Detection of individual ploidy levels with genotyping-by-sequencing (GBS) analysis.
Identifieur interne : 001449 ( Main/Exploration ); précédent : 001448; suivant : 001450Detection of individual ploidy levels with genotyping-by-sequencing (GBS) analysis.
Auteurs : Zachariah Gompert [États-Unis] ; Karen E. Mock [États-Unis]Source :
- Molecular ecology resources [ 1755-0998 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Populus.
- méthodes : Analyse de séquence d'ADN, Biostatistiques, Techniques de génotypage.
- Ploïdies, Simulation numérique.
English descriptors
- KwdEn :
- MESH :
- genetics : Populus.
- methods : Biostatistics, Genotyping Techniques, Sequence Analysis, DNA.
- Computer Simulation, Ploidies.
Abstract
Ploidy levels sometimes vary among individuals or populations, particularly in plants. When such variation exists, accurate determination of cytotype can inform studies of ecology or trait variation and is required for population genetic analyses. Here, we propose and evaluate a statistical approach for distinguishing low-level ploidy variants (e.g. diploids, triploids and tetraploids) based on genotyping-by-sequencing (GBS) data. The method infers cytotypes based on observed heterozygosity and the ratio of DNA sequences containing different alleles at thousands of heterozygous SNPs (i.e. allelic ratios). Whereas the method does not require prior information on ploidy, a reference set of samples with known ploidy can be included in the analysis if it is available. We explore the power and limitations of this method using simulated data sets and GBS data from natural populations of aspen (Populus tremuloides) known to include both diploid and triploid individuals. The proposed method was able to reliably discriminate among diploids, triploids and tetraploids in simulated data sets, and this was true for different levels of genetic diversity, inbreeding and population structure. Power and accuracy were minimally affected by low coverage (i.e. 2×), but did sometimes suffer when simulated mixtures of diploids, autotetraploids and allotetraploids were analysed. Cytotype assignments based on the proposed method closely matched those from previous microsatellite and flow cytometry data when applied to GBS data from aspen. An R package (gbs2ploidy) implementing the proposed method is available from CRAN.
DOI: 10.1111/1755-0998.12657
PubMed: 28150424
Affiliations:
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Mock</name><affiliation wicri:level="1"><nlm:affiliation>Wildland Resources Department and the Ecology Center, Utah State University, Logan, UT, 84322, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Wildland Resources Department and the Ecology Center, Utah State University, Logan, UT, 84322</wicri:regionArea><wicri:noRegion>84322</wicri:noRegion></affiliation></author></analytic><series><title level="j">Molecular ecology resources</title><idno type="eISSN">1755-0998</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Biostatistics (methods)</term><term>Computer Simulation (MeSH)</term><term>Genotyping Techniques (methods)</term><term>Ploidies (MeSH)</term><term>Populus (genetics)</term><term>Sequence Analysis, DNA (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de séquence d'ADN (méthodes)</term><term>Biostatistiques (méthodes)</term><term>Ploïdies (MeSH)</term><term>Populus (génétique)</term><term>Simulation numérique (MeSH)</term><term>Techniques de génotypage (méthodes)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Biostatistics</term><term>Genotyping Techniques</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Analyse de séquence d'ADN</term><term>Biostatistiques</term><term>Techniques de génotypage</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term><term>Ploidies</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Ploïdies</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Ploidy levels sometimes vary among individuals or populations, particularly in plants. When such variation exists, accurate determination of cytotype can inform studies of ecology or trait variation and is required for population genetic analyses. Here, we propose and evaluate a statistical approach for distinguishing low-level ploidy variants (e.g. diploids, triploids and tetraploids) based on genotyping-by-sequencing (GBS) data. The method infers cytotypes based on observed heterozygosity and the ratio of DNA sequences containing different alleles at thousands of heterozygous SNPs (i.e. allelic ratios). Whereas the method does not require prior information on ploidy, a reference set of samples with known ploidy can be included in the analysis if it is available. We explore the power and limitations of this method using simulated data sets and GBS data from natural populations of aspen (Populus tremuloides) known to include both diploid and triploid individuals. The proposed method was able to reliably discriminate among diploids, triploids and tetraploids in simulated data sets, and this was true for different levels of genetic diversity, inbreeding and population structure. Power and accuracy were minimally affected by low coverage (i.e. 2×), but did sometimes suffer when simulated mixtures of diploids, autotetraploids and allotetraploids were analysed. Cytotype assignments based on the proposed method closely matched those from previous microsatellite and flow cytometry data when applied to GBS data from aspen. An R package (gbs2ploidy) implementing the proposed method is available from CRAN.