A perennial leguminous forage, Medicago ruthenica has outstanding tolerance to abiotic stresses. The genome of Medicago ruthenica is large and has a complex genetic background, making it challenging to accurately determine genetic information. However, the chloroplast genome is widely used for researching issues related to evolution, genetic diversity, and other studies. To better understand its chloroplast characteristics and adaptive evolution, chloroplast genomes of 61 Medicago ruthenica were assembled (including 16 cultivated Medicago ruthenica germplasm and 45 wild Medicago ruthenica germplasm). These were used to construct the pan-chloroplast genome of Medicago ruthenica, and the chloroplast genomes of cultivated and wild Medicago ruthenica were compared and analyzed. Phylogenetic and haplotype analyses revealed two main clades of 61 Medicago ruthenica germplasm chloroplast genomes, distributed in eastern and western regions. Meanwhile, based on chloroplast variation information, 61 Medicago ruthenica germplasm can be divided into three genetic groups. Unlike the phylogenetic tree constructed from the chloroplast genome, a new intermediate group has been identified, mainly consisting of samples from the eastern region of Inner Mongolia, Shanxi Province, and Hebei Province. Transcriptomic analysis showed that 29 genes were upregulated and three genes were downregulated. The analysis of these genes mainly focuses on enhancing plant resilience and adapting adversity by stabilizing the photosystem structure and promoting protein synthesis. Additionally, in the analysis of adaptive evolution, the accD, clpP and ycf1 genes showed higher average Ka/Ks ratios and exhibited significant nucleotide diversity, indicating that these genes are strongly positively selected. The editing efficiency of the ycf1 and clpP genes significantly increases under abiotic stress, which may positively contribute to plant adaptation to the environment. In conclusion, the construction and comparative analysis of the complete chloroplast genomes of 61 Medicago ruthenica germplasm from different regions not only revealed new insights into the genetic variation and phylogenetic relationships of Medicago ruthenica germplasm, but also highlighted the importance of chloroplast transcriptome analysis in elucidating the model of chloroplast responses to abiotic stress. These provide valuable information for further research on the adaptive evolution of Medicago ruthenica.

Alfalfa (Medicago sativa) is a perennial forage legume that is widely distributed all over the world; therefore, it has an extremely complex genetic background. Though population structure and phylogenetic studies have been conducted on a large group of alfalfa nuclear genomes, information about the chloroplast genomes is still lacking. Chloroplast genomes are generally considered to be conservative and play an important role in population diversity analysis and species adaptation in plants. Here, 231 complete alfalfa chloroplast genomes were successfully assembled from 359 alfalfa resequencing data, on the basis of which the alfalfa chloroplast pan-genome was constructed. We investigated the genetic variations of the alfalfa chloroplast genome through comparative genomic, genetic diversity, phylogenetic, population genetic structure, and haplotype analysis. Meanwhile, the expression of alfalfa chloroplast genes under cold stress was explored through transcriptome analysis. As a result, chloroplast genomes of 231 alfalfa lack an IR region, and the size of the chloroplast genome ranges from 125,192 bp to 126,105 bp. Using population structure, haplotypes, and construction of a phylogenetic tree, it was found that alfalfa populations could be divided into four groups, and multiple highly variable regions were found in the alfalfa chloroplast genome. Transcriptome analysis showed that tRNA genes were significantly up-regulated in the cold-sensitive varieties, while rps7, rpl32, and ndhB were down-regulated, and the editing efficiency of ycf1, ycf2, and ndhF was decreased in the cold-tolerant varieties, which may be due to the fact that chloroplasts store nutrients through photosynthesis to resist cold. The huge number of genetic variants in this study provide powerful resources for molecular markers.

Agronomic traits and quality traits of alfalfa are of great importance to the feed industry. Genomic selection (GS) based on genotyping-by-sequencing (GBS) data, if it achieves moderate to high accuracy, has the potential to significantly shorten breeding cycles for complex traits and accelerate genetic progress. This study aims to investigate the effect of different reference genomes on the prediction accuracy of genomic selection. A total of 11 Bayesian and machine learning models and nine different reference genomes were used to conduct genomic selection on five traits in 385 alfalfa accessions. The accuracy of GS was evaluated using five-fold cross-validation, based on the correlation between genomic estimated breeding values (GEBVs) and estimated breeding values (EBVs). For the five traits, it was found that traits with high heritability exhibited significantly higher prediction accuracy. The prediction accuracy fluctuated minimally across different reference genomes, with the diploid genome showing relatively higher accuracy. For two high-heritability traits, fall dormancy and plant height, predictions were made after SNP density reduction, and it was observed that density had little effect on prediction accuracy. However, for the fall dormancy trait in the diploid genome, more than half of the models showed regular fluctuations, with prediction accuracy increasing as SNP density increased. In conclusion, this study provides a theoretical basis for precision breeding of alfalfa and other polyploid crops by combining different reference genomes and models, and offers important guidance for optimizing future genomic selection strategies.