Biology subjects

Denning-James, K.

Publications and source records attributed to Denning-James, K..

3 recordsLinked to original sources

bioRxiv2026

Developmental and physiological profiles define drought response diversity and genomic associations in common bean

Common bean (Phaseolus vulgaris L.) yields are strongly impacted by water deficit, yet there is substantial within-species diversity in how development, biomass accumulation, gas exchange, and photosynthetic performance are coordinated under stress. Based on this variation within a diversity panel, we characterise response profiles, define drought-response strategies, assess how these strategies relate to population structure and gene flow, and identify associated loci. A panel of 142 common bean accessions representing diverse genetic backgrounds was grown outdoors under controlled water-deficit conditions. Over five weeks, plants were monitored for phenology, biomass, pod production, and leaf traits related to stomatal and photosynthetic performance, including a brief recovery period. Genome-wide association analyses were then performed for developmental, physiological and recovery-related traits. Declining soil water availability revealed marked variation among accessions in developmental progression, biomass partitioning, stomatal behaviour and photosynthetic performance. We combined developmental and physiological traits to define above-ground response profiles and classify drought-response strategies in common bean, which were distributed across the diversity panel. GWAS identified multiple QTL and candidate loci associated with developmental, physiological and recovery-related traits. Common bean exhibits extensive diversity in overall above-ground responses to water deficit, likely reflecting local adaptation rather than population structure. Developmental data were essential for differentiating response strategies and, when combined with porometer and fluorometer measurements indicating the level of water stress experienced by the plants, for connecting traits and strategies to genomic variation. These results provide trait relationships, candidate loci and testable hypotheses for validation across environments and for future breeding-oriented studies.

plant biology↗

bioRxiv2024

Selective breeding for determinacy and photoperiod sensitivity in common bean (Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) is a legume pulse crop that provides significant dietary and ecosystem benefits globally. We investigated two key traits, determinacy and photoperiod sensitivity, that are integral to its management and crop production, and that were early selected during the domestication of both Mesoamerican and Andean gene pools. Still, significant variation exists among common bean landraces for these traits. Since landraces form the basis for trait introgression in pre-breeding, understanding these traits genetic underpinnings and relation with population structure is vital for guiding breeding and genetic studies. We explored genetic admixture, principal component, and phylogenetic analyses to define subpopulations and gene pools, and genome-wide association mapping (GWAS) to identify marker-trait associations in a diversity panel of common bean landraces. We observed a clear correlation between these traits, gene pool and subpopulation structure. We found extensive admixture between the Andean and Mesoamerican gene pools in some regions. We identified 13 QTLs for determinacy and 10 QTLs for photoperiod sensitivity, and underlying causative genes. Most QTLs appear to be firstly described. Our study identified known and novel causative genes and a high proportion of pleiotropic effects for these traits in common bean, and likely translatable to other legume species. HighlightWe identified and explored QTLs for the domestication-related determinacy and photoperiod sensitivity traits, which are traits critically associated with population structure and management and crop production.

plant biology↗

bioRxiv2024

AutoXAI4Omics: an Automated Explainable AI tool for Omics and tabular data

Machine learning (ML) methods have the potential of detailed insights of complex biological systems and today are increasingly used to analyse omics data for tasks such as the discovery of novel biomarkers and phenotype prediction. It can be extremely beneficial and powerful for scientists, domain experts, to easily run sophisticated, robust, and interpretable ML pipelines without the need for an in depth understanding of the code needed to train, tune, optimise ML algorithms. They can then focus on the biological interpretation and validation of the results and insights generated by ML models. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g., selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation e.g., predicted phenotypes, facilitating the discovery of actionable insights. AutoXAI4Omics is at: https://github.com/IBM/AutoXAI4Omics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/586460v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@366327org.highwire.dtl.DTLVardef@a7b559org.highwire.dtl.DTLVardef@7319aforg.highwire.dtl.DTLVardef@9b5030_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