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Efficient genetic value prediction using incomplete omics data

机译:使用不完整的OMICS数据有效的遗传值预测

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Key messageCovering a subset of individuals with a quantitative predictor, while imputing records for all others using pedigree or genomic data, could improve the precision of predictions while controlling for costs.AbstractPredicting genetic values with high accuracy is pivotal for effective candidate selection in animal and plant breeding. Novel omics'-based predictors have been shown to improve upon established genome-based predictions of important complex traits but require laborious and expensive assays. As a consequence, there are various datasets with full genetic marker coverage of all studied individuals but incomplete coverage with other omics' data. In animal breeding, single-step prediction was introduced to efficiently combine pedigree information, collected on a large number of animals, with genomic information, collected on a smaller subset of animals, for breeding value estimation without bias. Using two maize datasets of inbred lines and hybrids, we show that the single-step framework facilitates imputing transcriptomic data, boosting forecasts when their predictive ability exceeds that of pedigree or genomic data. Our results suggest that covering only a subset of inbred lines with omics' predictors and imputing all others using pedigree or genomic data could enable breeders to improve trait predictions while keeping costs under control. Employing omics' predictors could particularly improve candidate selection in hybrid breeding because the success of forecasts is a strongly convex function of predictive ability.
机译:关键词Messagecovering具有定量预测因子的个体子集,同时使用血统或基因组数据抵御所有其他其他人的记录,可以提高预测的精度,同时控制成本。在动物和植物中具有高精度的遗传值为具有高精度的遗传值配种。已经证明了新的常规基于常规的预测因子在建立基于基于基于复杂性状的基于基因组的预测,而是需要费力和昂贵的测定。因此,有各种数据集,具有全部研究的全部遗传标记覆盖,但与其他OMICS数据的覆盖不完全覆盖。在动物繁殖中,引入单步预测以有效地结合在大量动物上收集的血迹信息,其中基因组信息收集在较小的动物的小组上,用于无偏压的繁殖价值估计。使用两种跨交线和混合动力车的玉米数据集,我们表明单步框架有助于抵御转录组数据,当他们的预测能力超过血统或基因组数据时,提高预测。我们的结果表明,仅涵盖了常备线的近交系的子集,并使用血统或基因组数据抵御所有其他人可以使育种者能够改善特征预测,同时保持控制的成本。采用OMICS的预测因子可以特别改善混合育种中的候选选择,因为预测的成功是预测能力的强凸起的函数。

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