Genetic dissection of grain yield and correlated proxy traits under suboptimal conditions - Yan-Cheng Lin; Claude Urbany; Antonina Shlykova; Armin C. Hölker; Milena Ouzunova; Thomas Presterl; Torsten Pook; Manfred Mayer; Sebastian Urzinger; Chris-Carolin Schön (Theoretical and Applied Genetics, 2026)
Key message This eight-founder MAGIC population represents a powerful resource for dissecting complex traits in maize, assessing the utility of drought proxy traits, and optimizing low-coverage whole-genome sequencing approaches. Abstract Securing sustainable crop production requires the genetic improvement of abiotic stress tolerance. Due to the broad range of environmental factors causing abiotic stress and complex genotype-by-environment interactions, it is crucial to understand the genetic basis of crop yield under suboptimal conditions. Here, we developed a dent maize Multi-parent Advanced Generation Inter-Cross (MAGIC) population comprising 388 doubled-haploid (DH) lines. The population was derived from eight founders with varying stress tolerance, selected from a dent diversity panel evaluated for yield performance across a wide range of European environments. The MAGIC DH lines were genotyped via whole-genome sequencing (~ 5× coverage) and evaluated in seven testcross and 14 line per se trials, for grain yield, leaf senescence, leaf rolling, anthesis-silking interval, and six additional agronomic traits. Genetic dissection identified 22 grain yield QTL, explaining 45% of the genetic variance. Under heat and drought stress, testcross grain yield correlated significantly with leaf senescence and leaf rolling measured in line per se trials. Bivariate multi-trait analysis showed that alleles for delayed senescence and reduced rolling at detected QTL generally exhibited positive effects on grain yield, suggesting that accumulating these favorable alleles could enhance yield performance. Incorporating these proxies into multi-trait genomic prediction models improved yield prediction accuracy, although gains were constrained by modest trait correlations. Given the comprehensive data, we also provide recommendations for optimizing sequencing depth and QTL mapping strategies in experimental maize populations.

Assessing the potential of quantum computing in agriculture - Torsten Pook; Jeremie Vandenplas; Juan Carlos Boschero; Esteban Aguilera; Koen Leijnse; Aneesh Chauhan; Yamine Bouzembrak; Rob Knapen; Michael Aldridge (Computers and Electronics in Agriculture, 2025)

Alleviating the Concerns About Pollen‐Based Assisted Migration - David Chludil; Curt Almqvist; Mats Berlin; Arne Steffenrem; Steven E. McKeand; Jiří Korecký; Jan Stejskal; Jaroslav Čepl; Fikret Isik; Debojyoti Chakraborty; Silvio Schueler; Torsten Pook; et al. (Global Change Biology, 2025)
Our recent publication on pollen‐based assisted migration has prompted concerns about its feasibility and effectiveness. In this response article, we address those concerns by presenting large‐scale examples of supplemental and controlled pollination from Sweden and the United States, demonstrating its practical application. We also highlight the existence of an extensive global network of seed orchards and expand on the benefits of pollen‐based assisted migration, further reinforcing its potential and addressing the raised concerns.

A Pollen‐Based Assisted Migration for Rapid Forest Adaptation - David Chludil; Jaroslav Čepl; Arne Steffenrem; Jan Stejskal; Christi Sagariya; Torsten Pook; Silvio Schueler; Jiří Korecký; Curt Almqvist; Debojyoti Chakraborty; Mats Berlin; Milan Lstibůrek (Global Change Biology, 2025)
Climate change poses an unprecedented threat to forest ecosystems, necessitating innovative adaptation strategies. Traditional assisted migration approaches, while promising, face challenges related to environmental constraints, forestry practices, phytosanitary risks, economic barriers, and legal constraints. This has sparked debate within the scientific community, with some advocating for the broader implementation of assisted migration despite these limitations, while others emphasize the importance of local adaptation, which may not keep pace with the rapid rate of climate change. This opinion paper proposes a novel pollen‐based assisted migration strategy as a potential middle ground in this debate. By leveraging existing seed orchard infrastructure for controlled pollen transfer, this approach aims to enhance forest resilience through the introduction of genetic material from climatically suitable sources while acknowledging local adaptation. We assess the genetic implications of the proposed strategy through computer simulation. Additionally, we examine the ecological implications of assisted gene flow, discussing the potential benefits of heterosis and the risks of outbreeding depression in intra‐specific hybrid populations. We further explore the advantages of pollen‐based migration in mitigating phytosanitary risks, reducing economic barriers, and simplifying legal considerations compared to traditional seed or seedling transfer methods. Regional perspectives on adapting pollen‐based assisted migration are provided, with specific examples from Northern and Central Europe. We highlight how this approach could be integrated into existing forestry practices and regulatory frameworks within the European Union. We conclude by advocating for the inclusion of pollen‐based assisted migration in future international projects and operational forestry, emphasizing the need for adaptable policies that can support innovative forest management strategies in the face of climate change.

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments - Elaheh Vojgani; Torsten Pook; Johannes W. R. Martini; Armin C. Hölker; Manfred Mayer; Chris-Carolin Schön; Henner Simianer (Theoretical and Applied Genetics, 2021)
Key Message The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. Abstract We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from −0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for “sparse testing” approaches in which only a subset of the lines/hybrids of interest is observed at each location.