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">28150424</PMID><DateCompleted><Year>2018</Year><Month>06</Month><Day>29</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1755-0998</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>6</Issue><PubDate><Year>2017</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Molecular ecology resources</Title><ISOAbbreviation>Mol Ecol Resour</ISOAbbreviation></Journal><ArticleTitle>Detection of individual ploidy levels with genotyping-by-sequencing (GBS) analysis.</ArticleTitle><Pagination><MedlinePgn>1156-1167</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/1755-0998.12657</ELocationID><Abstract><AbstractText>Ploidy levels sometimes vary among individuals or populations, particularly in plants. When such variation exists, accurate determination of cytotype can inform studies of ecology or trait variation and is required for population genetic analyses. Here, we propose and evaluate a statistical approach for distinguishing low-level ploidy variants (e.g. diploids, triploids and tetraploids) based on genotyping-by-sequencing (GBS) data. The method infers cytotypes based on observed heterozygosity and the ratio of DNA sequences containing different alleles at thousands of heterozygous SNPs (i.e. allelic ratios). Whereas the method does not require prior information on ploidy, a reference set of samples with known ploidy can be included in the analysis if it is available. We explore the power and limitations of this method using simulated data sets and GBS data from natural populations of aspen (Populus tremuloides) known to include both diploid and triploid individuals. The proposed method was able to reliably discriminate among diploids, triploids and tetraploids in simulated data sets, and this was true for different levels of genetic diversity, inbreeding and population structure. Power and accuracy were minimally affected by low coverage (i.e. 2×), but did sometimes suffer when simulated mixtures of diploids, autotetraploids and allotetraploids were analysed. Cytotype assignments based on the proposed method closely matched those from previous microsatellite and flow cytometry data when applied to GBS data from aspen. An R package (gbs2ploidy) implementing the proposed method is available from CRAN.</AbstractText><CopyrightInformation>© 2017 John Wiley & Sons Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gompert</LastName><ForeName>Zachariah</ForeName><Initials>Z</Initials><Identifier Source="ORCID">http://orcid.org/0000-0003-2248-2488</Identifier><AffiliationInfo><Affiliation>Department of Biology and the Ecology Center, Utah State University, 5305 Old Main Hill, Logan, UT, 84322-5305, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mock</LastName><ForeName>Karen E</ForeName><Initials>KE</Initials><AffiliationInfo><Affiliation>Wildland Resources Department and the Ecology Center, Utah State University, Logan, UT, 84322, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D023362">Evaluation Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>03</Month><Day>09</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Mol Ecol Resour</MedlineTA><NlmUniqueID>101465604</NlmUniqueID><ISSNLinking>1755-098X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D056808" MajorTopicYN="N">Biostatistics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D060005" MajorTopicYN="N">Genotyping Techniques</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011003" MajorTopicYN="Y">Ploidies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Populus tremuloides
</Keyword><Keyword MajorTopicYN="N">Bayesian inference</Keyword><Keyword MajorTopicYN="N">cytotype</Keyword><Keyword MajorTopicYN="N">polyploid</Keyword><Keyword MajorTopicYN="N">population genetics</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>08</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2017</Year><Month>01</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>01</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>2</Month><Day>6</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>6</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>2</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28150424</ArticleId><ArticleId IdType="doi">10.1111/1755-0998.12657</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Gompert, Zachariah" sort="Gompert, Zachariah" uniqKey="Gompert Z" first="Zachariah" last="Gompert">Zachariah Gompert</name></noRegion><name sortKey="Mock, Karen E" sort="Mock, Karen E" uniqKey="Mock K" first="Karen E" last="Mock">Karen E. Mock</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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