David Gerard1, Luís Felipe Ventorim Ferrão2. 1. Department of Mathematics and Statistics, American University, Washington, DC 20016, USA. 2. Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA.
Abstract
MOTIVATION: Empirical Bayes techniques to genotype polyploid organisms usually either (i) assume technical artifacts are known a priori or (ii) estimate technical artifacts simultaneously with the prior genotype distribution. Case (i) is unappealing as it places the onus on the researcher to estimate these artifacts, or to ensure that there are no systematic biases in the data. However, as we demonstrate with a few empirical examples, case (ii) makes choosing the class of prior genotype distributions extremely important. Choosing a class is either too flexible or too restrictive results in poor genotyping performance. RESULTS: We propose two classes of prior genotype distributions that are of intermediate levels of flexibility: the class of proportional normal distributions and the class of unimodal distributions. We provide a complete characterization of and optimization details for the class of unimodal distributions. We demonstrate, using both simulated and real data that using these classes results in superior genotyping performance. AVAILABILITY AND IMPLEMENTATION: Genotyping methods that use these priors are implemented in the updog R package available on the Comprehensive R Archive Network: https://cran.r-project.org/package=updog. All code needed to reproduce the results of this article is available on GitHub: https://github.com/dcgerard/reproduce_prior_sims. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Empirical Bayes techniques to genotype polyploid organisms usually either (i) assume technical artifacts are known a priori or (ii) estimate technical artifacts simultaneously with the prior genotype distribution. Case (i) is unappealing as it places the onus on the researcher to estimate these artifacts, or to ensure that there are no systematic biases in the data. However, as we demonstrate with a few empirical examples, case (ii) makes choosing the class of prior genotype distributions extremely important. Choosing a class is either too flexible or too restrictive results in poor genotyping performance. RESULTS: We propose two classes of prior genotype distributions that are of intermediate levels of flexibility: the class of proportional normal distributions and the class of unimodal distributions. We provide a complete characterization of and optimization details for the class of unimodal distributions. We demonstrate, using both simulated and real data that using these classes results in superior genotyping performance. AVAILABILITY AND IMPLEMENTATION: Genotyping methods that use these priors are implemented in the updog R package available on the Comprehensive R Archive Network: https://cran.r-project.org/package=updog. All code needed to reproduce the results of this article is available on GitHub: https://github.com/dcgerard/reproduce_prior_sims. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.