Pavel Kuznetsov, Harald Meller, Oleg Mochalov, Vayacheslav Moiseyev, Nicole Nicklisch5, Sandra Pichler12, Roberto Risch13, Manuel Ángel Rojo Guerra14, Christina Roth5, Anna Szécsényi-Nagy5, Joachim Wahl, Matthias Meyer6, Johannes Krause6, Dorcas Brown15, David W. Anthony15, Alan Cooper1, Kurt W. Alt5, David Reich4 •
University of Adelaide1, Harvard University2, Broad Institute3, Howard Hughes Medical Institute4, University of Mainz5, Max Planck Society6, University of Tübingen7, Hungarian Academy of Sciences8, Stockholm University9, Facultad de Filosofía y Letras10, The Heritage Foundation11, University of Basel12, Autonomous University of Barcelona13, University of Valladolid14, Hartwick College15
11 Jun 2015-Nature
TL;DR: In this paper, the authors generated genome-wide data from 69 Europeans who lived between 8,000-3,000 years ago by enriching ancient DNA libraries for a target set of almost 400,000 polymorphisms.
Abstract: We generated genome-wide data from 69 Europeans who lived between 8,000-3,000 years ago by enriching ancient DNA libraries for a target set of almost 400,000 polymorphisms. Enrichment of these positions decreases the sequencing required for genome-wide ancient DNA analysis by a median of around 250-fold, allowing us to study an order of magnitude more individuals than previous studies and to obtain new insights about the past. We show that the populations of Western and Far Eastern Europe followed opposite trajectories between 8,000-5,000 years ago. At the beginning of the Neolithic period in Europe, ∼8,000-7,000 years ago, closely related groups of early farmers appeared in Germany, Hungary and Spain, different from indigenous hunter-gatherers, whereas Russia was inhabited by a distinctive population of hunter-gatherers with high affinity to a ∼24,000-year-old Siberian. By ∼6,000-5,000 years ago, farmers throughout much of Europe had more hunter-gatherer ancestry than their predecessors, but in Russia, the Yamnaya steppe herders of this time were descended not only from the preceding eastern European hunter-gatherers, but also from a population of Near Eastern ancestry. Western and Eastern Europe came into contact ∼4,500 years ago, as the Late Neolithic Corded Ware people from Germany traced ∼75% of their ancestry to the Yamnaya, documenting a massive migration into the heartland of Europe from its eastern periphery. This steppe ancestry persisted in all sampled central Europeans until at least ∼3,000 years ago, and is ubiquitous in present-day Europeans. These results provide support for a steppe origin of at least some of the Indo-European languages of Europe.

1,332 citations

Cites methods from "Ancient human genomes suggest three..."

