Genetic Mapping in Autohexaploid Sweet Potato with Low-Coverage NGS-Based Genotyping Data

Abstract

Next-generation sequencing (NGS)-based genotyping methods can generate numerous genetic markers in a single experiment and have contributed to plant genetic mapping. However, for high precision genetic analysis, the complicated genetic segregation mode in polyploid organisms requires high-coverage NGS data and elaborate analytical algorithms. In the present study, we propose a simple strategy for the genetic mapping of polyploids using low-coverage NGS data. The validity of the strategy was investigated using simulated data. Previous studies indicated that accurate allele dosage estimation from low-coverage NGS data (read depth < 40) is difficult. Therefore, we used allele dosage probabilities calculated from read counts in association analyses to detect loci associated with phenotypic variations. The allele dosage probabilities showed significant detection power, although higher allele dosage estimation accuracy resulted in higher detection power. On the contrary, differences in the segregation patterns between the marker and causal genes resulted in a drastic decrease in detection power even if the marker and casual genes were in complete linkage and the allele dosage estimation was accurate. These results indicated that the use of a larger number of markers is advantageous, even if the accuracy of allele dosage estimation is low. Finally, we applied the strategy for the genetic mapping of autohexaploid sweet potato (Ipomoea batatas) populations to detect loci associated with agronomic traits. Our strategy could constitute a cost-effective approach for preliminary experiments done performed to large-scale studies.