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SNP development and diversity analysis for Ginkgo biloba based on transcriptome sequencing

Wu, Yaqiong, Zhou, Qi, Huang, Shujing, Wang, Guibin, Xu, Li-an
Trees 2019 v.33 no.2 pp. 587-597
Ginkgo biloba, assets, biomarkers, data collection, dioecy, genetic variation, genomics, genotyping, heterozygosity, loci, melting, polymerase chain reaction, sequence analysis, single nucleotide polymorphism, transcriptome, transcriptomics, unigenes
KEY MESSAGE: Based on Ginkgo biloba transcriptome data, 22 SNP loci using high-resolution melting curve technology were validated and genetic diversity was analyzed in three populations. Ginkgo biloba (Ginkgo) is a long-lived dioecious gymnosperm with unique morphological characteristics and play a significant role in evolutionary relationship research. In this study, we used Illumina paired-end RNA sequencing technology for facilitating the gene discovery and single nucleotide polymorphism (SNP) development in Ginkgo. We collected 44.08 G clean bases from six transcriptome datasets. The transcriptome data generated 98,919 unigenes among which 42,667 (43.13%) were successfully annotated. A total of 139,854 putative SNPs were identified; most of the SNPs were transition-type with the nucleotide transitions C–T or A–G. Further, 54532 (38.99%) SNPs were found in protein-coding regions: 23483 (43.06%) were synonymous and 31049 (56.94%) were nonsynonymous. 22 SNPs were subjected to PCR amplification and Sanger sequencing, and all of them were validated. To test the practicability of identified SNPs, these validated SNPs were also assessed by genotyping three natural populations with 84 individuals by high-resolution melting curve (HRM) analysis. Observed and expected heterozygosity varied from 0.0119 to 0.9643 and from 0.0581 to 0.5024, respectively. HRM technology was first applied for the SNP genotyping in Ginkgo. SNPs identified by RNA-Seq provided a useful resource for genetic and genomic studies in Ginkgo. Moreover, the collection of nonsynonymous SNPs annotated with their predicted functional effects also provided a valuable asset for further discovery of genes, identification of gene variants, and development of functional markers.