  • ...Country Sex mt-hg Y-hg Autosomal SNPs I0061 Karelia_HG Russian Mesolithic EHG Yuzhnyy Oleni Ostrov, Karelia, Russia; UzOO74, grave 142, MAE RAS 5773-74 5500-5000 BCE Russia M C1g (formerly C1f) R1a1 341554 I0124 Samara_HG Russian Neolithic HG EHG Sok River, Samara, Russia; SVP44 5650-5555 cal BCE (Beta – 392490) Russia M U5a1d R1b1a 206748 I0011 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 1 5898-5531 cal BCE Sweden F U5a1 228271 I0012 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 2 5898-5531 cal BCE Sweden M U2e1 I2c2 292853 I0013 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 3 5898-5531 cal BCE Sweden M U5a1 I2a1b 251108 I0014 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 4 5898-5531 cal BCE Sweden F U5a2d 311299 I0015 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 6 5898-5531 cal BCE Sweden M U5a2d I2a1 285307 I0016 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 9 5898-5531 cal BCE Sweden M U5a2 I2a1 275233 I0017 Motala_HG Swedish Mesolithic SHG Motala, Sweden; Motala 12 5898-5531 cal BCE Sweden M U2e1 I2a1b 337794 I0174 Starcevo_EN Starcevo EN Alsónyék-Bátaszék, Mérnöki telep, Hungary; BAM25a, feature 1532 5710-5550 cal BCE (MAMS 11939 ) Hungary M N1a1a1b H2 101653 I0176 LBKT_EN LBKT EN Szemely-Hegyes, Hungary: SZEH4b, feature 1001 5210-4940 cal BCE (Beta - 310038) Hungary N1a1a1a3 30718 I0046 LBK_EN LBK EN Halberstadt-Sonntagsfeld, Germany: HAL5, grave 2, feature 241.1 5206-5004 cal BCE (MAMS 21479) Germany F T2c1d'e'f 266764 I0048 LBK_EN LBK EN Halberstadt-Sonntagsfeld, Germany: HAL25, grave 28, feature 861 5206-5052 cal BCE (MAMS 21482) Germany M K1a G2a2a 123828 I0056 LBK_EN LBK EN Halberstadt-Sonntagsfeld, Germany; HAL14, grave 15, feature 430 5206-5052 cal BCE (MAMS 21480) Germany M T2b(8) G2a2a 136578 I0057 LBK_EN LBK EN Halberstadt-Sonntagsfeld; HAL34, grave 38, feature 992 5207-5067 cal BCE (MAMS 21483) Germany F N1a1a1 55802 I0100 LBK_EN LBK EN Halberstadt-Sonntagsfeld; HAL4, grave 1, feature 139 5032-4946 cal BCE (KIA40341) Germany F N1a1a1a 342342 I0659 LBK_EN LBK EN Halberstadt-Sonntagsfeld, Germany; HAL2, grave 35, feature 999 5079-4997 cal BCE (KIA40350) 5066-4979 cal BCE (KIA30408) Germany M N1a1a G2a2a1 191007 0821 LBK_EN LBK EN Halberstadt-Sonntagsfeld, Germany; HAL24, grave 27, feature 867 5034-4942 cal BCE (KIA40348) Germany M Pre-X2d1 G2a2a1 55914 I0795 LBK_EN LBK EN Karsdorf, Germany; KAR6a, feature 170 5207-5070 cal BCE (MAMS 22823) Germany M H1 T1a 47804 I0054 LBK_EN LBK EN Oberwiederstedt-Unterwiederstedt, UWS4, Germany, grave 6, feature 1 14 5209-5070 cal BCE (MAMS 21485) Germany F J1c17 337625 I0022 LBK_EN LBK EN Viesenhäuser Hof, Stuttgart-Mühlhausen, Germany; LBK1976 5500-4800 BCE Germany F T2e 160852 I0025 LBK_EN LBK EN Viesenhäuser Hof, Stuttgart-Mühlhausen, Germany; LBK1992 5500-4800 BCE Germany F T2b 307686 I0026 LBK_EN LBK EN Viesenhäuser Hof, Stuttgart-Mühlhausen, Germany; LBK2155 5500-4800 BCE Germany F T2b 315484 I0409 Spain_EN Els_Trocs EN Els Trocs, Spain; Troc1 5311-5218 cal BCE (MAMS 16159) Spain F J1c3 172903 I0410 Spain_EN Els_Trocs EN Els Trocs, Spain; Troc3 5178-5066 cal BCE (MAMS 16161) Spain M pre-T2c1d2 R1b1 297595 I0411 Spain_EN_relative_of_I0410 Els_Trocs EN Els Trocs, Spain; Troc4 5177-5068 cal BCE (MAMS 16162) Spain M K1a2a F* 31507 I0412 Spain_EN Els_Trocs EN Els Trocs, Spain; Troc 5 5310-5206 cal BCE (MAMS 16164) Spain M N1a1a1 I2a1b1 333940 I0413 Spain_EN Els_Trocs EN Els Trocs, Spain; Troc7 5303-5204 cal BCE (MAMS 16166) Spain F V 295844 I0405 Spain_MN La_Mina MN La Mina, Spain; Mina3 3900-3600 BCE Spain M K1a1b1 I2a1a1/H2?...

