First core microsatellite panel identification in Apennine brown bears (Ursus arctos marsicanus): a collaborative approach.

Conservation genetics Cross-species amplification Discriminatory power Individual identification Interlaboratory comparison Italy Non-invasive genetic profiles Reproducibility STR calibration

Journal

BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258

Informations de publication

Date de publication:
18 Aug 2021
Historique:
received: 11 02 2021
accepted: 28 07 2021
entrez: 19 8 2021
pubmed: 20 8 2021
medline: 21 8 2021
Statut: epublish

Résumé

The low cost and rapidity of microsatellite analysis have led to the development of several markers for many species. Because in non-invasive genetics it is recommended to genotype individuals using few loci, generally a subset of markers is selected. The choice of different marker panels by different research groups studying the same population can cause problems and bias in data analysis. A priority issue in conservation genetics is the comparability of data produced by different labs with different methods. Here, we compared data from previous and ongoing studies to identify a panel of microsatellite loci efficient for the long-term monitoring of Apennine brown bears (Ursus arctos marsicanus), aiming at reducing genotyping uncertainty and allowing reliable individual identifications overtimes. We examined all microsatellite markers used up to now and identified 19 candidate loci. We evaluated the efficacy of 13 of the most commonly used loci analyzing 194 DNA samples belonging to 113 distinct bears selected from the Italian national biobank. We compared data from 4 different marker subsets on the basis of genotyping errors, allelic patterns, observed and expected heterozygosity, discriminatory powers, number of mismatching pairs, and probability of identity. The optimal marker set was selected evaluating the low molecular weight, the high discriminatory power, and the low occurrence of genotyping errors of each primer. We calibrated allele calls and verified matches among genotypes obtained in previous studies using the complete set of 13 STRs (Short Tandem Repeats), analyzing six invasive DNA samples from distinct individuals. Differences in allele-sizing between labs were consistent, showing a substantial overlap of the individual genotyping. The proposed marker set comprises 11 Ursus specific markers with the addition of cxx20, the canid-locus less prone to genotyping errors, in order to prevent underestimation (maximizing the discriminatory power) and overestimation (minimizing the genotyping errors) of the number of Apennine brown bears. The selected markers allow saving time and costs with the amplification in multiplex of all loci thanks to the same annealing temperature. Our work optimizes the available resources by identifying a shared panel and a uniform methodology capable of improving comparisons between past and future studies.

Sections du résumé

BACKGROUND BACKGROUND
The low cost and rapidity of microsatellite analysis have led to the development of several markers for many species. Because in non-invasive genetics it is recommended to genotype individuals using few loci, generally a subset of markers is selected. The choice of different marker panels by different research groups studying the same population can cause problems and bias in data analysis. A priority issue in conservation genetics is the comparability of data produced by different labs with different methods. Here, we compared data from previous and ongoing studies to identify a panel of microsatellite loci efficient for the long-term monitoring of Apennine brown bears (Ursus arctos marsicanus), aiming at reducing genotyping uncertainty and allowing reliable individual identifications overtimes.
RESULTS RESULTS
We examined all microsatellite markers used up to now and identified 19 candidate loci. We evaluated the efficacy of 13 of the most commonly used loci analyzing 194 DNA samples belonging to 113 distinct bears selected from the Italian national biobank. We compared data from 4 different marker subsets on the basis of genotyping errors, allelic patterns, observed and expected heterozygosity, discriminatory powers, number of mismatching pairs, and probability of identity. The optimal marker set was selected evaluating the low molecular weight, the high discriminatory power, and the low occurrence of genotyping errors of each primer. We calibrated allele calls and verified matches among genotypes obtained in previous studies using the complete set of 13 STRs (Short Tandem Repeats), analyzing six invasive DNA samples from distinct individuals. Differences in allele-sizing between labs were consistent, showing a substantial overlap of the individual genotyping.
CONCLUSIONS CONCLUSIONS
The proposed marker set comprises 11 Ursus specific markers with the addition of cxx20, the canid-locus less prone to genotyping errors, in order to prevent underestimation (maximizing the discriminatory power) and overestimation (minimizing the genotyping errors) of the number of Apennine brown bears. The selected markers allow saving time and costs with the amplification in multiplex of all loci thanks to the same annealing temperature. Our work optimizes the available resources by identifying a shared panel and a uniform methodology capable of improving comparisons between past and future studies.

Identifiants

pubmed: 34407764
doi: 10.1186/s12864-021-07915-5
pii: 10.1186/s12864-021-07915-5
pmc: PMC8371798
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

623

Subventions

Organisme : PNALM
ID : Research Project No. L00CGE11 - CUP C36C18000460005
Organisme : PNM
ID : Research Project No. L00CGE11 - CUP C36C18000460005
Organisme : Regione Lazio
ID : Research Project No. L00CGE18

Informations de copyright

© 2021. The Author(s).

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Auteurs

Erminia Scarpulla (E)

Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.

Alessio Boattini (A)

Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy.

Mario Cozzo (M)

Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE), Italian Institute for Environmental Protection and Research (ISPRA), Via Ca' Fornacetta, 9 - 40064 Ozzano dell'Emilia, Bologna, Italy.

Patrizia Giangregorio (P)

Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE), Italian Institute for Environmental Protection and Research (ISPRA), Via Ca' Fornacetta, 9 - 40064 Ozzano dell'Emilia, Bologna, Italy.

Paolo Ciucci (P)

Department of Biology and Biotechnology "Charles Darwin" (BBCD), Sapienza University of Rome, Rome, Italy.

Nadia Mucci (N)

Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE), Italian Institute for Environmental Protection and Research (ISPRA), Via Ca' Fornacetta, 9 - 40064 Ozzano dell'Emilia, Bologna, Italy.

Ettore Randi (E)

Faculty of Engineering and Science, Department of Chemistry and Bioscience, University of Aalborg, Aalborg, Denmark.

Francesca Davoli (F)

Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE), Italian Institute for Environmental Protection and Research (ISPRA), Via Ca' Fornacetta, 9 - 40064 Ozzano dell'Emilia, Bologna, Italy. francesca.davoli@isprambiente.it.

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Classifications MeSH