-
Journal of Dental Research Jul 2021Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by...
Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by the recent availability of high-throughput genomics and multi-omics analyses and the companion tools of advanced informatics and computational science. Understanding of the human genome and its influence on phenotype continues to advance through genotyping large populations and using "light phenotyping" approaches in combination with smaller subsets of the population being evaluated using "deep phenotyping" approaches. Using our capability to integrate and jointly analyze genomic data with other multi-omic data, the knowledge of genotype-phenotype relationships and associated genetic pathways and functions is being advanced. Understanding genotype-phenotype relationships that discriminate human health from disease is speculated to facilitate predictive, precision health care and change modes of health care delivery. The American Association for Dental Research Fall Focused Symposium assembled experts to discuss how studies of genotype-phenotype relationships are illuminating the pathophysiology of craniofacial diseases and developmental biology. Although the breadth of the topic did not allow all areas of dental, oral, and craniofacial research to be addressed (e.g., cancer), the importance and power of integrating genomic, phenomic, and other -omic data are illustrated using a variety of examples. The 8 Fall Focused talks presented different methodological approaches for ascertaining study populations and evaluating population variance and phenotyping approaches. These advances are reviewed in this summary.
Topics: Genome, Human; Genomics; Genotype; Humans; Phenotype
PubMed: 33749358
DOI: 10.1177/00220345211001850 -
PLoS Computational Biology Nov 2023Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic...
Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.
Topics: Genotype; Phenotype; Genome; Biological Evolution; Multifactorial Inheritance
PubMed: 37948461
DOI: 10.1371/journal.pcbi.1011631 -
Journal of Human Hypertension Oct 2023The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-naïve primary hypertensive...
The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-naïve primary hypertensive patients without co-morbidities (aged 18-55, n = 73), and matched normotensive controls (n = 79) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24 h ambulatory monitoring. Vascular phenotyping included SphygmoCor® measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT™-2000® provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing 'arterially stiffened', 'vaso-protected', and 'non-dipper' patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification.
Topics: Humans; Carotid Intima-Media Thickness; Pulse Wave Analysis; Blood Pressure Monitoring, Ambulatory; Hypertension; Blood Pressure; Phenotype; Vascular Stiffness
PubMed: 36528682
DOI: 10.1038/s41371-022-00794-7 -
Sensors (Basel, Switzerland) Jun 2021Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which... (Review)
Review
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
Topics: Artificial Intelligence; Machine Learning; Phenomics; Phenotype; Software
PubMed: 34202291
DOI: 10.3390/s21134363 -
The New Phytologist Nov 2019Ecological interaction and adaptation both depend on phenotypic characteristics. In contrast with the common conception of the 'adult' phenotype, plant bodies develop... (Review)
Review
Ecological interaction and adaptation both depend on phenotypic characteristics. In contrast with the common conception of the 'adult' phenotype, plant bodies develop continuously during their lives. Furthermore, the different units (metamers) that comprise plant bodies are not identical copies, but vary extensively within individuals. These characteristics foster recognition of plant phenotypes as dynamic mosaics. We elaborate this conception based largely on a wide-ranging review of developmental, ecological and evolutionary studies of plant reproduction, and identify its utility in the analysis of plant form, function and diversification. An expanded phenotypic conception is warranted because dynamic mosaic features affect plant performance and evolve. Evidence demonstrates that dynamic mosaic phenotypes enable functional ontogeny, division of labour, resource and mating efficiency. In addition, dynamic mosaic features differ between individuals and experience phenotypic selection. Investigation of the characteristics and roles of dynamic and mosaic features of plant phenotypes benefits from considering within-individual variation as a function-valued trait that can be analysed with functional data methods. Phenotypic dynamics and within-individual variation arise despite an individual's genetic uniformity, and develop largely by heterogeneous gene expression and associated hormonal control. These characteristics can be heritable, so that dynamic mosaic phenotypes can evolve and diversify by natural selection.
Topics: Biodiversity; Inflorescence; Magnoliopsida; Mosaicism; Phenotype; Reproduction
PubMed: 31087328
DOI: 10.1111/nph.15916 -
Computers in Biology and Medicine Nov 2021Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients...
Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.
Topics: Cluster Analysis; Humans; Phenotype; Semantics
PubMed: 34600327
DOI: 10.1016/j.compbiomed.2021.104904 -
PLoS Computational Biology Oct 2021Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of...
Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory-the ability to track and condition the time to switch to a different phenotypic state-can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.
Topics: Algorithms; Bacteria; Bacterial Physiological Phenomena; Computational Biology; Models, Biological; Phenotype
PubMed: 34597291
DOI: 10.1371/journal.pcbi.1009431 -
Animal Models and Experimental Medicine Apr 2023Revealing the entire dynamics of pathogenesis is critical for understanding, preventing and treating human disease but is limited by systematic clinical sampling. This...
Revealing the entire dynamics of pathogenesis is critical for understanding, preventing and treating human disease but is limited by systematic clinical sampling. This drawback can be overcome with animal model studies. Recent advances in phenotyping, omics and bioinformatics technologies promote the development of the 4D animal model to simulate and digitally display the spatiotemporal landscapes of phenotypes and molecular dynamics in human diseases and reveal novel targets for diagnosis and therapy. In this commentary, the origin, supporting technologies, content, function and application, and advantages of 4D animal models over clinical studies and traditional animal models, as well as their limitations, are presented.
Topics: Animals; Humans; Computational Biology; Phenotype; Models, Animal
PubMed: 36852490
DOI: 10.1002/ame2.12306 -
Journal of Animal Science Feb 2021Genetic strategies aimed at improving general immune competence (IC) have the potential to reduce the incidence and severity of disease in beef production systems, with...
Genetic strategies aimed at improving general immune competence (IC) have the potential to reduce the incidence and severity of disease in beef production systems, with resulting benefits of improved animal health and welfare and reduced reliance on antibiotics to prevent and treat disease. Implementation of such strategies first requires that methodologies be developed to phenotype animals for IC and demonstration that these phenotypes are associated with health outcomes. We have developed a methodology to identify IC phenotypes in beef steers during the yard weaning period, which is both practical to apply on-farm and does not restrict the future sale of tested animals. In the current study, a total of 838 Angus steers, previously IC phenotyped at weaning, were categorized as low (n = 98), average (n = 653), or high (n = 88) for the IC phenotype. Detailed health and productivity data were collected on all steers during feedlot finishing, and associations between IC phenotype, health outcomes, and productivity were investigated. A favorable association between IC phenotype and number of mortalities during feedlot finishing was observed with higher mortalities recorded in low IC steers (6.1%) as compared with average (1.2%, P < 0.001) or high (0%, P = 0.018) IC steers. Disease incidence was numerically highest in low IC steers (15.3 cases/100 animals) and similar in average IC steers (10.1 cases/100 animals) and high IC steers (10.2 cases/100 animals); however, differences between groups were not significant. No significant influence of IC phenotype on average daily gain was observed, suggesting that selection for improved IC is unlikely to incur a significant penalty to production. The potential economic benefits of selecting for IC in the feedlot production environment were calculated. Health-associated costs were calculated as the sum of lost production costs, lost capital investment costs, and disease treatment costs. Based on these calculations, health-associated costs were estimated at AUS$103/head in low IC steers, AUS$25/head in average IC steers, and AUS$4/head in high IC steers, respectively. These findings suggest that selection for IC has the potential to reduce mortalities during feedlot finishing and, as a consequence, improve the health and welfare of cattle in the feedlot production environment and reduce health-associated costs incurred by feedlot operators.
Topics: Animal Feed; Animals; Cattle; Diet; Phenotype; Weaning
PubMed: 33476384
DOI: 10.1093/jas/skab016 -
BMC Bioinformatics Mar 2022Ontologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be...
BACKGROUND
Ontologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease.
RESULTS
Here we present the Xenopus phenotype ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated.
CONCLUSIONS
The XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype-phenotype data that can be directly related to other uPheno compliant resources.
Topics: Animals; Biological Ontologies; Gene Ontology; Humans; Phenotype; Xenopus laevis
PubMed: 35317743
DOI: 10.1186/s12859-022-04636-8