    [...]

  • ...133230 I0406 Spain_MN La_Mina MN La Mina, Spain; Mina4 3900-3600 BCE Spain M H1 I2a2a1 324169 I0407 Spain_MN La_Mina MN La Mina, Spain; Mina6b 3900-3600 BCE Spain F K1b1a1 236225 I0408 Spain_MN La_Mina MN La Mina, Spain; Mina18a 3900-3600 BCE Spain F pre-U5b1i 321761 I0172 Esperstedt_MN Salzmünde/Bernburg MN Esperstedt, Germany; ESP24, feature 1841 3360-3086 cal BCE (Erl8699) Germany M T2b I2a1b1a 279147 I0559 Baalberge_MN Baalberge MN Quedlinburg, Germany; QLB15D, feature 21033 3645-3537 cal BCE (MAMS 22818) Germany M HV6’17 R*? 64304 I0560 Baalberge_MN Baalberge MN Quedlinburg, Germany; QLB18A, feature 21039 3640-3510 cal BCE (Er7856) Germany F T2e1 133305 I0807 Baalberge_MN Baalberge MN Esperstedt, Germany; ESP30, feature 6220 3887-3797 cal BCE (Er7784) Germany M H1e1a F* 33481 I0231 Yamnaya Yamnaya EBA Ekaterinovka,_Southern Steppe, Samara , Russia, SVP3 2910-2875 cal BCE (Beta 392487) Russia M U4a1 R1b1a2a2 348142 I0357 Yamnaya Yamnaya EBA Lopatino I, Sok_River, Samara, Russia; SVP5 same sample as SVP37 3090-2910 BCE (Beta 392489) Russia F W6 163845 I0370 Yamnaya Yamnaya EBA Ishkinovka I, Eastern Orenburg, Pre-Ural steppe, Samara, Russia: SVP10 3300-2700 BCE Russia M H13a1a1a R1b1a2a2 199345 I0429 Yamnaya Yamnaya EBA Lopatino I, Sok River, Samara, Russia, SVP38 3339-2917 cal BCE (AA47804) Russia M T2c1a2 R1b1a2a2 217664 I0438 Yamnaya Yamnaya EBA Luzhki I, Samara River, Samara, Russia; SVP50 3021-2635 cal BCE (AA47807) Russia M U5a1a1 R1b1a2a2 213493 I0439 Yamnaya Yamnaya EBA Lopatino I, Sok River, Samara, Russia, SVP52 3305-2925 cal BCE (Beta 392491) Russia M U5a1a1 R1b1a 98900 I0441 Yamnaya Yamnaya EBA Kurmanaevskii III, Buzuluk, Samara, Russia; SVP54 3010-2622 cal BCE (AA47805) Russia F H2b 51326 I0443 Yamnaya Yamnaya EBA Lopatino II, Sok River, Samara, Russia; SVP57 3300-2700 BCE Russia M W3a1a R1b1a2a 343890 I0444 Yamnaya Yamnaya EBA Kutuluk I, Kutuluk River, Samara, Russia; SVP58 3300-2700 BCE Russia M H6a1b R1b1a2a2 187126 I0550 Karsdorf_LN unknown LN Karsdorf, Germany; KAR22a, feature 191 3950-3400 BCE?, C14C pending Germany F T1a1 59907 I0103 Corded_Ware_LN Corded Ware LN Esperstedt, Germany; ESP16, feature 6236 2566-2477 cal BCE (MAMS 21488) Germany F W6a 336918 I0049 Corded_Ware_LN Corded Ware LN Esperstedt, Germany; ESP22, feature 6140 2454-2291 cal BCE (MAMS 21489) Germany F X2b4 167170 I0106 Corded_Ware_LN Corded Ware LN Esperstedt, Germany; ESP26, feature 6233.1 2454-2291 cal BCE (MAMS 21490) Germany F T2a1b1 69886 I0104 Corded_Ware_LN Corded Ware LN Esperstedt, Germany; ESP11, feature 6216 2473-2348 cal BCE (MAMS 21487) Germany M U4b1a1a1 R1a1a1 336637 I0059 BenzigerodeHeimburg_LN Bell Beaker?...

