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Abstract: Genotyping errors are rules rather than exceptions in reality, and are found in virtually all but very small datasets. These errors, even when occurring at an extremely low rate, can derail many genetic analyses such as parentage/sibship assignments and linkage/association studies. Nonetheless, few robust and accurate methods are available for estimating the rate of occurrence of genotyping errors and for identifying individual erroneous genotypes at a locus. Methods based on duplicate genotyping are expensive, and estimate genotype inconsistency rather than error rate at a locus. Methods based on Hardy–Weinberg equilibrium tests have low robustness and low power, and apply only to those particular errors that cause excessive homozygosity. Methods based on pedigrees are powerful, robust and accurate. However, they rely on known and complete pedigrees that are unfortunately rarely available from natural populations in the wild. I proposed a maximum likelihood method to reconstruct pedigrees from genotype data with errors occurring at a roughly estimated (presumed) rate. In this paper, I describe how to use the method and inferred pedigree in estimating allelic dropout (or null allele) rate and false allele rate jointly at each marker locus, in identifying the erroneous genotypes, and in inferring the most likely genotypes at each locus of each individual. I examine the power, accuracy and robustness of the method by extensive simulations, and demonstrate the usefulness of the method by analysing three empirical datasets. It is concluded that, both pedigrees and the rates of genotyping errors at each locus can be reliably estimated from the same genotype data by the same likelihood method, when marker information is sufficient and some sampled individuals are first‐degree relatives. The erroneous genotypes are however inferred conservatively, and are reliably detected only when they occur in large families and/or at highly polymorphic loci. Estimation of genotyping error rates per locus and identification of erroneous genotypes of each individual at each locus should be routinely conducted to assess and improve data quality, to highlight markers for optimization of genotyping protocols or for replacement, and to enable the integration of genotyping errors in a robust statistical analysis. [ABSTRACT FROM AUTHOR]

Microsatellite genetic marker data are exploited in a variety of fields, including forensics, gene mapping, kinship inference and population genetics. In all of these fields, inference can be thwarted by failure to quantify and account for data errors, and kinship inference in particular can benefit from separating errors into two distinct classes: allelic dropout and false alleles. Pedant is MS Windows software for estimating locus-specific maximum likelihood rates of these two classes of error. Estimation is based on comparison of duplicate error-prone genotypes: neither reference genotypes nor pedigree data are required. Other functions include: plotting of error rate estimates and confidence intervals; simulations for performing power analysis and for testing the robustness of error rate estimates to violation of the underlying assumptions; and estimation of expected heterozygosity, which is a required input. The program, documentation and source code are available from http://www.stats.gla.ac.uk/~paulj/pedant.html.

Non-invasive genetic sampling using materials such as faeces or hair can be used to monitor wildlife populations, although DNA quality is often poor. Improving sampling efficiency and minimising factors that reduce DNA quality are therefore critical. After a severe decline, the European pine marten, Martes martes, has reclaimed much of its former range in Scotland, UK. Recording this rapid range expansion requires developing techniques for accurate monitoring, but this is hampered by the species' elusive behaviour. We tested two sampling methods, hair collected from hair tubes and faeces (scat) collected along tracks, to assess the effects of key environmental and sampling variables on DNA quality and sampling efficiency. For hair, we tested the influence of hair tube location (distance from forest tracks) on collection rate and sex ratio of animals successfully sampled. For scats, we assessed the effect of time since defecation (1 to 16 days) on genotyping error rates and success under two contrasting environmental conditions (exposed to rainfall or sheltered). We found no bias in the collection rate or sex ratio of animals detected by hair samples with differing proximity to forest tracks. DNA amplification failure for scats exposed to rainfall increased from 28 to 65 % over the 16-day experimental period. During periods of low rainfall, the length of collection sessions could therefore be extended to increase sample number without risk of DNA degradation. Lack of bias in hair collection rates with proximity to forest tracks provides justification for tube placement close to tracks, as this reduces survey effort. These findings provide guidance for the development of efficient and cost-effective non-invasive sampling of Scottish pine martens. [ABSTRACT FROM AUTHOR]

Skin swabbing, a minimally invasive DNA sampling method recently developed on adult amphibians, was tested on larvae of fire salamanders (Salamandra salamandra). The quality and quantity of the sampled DNA was evaluated by (i) measuring DNA concentration in DNA extracts, (ii) sequencing part of the mtDNA cytochrome b gene (692 bp) and (iii) genotyping eight polymorphic nuclear microsatellite loci. The multiple-tubes approach was used for calculating allelic dropout (ADO) and false allele (FA) rates to evaluate the reliability of the genotypes. DNA extracts from tissue samples of road-killed individuals were included in the study as positive controls. Our results showed that skin swabs of fire salamander larvae can provide DNA in sufficient quantity and quality, as sequencing was successful and no allelic dropouts or false alleles were detected. This method, tested for the first time on amphibian larvae, has proven to be an efficient and reliable alternative to the controversial tail fin clipping procedure. [ABSTRACT FROM AUTHOR]

