Genotyping errors: causes, consequences and solutions

By F. Pompanon, Aurélie Bonin, E. Bellemain, P. Taberlet. Nature reviews genetics, 2005

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Citations in the corpus

Current trends in microsatellite genotyping (2011, background): “Genotyping error rates can be estimated by counting Mendelian inconsistencies in parent–offspring pairs or by counting mismatches between duplicated genotypes (Bonin et al. 2004; Hoffman & Amos 2005; Pompanon et al. 2005; DeWoody et al. 2006; Johnson & Haydon 2007).”
Abundance of humpback whales in Oceania using photo-identification and microsatellite genotyping (2012, methodology): “Genotypic error rates were calculated per allele (Pompanon et al. 2005) using the internal control samples amplified in every PCR. Unique genotypes were resolved with the program CERVUS using criteria that required exact matching for at least 8 loci, supported by control region haplotypes and sex.”
Population monitoring of snow leopards using noninvasive collection of scat samples: a pilot study (2008, background): “Most errors have been attributed to the low quantity and quality of target DNA typically extracted from noninvasive samples (Taberlet et al., 1999; Pompano et al., 2005).”
PCR-based sexing in conservation biology: Wrong answers from an accurate methodology? (2006, background): “Given the clear potential for sexing errors and that non-detection of a sex-specific fragment alters an individual’s designated sex, we should ask how common is error in molecular sexing? Unlike the recent interest in microsatellite genotyping error (e.g., Hoffman and Amos 2005; Pompanon et al. 2005”
Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface (2008, methodology): “Bayesian clustering algorithms have become extremely useful tools to investigate the structure of population genetics data [Excofﬁer and Henkel, 2006] but the conclusion drawn from the use of such algorithms can be markedly inﬂuenced by the presence of genotyping errors [Pompanon et al., 2005].”
Marking hard-shelled gastropods: tag loss, impact on life-history traits, and perspectives in biology (2007, methodology): “…with non-invasive tissue sampling (e.g., mucus; Kawai Invertebrate Biology vol. 126, no. 2, spring 2007 et al. 2004) and DNA fingerprinting methods might be considered (Lukacs & Burnham 2005), although they are technologically more demanding than simpler, direct methods (Pompanon et al. 2005).”
Revising how the computer program cervus accommodates genotyping error increases success in paternity assignment (2007, background): “This problem has long been recognized (see Pompanon et al .”
Strong maternal fidelity and natal philopatry shape genetic structure in North Pacific humpback whales (2013, neutral): “From these capture-recapture events, we calculated an initial error rate of 1.6% per allele, most of which was attributable to allelic dropout (Pompanon et al. 2005).”
Almost forgotten or latest practice? AFLP applications, analyses and advances. (2007, methodology): “genotype of an individual, error rates cannot be assessed directly but instead must be estimated using replicates [ 87 ].”
lobSTR: A short tandem repeat profiler for personal genomes (2012, methodology): “We focused on two measurements: the genotype discordance rate and the allelic discordance rate (Pompanon et al. 2005).”
A consensus list of microsatellite markers for olive genotyping (2009, methodology): “number of allelic mismatches and 2nt, the number of replicated alleles, in our case being nt = 4 (Pompanon et al. 2005), has been quantified on the panel of the selected SSRs. PCR amplification and visualization Each lab carried out independent SSR analyses on the common set of 21 DNA samples,…”
Automated binning of microsatellite alleles: problems and solutions (2006, neutral): “2004; Pompanon et al .”
An evaluation of field and noninvasive genetic methods for estimating Eurasian otter population size (2009, background): “An important issue in NGS studies is the detection and elimination of genotyping errors, as their occurrence can strongly bias population size estimates (Waits and Leberg 2000; Creel et al. 2003; Pompanon et al. 2005 ).”
Inter-simple sequence repeat (ISSR) markers: are we doing it right? (2015, background): “(e) Scoring of bands The standards or criteria of scoring DNA bands generated for most dominant DNA markers have so far been very much subjective, and band-scoring results may differ from person to person (Pompanon et al. 2005; Meudt & Clarke 2007).”
Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple‐tube approach (2006, background): “Recent studies pointed out the need of standardizing the estimation of error rates in genotype data sets (Broquet & Petit 2004; Pompanon et al . 2005 ).”