    [...]

  • ...The ten new samples from Els Trocs (5) and La Mina (5) were pooled with the Epi-Cardial (CAR) and Middle Neolithic (MNS) data from Spain, respectively....

    [...]

  • ...XC, T669C, A769G, A825t, A1018G, G1719A, G2702A, A2758G, C2885T, T3336C, T3594C, G4104A, T4312C, A5315G, G7146A, T7256C, A7521G, T8227C, T8468C, T8655C, G8701A, A8901G, C9540T, G10143A, T10238C, T10664C, A10688G, C10810T, C10873T, C10915T, A11884G, A11914G, G12501A, G13105A, G13276A, T13506C, T13650C, A13780G, G15043A, A16129G, C16147A, T16172C, T16187C, C16189T, G16230A, C16248T, T16278C, C16311T I0551 SALZ3B U3a1 none C146T, C150T, C152T, C195T, A247G, A769G, A825t, A1018G, A1811G, A2294G, A2758G, C2885T, G3010A, T3594C, G4104A, T4312C, T4703C, C6518T, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, G9266A, C9540T, G10398A, A10506G, T10664C, A10688G, C10810T, C10873T, C10915T, A11467G, A11914G, A12308G, G12372A, T12705C, G13105A, G13276A, T13506C, T13650C, C13934T, A14139G, T15454C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T, A16343G, G16390A I0800 SALZ57A H3 T152C!, A5515G G73A, C146T, C195T, A247G, A769G, A825t, A1018G, G2706A, A2758G, C2885T, T3594C, G4104A, T4312C, A5515G, T6776C, T7028C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, T13506C, T13650C, T14766C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T I0802 SALZ77A H3 none G73A, C146T, C152T, C195T, A247G, A769G, A825t, A1018G, G2706A, A2758G, C2885T, T3594C, G4104A, T4312C, T6776C, T7028C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, T13506C, T13650C, T14766C, A16129G, T16187C, C16189T, Lab ID Individual ID mtDNA-hg call Private mutations SNPs against RSRS T16223C, G16230A, T16278C, C16311T I0552 SALZ7A H5 C5993T, A14566G, C16519T G73A, C146T, C152T, C195T, A247G, C456T, A769G, A825t, A1018G, G2706A, A2758G, C2885T, T3594C, G4104A, T4312C, C5993T, T7028C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, T13506C, T13650C, A14566G, T14766C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, T16304C, C16311T, C16519T I0554 SALZ88A J1c A235G, T4083C, A7049G, A15799G, C16519T C146T, C152T, C195T, G228A, A235G, A247G, C295T, C462T, T489C, A769G, A825t, A1018G, A2758G, C2885T, G3010A, T3594C, T4083C, G4104A, T4216C, T4312C, A7049G, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, T10664C, A10688G, C10810T, C10873T, C10915T, A11251G, A11914G, A12612G, T12705C, G13105A, G13276A, T13506C, T13650C, G13708A, T14798C, C15452a, A15799G, C16069T, T16126C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T, C16519T, I0798 SALZ18A H10e'f'g C13503T G73A, C146T, C152T, C195T, A247G, A769G, A825t, A1018G, G2706A, A2758G, C2885T, T3594C, G4104A, T4312C, T7028C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, C13503T, T13506C, T13650C, T14470a, T14766C, T16093C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T I0799 SALZ21B H1e T1766C G73A, C146T, C152T, C195T, A247G, A769G, A825t, A1018G, T1766C, G2706A, A2758G, C2885T, G3010A, T3594C, G4104A, T4312C, G5460A, T7028C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, T13506C, T13650C, T14766C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T I0408 Mina18a pre-U5b1i T13656C, T16209C, A16399G (topologically missing C3498T, T6674G, G15777A, T16311C, T16356C C146T, C150T, C152T, C195T, A247G, A769G, A825t, A1018G, A2758G, C2885T, A3105G, T3197C, T3594C, G4104A, T4312C, A5656G, G7146A, T7256C, A7521G, A7768G, T8468C, T8655C, G8701A, G9477A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11467G, A11914G, A12308G, G12372A, T12705C, G13105A, G13276A, T13506C, T13617C, T13650C, T13656C, T14182C, A16129G, C16167T, T16187C, C16189T, C16192T, T16209C, T16223C, G16230A, C16270T, T16278C, C16311T, A16399G