Large amounts of data (“big data”) offer enormous potential for improving the accuracy of genome-wide predictions in plant breeding. Encouraged by successful results with wheat hybrids, researchers at the IPK Leibniz Institute have now extended this approach to so-called inbred lines. For the first time, they combined phenotypic and genotypic data from four commercial wheat breeding programmes. The study results were published in the “Plant Biotechnology Journal”.
Deep learning methods have become increasingly crucial in genomic prediction in recent years. In contrast to conventional methods, deep learning approaches work with flexible, non-linear transformations of the input data. The aim is to recognize patterns in the data and link these to observable characteristics such as yield or plant height. The parameters required for this are optimized based on extensive training data. Such methods promise particular advantages when plant characteristics are strongly influenced by complex interactions that are insufficiently considered in conventional models.