Subsequent projects | Special projects

Start of funding 01.01.2024

Genome evolution and bridging the genotype-phenotype divide with machine learning

Prof. Dr. Claude Becker
Ludwig-Maximilians-University of Munich
Genetik

Prof. Dr. Daniel Koenig
University of California, Riverside
Department of Botany and Plant Sciences


This project addresses two factors that affect crop productivity. First, in agricultural settings, plants growing densely next to one another engage in often yield-inhibiting plant-plant interactions. Selection for less competitive genotypes by breeders over the last one hundred years has been a major component of yield gains but has not been properly developed in many crops. Second, plant genomes are under constant attack from genomic parasites, retrotransposons, which replicate by inserting new copies of themselves into the genome at a new position and can have major impacts on plant diversity and adaptation. Leveraging the diverse expertise of the involved PIs, we will use machine-learning-based methods to (i) to combine genetic variation, microbiome interactions, phenomic, and functional genomic datasets to predict winners in competition plots and to (ii) develop a predictive framework for transposon insertion bias in crop genomes.