Halll, A. | bioRxiv Science

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↗

bioRxiv2021

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

The circadian clock is an important adaptation to life on earth. Here, we use machine learning to predict complex temporal circadian gene expression patterns in Arabidopsis. Most significantly, we classify circadian genes using DNA sequence features generated from public genomic resources, with no experimental work or prior knowledge needed. We use model explanation to rank DNA sequence features, observing transcript-specific combinations of potential circadian regulatory elements that discriminate temporal phase of expression. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints, saving both time and money. Finally, we predict the circadian time from a single transcriptomic timepoint, deriving novel marker transcripts that are most impactful for accurate prediction, this could facilitate the identification of altered clock function from existing datasets.

genomics↗