Genes reveal traces of common recent demographic history for most of the Uralic-speaking populations

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Abstract

Background

The genetic origins of Uralic speakers from across a vast territory in the temperate zone of North Eurasia have remained elusive. Previous studies have shown contrasting proportions of Eastern and Western Eurasian ancestry in their mitochondrial and Y chromosomal gene pools. While the maternal lineages reflect by and large the geographic background of a given Uralic-speaking population, the frequency of Y chromosomes of Eastern Eurasian origin is distinctively high among European Uralic speakers. The autosomal variation of Uralic speakers, however, has not yet been studied comprehensively.

Results

Here, we present a genome-wide analysis of 15 Uralic-speaking populations which cover all main groups of the linguistic family. We show that contemporary Uralic speakers are genetically very similar to their local geographical neighbours. However, when studying relationships among geographically distant populations, we find that most of the Uralic speakers and some of their neighbours share a genetic component of possibly Siberian origin. Additionally, we show that most Uralic speakers share significantly more genomic segments identity-by-descent with each other than with geographically equidistant speakers of other languages. We find that correlated genome-wide genetic and lexical distances among Uralic speakers suggest co-dispersion of genes and languages. Yet, we do not find long-range genetic ties between Estonians and Hungarians with their linguistic sisters that would distinguish them from their non-Uralic-speaking neighbours.

Conclusions

We show that most Uralic speakers share a distinct ancestry component of likely Siberian origin, which suggests that the spread of Uralic languages involved at least some demic component.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1522-1) contains supplementary material, which is available to authorized users.

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Most cited references 85

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MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

Sudhir Kumar, Glen Stecher, Koichiro Tamura (2016)

We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.

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PLINK: a tool set for whole-genome association and population-based linkage analyses.

Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)

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.

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Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

R. Edgar (2002)

The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

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Author and article information

Contributors

Kristiina Tambets:

ORCID: http://orcid.org/0000-0002-8173-6380

+372 7 375054 , ktambets@ebc.ee

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 21 September 2018

Publication date PMC-release: 21 September 2018

Publication date Collection: 2018

Volume: 19

Electronic Location Identifier: 139

Affiliations

[1 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Estonian Biocentre, Institute of Genomics, , University of Tartu, ; Riia 23b, 51010 Tartu, Estonia

[2 ]Ufa Scientific Center of RAS, Ufa, 450054 Russia

[3 ]ISNI 0000 0001 0696 9806, GRID grid.148374.d, Statistics and Bioinformatics Group, Institute of Fundamental Sciences, , Massey University, ; Palmerston North, 4442 New Zealand

[4 ]ISNI 0000 0001 2097 1371, GRID grid.1374.1, Department of Biology, , University of Turku, ; 20014 Turku, Finland

[5 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Institute of Estonian and General Linguistics, , University of Tartu, ; 51014 Tartu, Estonia

[6 ]ISNI 0000 0004 0372 3343, GRID grid.9654.e, School of Psychology, , University of Auckland, ; Auckland, 1142 New Zealand

[7 ]ISNI 0000 0004 4914 1197, GRID grid.469873.7, Department of Linguistic and Cultural Evolution, , Max Planck Institute for the Science of Human History, ; D-07745 Jena, Germany

[8 ]ISNI 0000 0004 1795 1830, GRID grid.451388.3, The Francis Crick Institute, ; 1 Midland Road, London, NW1 1AT UK

[9 ]ISNI 0000 0001 2271 2138, GRID grid.410300.6, Institute of Genetics and Cytology of the National Academy of Sciences of Belarus, ; Minsk, 220072 Republic of Belarus

[10 ]Institute of Biochemistry and Genetics, Ufa Scientific Center of RAS, Ufa, 450054 Russia

[11 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Department of Evolutionary Biology, Institute of Molecular and Cell Biology, , University of Tartu, ; 51010 Tartu, Estonia

[12 ]ISNI 0000 0001 2097 1371, GRID grid.1374.1, Department of Geography and Geology, , University of Turku, ; 20014 Turku, Finland

[13 ]GRID grid.416167.3, Department of Radiology, , The Mount Sinai Medical Center, ; New York, NY 10029 USA

