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Software for Quantifying and Simulating Microsatellite Genotyping Error

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Publication Date: 12 Nov 2007

Journal: Bioinformatics and Biology Insights

Citation: Bioinformatics and Biology Insights 2007:1 71-75

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Paul C.D. Johnson and Daniel T. Haydon

Division of Environmental and Evolutionary Biology, Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ, U.K.

Abstract: 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.


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Posted by Paul Johnson - 11:59,October 23, 2009

Author's note Table 1 comparing the accuracy of MasterBayes and Pedant appears to be misleading, in that I didn't use the correct summary of the posterior distribution. This led me to the erroneous conclusion that MasterBayes gives more biased estimates than Pedant. In fact, while my estimates of Pedant's bias are accurate, MasterBayes turns out to be unbiased. See this note from Jarrod Hadfield's tutorial on MasterBayes: "Johnson and Haydon [2007] find that MasterBayes overestimates error rates by 31% when error rates are low (compared to a downward bias of 18% for Pedant). However, large scale simulation shows that MasterBayes is unbiased (if such a thing exists in a Bayesian analysis) if the posterior mode is used rather than the posterior mean." p29 of MasterBayes: Maximum Likelihood and Markov chain Monte Carlo methods for pedigree reconstruction, analysis and simulation. J. D. Hadfield, July 21, 2009 http://cran.es.r-project.org/web/packages/MasterBayes/vignettes/Tutorial.pdf (A minor point: I actually used the posterior median, which is likely to be less biased than the mean, but presumably still not as accuarate as using the mode.) I re-ran the analysis for Table 1 on the same simulated data, using the mode instead of the median. The results agree with Jarrod's conclusions: there was no bias in any of the estimates (all 4 mean parameter estimates were within 2 SEs of their target). With hindsight it isn't surprising that MasterBayes is more accurate, given that MasterBayes' error model is more realistic than Pedant's. Pedant makes a few simplifying assumptions (i.e. convenient shortcuts), such as assuming that the only way to mistype an AB genotype as AA is via allelic dropout (AD), whereas in reality it's possible (if unlikely for variable microsatellites) for this error to arise via a false allele (FA) error. This is presumably why Pedant overestimates the number of ADs and underestimates FAs. Paul Johnson 23/10/09


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