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The chromosome-scale genome of Magnolia sinica (Magnoliaceae) provides insights into the conservation of plant species with extremely small populations (PSESP).

Lei CaiDe-Tuan LiuFengmao YangRen-Gang ZhangQuanzheng YunZhiling DaoYong-Peng MaWei Bang Sun
Published in: GigaScience (2024)
Magnolia sinica (Magnoliaceae) is a highly threatened tree endemic to southeast Yunnan, China. In this study, we generated for the first time a high-quality chromosome-scale genome sequence from M. sinica, by combining Illumina and ONT data with Hi-C scaffolding methods. The final assembled genome size of M. sinica was 1.84 Gb, with a contig N50 of ca. 45 Mb and scaffold N50 of 92 Mb. Identified repeats constituted approximately 57% of the genome, and 43,473 protein-coding genes were predicted. Phylogenetic analysis shows that the magnolias form a sister clade with the eudicots and the order Ceratophyllales, while the monocots are sister to the other core angiosperms. In our study, a total of 21 individuals from the 5 remnant populations of M. sinica, as well as 22 specimens belonging to 8 related Magnoliaceae species, were resequenced. The results showed that M. sinica had higher genetic diversity (θw = 0.01126 and θπ = 0.01158) than other related species in the Magnoliaceae. However, population structure analysis suggested that the genetic differentiation among the 5 M. sinica populations was very low. Analyses of the demographic history of the species using different models consistently revealed that 2 bottleneck events occurred. The contemporary effective population size of M. sinica was estimated to be 10.9. The different patterns of genetic loads (inbreeding and numbers of deleterious mutations) suggested constructive strategies for the conservation of these 5 different populations of M. sinica. Overall, this high-quality genome will be a valuable genomic resource for conservation of M. sinica.
Keyphrases
  • genetic diversity
  • genome wide
  • copy number
  • dna methylation
  • gene expression
  • small molecule
  • machine learning
  • amino acid
  • single cell
  • data analysis
  • protein kinase
  • fine needle aspiration