Bayesian parentage analysis with systematic accountability of genotyping error, missing data, and false matching Public Deposited

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  • Motivation: The goal of any parentage analysis is to identify as many parent-offspring relationships as possible, while minimizing incorrect assignments. Existing methods can achieve these ends, but require additional information in the form of demographic data, thousands of markers, and/or estimates of genotyping error rates. For many non-model systems, it is simply not practical, cost-effective, or logistically feasible to obtain this information. Here, we develop a Bayesian parentage method that only requires the sampled genotypes in order to account for genotyping error, missing data, and false matches. Results: Extensive testing with microsatellite and SNP data sets reveals that our Bayesian parentage method reliably controls for the number of false assignments, irrespective of the genotyping error rate. When the number of loci is limiting, our approach maximizes the number of correct assignments by accounting for the frequencies of shared alleles. Comparisons with exclusion and likelihood-based methods on an empirical salmon data set revealed that our Bayesian method had the highest ratio of correct to incorrect assignments. Availability: Our program SOLOMON is available as an R package from the CRAN website. SOLOMON comes with a fully functional graphical user interface, requiring no user knowledge about the R programming environment. In addition to performing Bayesian parentage analysis, SOLOMON includes Mendelian exclusion and a priori power analysis modules. Further information and user support can be found at
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  • Christie, M., Tennessen, J., & Blouin, M. (2013). Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching. Bioinformatics, 29(6), 725-732. doi:10.1093/bioinformatics/btt039
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  • 29
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  • 6
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  • This research was supported by a grant to M.S. Blouin from the Bonneville Power Administration.
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