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Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

机译：利用基因组数据可以高精度预测甘蓝型油菜的种子品质性状

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Improving seed oil yield and quality are central targets in rapeseed (Brassica napus) breeding. The primary goal of our study was to examine and compare the potential and the limits of marker-assisted selection and genome-wide prediction of six important seed quality traits of B. napus. Our study is based on a bi-parental population comprising 202 doubled haploid lines and a diverse validation set including 117 B. napus inbred lines derived from interspecific crosses between B. rapa and B. carinata. We used phenotypic data for seed oil, protein, erucic acid, linolenic acid, stearic acid, and glucosinolate content. All lines were genotyped with a 60k SNP array. We performed five-fold cross-validations in combination with linkage mapping and four genome-wide prediction approaches in the bi-parental population. Quantitative trait loci (QTL) with large effects were detected for erucic acid, stearic acid, and glucosinolate content, blazing the trail for marker-assisted selection. Despite substantial differences in the complexity of the genetic architecture of the six traits, genome-wide prediction models had only minor impacts on the prediction accuracies. We evaluated the effects of training population size, marker density and phenotyping intensity on the prediction accuracy. The prediction accuracy in the independent and genetically very distinct validation set still amounted to 0.14 for protein content and 0.17 for oil content reflecting the utility of the developed calibration models even in very diverse backgrounds.
机译：提高菜籽油产量和质量是油菜（甘蓝型油菜）育种的主要目标。我们研究的主要目标是检验和比较标记辅助选择和油菜六种重要种子品质性状的全基因组预测的潜力和局限性。我们的研究基于双亲种群，其中包括202个双倍单倍体系和多样化的验证集，其中包括从B. rapa和B. carinata之间的种间杂交衍生的117个B. napus自交系。我们使用了种子油，蛋白质，芥酸，亚麻酸，硬脂酸和芥子油苷含量的表型数据。用60k SNP阵列对所有品系进行基因分型。我们在双亲群体中结合了连锁图谱和四种全基因组预测方法，进行了五次交叉验证。对芥酸，硬脂酸和芥子油苷含量检测到影响较大的数量性状基因座（QTL），为标记辅助选择开辟了道路。尽管这六个性状的遗传结构的复杂性存在显着差异，但全基因组范围的预测模型对预测准确性的影响很小。我们评估了训练人口规模，标记物密度和表型强度对预测准确性的影响。在独立且遗传上非常不同的验证集中，蛋白质含量的预测准确性仍为0.14，油质含量的预测准确性仍为0.17，这反映了即使在非常不同的背景下开发的校准模型的效用。

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