High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms.

翻译：高维时间序列表型数据在植物育种项目中正变得越来越普遍。然而，对此类数据进行分析与整合以用于遗传分析和基因组预测仍然存在困难。本文展示了如何通过对高光谱次级表型数据的遗传相关矩阵进行因子分析并结合普洛克鲁斯特旋转，来提取用于试验内预测的相关特征。我们使用了国际玉米小麦改良中心2014-2015年度小麦精英产量试验的一个子集，包含1,033个基因型。这些基因型在生长季的多个时间点，于三种灌溉处理下，通过搭载高光谱传感器（可捕获385-850 nm光谱范围内的62个波段）的有人驾驶飞机进行了测量。我们利用潜在变量进行多变量基因组预测，以提高三种不同水分处理下小麦籽粒产量的试验内基因组预测能力。通过将高光谱数据的潜在变量整合到多变量基因组预测模型中，与单变量基因组预测相比，我们能够在预测能力（基于相关性尺度）上实现0.1至0.3的绝对增益。此外，我们揭示了试验中哪些时间点是重要的，以及这些时间点如何与植物生长阶段相关联。本文展示了如何将领域知识与数据驱动方法相结合，以提高高通量表型平台传感器数据的预测能力并从中获得新的见解。