@article{vonmaydell2026population,

title = {Population structure, genetic diversity and core set construction of an international collection of 256 Melissa officinalis genotypes},

author = {Daniel Maydell and Johannes Schwerdt and Heike Lehnert and Yvonne P\"{o}schl-Grau and Thomas Schmutzer and Frank Marthe},

doi = {10.1186/s12870-026-08853-8},

issn = {1471-2229},

year  = {2026},

date = {2026-05-01},

urldate = {2026-05-01},

journal = {BMC Plant Biology},

volume = {26},

number = {1},

pages = {794},

abstract = {BackgroundMelissa officinalis (balm) is a perennial medicinal. Climate change and high cultivation costs necessitate the breeding of new cultivars with improved stress tolerance and high metabolite content. However, the high costs of phenotyping large collections limit breeding progress. This study characterized the genetic diversity and population structure of 256 genotypes from 215 international accessions using flow cytometry and genotyping by sequencing (GBS). The primary objective was to identify untapped genetic resources and establish core sets to streamline future breeding and research efforts.ResultsMorphological characterization and flow cytometry revealed a clear division by ploidy and subspecies. The collection comprised 209 diploid and three polyploid genotypes of ssp. officinalis (2C = 1.79 pg) and 44 tetraploid genotypes of ssp. altissima (4C = 3.57 pg). GBS generated 29,307 and 9,909 SNPs for the entire collection and a subset of ssp. officinalis genotypes, respectively. We identified significant genetic differentiation between the subspecies, as confirmed by PCA (PC1 = 69.9%), STRUCTURE, and hierarchical clustering. With 21,770 private alleles and HE = 0.38 subspecies altissima exhibited greater genetic divergence than ssp. officinalis (2,953 private alleles},

keywords = {DiP-DIAMANT, DiP-OptiLamia, Domestication, Gene flow, Gene pool, Genetic diversity, Identification (biology), Melissa officinalis, Officinalis, Population, Subspecies},

pubstate = {published},

tppubtype = {article}

}

@article{niels2025harnessing,

title = {Harnessing historical genebank data to accelerate pea breeding},

author = {Lanique Niels and Jochen Christoph Reif and Lars-Gernot Otto and Vilson Mirdita and Markus Oppermann and Ulrike Lohwasser and Matthias Kotter and Stephan Weise and Samira El Hanafi},

doi = {10.1007/s00122-025-05032-5},

issn = {0040-5752, 1432-2242},

year  = {2025},

date = {2025-09-09},

urldate = {2025-09-09},

journal = {Theoretical and Applied Genetics},

volume = {138},

number = {10},

pages = {243},

abstract = {The German Federal Ex Situ Genebank for Agricultural and Horticultural Crops (IPK) harbours over 3000 pea plant genetic resources (PGRs), backed up by corresponding information across 16 key agronomic and economical traits. The unbalanced structure and inconsistent format of this historical data has precluded effective leverage of genebank accessions, despite the opportunities contained in its genetic diversity. Therefore, a three-step statistical approach founded in linear mixed models was implemented to enable a rigorous and targeted data curation. Spring accessions revealed considerable breeding potential, with protein content exceeding market standards by almost one-fifth and with hundred grain weight that could match the upper limits reported for European elite varieties. This variation is embedded within structured populations, comprising five convarieties including sugar snaps and field pea, adding value for breeding across diverse morphotypes and market segments. Winter accessions demonstrated cold resilience, with post-winter survival rate up to 79.27% under minimum temperatures as low as − 17.1 °C. This variation is of particular relevance given the limited availability of winter-hardy cultivars able to evade summer drought and heat stresses. Transformation of the IPK Genebank into a bio-digital resource redirects formerly static material into central leverage for plant breeding in view of contemporary challenges. As such, this investigation activated the IPK pea population for use in among others breeding for a wide variety of ideotypes, research into adaptation, and future combination with omics studies.},

keywords = {Agriculture, Breeding program, Cultivar, DiP-DiPisum, Germplasm, Leverage (statistics), Plant Breeding, Population},

pubstate = {published},

tppubtype = {article}

}