Search for: All records

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’. 

Dry bean is a nutrient-dense food targeted in biofortification programs to increase seed iron and zinc levels. The underlying assumption of breeding for higher mineral content is that enhanced iron and zinc levels will deliver health benefits to the consumers of these biofortified foods. This study characterized a diversity panel of 275 genotypes comprising the Yellow Bean Collection (YBC) for seed Fe and Zn concentration, Fe bioavailability (FeBio), and seed yield across 2 years in two field locations. The genetic architecture of each trait was elucidated via genome-wide association studies (GWAS) and the efficacy of genomic prediction (GP) was assessed. Moreover, 82 yellow breeding lines were evaluated for seed Fe and Zn concentrations as well as seed yield, serving as a prediction set for GP models. Large phenotypic variability was identified in all traits evaluated, and variations of up to 2.8 and 13.7-fold were observed for Fe concentration and FeBio, respectively. Prediction accuracies in the YBC ranged from a low of 0.12 for Fe concentration, to a high of 0.72 for FeBio, and an accuracy improvement of 0.03 was observed when a QTN, identified through GWAS, was used as a fixed effect for FeBio. This study provides evidence of the lack of correlation between FeBio estimatedin vitroand Fe concentration and highlights the potential of GP in accurately predicting FeBio in yellow beans, offering a cost-effective alternative to the traditional assessment of using Caco2 cell methodologies. 

Abstract PremiseThe selection ofArabidopsisas a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled research. MethodsHere, we examine the ability of models based onArabidopsisgene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested onArabidopsisdata achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained onArabidopsisdata, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. ResultsThe identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance fromArabidopsis.k‐nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. DiscussionOur data‐driven results highlight that the assertion that knowledge fromArabidopsisis translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis onArabidopsisand prioritize plant diversity. 

Abstract Common bean (Phaseolus vulgarisL.) is a nutrient-rich food, but its long cooking times hinder its wider utilization. The Yellow Bean Collection (YBC) was assembled with 295 genotypes from global sources to assess the genetic and phenotypic diversity for end-use quality traits in yellow beans. The panel was genotyped with over 2,000 SNPs identified via Genotyping-By-Sequencing (GBS). Through population structure analyses with the GBS markers, the YBC was determined to be 69% Andean, 26% Middle American, and 5% admixture. The YBC was grown in two major bean production regions in the U.S., Michigan (MI) and Nebraska (NE) over two years. The genotypes exhibited a wide diversity in days to flower, seed weight, water uptake, and cooking time. The cooking times of the YBC ranged from 17–123 min. The cooking time were longer and varied more widely in NE with many more genotypes exhibiting hardshell than in MI. Fast-cooking genotypes were identified with various yellow colors; 20 genotypes cooked within 20 min in MI, and eight genotypes cooked within 31 min in NE. Water uptake and cooking time were significantly affected by the environment, which included both the growing and cooking environment, and notably in relation to cooking, NE is higher elevation than MI. SNPs associated with cooking time were identified with genome-wide association analyses and a polygalacturonase gene on Pv04 was considered to be a candidate gene. The genotypic and phenotypic variability, fast-cooking genotypes, and the associated SNPs of the YBC will lay the foundation for utilizing yellow beans for breeding and genetic analyses.