ConGenR: rapid determination of consensus genotypes and estimates of genotyping errors from replicated genetic samples
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TECHNICAL NOTE
ConGenR: rapid determination of consensus genotypes and estimates of genotyping errors from replicated genetic samples Robert C. Lonsinger1 • Lisette P. Waits1
Received: 31 May 2015 / Accepted: 4 November 2015 / Published online: 13 November 2015 Ó Springer Science+Business Media Dordrecht 2015
Abstract ConGenR (available at http://www.uidaho.edu/ cnr/research-outreach/facilities/leecg/publications-and-soft ware) is an R based conservation genetics script that facilitates rapid determination of consensus genotypes from replicated samples, determines overall (successful amplifications/amplification attempted) and individual sample level (proportion of samples with successful amplifications at n loci) amplification success rates, and quantifies genotyping error rates. ConGenR is intended for use with codominant, multilocus microsatellite data generated primarily through noninvasive genetic sampling and processed with a multi-tubes approach. ConGenR handles input that can be easily exported from GENEMAPPER, a program commonly used to score allele sizes. Amplification success and genotyping error rates can be evaluated by sample class (i.e., any identifiable and meaningful subdivision of samples; e.g., sex, season, region, or sample condition), offering insights into processes driving amplification success and genotyping error rates. Additionally, amplification success and genotyping error rates are calculated by locus, expediting the identification of problematic loci during pilot studies. Keywords Allelic dropout Consensus genotypes False alleles Genotyping errors Noninvasive genetic sampling PCR success Noninvasive genetic sampling is an appealing monitoring strategy when working with species that are difficult to & Robert C. Lonsinger [email protected] 1
Department of Fish and Wildlife Sciences, University of Idaho, Moscow, ID 83844-1136, USA
observe or capture and provides the opportunity to identify individuals (Waits et al. 2001), estimate population demographic parameters (Marucco et al. 2011), and evaluate genetic health without observing or handling individuals (Waits and Paetkau 2005; Beja-Pereira et al. 2009). Noninvasive genetic samples are typically characterized by low quantity and quality DNA, leading to low polymerase chain reaction (PCR) success and the presence of genotyping errors, making it challenging to obtain reliable genotypes (Pompanon et al. 2005; Waits and Paetkau 2005; Broquet et al. 2006). A multi-tubes approach is frequently used to establish reliable consensus genotypes and minimize the influence of genotyping errors (Taberlet et al. 1996). Genotyping errors are typically classified as a false allele (FA), where an allele is observed within a replicate that is not present in the consensus or reference genotype, or allelic dropout (ADO), where an allele present in the consensus or reference genotype fails to amplify in a successful PCR replicate (Broquet and Petit 2004). Prior to initiating noninvasive genetic monitoring, pilot studies are recommended
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