The Influence of Missing Hills on Potato Yield and Evaluation of Correction Methods - Kathrin Thelen; Po-Ya Wu; Alessio Maggiorelli; Vanessa Prigge; Julien Bruckmüller; Katja Muders; Bernd Truberg; Stefanie Hartje; Juliane Renner; Delphine Van Inghelandt; Benjamin Stich (Potato Research, 2026)
Missing hills affect the yield of a single plant in experimental plots, as the remaining plants have more space and resources compared to plants in plots without missing hills. However, up to date, no universal way on how to correct for missing hills exists in the context of potato breeding programs. The aim of our study was to (i) compare different approaches to adjust the yield when hills are missing in a plot, (ii) propose a universal approach for correcting missing hills in potato plots, and (iii) test if the estimated parameters are transferable from a training population to an independent validation population. We evaluated in a set of 1066 different clones approaches to adjust single plant yield for the proportion of missing hills in a plot. These approaches were a grid search, a linear mixed effects model, or approaches previously proposed in the literature. We compared these approaches based on the heritability of single plant yield. Our results showed that all advanced correction approaches improved the heritability and reduced the residuals compared to the standard approaches, where the yield of the plot was just divided by the actual plant number or the intended plant number. The most promising results were observed for the four-parameter Weibull type 2 regression of the effects for each proportion of missing hills derived by a linear mixed model analysis with the proportion of missing hills used as a cofactor. The approach derived here is transferable across populations and increases heritability of yield estimates without new parameter adaptation. Therefore, our approach provides an easily applicable procedure to improve the comparison of single plant yield between clones in potato breeding programs.

Introgression of rye chromosome arm 1RS enhances climate resilience in German winter wheat - Yeneneh Bekele-Reba; Lorenz Bülow; Anne Zaar; Michael Koch; Adalbert Bund; Carsten Reinbrecht; Jost Dörnte; Hubert Kempf; Josef Holzapfel; Marco Stucke; Jörg Plieske; Lorenz Hartl; et al. (Theoretical and Applied Genetics, 2026)
Key message We provide quantitative evidence that rye chromosome arm 1RS exerts background- and context-dependent effects on yield, stability, and grain protein content in contemporary elite winter wheat. Integrated multi-environment analyses demonstrate that translocation lines exhibit pronounced yield stability across 14 contrasting environments, with ‘Insave’- and ‘Petkus’-derived segments contributing through distinct performance profiles relevant for climate-resilient wheat breeding. Abstract Climate extremes increasingly threaten wheat production and yield stability globally. Rye ( Secale cereale L.) chromosome arm 1RS has long been deployed in wheat breeding, yet its agronomic performance under drought and its interaction with elite genetic backgrounds remain insufficiently characterized. We evaluated 1RS translocations in elite German winter wheat using integrated molecular diversity analyses, multi-environment field phenotyping, and genomic modeling. Under near-optimal precipitation in 2021, matching the 1961–1990 reference period, the investigated winter wheat panel exhibited considerable genetic variation for yield and agronomic traits, indicating that high yield potential is still present in current German winter wheat breeding germplasm. In contrast, severe drought conditions in 2022 resulted in a 13.7% average yield decline, underscoring the sensitivity of current germplasm to drought stress. We show that 1RS translocations exhibit background-dependent effects on grain yield and stability. In particular, T1AL.1RS rye translocation originating from ‘Insave’ rye and stacked T1AL.1RS/T1BL.1RS translocations combining ‘Insave’ and ‘Petkus’ rye segments demonstrated favorable yield performance under drought conditions, although effects were context-dependent. Genetic modeling confirmed a significant interaction between 1RS translocations and the wheat genetic background, indicating that deployment of 1RS requires consideration of recipient genetic background. Our results highlight substantial response diversity within elite germplasm and demonstrate that targeted introgression of rye chromatin can contribute to improved climate resilience when systematically introgressed into adapted genetic backgrounds. The robust multi-environment field evaluation provides a strong foundation for interpreting translocation effects in modern elite germplasm. Complementary trait-level analyses, particularly root phenotyping and evaluation of double 1RS translocations within uniform genetic backgrounds, will further elucidate the physiological mechanisms underlying the observed responses.

Bridging tradition and innovation: a review of computer simulations in plant breeding - Benjamin Stich; Delphine Van Inghelandt; Po-Ya Wu (Theoretical and Applied Genetics, 2026)
The wide range of tools and methods available to plant breeders today has the potential to increase the gain of selection. However, they also result in numerous complex choices in the design of efficient crossing and selection strategies. Computer simulations are essential to optimize breeding programs that are multi-year, high-effort endeavors and to compare process efficiencies without going through field experiments, thus saving both time and field resources. In addition, computer simulations are key to evaluate statistical properties of new methods and procedures as well as to exploit crop growth models and G*E interactions. The first two areas are discussed in great detail in our review. Furthermore, the review evaluates the capabilities, assumptions, and limitations of all publicly available simulation tools highlighting their relevance for the different areas of application.

Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population - Christopher Arlt; Delphine van Inghelandt; Jinquan Li; Benjamin Stich (Theoretical and Applied Genetics, 2025)
Key message Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. Abstract The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations. We investigated the potential of increasing prediction ability by combining genomic and transcriptomic datasets, adding whole-genome sequencing (WGS) SNP data, functional annotation-based filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small-footprint plant cultivation, high-throughput RNA extraction, and Library preparation miniaturization. We also examined sequencing depth reduction as an additional cost-saving measure. We used fivefold cross-validation to evaluate the prediction ability of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in prediction abilities between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training and validation sets originated from different RIL sub-populations. We were therefore able to not only show that RNA-Seq data alone are able to predict various complex traits in barley using RILs, but also that the performance can be further increased with WGS data for which the public availability will steadily increase.

Transferability of genomic prediction models across market segments in potato and the effect of selection - Kathrin Thelen; Po-Ya Wu; Nadia Baig; Vanessa Prigge; Julien Bruckmüller; Katja Muders; Bernd Truberg; Stefanie Hartje; Juliane Renner; Delphine Van Inghelandt; Benjamin Stich (Theoretical and Applied Genetics, 2025)
Genomic prediction (GP) can help increase the efficiency of breeding programs, as genotypes can be selected based on their predicted performance. However, to the best of our knowledge, this procedure is not yet routine in commercial breeding programs in tetraploid organisms like potato ( Solanum tuberosum L.). The objectives of this study were to Estimate the prediction accuracy for 26 different potato traits in a panel of about 1000 genotypes based on 202,008 single nucleotide polymorphisms, Evaluate the influence of the size and constitution of the training set on the prediction accuracy, and Investigate how the effect of selection in the training set influences the outcome of GP. GP revealed high prediction accuracies using genomic best linear unbiased prediction. Our results indicated that a training set of 280–480 clones and 10,000 markers was sufficient. Prediction within a specific market segment led to a higher prediction accuracy compared to adding clones from other market segments to the training set or to predict between different market segments. Lastly, we found a higher prediction accuracy when in a training set of selected clones, i.e., a training set that consists of clones with high trait values, 20% of the clones were replaced by clones that were sampled from the clones that showed the lowest 10% trait values. This observation shows that clones from advanced breeding stages can be used as training set, if some clones specifically from the other side of the distribution range are added to the training set.

Potatome: Harnessing natural variation of potatoes - Delphine Van Inghelandt; Benjamin Stich (Molecular Plant, 2025)

Fine mapping a QTL for BYDV-PAV resistance in maize - Maria Schmidt; Ricardo Guerreiro; Nadia Baig; Antje Habekuß; Torsten Will; Britta Ruckwied; Benjamin Stich (Theoretical and Applied Genetics, 2024)
Barley yellow dwarf (BYD) is one of the economically most important virus diseases of cereals worldwide, causing yield losses up to 80%. The means to control BYD are limited, and the use of genetically resistant cultivars is the most economical and environmentally friendly approach. The objectives of this study were i) to identify the causative gene for BYD virus (BYDV)-PAV resistance in maize, ii) to identify single nucleotide polymorphisms and/or structural variations in the gene sequences, which may cause differing susceptibilities to BYDV-PAV of maize inbreds, and iii) to characterize the effect of BYDV-PAV infection on gene expression of susceptible, tolerant, and resistant maize inbreds. Using two biparental mapping populations, we could reduce a previously published quantitative trait locus for BYDV-PAV resistance in maize to ~ 0.3 Mbp, comprising nine genes. Association mapping and gene expression analysis further reduced the number of candidate genes for BYDV-PAV resistance in maize to two: Zm00001eb428010 and Zm00001eb428020. The predicted functions of these genes suggest that they confer BYDV-PAV resistance either via interfering with virus replication or by inducing reactive oxygen species signaling. The gene sequence of Zm00001eb428010 is affected by a 54 bp deletion in the 5`-UTR and a protein altering variant in BYDV-PAV-resistant maize inbreds but not in BYDV-PAV-susceptible and -tolerant inbreds. This finding suggests that altered abundance and/or properties of the proteins encoded by Zm00001eb428010 may lead to BYDV-PAV resistance.

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes - Venkata Suresh Bonthala; Benjamin Stich (Plant Cell Reports, 2024)
Key message We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Abstract Potato ( Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.

