Literature DB >> 15643963

Evaluating mixed samples as a source of error in non-invasive genetic studies using microsatellites.

David A Roon1, Miranda E Thomas, Katherine C Kendall, Lisette P Waits.   

Abstract

The use of noninvasive genetic sampling (NGS) for surveying wild populations is increasing rapidly. Currently, only a limited number of studies have evaluated potential biases associated with NGS. This paper evaluates the potential errors associated with analysing mixed samples drawn from multiple animals. Most NGS studies assume that mixed samples will be identified and removed during the genotyping process. We evaluated this assumption by creating 128 mixed samples of extracted DNA from brown bear (Ursus arctos) hair samples. These mixed samples were genotyped and screened for errors at six microsatellite loci according to protocols consistent with those used in other NGS studies. Five mixed samples produced acceptable genotypes after the first screening. However, all mixed samples produced multiple alleles at one or more loci, amplified as only one of the source samples, or yielded inconsistent electropherograms by the final stage of the error-checking process. These processes could potentially reduce the number of individuals observed in NGS studies, but errors should be conservative within demographic estimates. Researchers should be aware of the potential for mixed samples and carefully design gel analysis criteria and error checking protocols to detect mixed samples.

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Year:  2005        PMID: 15643963     DOI: 10.1111/j.1365-294X.2004.02385.x

Source DB:  PubMed          Journal:  Mol Ecol        ISSN: 0962-1083            Impact factor:   6.185


  7 in total

1.  Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism panels.

Authors:  Alina von Thaden; Carsten Nowak; Annika Tiesmeyer; Tobias E Reiners; Paulo C Alves; Leslie A Lyons; Federica Mattucci; Ettore Randi; Margherita Cragnolini; José Galián; Zsolt Hegyeli; Andrew C Kitchener; Clotilde Lambinet; José M Lucas; Thomas Mölich; Luana Ramos; Vinciane Schockert; Berardino Cocchiararo
Journal:  Mol Ecol Resour       Date:  2020-01-30       Impact factor: 7.090

2.  Estimating Size and Trend of the North Interlake Woodland Caribou Population Using Fecal-DNA and Capture-Recapture Models.

Authors:  Peter N Hettinga; Arni Neil Arnason; Micheline Manseau; Dale Cross; Kent Whaley; Paul J Wilson
Journal:  J Wildl Manage       Date:  2012-04-05       Impact factor: 2.469

3.  How Behavior of Nontarget Species Affects Perceived Accuracy of Scat Detection Dog Surveys.

Authors:  Karen E DeMatteo; Linsey W Blake; Julie K Young; Barbara Davenport
Journal:  Sci Rep       Date:  2018-09-14       Impact factor: 4.379

4.  Testing the effectiveness of genetic monitoring using genetic non-invasive sampling.

Authors:  Anthony James Schultz; Kasha Strickland; Romane H Cristescu; Jonathan Hanger; Deidre de Villiers; Céline H Frère
Journal:  Ecol Evol       Date:  2021-12-27       Impact factor: 2.912

5.  Genetic Analysis and Status of Brown Bear Sub-Populations in Three National Parks of Greece Functioning as Strongholds for the Species' Conservation.

Authors:  Tzoulia-Maria Tsalazidou-Founta; Evangelia A Stasi; Maria Samara; Yorgos Mertzanis; Maria Papathanassiou; Pantelis G Bagos; Spyros Psaroudas; Vasiliki Spyrou; Yorgos Lazarou; Athanasios Tragos; Yannis Tsaknakis; Elpida Grigoriadou; Athanasios Korakis; Maria Satra; Charalambos Billinis
Journal:  Genes (Basel)       Date:  2022-08-04       Impact factor: 4.141

6.  Scale-dependent effects of a heterogeneous landscape on genetic differentiation in the Central American squirrel monkey (Saimiri oerstedii).

Authors:  Mary E Blair; Don J Melnick
Journal:  PLoS One       Date:  2012-08-15       Impact factor: 3.240

7.  Environmental DNA from Residual Saliva for Efficient Noninvasive Genetic Monitoring of Brown Bears (Ursus arctos).

Authors:  Rachel E Wheat; Jennifer M Allen; Sophie D L Miller; Christopher C Wilmers; Taal Levi
Journal:  PLoS One       Date:  2016-11-09       Impact factor: 3.240

  7 in total

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