Several methods have been applied to calculate genotyping error rates (GER) for non-invasive population size estimations. However, there is a lack of comparability between these methods. Here we focused on the comparison of methods for determination of GER within one study using faeces samples of wild boars ( Sus scrofa). Error rates were calculated by (1) comparison of reference tissue samples and rectum faeces samples (2) the number of deviations between replicates and the assumed consensus genotypes, (3) re-analysis of a subsample interpreted by allelic and genotype comparisons, and (4) a blind-test of anonymously subdivided faecal samples. The error rates differed widely between these four methods (0-57.5 %) and underline the need of a consensus approach. The blind-test resulted in a GER of 4.3 %. We recommend conducting such a blind-test for estimating realistic GER when starting a pilot study in wildlife forensics. [ABSTRACT FROM AUTHOR]

Non-invasive DNA genotyping using hair samples has become a common method in population surveys of Asiatic black bears ( Ursus thibetanus) in Japan; however, the accuracy of the genotyping data has rarely been discussed in empirical studies. Therefore, we conducted a large-scale pilot study to examine genotyping accuracy and sought an efficient way of error-checking hair-trapping data. We collected 2,067 hair samples, successfully determined the genotypes of 1,245 samples, and identified 295 individuals. The genotyping data were further divided into 3 subsets of data according to the number of hairs used for DNA extraction in each sample (1-4, 5-9, and ≥10 hairs), and the error rates of allelic dropout and false alleles were estimated for each subset using a maximum likelihood method. The genotyping error rates in the samples with ≥10 hairs were found to be lower than those in the samples with 1-4 and 5-9 hairs. The presence of erroneous genotypes among the identified individuals was further checked using a post hoc goodness-of-fit test that determined the match between the expected and observed frequencies of individual homozygotes at 0-6 loci. The results indicated the presence of erroneous genotypes, possibly as a result of allelic dropout, in the samples. Therefore, for improved accuracy, it is recommended that samples containing ≥10 hairs should be used for genotyping and a post hoc goodness-of-fit test should be performed to exclude erroneous genotypes before proceeding with downstream analysis such as capture-mark-recapture estimation. [ABSTRACT FROM AUTHOR]

Genetic markers are widely used to determine the parentage of individuals in studies of mating systems, reproductive success, dispersals, quantitative genetic parameters and in the management of conservation populations. These markers are, however, imperfect for parentage analyses because of the presence of genotyping errors and undetectable alleles, which may cause incompatible genotypes (mismatches) between parents and offspring and thus result in false exclusions of true parentage. Highly polymorphic markers widely used in parentage analyses, such as microsatellites, are especially prone to genotyping errors. In this investigation, I derived the probabilities of excluding a random (related) individual from parentage and the probabilities of Mendelian-inconsistent errors (mismatches) and Mendelian-consistent errors (which do not cause mismatches) in parent-offspring dyads, when a marker having null alleles, allelic dropouts and false alleles is used in a parentage analysis. These probabilities are useful in evaluating the impact of various types of genotyping errors on the information content of a set of markers in and thus the power of a parentage analysis, in determining the threshold number of genetic mismatches that is appropriate for a parentage exclusion analysis and in estimating the rates of genotyping errors and frequencies of null alleles from observed mismatches between known parent-offspring dyads. These applications are demonstrated by numerical examples using both hypothetical and empirical data sets and discussed in the context of practical parentage exclusion analyses. [ABSTRACT FROM AUTHOR]

The article discusses research on the non-invasive DNA sampling of hair and faecal material from the Pteropus poliocephalus or grey-headed flying-fox. It references a study by Heather J. Baldwin at the Macquarie University Department of Biological Sciences in Sydney, New South Wales, and her colleagues, published in an issue of "Australian Mammalogy." The researchers concluded that the hair and faeces of P. poliocephalus serve as a good source of high-quality mitochondrial DNA. They also found that the low quality of the extracted nuclear DNA was influenced by a number of factors including spurious microsatellite profiles, false alleles and reduced amplification success.

ConGenR (available at ) 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. [ABSTRACT FROM AUTHOR]

Microsatellite genetic marker data are exploited in a variety of fields, including forensics, gene mapping, kinship inference and population genetics. In all of these fields, inference can be thwarted by failure to quantify and account for data errors, and kinship inference in particular can benefit from separating errors into two distinct classes: allelic dropout and false alleles. Pedant is MS Windows software for estimating locus-specific maximum likelihood rates of these two classes of error. Estimation is based on comparison of duplicate error-prone genotypes: neither reference genotypes nor pedigree data are required. Other functions include: plotting of error rate estimates and confidence intervals; simulations for performing power analysis and for testing the robustness of error rate estimates to violation of the underlying assumptions; and estimation of expected heterozygosity, which is a required input. The program, documentation and source code are available from http://www.stats.gla.ac.uk/~paulj/pedant.html.