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Genome-wide association study and genomic selection for yield and related traits in soybean

机译:大豆产量和相关性状的基因组 - 范围基因研究和基因组选择

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Soybean [ Glycine max (L . ) Merr .] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci ( E2 , E4 , and Dt1 ) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.
机译:大豆[甘氨酸Max(l。)merr。]是全世界兴趣的作物。探索产量遗传增益增加的分子方法是大豆育种者和遗传学家的主要挑战之一。已经发现农艺特征,如成熟度,植物高度和种子重量有助于产量。在这项研究中,总共250种大豆载体与通过测序(GBS)的基因分型假定了10,259种高质量的SNP,并在三年内评估了籽粒产量,成熟度,植物高度和种子重量。使用贝叶斯信息和联系不平衡迭代嵌套键槽(眨眼)模型进行基因组 - 宽协会研究(GWAs)。使用脊回归最佳线性无偏见的预测器(RRBLUP)模型来评估基因组选择(GS)。结果表明,20,31,37和23个SNP分别与成熟,植物高度,种子重量和产量显着相关;将许多SNP映射到先前描述的成熟度和植物高度基因座(E2,E4和DT1),并且将新的植物高度基因座映射到染色体20.在两个SNP附近发现候选基因,其具有与之相关的最高水平产量,成熟,植物高度,种子重量分别。染色体10的11.5mb区域与产量和种子重量有关。总体而言,GS的准确性取决于特征,年和人口结构,高精度表明这些农艺性质可以通过GS分子育种。本研究中鉴定的SNP标记可用于通过在繁殖计划中通过标记辅助选择和GS来改善产量和农艺性状。

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