Harnessing Empirical Bayes and Mendelian Segregation for Genotyping Autopolyploids from Messy Sequencing Data
Detecting and quantifying the differences in individual genomes (i.e. genotyping), plays a fundamental role in most modern bioinformatics pipelines. Many scientists now use reduced representation next-generation sequencing (NGS) approaches for genotyping. Genotyping diploid individuals using NGS is a well-studied field and similar methods for polyploid individuals are just emerging. However, there are many aspects of NGS data, particularly in polyploids, that remain unexplored by most methods. We provide two main contributions in this paper: (i) We draw attention to, and then model, common aspects of NGS data: sequencing error, allelic bias, overdispersion, and outlying observations. (ii) Many datasets feature related individuals, and so we use the structure of Mendelian segregation to build an empirical Bayes approach for genotyping polyploid individuals. We assess the accuracy of our method in simulations and apply it to a dataset of hexaploid sweet potatoes (Ipomoea batatas). An R package implementing our method is available at https://github.com/dcgerard/updog.