State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany.
KWS SAAT SE, Grimsehlstraße 31, 37574, Einbeck, Germany.
Theor Appl Genet. 2021 May;134(5):1409-1422. doi: 10.1007/s00122-021-03779-1. Epub 2021 Feb 17.
Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
高光谱数据是基因组数据的一种很有前途的补充，可以预测遗传相关性低的情况下的生物量。模型训练和验证所使用的数据之间需要有足够的环境连通性。全球对生物量可持续来源的需求正在增加。通过基于高光谱和/或基因组预测的干物质产量（DMY）间接选择来早期预测生物量对于经济地挖掘黑麦（Secale cereale L.）作为两用作物的潜力至关重要。然而，这种估计涉及多个遗传背景，遗传相关性是基因组选择（GS）的关键因素。为了评估利用反射率数据作为 GS 对生物量育种的合适补充的预测前景，比较了性状遗传力（[Formula: see text]）和遗传相关性的影响。模型基于基因组（GBLUP）和高光谱反射率衍生（HBLUP）关系矩阵来预测 DMY 和其他与生物量相关的性状，如干物质含量（DMC）和鲜物质产量（FMY）。为此，使用 10 k-SNP 阵列对来自九个相互连接的双亲系的 270 个黑麦精英系进行了基因型分析，并在两年中的四个地点（八个环境）作为测验杂交种进行了表型分析。在每个环境的两个日期，由无人飞行器（UAV）收集的 400 个离散窄带（410nm-993nm）中，包含了 32 个先前通过套索选择的高光谱带，这些带被纳入预测模型中。与 GBLUP（0.14-0.28）相比，在遗传关系减弱的情况下，HBLUP 显示出更高的预测能力（0.41-0.61），特别是对于中遗传力的性状（FMY 和 DMY），这表明 HBLUP 受相关性和[Formula: see text]的影响较小。然而，两种模型的预测能力都受到环境方差的很大影响。通过将两个矩阵和株高集成到双变量模型中，DMY 的预测能力进一步提高（高达 20%）。因此，从高通量表型中获得的数据是有效利用黑麦生物量育种中选择增益的一种合适策略；然而，需要有足够的环境连通性。