I0404 Mina2 J2a1a1 T195C!, A10499G, G10586A, G11377A, C16519T; heteroplasmy at np 16274 (54% A, 46% G) C146T, C150T, A215G, A247G, C295T, T319C, T489C, G513A, A769G, A825t, A1018G, A2758G, C2885T, T3594C, G4104A, T4216C, T4312C, G7146A, T7256C, C7476T, A7521G, G7789A, T8468C, T8655C, G8701A, C9540T, A10499G, G10586A, T10664C, A10688G, C10810T, C10873T, C10915T, A11251G, G11377A, A11914G, A12612G, T12705C, G13105A, G13276A, T13506C, T13650C, G13708A, A13722G, A14133G, G15257A, C15452a, C16069T, T16126C, A16129G, G16145A, T16187C, C16189T, T16223C, G16230A, T16231C, C16261T, T16278C, C16311T, C16519T I0405 Mina3 K1a1b1 C16301T; C114T, C146T, C152T, C195T, A247G, C497T, A769G, A825t, A1018G, T1189C, A1811G, A2758G, C2885T, A3480G, T3594C, G4104A, T4312C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, G9055A, C9540T, T9698C, A10550G, T10664C, A10688G, C10810T, C10873T, C10915T, T11299C, A11467G, A11470G, A12308G, G12372A, T12705C, G13105A, G13276A, T13506C, T13650C, C14167T, T14798C, A15924G, T16093C, A16129G, T16187C, C16189T, T16223T, T16224C, G16230A, T16278C, C16301T I0406 Mina4 H1 C722T G73A, C146T, C152T, C195T, A247G, C722T, A769G, A825t, A1018G, G2706A, A2758G, C2885T, G3010A, T3594C, G4104A, T4312C, T7028C, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, T13506C, T13650C, T14766C, A16129G, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T I0407 Mina6b K1b1a1 none C146T, C195T, A247G, A769G, A825t, A1018G, T1189C, A1811G, A2758G, C2885T, A3480G, T3594C, G4104A, T4312C, G5913A, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, G9055A, C9540T, T9698C, G9962A, A10289G, A10550G, T10664C, A10688G, C10810T, C10873T, C10915T, T11299C, A11467G, A11914G, A11923G, A12308G, G12372A, T12705C, G13105A, G13276A, T13506C, T13650C, C13967T, C14167T, T14798C, G15257A, C15946T, T16093C, A16129G, T16187C, C16189T, T16223T, T16224C, Lab ID Individual ID mtDNA-hg call Private mutations SNPs against RSRS G16230A, T16278C, G16319A, A16463G I0370 SVP10 H13a1a1 C14809T, C16261T, C16519T G73A, C146T, C152T, C195T, A247G, A769G, A825t, A1018G, C2259T, G2706A, A2758G, C2885T, T3594C, G4104A, T4312C, A4745G, T7028C, G7146A, T7256C, G7337A, A7521G, T8468C, T8655C, G8701A, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, A11719G, A11914G, T12705C, G13105A, G13276A, T13326C, T13506C, T13650C, C13680T, T14766C, C14809T, C14872T, A16129G, T16187C, C16189T, T16223C, G16230A, C16261T, T16278C, C16311T, C16519T I0355 SVP2 K1b2a none C152T, A247G, A769G, A825t, A1018G, T1189C, A1811G, A2758G, C2885T, A3480G, T3594C, G4104A, T4312C, G5913A, G7146A, T7256C, A7521G, T8468C, T8655C, G8701A, G9055A, C9540T, T9698C, A10550G, T10664C, A10688G, C10810T, C10873T, C10915T, T11299C, A11467G, A11914G, A12308G, G12372A, T12705C, T12738g, G12771A, G13105A, G13276A, T13506C, T13650C, C14167T, T14798C, A16129G, T16187C, C16189T, T16223T, T16224C, G16230A, T16278C I0231 SVP3 U4a1 T7153C C146T, A247G, G499A, A769G, A825t, A1018G, A1811G, A2758G, C2885T, T3594C, G4104A, T4312C, T4646C, T5999C, A6047G, G7146A, T7153C, T7256C, A7521G, T8468C, T8655C, G8701A, C8818T, C9540T, G10398A, T10664C, A10688G, C10810T, C10873T, C10915T, C11332T, A11467G, A11914G, A12308G, G12372A, T12705C, A12937G, G13105A, G13276A, T13506C, T13650C, C14620T, T15693C, A16129G, C16134T, T16187C, C16189T, T16223C, G16230A, T16278C, C16311T, T16356C I0429 SVP38 T2c1a2 none C146T, C152T, C195T, A247G, 573....

