Population genetic structure of Hymenopellis radicata germplasm resources based on genome re-sequencing.

Through whole-genome re-sequencing of 18 Hymenopellis radicata germplasm resources collected from diverse regions in China, we identified significant variations in the form of Single Nucleotide Polymorphisms (SNPs) and Insertions and Deletions (InDels). These variations were comprehensively annotated, shedding light on the mutation types present in the entire genome of the H. radicata germplasm. This analysis revealed the number and position information of each mutation and provided insights into the overall genomic landscape of H. radicata germplasm. Utilizing SNP data, we delved into the population structure of the 18 H. radicata germplasm resources. The results indicated the presence of 2,335,179 Indel sites and 12,050,448 SNP sites. The population structure analysis unveiled two distinct subgroups among the H. radicata germplasm resources. Phenotypic statistics, principal component analysis, and phylogenetic tree results echoed the findings of the population structure analysis. Different strains of H. radicata from various regions in China exhibited notable differences in genetic diversity, mycelial growth rate, yield, and fruiting body characteristics. Significant disparities were observed between the two subgroups, while strains within each subgroup shared common characteristics. This research establishes a solid foundation for integrating H. radicata into diverse breeding programs. The data underscore the potential of H. radicata for genetic improvement and exploitation in breeding initiatives, paving the way for future advancements in this field.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer QL declared a shared affiliation with the authors YN and WL to the handling editor at the time of review.
(Copyright © 2024 Cao, Yang, Zhang, Ni, Li, Feng, Mu and Zhao.)