Biology subjects

Hoh, D.

Publications and source records attributed to Hoh, D..

3 recordsLinked to original sources

bioRxiv2024

From Quantitative Trait Loci towards Mechanisms: Linkage Integration Hypothesis Testing (LIgHT) Sheds Light on the Mechanisms of Genetically Modulated Stress Tolerance

The goal of this work is to assess the mechanistic bases of natural genetic variations in plant responses of photosynthesis to stress. To achieve this goal, we devised the Linkage Integration Hypothesis Testing (LIgHT) approach, comparing chromosomal locations of quantitative trait loci (QTL) for multiple phenotypes to distinguish between hypothetical mechanisms. As a use case, we explored genetic variations in photosynthesis-related processes under chilling stress in recombinant inbred lines of cowpea (Vigna unguiculata L. Walp.). We focused on photosynthesis-related parameters measurable in high throughput and indicative of proposed chilling responses, including the states of photosystems I (PSI) and II (PSII), photoprotective nonphotochemical quenching, PSII photodamage, and nyctinastic leaf movements (NLM). The patterns of QTL linkages indicated chilling stress tolerance is genetically controlled by avoiding PSII photodamage rather than PSI damage or NLM. This model was validated in a separate experiment measuring the rates of PSII photodamage and repair. Additional linkages suggest that chilling-induced damage to PSII is controlled by the thylakoid proton motive force and redox state of PSII. This regulation is modulated by thylakoid fatty acid composition, as suggested in Hoh et al., 2022. We propose the LIgHT approach can be broadly applied to test mechanisms underlying genetic variations. HighlightThis work Introduces the Linkage Integration Hypothesis Testing (LIgHT) approach for mechanistic studies of natural variations, identifying photosynthetic regulatory mechanisms underlying natural variations to abiotic stresses by applying this approach.

plant biology↗

bioRxiv2020

Composite modeling of leaf shape across shoots discriminates Vitis species better than individual leaves

Premise of studyLeaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. We measured leaf morphology from over 200 vines over four years, and modeled changes in leaf shape along the shoot to determine if a composite "shape of shapes" can better capture variation and predict species identity compared to individual leaves. MethodsUsing homologous universal landmarks found in grapevine leaves, we modeled various morphological features as a polynomial function of leaf node. The resulting functions are used to reconstruct modeled leaf shapes across shoots, generating composite leaves that comprehensively capture the spectrum of possible leaf morphologies. ResultsWe found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict species identity of previously unassigned vines, which were verified with genotyping. DiscussionObservations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape--an assemblage of modeled leaf snapshots across the shoot--is a better representation of the dynamic and essential shapes of leaves, as well as serving as a better predictor of species identity than individual leaves.

plant biology↗

bioRxiv2018

Identifying Emerging Phenomenon in Plant Long Temporal Phenotyping Experiments

The rapid improvement of phenotyping capability, accuracy, and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust, and biologically significant. The source code, manual, and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/.

bioinformatics↗