Suppr超能文献

小麦(L.)抗条锈病的基因组预测评估及新位点鉴定

Evaluations of Genomic Prediction and Identification of New Loci for Resistance to Stripe Rust Disease in Wheat ( L.).

作者信息

Tomar Vipin, Dhillon Guriqbal Singh, Singh Daljit, Singh Ravi Prakash, Poland Jesse, Chaudhary Anis Ahmad, Bhati Pradeep Kumar, Joshi Arun Kumar, Kumar Uttam

机构信息

Borlaug Institute for South Asia, Ludhiana, India.

International Maize and Wheat Improvement Center, New Delhi, India.

出版信息

Front Genet. 2021 Sep 28;12:710485. doi: 10.3389/fgene.2021.710485. eCollection 2021.

Abstract

Stripe rust is one of the most destructive diseases of wheat ( L.), caused by f. sp. (), and responsible for significant yield losses worldwide. Single-nucleotide polymorphism (SNP) diagnostic markers were used to identify new sources of resistance at adult plant stage to wheat stripe rust (YR) in 141 CIMMYT advanced bread wheat lines over 3 years in replicated trials at Borlaug Institute for South Asia (BISA), Ludhiana. We performed a genome-wide association study and genomic prediction to aid the genetic gain by accumulating disease resistance alleles. The responses to YR in 141 advanced wheat breeding lines at adult plant stage were used to generate G × E (genotype × environment)-dependent rust scores for prediction and genome-wide association study (GWAS), eliminating variation due to climate and disease pressure changes. The lowest mean prediction accuracies were 0.59 for genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP (RRBLUP), while the highest mean was 0.63 for extended GBLUP (EGBLUP) and random forest (RF), using 14,563 SNPs and the G × E rust score results. RF and EGBLUP predicted higher accuracies (∼3%) than did GBLUP and RRBLUP. Promising genomic prediction demonstrates the viability and efficacy of improving quantitative rust tolerance. The resistance to YR in these lines was attributed to eight quantitative trait loci (QTLs) using the FarmCPU algorithm. Four (, , , and ) of eight QTLs linked to the diagnostic markers were mapped at unique loci (previously unidentified for resistance) and possibly new loci. The statistical evidence of effectiveness and distribution of the new diagnostic markers for the resistance loci would help to develop new stripe rust resistance sources. These diagnostic markers along with previously established markers would be used to create novel DNA biosensor-based microarrays for rapid detection of the resistance loci on large panels upon functional validation of the candidate genes identified in the present study to aid in rapid genetic gain in the future breeding programs.

摘要

条锈病是小麦最具毁灭性的病害之一,由小麦条锈菌(Puccinia striiformis f. sp. tritici)引起,在全球范围内导致显著的产量损失。在位于卢迪亚纳的南亚博洛格研究所(BISA)进行的为期3年的重复试验中,利用单核苷酸多态性(SNP)诊断标记,在141份国际玉米小麦改良中心(CIMMYT)的优质面包小麦品系中,鉴定了成年植株期对小麦条锈病(YR)的新抗性来源。我们进行了全基因组关联研究和基因组预测,以通过积累抗病等位基因来促进遗传增益。利用141个先进小麦育种品系在成年植株期对条锈病的反应,生成了依赖于基因型×环境(G×E)的锈病评分,用于预测和全基因组关联研究(GWAS),消除了气候和病害压力变化带来的变异。使用14563个SNP和G×E锈病评分结果,基因组最佳线性无偏预测(GBLUP)和岭回归BLUP(RRBLUP)的最低平均预测准确率为0.59,而扩展GBLUP(EGBLUP)和随机森林(RF)的最高平均预测准确率为0.63。RF和EGBLUP的预测准确率比GBLUP和RRBLUP高约3%。有前景的基因组预测证明了提高定量锈病耐受性的可行性和有效性。利用FarmCPU算法,这些品系对条锈病的抗性归因于8个数量性状位点(QTL)。与诊断标记连锁的8个QTL中的4个(QYr.cim-2B、QYr.cim-5A、QYr.cim-6B和QYr.cim-7B)定位在独特的位点(以前未鉴定出对条锈病的抗性),可能是新位点。抗性位点新诊断标记有效性和分布的统计证据将有助于开发新的条锈病抗性来源。这些诊断标记与先前建立的标记一起,将用于创建基于新型DNA生物传感器的微阵列,以便在对本研究中鉴定的候选基因进行功能验证后,在大型群体上快速检测抗性位点,从而有助于未来育种计划中的快速遗传增益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/961a/8505882/0b5561ad3159/fgene-12-710485-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验