Digitalizing Trait Stability in Plant Breeding: Tackling Gynoecy Instability with Pangenome Graphs and FAIR Data Integration

December 3, 2025

In hybrid breeding programs gynoecy is a highly desirable trait, offering significant advantages such as increased yield, streamlined hybrid seed production, and reduced labor costs. However, gynoecy instability remains a major challenge for consistent trait expression and breeding reliability resulting in phenotypic reversion and complicating trait inheritance across generations. Next-generation sequencing (NGS) technology offers fast, sensitive, and scalable method to monitor gynoecy instability but presentssubstantial computational challenges in processing, analyzing, and managing NGS data.

Here we present a joined effort of adopting Genedata Selector^® to harmonize access to complex NGS-based genomic and phenotypic datasets using pangenome graph representations. These graphs enable breeders to visualize and interrogate CNVs, tandem duplications, and other structural variants across diverse breeding lines. By integrating genotype-phenotype relationships into an intuitive, FAIR-compliant interface, the platform facilitates rapid identification of instability patterns and supports informed decision-making. This digital transformation empowers interdisciplinary teams to accelerate trait development, overcome breeding bottlenecks, and deliver stable, high-performing crop varieties with reduced time to market.