Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.

Genetic stock identification (GSI) using molecular markers is an important tool for management of migratory species. Here, we tested a cost-effective alternative to individual genotyping, known as allelotyping, for identification of highly informative SNPs for accurate genetic stock identification....

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Autores principales: Mikhail Ozerov, Anti Vasemägi, Vidar Wennevik, Rogelio Diaz-Fernandez, Matthew Kent, John Gilbey, Sergey Prusov, Eero Niemelä, Juha-Pekka Vähä
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Publicado: Public Library of Science (PLoS) 2013
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Acceso en línea:https://doaj.org/article/e7cf3db1bb424cd1a03c00320c4697e9
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spelling oai:doaj.org-article:e7cf3db1bb424cd1a03c00320c4697e92021-11-18T08:41:49ZFinding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.1932-620310.1371/journal.pone.0082434https://doaj.org/article/e7cf3db1bb424cd1a03c00320c4697e92013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24358184/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Genetic stock identification (GSI) using molecular markers is an important tool for management of migratory species. Here, we tested a cost-effective alternative to individual genotyping, known as allelotyping, for identification of highly informative SNPs for accurate genetic stock identification. We estimated allele frequencies of 2880 SNPs from DNA pools of 23 Atlantic salmon populations using Illumina SNP-chip. We evaluated the performance of four common strategies (global F ST, pairwise F ST, Delta and outlier approach) for selection of the most informative set of SNPs and tested their effectiveness for GSI compared to random sets of SNP and microsatellite markers. For the majority of cases, SNPs selected using the outlier approach performed best followed by pairwise F ST and Delta methods. Overall, the selection procedure reduced the number of SNPs required for accurate GSI by up to 53% compared with randomly chosen SNPs. However, GSI accuracy was more affected by populations in the ascertainment group rather than the ranking method itself. We demonstrated for the first time the compatibility of different large-scale SNP datasets by compiling the largest population genetic dataset for Atlantic salmon to date. Finally, we showed an excellent performance of our top SNPs on an independent set of populations covering the main European distribution range of Atlantic salmon. Taken together, we demonstrate how combination of DNA pooling and SNP arrays can be applied for conservation and management of salmonids as well as other species.Mikhail OzerovAnti VasemägiVidar WennevikRogelio Diaz-FernandezMatthew KentJohn GilbeySergey PrusovEero NiemeläJuha-Pekka VähäPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82434 (2013)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Mikhail Ozerov
Anti Vasemägi
Vidar Wennevik
Rogelio Diaz-Fernandez
Matthew Kent
John Gilbey
Sergey Prusov
Eero Niemelä
Juha-Pekka Vähä
Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.
description Genetic stock identification (GSI) using molecular markers is an important tool for management of migratory species. Here, we tested a cost-effective alternative to individual genotyping, known as allelotyping, for identification of highly informative SNPs for accurate genetic stock identification. We estimated allele frequencies of 2880 SNPs from DNA pools of 23 Atlantic salmon populations using Illumina SNP-chip. We evaluated the performance of four common strategies (global F ST, pairwise F ST, Delta and outlier approach) for selection of the most informative set of SNPs and tested their effectiveness for GSI compared to random sets of SNP and microsatellite markers. For the majority of cases, SNPs selected using the outlier approach performed best followed by pairwise F ST and Delta methods. Overall, the selection procedure reduced the number of SNPs required for accurate GSI by up to 53% compared with randomly chosen SNPs. However, GSI accuracy was more affected by populations in the ascertainment group rather than the ranking method itself. We demonstrated for the first time the compatibility of different large-scale SNP datasets by compiling the largest population genetic dataset for Atlantic salmon to date. Finally, we showed an excellent performance of our top SNPs on an independent set of populations covering the main European distribution range of Atlantic salmon. Taken together, we demonstrate how combination of DNA pooling and SNP arrays can be applied for conservation and management of salmonids as well as other species.
format article
author Mikhail Ozerov
Anti Vasemägi
Vidar Wennevik
Rogelio Diaz-Fernandez
Matthew Kent
John Gilbey
Sergey Prusov
Eero Niemelä
Juha-Pekka Vähä
author_facet Mikhail Ozerov
Anti Vasemägi
Vidar Wennevik
Rogelio Diaz-Fernandez
Matthew Kent
John Gilbey
Sergey Prusov
Eero Niemelä
Juha-Pekka Vähä
author_sort Mikhail Ozerov
title Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.
title_short Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.
title_full Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.
title_fullStr Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.
title_full_unstemmed Finding markers that make a difference: DNA pooling and SNP-arrays identify population informative markers for genetic stock identification.
title_sort finding markers that make a difference: dna pooling and snp-arrays identify population informative markers for genetic stock identification.
publisher Public Library of Science (PLoS)
publishDate 2013
url https://doaj.org/article/e7cf3db1bb424cd1a03c00320c4697e9
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