Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection
Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection
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DOI：
10.2135/cropsci2018.09.0602
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发表时间：
2019-05-01
期刊：
影响因子：
2.3
通讯作者：
Dawson, J. C.
中科院分区：
文献类型：
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作者：
Corak, K. E.;Ellison, S. L.;Dawson, J. C.
作物育种计划对利用遗传资源感兴趣，但难以从种质收集中识别有用的种质。为了有效地利用大型种质资源中存在的多样性，育种者通常识别代表较大种质资源的一个子集。识别这些子集的方法被称为核心集合，并不能始终如一地捕捉功能多样性，育种者将受益于使用品种试验或育种计划的现有数据帮助创建定制核心集合的方法。利用433个驯化胡萝卜（Daucus carota L.）为了获得更多的种质，我们测试了是否有可能为特定的育种目的开发定制的种质子集。我们发现，对于这个集合，代表性策略在开发捕捉集合多样性的核心集合方面是有效的，但它们并不比随机抽样更好，可能是因为集合本身没有被强烈细分。自定义策略产生的子集，从总集合不同，改变了遗传，地理和表型组成。然而，当用作集合中其他加入物的基因组预测的训练群体时，这些定制核心并没有产生比传统核心集合实质性的改进。增加核心的大小确实提高了预测的准确性，这表明有可能通过识别足够大的定制子集来提高核心集合的有用性，以代表集合中存在的功能遗传多样性。
Crop breeding programs are interested in using genetic resources but have difficulty identifying useful accessions from germplasm collections. To efficiently use the diversity present in large germplasm collections, breeders often identify a subset of accessions that represent the larger collection. Methods to identify these subsets, which are called core collections, do not consistently capture functional diversity, and breeders would benefit from methods that help create custom core collections using existing data from variety trials or breeding programs. Making use of high-density genomic data and existing phenotypic data from a collection of 433 domesticated carrot (Daucus carota L.) accessions, we tested whether it is possible to develop custom subsets of accessions for specific breeding purposes. We found that for this collection, representative strategies were effective in developing core collections that capture the diversity of the collection, but they were no better than random sampling, likely because the collection itself is not strongly subdivided. Custom strategies generated subsets that differed from the total collection with altered genetic, geographic, and phenotypic compositions. When used as training populations for genomic prediction of the other accessions in the collection, however, these custom cores did not produce a substantial improvement over traditional core collections. Increasing the size of the core did improve prediction accuracy, suggesting that it is possible to improve the usefulness of core collections by identifying custom subsets that are large enough to represent the functional genetic diversity present in the collection.