    [...]

  • ...Another heteroplasmic site was found in sample La Mina 2 at np 16274, where 54% of the reads show and A and 46% a G in a UDG-treated libraries, whereas all neighbouring D-loop SNP are consistent throughout all reads, again rendering contamination unlikely....

    [...]

Journal Article•DOI•
Population genomics of Bronze Age Eurasia

[...]

Morten E. Allentoft1, Martin Sikora1, Karl-Göran Sjögren2, Simon Rasmussen3, Morten Rasmussen1, Jesper Stenderup1, Peter de Barros Damgaard1, Hannes Schroeder4, Hannes Schroeder1, Torbjörn Ahlström5, Lasse Vinner1, Anna-Sapfo Malaspinas1, Ashot Margaryan1, Thomas Higham6, David Chivall6, Niels Lynnerup1, Lise Harvig1, Justyna Baron7, Philippe Della Casa8, Paweł Dąbrowski9, Paul R. Duffy10, Alexander V. Ebel11, Andrey Epimakhov12, Karin Margarita Frei, Mirosław Furmanek7, Tomasz Gralak7, Andrey Gromov, Stanisław Gronkiewicz13, Gisela Grupe14, Tamás Hajdu15, Tamás Hajdu16, Radosław Jarysz, Valeri Khartanovich, Alexandr Khokhlov, Viktória Kiss17, Jan Kolář18, Jan Kolář19, Aivar Kriiska20, Irena Lasak7, Cristina Longhi, George McGlynn14, Algimantas Merkevicius, Inga Merkyte1, Mait Metspalu20, Ruzan Mkrtchyan21, Vyacheslav Moiseyev, László Paja22, László Paja23, György Pálfi22, Dalia Pokutta2, Łukasz Pospieszny13, T. Douglas Price24, Lehti Saag20, Mikhail V. Sablin25, N. I. Shishlina, Václav Smrčka26, Vasilii I. Soenov11, Vajk Szeverényi17, Gusztáv Tóth15, Synaru V. Trifanova11, Liivi Varul20, Magdolna Vicze, Levon Yepiskoposyan27, Vladislav S. Zhitenev28, Ludovic Orlando1, Thomas Sicheritz-Pontén3, Søren Brunak1, Søren Brunak3, Rasmus Nielsen29, Kristian Kristiansen2, Eske Willerslev1 •
University of Copenhagen1, University of Gothenburg2, Technical University of Denmark3, Leiden University4, Lund University5, University of Oxford6, University of Wrocław7, University of Zurich8, Wrocław Medical University9, University of Toronto10, Gorno-Altaisk State University11, South Ural State University12, Polish Academy of Sciences13, Ludwig Maximilian University of Munich14, Eötvös Loránd University15, Hungarian Natural History Museum16, Hungarian Academy of Sciences17, Masaryk University18, Academy of Sciences of the Czech Republic19, University of Tartu20, Yerevan State University21, University of Szeged22, Hungarian National Museum23, University of Wisconsin-Madison24, Russian Academy of Sciences25, First Faculty of Medicine, Charles University in Prague26, Armenian National Academy of Sciences27, Moscow State University28, University of California, Berkeley29
11 Jun 2015-Nature
TL;DR: It is shown that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia.
Abstract: The Bronze Age of Eurasia (around 3000-1000 BC) was a period of major cultural changes. However, there is debate about whether these changes resulted from the circulation of ideas or from human migrations, potentially also facilitating the spread of languages and certain phenotypic traits. We investigated this by using new, improved methods to sequence low-coverage genomes from 101 ancient humans from across Eurasia. We show that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia. Our findings are consistent with the hypothesized spread of Indo-European languages during the Early Bronze Age. We also demonstrate that light skin pigmentation in Europeans was already present at high frequency in the Bronze Age, but not lactose tolerance, indicating a more recent onset of positive selection on lactose tolerance than previously thought.

