Plant Genetic Resources Unit, United States Department of Agriculture, Agricultural Research Service, 630 W. North St., Geneva, NY 14456, USA.
Institute of Biotechnology, 526 Campus Rd., Cornell University, Ithaca, NY 14853, USA.
Genome. 2020 Dec;63(12):607-613. doi: 10.1139/gen-2020-0011. Epub 2020 Aug 27.
Onion () is not highly tractable for development of molecular markers due to its large (16 gigabases per 1C) nuclear genome. Single nucleotide polymorphisms (SNPs) are useful for genetic characterization and marker-aided selection of onion because of codominance and common occurrence in elite germplasm. We completed genotyping by sequencing (GBS) to identify SNPs in onion using 46 F plants, parents of the F plants (Ailsa Craig 43 and Brigham Yellow Globe 15-23), two doubled haploid (DH) lines (DH2107 and DH2110), and plants from 94 accessions in the USDA National Plant Germplasm System (NPGS). SNPs were called using the TASSEL 3.0 Universal Network Enabled Analysis (UNEAK) bioinformatics pipeline. Sequences from the F and DH plants were used to construct a pseudo-reference genome against which genotypes from all accessions were scored. Quality filters were used to identify a set of 284 high quality SNPs, which were placed onto an existing genetic map for the F family. Accessions showed a moderate level of diversity (mean = 0.341) and evidence of inbreeding (mean = 0.592). GBS is promising for SNP discovery in onion, although lack of a reference genome required extensive custom scripts for bioinformatics analyses to identify high quality markers.
洋葱的核基因组较大(每个 1C 为 16 千兆碱基),因此在开发分子标记方面难度较大。由于单核苷酸多态性 (SNP) 具有共显性和在优良种质中常见的特点,因此非常适用于洋葱的遗传特征分析和标记辅助选择。我们使用 46 个 F 代植物、F 代植物的亲本(Ailsa Craig 43 和 Brigham Yellow Globe 15-23)、两个双单倍体 (DH) 系(DH2107 和 DH2110)以及来自美国农业部国家植物种质系统(NPGS)的 94 个品种进行了测序基因型分析 (GBS),以鉴定洋葱中的 SNP。使用 TASSEL 3.0 通用网络启用分析 (UNEAK) 生物信息学管道对 SNP 进行了调用。来自 F 代和 DH 代植物的序列被用来构建一个伪参考基因组,所有品种的基因型都根据该基因组进行评分。使用质量过滤器来识别出 284 个高质量 SNP,将其放置在 F 家系的现有遗传图谱上。品种表现出中等程度的多样性(平均值为 0.341)和自交程度(平均值为 0.592)。GBS 有望在洋葱中发现 SNP,尽管缺乏参考基因组,需要大量的自定义脚本进行生物信息学分析,以识别高质量的标记。