Resolving microsatellite genotype ambiguity in populations of allopolyploid and diploidized autopolyploid organisms using negative correlations between allelic variables.
Identifieur interne : 000039 ( Main/Exploration ); précédent : 000038; suivant : 000040Resolving microsatellite genotype ambiguity in populations of allopolyploid and diploidized autopolyploid organisms using negative correlations between allelic variables.
Auteurs : Lindsay V. Clark [États-Unis] ; Andrea Drauch Schreier [États-Unis]Source :
- Molecular ecology resources [ 1755-0998 ] ; 2016.
Abstract
A major limitation in the analysis of genetic marker data from polyploid organisms is non-Mendelian segregation, particularly when a single marker yields allelic signals from multiple, independently segregating loci (isoloci). However, with markers such as microsatellites that detect more than two alleles, it is sometimes possible to deduce which alleles belong to which isoloci. Here, we describe a novel mathematical property of codominant marker data when it is recoded as binary (presence/absence) allelic variables: under random mating in an infinite population, two allelic variables will be negatively correlated if they belong to the same locus, but uncorrelated if they belong to different loci. We present an algorithm to take advantage of this mathematical property, sorting alleles into isoloci based on correlations, then refining the allele assignments after checking for consistency with individual genotypes. We demonstrate the utility of our method on simulated data, as well as a real microsatellite data set from a natural population of octoploid white sturgeon (Acipenser transmontanus). Our methodology is implemented in the R package polysat version 1.6.
DOI: 10.1111/1755-0998.12639
PubMed: 27868358
Affiliations:
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Clark</name><affiliation wicri:level="1"><nlm:affiliation>Department of Crop Sciences, University of Illinois, Urbana-Champaign, 1201 W. Gregory Drive, Urbana, IL, 61801, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Crop Sciences, University of Illinois, Urbana-Champaign, 1201 W. Gregory Drive, Urbana, IL, 61801</wicri:regionArea><wicri:noRegion>61801</wicri:noRegion></affiliation></author><author><name sortKey="Schreier, Andrea Drauch" sort="Schreier, Andrea Drauch" uniqKey="Schreier A" first="Andrea Drauch" last="Schreier">Andrea Drauch Schreier</name><affiliation wicri:level="1"><nlm:affiliation>Department of Animal Science, University of California - Davis, Davis, CA, 95616, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Animal Science, University of California - Davis, Davis, CA, 95616</wicri:regionArea><wicri:noRegion>95616</wicri:noRegion></affiliation></author></analytic><series><title level="j">Molecular ecology resources</title><idno type="eISSN">1755-0998</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A major limitation in the analysis of genetic marker data from polyploid organisms is non-Mendelian segregation, particularly when a single marker yields allelic signals from multiple, independently segregating loci (isoloci). However, with markers such as microsatellites that detect more than two alleles, it is sometimes possible to deduce which alleles belong to which isoloci. Here, we describe a novel mathematical property of codominant marker data when it is recoded as binary (presence/absence) allelic variables: under random mating in an infinite population, two allelic variables will be negatively correlated if they belong to the same locus, but uncorrelated if they belong to different loci. We present an algorithm to take advantage of this mathematical property, sorting alleles into isoloci based on correlations, then refining the allele assignments after checking for consistency with individual genotypes. We demonstrate the utility of our method on simulated data, as well as a real microsatellite data set from a natural population of octoploid white sturgeon (Acipenser transmontanus). Our methodology is implemented in the R package polysat version 1.6.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Clark, Lindsay V" sort="Clark, Lindsay V" uniqKey="Clark L" first="Lindsay V" last="Clark">Lindsay V. Clark</name></noRegion><name sortKey="Schreier, Andrea Drauch" sort="Schreier, Andrea Drauch" uniqKey="Schreier A" first="Andrea Drauch" last="Schreier">Andrea Drauch Schreier</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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