1,088 citations

Journal Article•DOI•
Genome-wide patterns of selection in 230 ancient Eurasians

[...]

Iain Mathieson1, Iosif Lazaridis2, Iosif Lazaridis1, Nadin Rohland1, Nadin Rohland2, Swapan Mallick1, Swapan Mallick3, Swapan Mallick2, Nick Patterson2, Songül Alpaslan Roodenberg, Eadaoin Harney3, Eadaoin Harney1, Kristin Stewardson1, Kristin Stewardson3, Daniel Fernandes4, Mario Novak4, Kendra Sirak4, Kendra Sirak5, Cristina Gamba6, Cristina Gamba7, Cristina Gamba4, Eppie R. Jones6, Bastien Llamas8, Stanislav Dryomov9, Joseph K. Pickrell1, Juan Luis Arsuaga10, José María Bermúdez de Castro, Eudald Carbonell11, Fokke Gerritsen, Aleksandr Khokhlov, Pavel Kuznetsov, Marina Lozano11, Harald Meller, Oleg Mochalov, Vayacheslav Moiseyev, Manuel Ángel Rojo Guerra12, Jacob Roodenberg, Josep Maria Vergès11, Johannes Krause13, Johannes Krause14, Alan Cooper8, Kurt W. Alt15, Kurt W. Alt16, Dorcas Brown17, David W. Anthony17, Carles Lalueza-Fox18, Wolfgang Haak13, Wolfgang Haak8, Ron Pinhasi4, David Reich2, David Reich1, David Reich3 •
Harvard University1, Broad Institute2, Howard Hughes Medical Institute3, University College Dublin4, Emory University5, Trinity College, Dublin6, University of Copenhagen7, University of Adelaide8, Russian Academy of Sciences9, Complutense University of Madrid10, Rovira i Virgili University11, University of Valladolid12, Max Planck Society13, University of Tübingen14, University of Basel15, Danube Private University16, Hartwick College17, Pompeu Fabra University18
24 Dec 2015-Nature
TL;DR: A genome-wide scan for selection using ancient DNA is reported, capitalizing on the largest ancient DNA data set yet assembled: 230 West Eurasians who lived between 6500 and 300 bc, including 163 with newly reported data.
Abstract: Ancient DNA makes it possible to observe natural selection directly by analysing samples from populations before, during and after adaptation events. Here we report a genome-wide scan for selection using ancient DNA, capitalizing on the largest ancient DNA data set yet assembled: 230 West Eurasians who lived between 6500 and 300 bc, including 163 with newly reported data. The new samples include, to our knowledge, the first genome-wide ancient DNA from Anatolian Neolithic farmers, whose genetic material we obtained by extracting from petrous bones, and who we show were members of the population that was the source of Europe's first farmers. We also report a transect of the steppe region in Samara between 5600 and 300 bc, which allows us to identify admixture into the steppe from at least two external sources. We detect selection at loci associated with diet, pigmentation and immunity, and two independent episodes of selection on height.