Using drone-retrieved multispectral data for phenomic selection in potato breeding - Alessio Maggiorelli; Nadia Baig; Vanessa Prigge; Julien Bruckmüller; Benjamin Stich (Theoretical and Applied Genetics, 2024)
Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of − 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.

Improving photosynthetic efficiency toward food security: Strategies, advances, and perspectives - Edward N. Smith; Marvin van Aalst; Tiina Tosens; Ülo Niinemets; Benjamin Stich; Tomas Morosinotto; Alessandro Alboresi; Tobias J. Erb; Paul A. Gómez-Coronado; Dimitri Tolleter; Giovanni Finazzi; Gilles Curien; et al. (Molecular Plant, 2023)

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation - Marius Weisweiler; Christopher Arlt; Po-Ya Wu; Delphine Van Inghelandt; Thomas Hartwig; Benjamin Stich (Theoretical and Applied Genetics, 2022)
Key message Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. Abstract In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant ( P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly ( P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome - Wayne E. Clarke; Erin E. Higgins; Joerg Plieske; Ralf Wieseke; Christine Sidebottom; Yogendra Khedikar; Jacqueline Batley; Dave Edwards; Jinling Meng; Ruiyuan Li; Cynthia Taylor Lawley; Jérôme Pauquet; et al. (Theoretical and Applied Genetics, 2016)

First steps to understand heat tolerance of temperate maize at adult stage: identification of QTL across multiple environments with connected segregating populations - Felix P. Frey; Thomas Presterl; Patrick Lecoq; András Orlik; Benjamin Stich (Theoretical and Applied Genetics, 2016)

Genes involved in barley yellow dwarf virus resistance of maize - Frederike Horn; Antje Habekuß; Benjamin Stich (Theoretical and Applied Genetics, 2014)

Seedling development in a Brassica napus diversity set and its relationship to agronomic performance - Niklas Körber; Benjamin Wittkop; Anja Bus; Wolfgang Friedt; Rod J. Snowdon; Benjamin Stich (Theoretical and Applied Genetics, 2012)

Patterns of molecular variation in a species-wide germplasm set of Brassica napus - Anja Bus; Niklas Körber; Rod J. Snowdon; Benjamin Stich (Theoretical and Applied Genetics, 2011)

Population structure in sorghum accessions from West Africa differing in race and maturity class - Sankalp U. Bhosale; Benjamin Stich; H. Frederick W. Rattunde; Eva Weltzien; Bettina I. G. Haussmann; C. Thomas Hash; Albrecht E. Melchinger; Heiko K. Parzies (Genetica, 2011)

Population structure and genetic diversity in elite sugar beet germplasm investigated with SSR markers - Jinquan Li; Britta Schulz; Benjamin Stich (Euphytica, 2010)

Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers - Delphine Van Inghelandt; Albrecht E. Melchinger; Claude Lebreton; Benjamin Stich (Theoretical and Applied Genetics, 2010)

Optimum allocation of resources for QTL detection using a nested association mapping strategy in maize - Benjamin Stich; H. Friedrich Utz; Hans-Peter Piepho; Hans P. Maurer; Albrecht E. Melchinger (Theoretical and Applied Genetics, 2010)

Association mapping in multiple segregating populations of sugar beet (Beta vulgaris L.) - Benjamin Stich; Albrecht E. Melchinger; Martin Heckenberger; Jens Möhring; Axel Schechert; Hans-Peter Piepho (Theoretical and Applied Genetics, 2008)

Multi-trait association mapping in sugar beet (Beta vulgaris L.) - Benjamin Stich; Hans-Peter Piepho; Britta Schulz; Albrecht E. Melchinger (Theoretical and Applied Genetics, 2008)

Potential causes of linkage disequilibrium in a European maize breeding program investigated with computer simulations - Benjamin Stich; Albrecht E. Melchinger; Hans-Peter Piepho; Sonia Hamrit; Wolfgang Schipprack; Hans P. Maurer; Jochen C. Reif (Theoretical and Applied Genetics, 2007)

A new test for family-based association mapping with inbred lines from plant breeding programs - Benjamin Stich; Albrecht E. Melchinger; Hans-Peter Piepho; Martin Heckenberger; Hans P. Maurer; Jochen C. Reif (Theoretical and Applied Genetics, 2006)

Linkage disequilibrium in European elite maize germplasm investigated with SSRs - Benjamin Stich; Albrecht E. Melchinger; Matthias Frisch; Hans P. Maurer; Martin Heckenberger; Jochen C. Reif (Theoretical and Applied Genetics, 2005)