Biology subjects

Kupper, D.

Publications and source records attributed to Kupper, D..

2 recordsLinked to original sources

bioRxiv2026

Evolutionary signatures of self-incompatibility genes in grasses: high diversity, trans-generic polymorphism and co-evolution

In many flowering plants, self-incompatibility (SI) is a genetically controlled recognition system that prevents self-fertilization, thereby promoting outcrossing and maintaining genetic diversity within populations. The gametophytic SI system in grasses (Poaceae) is governed by two multiallelic loci, S and Z, each locus comprising of three genes governing the initial recognition between pollen and stigma. Identifying these genes establishes the molecular basis of SI in grasses, but how its unique two-locus architecture has shaped the evolution of the underlying loci and recognition genes remains largely unexplored. Here, we report targeted sequence capture of SI genes in 362 genotypes across nine forage grass species, revealing extensive allelic richness and sequence diversity. The SI genes exhibited elevated nucleotide diversity ({pi}) (0.134 < {pi} < 0.403) and great amino acid sequence divergence, ranging between 48.2-58.0% for the female determinants (sS and sZ) and 66.5-83.4% for the male determinants (SI-DUF247). The SI-DUF247 proteins also displayed trans-generic polymorphism, indicating that alleles predate speciation. Phylogenetic analysis of SI determinants showed congruence at both S- and Z-locus, consistent with co-evolution of recognition partners. Our results demonstrate that the unique two-locus SI system in grasses shares evolutionary characteristics with other SI systems, suggesting that their reproductive function is shaping their evolution. These findings also indicate that the grass SI loci are under long-term balancing selection to maintain recognition specificity while creating sufficient allelic diversity. Our study provides comprehensive sequence resources for SI genes and new insights into the evolution of the unique two-locus SI system of grasses.

plant biology↗

bioRxiv2024

Evaluation of genomic and phenomic prediction for application in apple breeding

Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. These models predictive ability depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic selection. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out validation, reflecting the application of genomic selection to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic selection approach is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.

genomics↗