Aug 2026· Molecular Ecology· Vol 35· 0 citations· 75 references
Medicine
TL;DR
The large diversity of methods at each stage of the EAA analysis is revealed, including marker filtering, EAA model selection, the significance criteria used, candidate gene selection and any downstream analyses conducted, highlighting the challenges in comparing the results of EAA studies.
Abstract
Climate change is predicted to impact existing ecological systems, leading to a requirement to improve our understanding of environmental adaptation in plants, particularly for conservation and the development of environmentally tolerant crop varieties. Environmental association analyses (EAAs) are a group of landscape genomic methods for detecting genetic markers or genes associated with environmental factors and therefore provide a top‐down approach of studying the genetics of environmental adaptation. Here we review 138 EAA studies in plants, revealing the large diversity of methods at each stage of the analysis, including marker filtering, EAA model selection, the significance criteria used, candidate gene selection and any downstream analyses conducted. Following this, we compared four frequently used EAA models using genetic data from a panel of traditional rice (Oryza sativa) varieties from Vietnam, revealing a greater proportion of significant markers for recent GWAS models (FarmCPU and BLINK) compared to single locus mixed linear models (MLM) and latent factor mixed models (LFMM). Between zero and 22 environmentally associated markers overlapped between models and several genes in proximity to these loci have putative links to abiotic factors. Overall, this highlights the challenges in comparing the results of EAA studies, particularly regarding applying the results to crop breeding for future climates.
A sustainable approach to address climate change and increasing water demand in agriculture is breeding for plant functional traits that conserve water and enhance climate resilience. Stomata regulate plant-water relations and are promising targets for crop improvement. Here, we investigate the variation in stomatal density (SD) in a diverse apple population (Malus domestica Borkh.) consisting of 269 accessions. Genome-wide association studies identified robust associations with SD on chromosomes 2, 9, and 10 (classified as SNPs with p value higher than adjusted Bonferroni threshold of -log10(p) > 8.78 that were consistently identified across datasets). On chromosome 9, a candidate gene that negatively regulates stomatal development, EPIDERMAL PATTERNING FACTOR 1 (EPF1), was identified inside a genomic region of 241 kb determined by six robust associations. On chromosome 10, a positive regulator candidate gene, EPIDERMAL PATTERNING FACTOR LIKE 9 (STOMAGEN), was identified 1680 kb from the robust association. Identification of positive (STOMAGEN) and negative (EPF1) regulators of SD suggest potential antagonistic roles at the population scale in determining SD. On chromosome 2, a gene co-expression analysis identified a gene cluster containing both EPF1 and STOMAGEN together with a novel candidate gene, CYTOCHROME P450 (CYP77A4), that was located 544 kb from the robust association. The percentage of SD phenotypic variance explained by each robust association was between 7% and 10%. These findings provide a foundation for understanding SD variation at the population scale and opportunities to modulate SD by genomics-assisted breeding strategies.
Francesca Zuffa, M. Jung, Steven Yates et al.· Horticulture Research· 1 citation
Proper analysis of phenotypic data is essential for reliable genomic prediction (GP) and sustained genetic gain in breeding programs. In this study, we used phenotypic and genotypic data generated from the winter wheat breeding program of Deutsche Saatveredelung AG (DSV), Lippstadt, Germany, comprising 1,941 genotypes and 6,335 SNP markers across three traits: grain yield, plant height, and heading date. We evaluated the impact of different two-stage analysis strategies: one using the environment (year × location combination) as the analysis unit (TS-S1) and the other using the breeding stage as the analysis unit (TS-S2), each with and without accounting for breeding-stage effects (-YesBS and -NoBS), on the computation of best linear unbiased estimates (BLUEs) and genome-wide prediction ability (PA), defined as the correlation between BLUEs and predicted values. The performance of these 4 different second stage models (TS-S1-NoBS, TS-S1-YesBS, TS-S2-NoBS, and TS-S2-YesBS) were evaluated using the extended genomic best linear prediction (EGBLUP) model under 5-fold cross validation (5-fold CV), leave one year out cross validation (LOY-CV) and leave one breeding stage out cross validation (LOBS-CV) scenarios. In the presence of the strong breeding stage effect ignoring the breeding stage in the model resulted in biased BLUEs and overestimated prediction abilities in the 5-fold CV, and underestimated prediction abilities in the LOY-CV and LOBS-CV. These unstable prediction abilities are driven by confounded environmental effects in the BLUEs due to omission of the breeding-stage effect. In contrast, models that accounted for the breeding stage produced more reliable BLUEs and more stable prediction results across all cross-validation scenarios, with TS-S1-YesBS performing best overall. Overall, our findings demonstrate that breeding stage effects mainly arise from differences in growing conditions and must be adequately considered. Therefore, including breeding stage in phenotypic models is critical to obtaining unbiased BLUEs and ensuring accurate genomic prediction and selection decisions.