1,083 citations

Journal Article•DOI•
Benefits and limitations of genome-wide association studies.

[...]

Vivian W.Y. Tam1, Nikunj Patel1, Michelle Turcotte1, Yohan Bossé2, Guillaume Paré1, David Meyre1, David Meyre3 •
McMaster University1, Laval University2, University of Lorraine3
01 Aug 2019-Nature Reviews Genetics
TL;DR: This Review comprehensively assess the benefits and limitations of GWAS in human populations and discusses the relevance of performing more GWAS, with a focus on the cardiometabolic field.
Abstract: Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype–phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS. Despite the success of human genome-wide association studies (GWAS) in associating genetic variants and complex diseases or traits, criticisms of the usefulness of this study design remain. This Review assesses the pros and cons of GWAS, with a focus on the cardiometabolic field.

1,002 citations

References
More filters
Journal Article•DOI•
The Sequence Alignment/Map format and SAMtools

[...]

Heng Li1, Bob Handsaker2, Alec Wysoker2, T. J. Fennell2, Jue Ruan3, Nils Homer2, Gabor T. Marth4, Gonçalo R. Abecasis2, Richard Durbin1 •
Wellcome Trust Sanger Institute1, University of California, Los Angeles2, Chinese Academy of Sciences3, Boston College4
01 Aug 2009-Bioinformatics
TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: [email protected]

45,957 citations

Journal Article•DOI•
Fast and accurate short read alignment with Burrows–Wheeler transform

[...]

Heng Li1, Richard Durbin1•
Wellcome Trust Sanger Institute1
01 Jul 2009-Bioinformatics
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

43,862 citations

Journal Article•DOI•
MEGA5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

[...]

Koichiro Tamura1, Daniel S. Peterson2, Nicholas Peterson2, Glen Stecher2, Masatoshi Nei3, Sudhir Kumar2 •
Tokyo Metropolitan University1, Arizona State University2, Pennsylvania State University3
01 Oct 2011-Molecular Biology and Evolution
TL;DR: The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models, inferring ancestral states and sequences, and estimating evolutionary rates site-by-site.
Abstract: Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.

39,110 citations

Journal Article•DOI•
PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

[...]

Shaun Purcell1, Shaun Purcell2, Benjamin M. Neale3, Benjamin M. Neale1, Kathe Todd-Brown2, Lori Thomas2, Manuel A. R. Ferreira2, David Bender2, David Bender1, Julian Maller2, Julian Maller1, Pamela Sklar1, Pamela Sklar2, Paul I.W. de Bakker1, Paul I.W. de Bakker2, Mark J. Daly1, Mark J. Daly2, Pak C. Sham4 •
Massachusetts Institute of Technology1, Harvard University2, University of London3, University of Hong Kong4
01 Sep 2007-American Journal of Human Genetics
TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

26,280 citations

Journal Article•DOI•
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

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Aaron McKenna1, Matthew Hanna, Eric Banks, Andrey Sivachenko, Kristian Cibulskis, Andrew Kernytsky, Kiran V. Garimella, David Altshuler, Stacey Gabriel, Mark J. Daly, Mark A. DePristo •
Broad Institute1
01 Sep 2010-Genome Research
TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

20,557 citations

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