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Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding

机译:全基因组中小麦草产量构成特征的全基因组关联研究及其在基因组选择和育种中的意义

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摘要

Intermediate wheatgrass (Thinopyrum intermedium, IWG) is a perennial grain crop with high biomass and grain yield, long seeds, and resistance to pests and diseases. It also reduces soil erosion, nitrate and mineral leaching into underground water tables, and sequesters carbon in its roots. The domestication timeline of IWG as a grain crop spans only 3 decades, hence it lags annual grain crops in yield and seed characteristics. One approach to improve its agronomic traits is by using molecular markers to uncover marker-trait associations. In this study, we performed association mapping on IWG breeding germplasm from the third recurrent selection cycle at the University of Minnesota. The IWG population was phenotyped in St Paul, MN in 2017 and 2018, and in Crookston, MN in 2018 for grain yield, seed length, width and weight, spike length and weight, and number of spikelets per spike. Strong positive correlations were observed among most trait pairs, with correlations as high as 0.76. Genotyping using high throughput sequencing identified 8,899 high-quality genome-wide SNPs which were combined with phenotypic data in association mapping to discover regions associated with the yield component traits. We detected 154 genetic loci associated with these traits of which 19 were shared between at least two traits. Prediction of breeding values using significant loci as fixed effects in genomic selection model improved predictive abilities by up to 14%. Genetic mapping of agronomic traits followed by using genomic selection to predict breeding values can assist breeders in selecting superior genotypes to accelerate IWG domestication.
机译:中级小麦草(Thinopyrum intermedium,IWG)是多年生粮食作物,具有较高的生物量和谷物产量,种子长,对病虫害具有抵抗力。它还可以减少土壤侵蚀,硝酸盐和矿物质浸入地下水位,并在其根部隔离碳。 IWG作为谷物作物的驯化时间跨度仅为3年,因此在产量和种子特性方面落后于年度谷物作物。改善其农艺性状的一种方法是通过使用分子标记物来揭示标记物-性状关联。在这项研究中,我们对来自明尼苏达大学的第三个重复选择周期的IWG育种种质进行了关联作图。 IWG群体分别于2017年和2018年在明尼苏达州圣保罗和2018年在明尼苏达州克鲁克斯顿的表型中进行分析,以了解谷物产量,种子长度,宽度和重量,穗长和重量以及每个穗的小穗数量。在大多数性状对之间观察到强正相关,相关性高达0.76。使用高通量测序进行基因分型鉴定出了8,899个全基因组范围内的高质量SNP,它们与表型数据结合在一起进行关联作图,以发现与产量成分性状相关的区域。我们检测到154个与这些性状相关的遗传基因座,其中至少有两个性状共有19个。在基因组选择模型中使用显着位点作为固定效应的育种值预测将预测能力提高了14%。农艺性状的遗传作图,然后使用基因组选择来预测育种值,可帮助育种者选择优良的基因型以加速IWG的驯化。

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