Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria.
Theor Appl Genet. 2023 Oct 25;136(11):235. doi: 10.1007/s00122-023-04479-8.
NIRS of wheat grains as phenomic predictors for grain yield show inflated prediction ability and are biased toward grain protein content. Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, phenomic technologies, including remote sensing and aerial hyperspectral imaging of plant canopies, have made it feasible to predict the breeding value of individuals in the absence of genetic marker data. This is commonly referred to as phenomic prediction. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 1980 s to predict compositional parameters of harvest products. Moreover, in recent studies NIRS from grains was used to predict grain yield. The same studies showed that phenomic prediction can outperform genomic prediction for grain yield. The genome is static and not environment dependent, thereby limiting genomic prediction ability. Gene expression is tissue specific and differs under environmental influences, leading to a tissue- and environment-specific phenome, potentially explaining the higher predictive ability of phenomic prediction. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions outperform genomic prediction for some traits. However, phenomic predictions are biased toward the information present in the predictor. Future studies on this topic should investigate whether population parameters are retained in phenomic prediction as they are in genomic prediction. Furthermore, we find that unbiased phenomic prediction abilities are considerably lower than previously reported and recommend a method to circumvent this issue.
利用近红外光谱（NIRS）对小麦籽粒进行非成像表型预测以估算籽粒产量的研究进展
作为表型预测因子的近红外光谱（NIRS）分析小麦籽粒，其对籽粒产量的预测能力过高，并且存在偏向于籽粒蛋白质含量的偏倚。利用全基因组标记数据（基因组预测）估算个体的育种值是目前主要作物育种进展的最重要驱动力之一。最近，表型技术（包括对植物冠层的遥感和航空高光谱成像）已经使得在没有遗传标记数据的情况下预测个体的育种值成为可能。这通常被称为表型预测。自 20 世纪 80 年代以来，近红外光谱技术已被用于预测收获产品的成分参数。此外，在最近的研究中，利用 NIRS 从谷物中预测籽粒产量。同样的研究表明，表型预测可以优于基因组预测来预测籽粒产量。基因组是静态的，不依赖于环境，从而限制了基因组预测的能力。基因表达是组织特异性的，并且在环境影响下会有所不同，从而导致组织和环境特异性的表型，这可能解释了表型预测更高的预测能力。在这里，我们比较了来自小麦籽粒高光谱测量的基因组预测和表型预测，以预测包括籽粒产量在内的多种性状。我们表明，表型预测在某些性状上优于基因组预测。然而，表型预测存在偏向于预测器中存在的信息的问题。关于这个主题的未来研究应该调查表型预测是否保留了群体参数，就像在基因组预测中一样。此外，我们发现无偏表型预测能力比以前报道的要低得多，并建议采用一种方法来解决这个问题。
