RT Journal Article
SR Electronic
T1 Genotyping Polyploids from Messy Sequencing Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 281550
DO 10.1101/281550
A1 David Gerard
A1 Luis Felipe Ventorim Ferrão
A1 Antonio Augusto Franco Garcia
A1 Matthew Stephens
YR 2018
UL http://biorxiv.org/content/early/2018/08/02/281550.abstract
AB 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. Our contributions in this paper are four-fold: (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. (iii) We develop novel models to account for preferential pairing of chromosomes and harness these for genotyping. (iv) We derive oracle genotyping error rates that may be used for read depth suggestions. 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://cran.r-project.org/package=updog.