Ravindra Reddy Gundala, Georg Witte, Jost Doernte et al.· Frontiers in Plant Science· 0 citations
Chickpea is one of the most consumed legumes due to its high nutritional value and accessibility to low-income populations. However, due to climate change, chickpea cultivation is exposed to various environmental stresses affecting its production and productivity. This study evaluates the agronomic performance of 168 MAGIC subset population across two Mediterranean environments, in Marchouch (Morocco) and Terbol (Lebanon). Genome-wide association studies (GWAS) were conducted to identify potential marker-trait associations (MTAs) using the general linear model (GLM), the mixed linear model (MLM), and the fixed- and random-effect circulant probability unification (FarmCPU), with kinship and principal components used as covariates. The results revealed high genetic variation among the genotypes tested, with significant genotype-by-environment interactions for most traits studied. Genotypes with good agronomic performance (M-1407, M-2038, M-2079, M-242, M-2551, and M-987) were identified under both environments. Early flowering and maturation resulted in a significant increase in grain yield of around 66%. Grain yield varied from 151.85 to 882.7 g m-2 and from 183.59 to 365.76 g m-2 under Marchouch and Terbol conditions, respectively, showing higher genetic variation under Marchouch than under Terbol. Correlation analysis revealed strong, significant correlations among the studied traits. GWAS revealed clear genetic variation across environments, with Marchouch showing stronger and more consistent association signals than Terbol. In total, 72 reliable MTAs were detected for phenological traits, 38 for plant height, 18 for grain yield, and 197 for hundred-seed weight across both sites. A major genomic hotspot on chromosome 4 (11.97–13.63 Mbp) harbored stable and pleiotropic MTAs. Functional annotation of regions surrounding significant SNPs revealed 59 putative candidate genes, highlighting potential biological processes related to growth, signaling, and stress responses that require further validation. These results provide a foundation for further research on marker-assisted selection to improve chickpea productivity and yield stability in stressed environments.
Fatoumata Farida Traore, Quahir Sohail, A. El Allali et al.· Frontiers in Plant Science· 0 citations
Abstract Genomic selection (GS) is a powerful tool for accelerating genetic gain in potato (Solanum tuberosum L.) breeding, particularly for complex traits. In this study, three practical aspects of GS implementation in a potato breeding program were examined. First, the predictive ability of GS models was evaluated for three key traits (total yield, marketable yield, and specific gravity) using two elite potato populations with shared ancestry, tested across seven location‐year environments. Two cross‐validation strategies were used to reflect practical breeding scenarios: predicting unphenotyped lines in known environments and predicting clonal performance in unknown environments. Four models were evaluated, two of which included genotype‐by‐environment interactions. Tuber specific gravity showed higher and more consistent prediction accuracy across environments, supporting the evidence that it is a more stable trait. Second, the impact of genotyping platforms and marker density on GS performance were examined, as the two populations were genotyped using two different targeted sequencing platforms: Flex‐seq (22K loci) and DArTag (4K loci), sharing ∼4K common loci, that allowed direct comparison. Prediction accuracies were comparable across platforms, indicating that both are suitable for GS implementation, with the choice depending on breeding goals, cost, and throughput considerations. Finally, the long‐term impact of GS on genetic gain was assessed through stochastic simulation of a 30‐year breeding pipeline, comparing conventional phenotypic selection with GS‐assisted selection scenarios. GS scenarios achieved higher long‐term genetic gains, though practical deployment should consider both cost and breeding objectives. Our findings for the three aspects of this study support the integration of GS into potato breeding programs, while highlighting key considerations for its effective implementation.
R. Dhakal, M. A. Peixoto, Leo Hoffmann et al.· The Plant Genome· 0 citations
Faba bean is a globally adapted legume protein crop with a high yield potential. Currently, yield variation across environments limits more widespread cultivation, and the underlying genetics remain poorly understood. Here, we identify major QTL for faba bean yield and yield stability. We genotype the ProFaba diversity panel with high resolution and carry out coordinated multi-year/location trials across Europe. Based on these data, we identify more than one hundred loci associated with mean performance and stability for 14 complex traits, including yield. Experimental validation supports the involvement of the candidate gene Vfaba.Hedin2.R2.1g002122 in plant architecture, with gene expression significantly associated with first pod position and plant height. Furthermore, we introduce a method for integrating environmental data in the analysis of trait stability based on a random regression mixed model, which enables prediction of performance in untested environments. Our study provides insights into the genetic architecture of yield, yield stability, and genotype-by-environment interaction in faba bean. The genomic resources, candidate loci, and weather-informed analytical framework provide practical tools for predicting performance across environments and accelerating breeding of resilient, high-yielding protein crops.
Elesandro Bornhofen, Troels W. Mouritzen, Sheila Alves et al.· Genome Biology· 0 citations
By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.
Muhammad Shahid Iqbal, Z. Sarfraz, Muhammad Mujahid et al.· Frontiers in Plant Science· 0 citations