[14 ]GRID grid.418953.2, Institute of Cytology and Genetics, Siberian Branch of RAS, ; Novosibirsk, 630090 Russia

[15 ]Research Institute of Medical and Social Problems and Control of the Healthcare Department of Tuva Republic, Kyzyl, 667003 Russia

[16 ]GRID grid.415876.9, Research Centre for Medical Genetics, Russian Academy of Medical Sciences, ; Moscow, 115478 Russia

[17 ]ISNI 0000 0001 0339 7822, GRID grid.412254.4, Northern State Medical University, ; Arkhangelsk, 163000 Russia

[18 ]ISNI 0000 0004 0623 6380, GRID grid.426412.7, Anthony Nolan, ; London, NW3 2NU UK

[19 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Research Centre of Estonian Genome Center, Institute of Genomics, , University of Tartu, ; 51010 Tartu, Estonia

[20 ]ISNI 0000 0004 4648 9892, GRID grid.419210.f, Latvian Biomedical Research and Study Centre, ; Riga, LV-1067 Latvia

[21 ]ISNI 0000 0001 2192 9124, GRID grid.4886.2, Vavilov Institute for General Genetics, RAS, ; Moscow, 119991 Russia

[22 ]ISNI 0000 0001 1015 7624, GRID grid.77269.3d, Department of Genetics and Fundamental Medicine, , Bashkir State University, ; Ufa, 450054 Russia

[23 ]ISNI 0000000121896553, GRID grid.4605.7, Novosibirsk State University, ; 2 Pirogova Str, Novosibirsk, 630090 Russia

[24 ]ISNI 0000 0001 2254 1834, GRID grid.415877.8, Institute of Internal Medicine, Siberian Branch of Russian Academy of Medical Sciences, ; Novosibirsk, 630090 Russia

[25 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Archaeology, , University of Cambridge, ; Cambridge, CB2 1QH UK

[26 ]ISNI 0000 0001 0668 7884, GRID grid.5596.f, Department of Human Genetics, KU Leuven, ; Leuven, 3000 Belgium

Author information

Kristiina Tambets http://orcid.org/0000-0002-8173-6380

Article

Publisher ID: 1522

DOI: 10.1186/s13059-018-1522-1

PMC ID: 6151024

PubMed ID: 30241495

SO-VID: eb011726-f20f-4fcc-882b-b1c1a7805546

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

History

Date received : 12 January 2018

Date accepted : 3 September 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002301, Eesti Teadusagentuur;

Award ID: PUT1217

Award ID: PUT1339

Award ID: IUT24

Award Recipient : Kristiina Tambets Alena Kushniarevich

Funded by: FundRef http://dx.doi.org/10.13039/501100008530, European Regional Development Fund;

Award ID: 2014-2020.4.01.15-0012

Award ID: 014-2020.4.01.16-0271

Award ID: 2014-2020.4.01.16-0125

Funded by: FundRef http://dx.doi.org/10.13039/501100005781, Koneen Säätiö;

Funded by: Russian Federation State Research Project

Award ID: 0324-2018-0016

Funded by: FundRef http://dx.doi.org/10.13039/100011264, FP7 People: Marie-Curie Actions;

Award ID: FP7-PEOPLE-2012-IRSES-number 318979

Award Recipient : Georgi Hudjashov Quentin Atkinson Monika Karmin Mait Metspalu

Funded by: FundRef http://dx.doi.org/10.13039/501100002261, Russian Foundation for Basic Research;

Award ID: 16-06-00303

Award Recipient : Elena Balanovska

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: population genetics,genome-wide analysis,haplotype analysis,ibd-segments,uralic languages

Data availability:

ScienceOpen disciplines: Genetics

Keywords: population genetics, genome-wide analysis, haplotype analysis, ibd-segments, uralic languages

Genes reveal traces of common recent demographic history for most of the Uralic-speaking populations

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Abstract

Background

Results

Conclusions

Electronic supplementary material

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G3: Genes|Genomes|Genetics

Most cited references 85

MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

PLINK: a tool set for whole-genome association and population-based linkage analyses.

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

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