利用连锁分析和关联分析发掘小麦抗旱优异等位基因

Wheat is the staple food crops for human, but frequently suffers drought stress during the growing season. It makes the improvement of drought tolerance (DT) in wheat especially important. Abundant wheat germplasm resources with diverse drought tolerance have been identified from the accessions conserved in the National Genebank. They are the vital gene resources for wheat DT improvement. However, we highly lack the knowledge on the drought tolerant resources, such as what DT genes these germplasm possess, in which accessions there are the superior alleles underlying drought tolerance, and what effects these favorable alleles play. Wheat is a species with the huge and complex hexaploid genome. A series of agronomic and physiologic traits are involved in the drought tolerance in wheat. Most of them are quantitative traits. Quantitative trait loci (QTL) for complex traits in plants generally have been mapped through biparental population-based linkage analysis or diverse germplasm-based linkage disequilibrium (LD) analysis. Linkage analysis also called linkage mapping, is powerful for detecting genetic effects at loci involved in the expression of target traits. LD analysis called association analysis or association mapping, has significant potential to simultaneously identify and evaluate the varying effects of many alleles. The major limitation of the linkage analysis is the limited biparental diversity in the genetic backgrounds. One of the key limitations of association analysis is the effect of genetic relatedness in the population, which often cause the spurious associations. Another problem is that rare alleles severely limit the power of QTL detection. Joint linkage-association analysis is an alternative approach to overcome some of their inherent limitations. The purpose of the present proposal is to investigate the efficient approach for discovering superior alleles underlying drought tolerance in wheat using joint linkage-association mapping. The linkage mapping of trait-molecular marker is conducted using a doubled haploid (DH) population (Hanxuan 10 × Lumai 14) with 150 lines. The association mapping is implemented using a natural population with 300 accessions diversed in drought tolerance, and the combinations of natural population with about 500 DT introgression lines, which are derived from backcrosses. The DT introgression lines are also used to verify the effects of favorable alleles. The research results will be beneficial to DT molecular assisted breeding, DT gene cloning, and will enhance the efficiency of drought tolerance improvement in wheat.

发掘利用抗旱基因，改良小麦抗旱性是应对干旱的重要途径。我国小麦种质资源中蕴藏着丰富的抗旱材料，这些材料中有哪些抗旱基因?哪些材料中具有最优异的等位基因?其遗传效应如何?目前尚不清楚。小麦抗旱性涉及诸多农艺性状和生理性状等，多数是复杂的数量性状。解析数量性状基因的主要研究方法包括连锁分析与关联分析，不过，连锁分析对目标基因的检测效率受限于双亲的遗传差异，关联分析受群体遗传结构的影响，导致伪关联，对低频率等位基因检出效率低。本项目以探讨联合利用连锁分析和关联分析高效发掘小麦抗旱优异等位基因的有效途径为目标，以DH群体为材料，进行性状/分子标记的连锁分析；以抗旱多态性高的种质资源及其与回交导入系群体组成的自然群体为材料，进行性状/分子标记的关联分析；以抗旱回交导入系群体为材料，进行连锁-关联分析，验证优异等位基因的遗传效应。为分子标记育种和基因克隆奠定基础，提高抗旱品种选育效率。

发掘利用优异基因改良小麦抗旱性是应对干旱的重要途径，如何高效发掘利用种质资源中的优异基因资源是亟待解决的重要瓶颈问题。小麦抗旱性是复杂的数量性状，解析数量性状基因的主要研究方法包括连锁分析与关联分析，不过，连锁分析对目标基因的检测效率受限于双亲的遗传差异，关联分析受群体遗传结构的影响，导致伪关联，对低频率等位基因检出效率低。本项目以探讨联合利用连锁分析和关联分析高效发掘小麦抗旱优异等位基因的有效途径为目标，认真实施研究计划，圆满完成了研究任务，取得以下主要成果：（1）明确了联合利用连锁分析和关联分析方法能够优势互补，有效克服两种方法在遗传分析方面的局限性，高效检测遗传群体和自然群体中同一基因位点上多个等位基因的效应，提高优异等位基因的发掘效率；（2）利用基因组分子标记对茎秆可溶性糖含量、株高进行连锁和关联分析，发掘出具有大效应的分子标记等位变异17个；（3）根据序列多态性开发了7个基因的功能分子标记，通过连锁与关联分析，发现了与糖代谢、株型、根系结构和产量等性状密切相关的大效应优异等位基因9个；（4）多数优异等位基因虽然在育种进程中已经受到一定程度的正向选择，但频率仍然较低，具有较大的应用潜力。项目组发表研究论文12篇，其中SCI收录期刊论文11篇；获得国家发明专利1项，申请发明专利2项；项目研究成果为进一步提高小麦优异等位基因发掘效率，利用分子标记选择聚合目标性状优异基因和基因克隆奠定了基础，为小麦抗旱高产分子育种提供了理论和技术支撑。培养研究生8名。1人作为“小麦种质资源与遗传改良创新团队”成员获2016年国家科学技术奖创新团队奖。通过本项目的实施，整体提升了团队成员的研究能力和水平。