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Application of Genomic Approaches to Improve Yield and Bacterial Leaf Streak Resistance in Winter Wheat

机译:基因组学方法在提高冬小麦产量和细菌叶片条纹抗性中的应用

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摘要

Global wheat production is threatened by the change in climate thus leading lead to the increase in the biotic and abiotic stresses. We need to increase wheat productivity at a faster pace and manage these challenges to meet the growing demand. Development of cultivars with durable disease resistance and enhancing the rate of genetic gain in wheat are the major goals in wheat breeding programs. Bacterial Leaf Streak (BLS) is one of the most threatening bacterial diseases to wheat in the US Northern Great Plains. Unlike fungal diseases, bacterial diseases cannot be effectively managed using chemicals and thus developing disease resistant cultivars would be the most economical control for BLS. Identification and characterization of genomic regions in wheat that confer resistance to BLS can be an effective way to mobilize resistance genes in wheat breeding. Here we performed Genome -- wide association mapping on a Hard Winter Wheat Association Panel (HWWMP) to identify genomic regions that confer resistance to BLS. The genotyped data for this panel of 300 winter wheat lines from the major breeding programs across the Midwestern region of the US was obtained from T3 Triticale Toolbox (under the GPL license). The responses of all these lines against Xanthomonas campestris pv. translucens in the greenhouse and field conditions were evaluated. Association Mapping (AM) was used to detect marker -- trait associations using ECMLM, and we identified five QTL regions (Q.bls.sdsu.1AL, Q.bls.sdsu.1BS, Q.bls.sdsu.3AL, Q.bls.sdsu.4AL and Q.bls.sdsu.7AS) conferring BLS resistance. In total, these five QTLs explained 42% of the variation. Eleven genotypes were identified, which could be used as a source of resistance against BLS. Comparative analysis of three of the identified QTLs (Q.bls.sdsu.1AL, Q.bls.sdsu.3AL and Q.bls.sdsu.4AL ) with rice showed BLS resistance genes in rice (qBLSr5d, qBLSr1, and qBLSr3d) located on syntenic regions in rice chromosomes 5R, 1R and 3R respectively. The 11 BLS resistant genotypes and SNP markers linked to QTLs identified in our study could facilitate breeding BLS resistance in wheat. For grain yield improvement, we assessed the robustness for genomic selection (GS) in the South Dakota State Winter Wheat Breeding program (SDSWWBP). We performed GS with a set of 434 advanced breeding lines (AYT and PYT nurseries) between the years 2014 -- 2017. These lines were genotyped by sequencing GBS and the yield data from 34 years x location combinations were used as a phenotype. We developed training and validation datasets for testing the genomic prediction accuracies. Single and multiyear analysis were done using several GS models (rrBLUP, PLSR, ELNET and Random Forest). The average predictions accuracies within a single year across locations were 0.62. However, with the multi-year-location analysis, the average genomic prediction accuracies were 0.26 for two-year combination, 0.32 for three-year combination and 0.36 for the four-year combination. Our results suggested several years of data is required to develop better genome-wide selection models.
机译:全球小麦产量受到气候变化的威胁,从而导致生物和非生物胁迫的增加。我们需要以更快的速度提高小麦的生产率,并应对这些挑战,以满足不断增长的需求。在小麦育种计划中,开发具有持久抗病性的品种以及提高小麦的遗传增益速率是其主要目标。细菌叶斑病(BLS)是美国北部大平原地区对小麦威胁最大的细菌性疾病之一。与真菌性疾病不同,细菌性疾病无法使用化学药品有效管理,因此开发抗病品种将是BLS最经济的控制方法。鉴定和鉴定赋予BLS抗性的小麦基因组区域可能是动员小麦育种中抗性基因的有效途径。在这里,我们在``硬冬小麦协会专家组''(HWWMP)上进行了基因组-广泛关联映射,以鉴定赋予BLS抗性的基因组区域。来自美国中西部地区主要育种计划的300个冬小麦品系的小组基因分型数据来自T3 Triticale Toolbox(已获得GPL许可)。所有这些反对派黄单胞菌光伏的反应。评估了温室和田间条件下的半透明蛋白。关联映射(AM)用于使用ECMLM检测标记-性状关联,我们确定了五个QTL区域(Q.bls.sdsu.1AL,Q.bls.sdsu.1BS,Q.bls.sdsu.3AL,Q。 bls.sdsu.4AL和Q.bls.sdsu.7AS)赋予BLS抗性。总共,这五个QTL解释了这种变化的42%。确定了11个基因型,可以用作对BLS的抗药性来源。对三个已鉴定的QTLs(Q.bls.sdsu.1AL,Q.bls.sdsu.3AL和Q.bls.sdsu.4AL)与水稻的比较分析表明,位于水稻中的BLS抗性基因(qBLSr5d,qBLSr1和qBLSr3d)位于分别位于水稻5R,1R和3R染色体的同义区域。在我们的研究中确定的与QTL相关的11种BLS抗性基因型和SNP标记可以促进小麦BLS抗性的育种。为了提高谷物产量,我们在南达科他州冬小麦育种计划(SDSWWBP)中评估了基因组选择(GS)的稳健性。在2014年至2017年之间,我们对434个高级育种系(AYT和PYT苗圃)进行了GS。这些系通过对GBS进行测序进行基因分型,并将34年x定位组合的产量数据用作表型。我们开发了用于测试基因组预测准确性的培训和验证数据集。使用几种GS模型(rrBLUP,PLSR,ELNET和Random Forest)进行了单年和多年分析。各个地区一年内的平均预测准确度为0.62。但是,通过多年定位分析,两年组合的平均基因组预测准确度为0.26,三年组合为0.32,四年组合为0.36。我们的结果表明,要开发更好的全基因组选择模型需要数年的数据。

著录项

  • 作者

    Ramakrishnan, Sai Mukund.;

  • 作者单位

    South Dakota State University.;

  • 授予单位 South Dakota State University.;
  • 学科 Plant sciences.
  • 学位 M.S.
  • 年度 2018
  • 页码 126 p.
  • 总页数 126
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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