Computomics helps to explore genetic diversity for improved maize breeding within BMBF-funded MAZE 3 grant

Genetic improvement is essential to secure sustainable crop production. Together with academic and industry partners, Computomics is helping to access native diversity in landraces to improve quantitative traits relevant for crop production in maize. 

MAZE Phase 3 follows the highly successful completion of Phase 1 and 2, where researchers gained deep knowledge on doubled-haploid flint lines, identified promising alleles from landraces that improve important traits in maize, and advanced both rapid breeding schemes as well as genomic prediction of traits coping with transcriptome data and genetically very diverged material.

Goals of MAZE Phase 3

Building on the achievements of previous project phases, MAZE 3.0 aims to molecularly characterize genes with a large effect on early development, root and drought-related traits. For European flint material, MAZE will generate a transcriptome atlas to help the search for additional alleles in the available maize gene pool and to advance the predictive functional analysis of the flint gene and intergenic space at a pan-genomic and targeted local level. Genome-based methods open new avenues for decreasing cycle length and upscaling the number of promising selection candidates. During the course of the MAZE project, a genome-based rapid cycling experiment was initiated and MAZE 3.0 will assess the potential and cost benefit of this pre-breeding scheme. Based on these results, MAZE 3.0 will optimize and accelerate the bridging process that guides integration of landrace-derived material into elite germplasm.

Computomics’ Contribution

During MAZE 3.0, Computomics will extend its pan-genome visualization technology Pantograph to serve as a public maize diversity browser of the genomic material generated within MAZE at candidate loci and on a broader haplotype level. It will enable the exploration of small and large scale genetic variation side by side with a collection of metadata generated within MAZE like gene annotations, expression levels, phenotypic traits as well as haplotype blocks. Computomics’ Pantograph can efficiently visualize a pan-genome even of large and complex genomes and implements zooming, scrolling, coloring and sorting by metadata to explore sub-population specific allele frequency differences. By providing insights on a candidate loci as well as broader haplotype level, and by including non-reference genomes, this resource will assist the search for candidate genes associated to important traits like drought and early development analyzed within the MAZE collaboration.

Outcome

Results will inform about novel, potentially beneficial alleles in landraces and non-adapted material and the usefulness of gene discovery in plant genetic resources. It will provide means to identify well suited donor individuals that minimize the gap between genetic resources and breeding material for specific alleles.

The results of this project will be freely available to researchers and breeders, to include the gained knowledge to generate optimized germplasm and breeding schemes. Computomics is excited to contribute to this project securing crop production in fast changing environments.

The project is led by Prof. Dr. Chris-Carolin Schön from Technische Universität München.

More about Pantograph, Computomics' interactive pangenome browser: view demo on YouTube