HGDP and HapMap Analysis by Ancestry Mapper Reveals Local and Global Population Relationships
Identifieur interne : 000124 ( Ncbi/Merge ); précédent : 000123; suivant : 000125HGDP and HapMap Analysis by Ancestry Mapper Reveals Local and Global Population Relationships
Auteurs : Tiago R. Magalhães [Irlande (pays)] ; Jillian P. Casey [Irlande (pays)] ; Judith Conroy [Irlande (pays)] ; Regina Regan [Irlande (pays)] ; Darren J. Fitzpatrick [Irlande (pays)] ; Naisha Shah [Irlande (pays)] ; João Sobral [Portugal] ; Sean Ennis [Irlande (pays)]Source :
- PLoS ONE [ 1932-6203 ] ; 2012.
Abstract
Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set.
Url:
DOI: 10.1371/journal.pone.0049438
PubMed: 23189146
PubMed Central: 3506643
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<front><div type="abstract" xml:lang="en"><p>Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">23189146</article-id>
<article-id pub-id-type="pmc">3506643</article-id>
<article-id pub-id-type="publisher-id">PONE-D-12-12438</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0049438</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Biology</subject>
<subj-group><subject>Computational Biology</subject>
<subj-group><subject>Genomics</subject>
<subj-group><subject>Genome Analysis Tools</subject>
</subj-group>
</subj-group>
<subj-group><subject>Population Genetics</subject>
<subject>Sequence Analysis</subject>
</subj-group>
</subj-group>
<subj-group><subject>Evolutionary Biology</subject>
<subj-group><subject>Organismal Evolution</subject>
<subj-group><subject>Human Evolution</subject>
</subj-group>
</subj-group>
<subj-group><subject>Evolutionary Genetics</subject>
<subject>Evolutionary Systematics</subject>
<subject>Evolutionary Theory</subject>
</subj-group>
</subj-group>
<subj-group><subject>Genetics</subject>
<subj-group><subject>Human Genetics</subject>
</subj-group>
</subj-group>
<subj-group><subject>Genomics</subject>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Social and Behavioral Sciences</subject>
<subj-group><subject>Psychology</subject>
<subj-group><subject>Human Relations</subject>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>HGDP and HapMap Analysis by Ancestry Mapper Reveals Local and Global Population Relationships</article-title>
<alt-title alt-title-type="running-head">HGDP and HapMap Analyzed by Ancestry Mapper</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Magalhães</surname>
<given-names>Tiago R.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Casey</surname>
<given-names>Jillian P.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Conroy</surname>
<given-names>Judith</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Regan</surname>
<given-names>Regina</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Fitzpatrick</surname>
<given-names>Darren J.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Shah</surname>
<given-names>Naisha</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sobral</surname>
<given-names>João</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ennis</surname>
<given-names>Sean</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>School of Medicine and Medical Science, University College Dublin, Dublin, Ireland</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>National Children’s Research Centre, Our Lady’s Childrens Hospital Crumlin, Dublin, Ireland</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>Instituto Gulbenkian de Ciência, Oeiras, Portugal</addr-line>
</aff>
<aff id="aff4"><label>4</label>
<addr-line>Clique Research Cluster, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland</addr-line>
</aff>
<aff id="aff5"><label>5</label>
<addr-line>Complex and Adaptive Systems Laboratory, and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Ellegren</surname>
<given-names>Hans</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>University of Uppsala, Sweden</addr-line>
</aff>
<author-notes><corresp id="cor1">* E-mail: <email>sean.ennis@ucd.ie</email>
</corresp>
<fn fn-type="conflict"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con"><p>Conceived and designed the experiments: TRM SE JPC. Performed the experiments: TRM JPC JC DJF. Analyzed the data: TRM JPC JC JS NS. Wrote the paper: TRM SE JPC RR. R package: TRM DJF.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2012</year>
</pub-date>
<pub-date pub-type="epub"><day>26</day>
<month>11</month>
<year>2012</year>
</pub-date>
<volume>7</volume>
<issue>11</issue>
<elocation-id>e49438</elocation-id>
<history><date date-type="received"><day>2</day>
<month>5</month>
<year>2012</year>
</date>
<date date-type="accepted"><day>9</day>
<month>10</month>
<year>2012</year>
</date>
</history>
<permissions><copyright-year>2012</copyright-year>
<copyright-holder>Magalhães et al</copyright-holder>
<license><license-p>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.</license-p>
</license>
</permissions>
<abstract><p>Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set.</p>
</abstract>
<funding-group><funding-statement>This work was funded with internal funding from University College of Dublin, Ireland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><page-count count="16"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Irlande (pays)</li>
<li>Portugal</li>
</country>
</list>
<tree><country name="Irlande (pays)"><noRegion><name sortKey="Magalhaes, Tiago R" sort="Magalhaes, Tiago R" uniqKey="Magalhaes T" first="Tiago R." last="Magalhães">Tiago R. Magalhães</name>
</noRegion>
<name sortKey="Casey, Jillian P" sort="Casey, Jillian P" uniqKey="Casey J" first="Jillian P." last="Casey">Jillian P. Casey</name>
<name sortKey="Casey, Jillian P" sort="Casey, Jillian P" uniqKey="Casey J" first="Jillian P." last="Casey">Jillian P. Casey</name>
<name sortKey="Conroy, Judith" sort="Conroy, Judith" uniqKey="Conroy J" first="Judith" last="Conroy">Judith Conroy</name>
<name sortKey="Ennis, Sean" sort="Ennis, Sean" uniqKey="Ennis S" first="Sean" last="Ennis">Sean Ennis</name>
<name sortKey="Fitzpatrick, Darren J" sort="Fitzpatrick, Darren J" uniqKey="Fitzpatrick D" first="Darren J." last="Fitzpatrick">Darren J. Fitzpatrick</name>
<name sortKey="Fitzpatrick, Darren J" sort="Fitzpatrick, Darren J" uniqKey="Fitzpatrick D" first="Darren J." last="Fitzpatrick">Darren J. Fitzpatrick</name>
<name sortKey="Regan, Regina" sort="Regan, Regina" uniqKey="Regan R" first="Regina" last="Regan">Regina Regan</name>
<name sortKey="Regan, Regina" sort="Regan, Regina" uniqKey="Regan R" first="Regina" last="Regan">Regina Regan</name>
<name sortKey="Shah, Naisha" sort="Shah, Naisha" uniqKey="Shah N" first="Naisha" last="Shah">Naisha Shah</name>
<name sortKey="Shah, Naisha" sort="Shah, Naisha" uniqKey="Shah N" first="Naisha" last="Shah">Naisha Shah</name>
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<country name="Portugal"><noRegion><name sortKey="Sobral, Joao" sort="Sobral, Joao" uniqKey="Sobral J" first="João" last="Sobral">João Sobral</name>
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