Genome-Wide Analysis of Seed Acid Detergent Lignin (ADL) and Hull Content in Rapeseed (Brassica napus L.).
Genome-Wide Analysis of Seed Acid Detergent Lignin (ADL) and Hull Content in Rapeseed (Brassica napus L.).
复制标题
油菜籽（Brassica napus L.）种子酸洗涤剂木质素 (ADL) 和壳含量的全基因组分析
DOI：
10.1371/journal.pone.0145045
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Liu L
中科院分区：
文献类型：
--
作者：
Wang J;Jian H;Wei L;Qu C;Xu X;Lu K;Qian W;Li J;Li M;Liu L
稳定的黄籽油菜品种是获得理想油菜品种的育种目标。种子中酸性洗涤木质素（ADL）含量和船体含量（HC）常作为黄籽油菜的筛选指标。在这项研究中，使用Q + K模型对520份材料进行了全基因组关联分析，共有31，839个单核苷酸多态性（SNP）位点。因此，在B上有三个显著的关联。检测了油菜A05、A09和C 05染色体种子ADL含量。在关键基因BnaA. PAL 4、BnaA.CAD2/BnaA.CAD3和BnaC. CCR 1的9.27、14.22和20.86 kb范围内出现SNPs峰值。对A05的主基因座进行了进一步分析，该主基因座也在种子HC检查中检测到。我们的全基因组关联研究（GWAS）的结果和以前的连锁图谱的比较显示，一个共同的染色体区域A09，这表明GWAS可以作为一个强大的互补策略，解剖复杂的性状在B。油菜。利用基于GWAS结果的显著SNP标记的基因组选择（GS）表现出增加的预测能力，表明通过使用GWAS和GS可以显著提高给定模型的预测能力。
A stable yellow-seeded variety is the breeding goal for obtaining the ideal rapeseed (Brassica napus L.) plant, and the amount of acid detergent lignin (ADL) in the seeds and the hull content (HC) are often used as yellow-seeded rapeseed screening indices. In this study, a genome-wide association analysis of 520 accessions was performed using the Q + K model with a total of 31,839 single-nucleotide polymorphism (SNP) sites. As a result, three significant associations on the B. napus chromosomes A05, A09, and C05 were detected for seed ADL content. The peak SNPs were within 9.27, 14.22, and 20.86 kb of the key genes BnaA.PAL4, BnaA.CAD2/BnaA.CAD3, and BnaC.CCR1, respectively. Further analyses were performed on the major locus of A05, which was also detected in the seed HC examination. A comparison of our genome-wide association study (GWAS) results and previous linkage mappings revealed a common chromosomal region on A09, which indicates that GWAS can be used as a powerful complementary strategy for dissecting complex traits in B. napus. Genomic selection (GS) utilizing the significant SNP markers based on the GWAS results exhibited increased predictive ability, indicating that the predictive ability of a given model can be substantially improved by using GWAS and GS.