Predicting individual performance from genomic information using Neural networks

Decisions in daily farm management practices may be optimized by predicting the performance (i.e. phenotype) of young individuals. Phenotypes may be accurately predicted from genomic data with Neural networks, because such models can fit complex relationships between genotypes.

In the GenoMiX project, Machine Learning experts from Radboud University worked in close collaboration with researchers from Wageningen University to develop a Neural network that can predict individual phenotypes from genotype data. The results showed that the Neural network was slightly more accurate than traditional genomic prediction models in scenarios where the phenotype was influenced by only a small number of genes, or when interactions between genes were abundant and strong. In other scenarios, the accuracies were comparable. The downside of the Neural network was its computation time, which was about 200 times as long as that of traditional methods. In conclusion, although the Neural network had robust performance across scenarios, computation time may hinder use in practice. 

Read the full article in Genetics, Selection, Evolution (GSE)

Contact person: This email address is being protected from spambots. You need JavaScript enabled to view it.

Check out our Partners