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American Journal of Human Genetics Jan 2023Although genomic research has predominantly relied on phenotypic ascertainment of individuals affected with heritable disease, the falling costs of sequencing allow... (Review)
Review
Although genomic research has predominantly relied on phenotypic ascertainment of individuals affected with heritable disease, the falling costs of sequencing allow consideration of genomic ascertainment and reverse phenotyping (the ascertainment of individuals with specific genomic variants and subsequent evaluation of physical characteristics). In this research modality, the scientific question is inverted: investigators gather individuals with a genomic variant and test the hypothesis that there is an associated phenotype via targeted phenotypic evaluations. Genomic ascertainment research is thus a model of predictive genomic medicine and genomic screening. Here, we provide our experience implementing this research method. We describe the infrastructure we developed to perform reverse phenotyping studies, including aggregating a super-cohort of sequenced individuals who consented to recontact for genomic ascertainment research. We assessed 13 studies completed at the National Institutes of Health (NIH) that piloted our reverse phenotyping approach. The studies can be broadly categorized as (1) facilitating novel genotype-disease associations, (2) expanding the phenotypic spectra, or (3) demonstrating ex vivo functional mechanisms of disease. We highlight three examples of reverse phenotyping studies in detail and describe how using a targeted reverse phenotyping approach (as opposed to phenotypic ascertainment or clinical informatics approaches) was crucial to the conclusions reached. Finally, we propose a framework and address challenges to building collaborative genomic ascertainment research programs at other institutions. Our goal is for more researchers to take advantage of this approach, which will expand our understanding of the predictive capability of genomic medicine and increase the opportunity to mitigate genomic disease.
Topics: Phenotype; Genotype; Genome; Genomics; Medical Informatics
PubMed: 36608682
DOI: 10.1016/j.ajhg.2022.12.004 -
Molecular Plant Feb 2020Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However,... (Review)
Review
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
Topics: Crops, Agricultural; Genome, Plant; Genomics; Genotype; Phenomics; Phenotype; Plant Breeding
PubMed: 31981735
DOI: 10.1016/j.molp.2020.01.008 -
Evolution; International Journal of... Feb 2022Forms of phenotypic plasticity in key traits, and forms of selection on and genetic variation in such plasticity, fundamentally underpin phenotypic, population dynamic,...
Forms of phenotypic plasticity in key traits, and forms of selection on and genetic variation in such plasticity, fundamentally underpin phenotypic, population dynamic, and evolutionary responses to environmental variation and directional change. Accordingly, numerous theoretical and empirical studies have examined properties and consequences of plasticity, primarily considering traits that are continuously distributed on observed phenotypic scales with linear reaction norms. However, many environmentally sensitive traits are expressed as discrete alternative phenotypes and are appropriately characterized as quantitative genetic threshold traits. Here, we highlight that forms of phenotypic plasticity, genetic variation, and inheritance in plasticity, and outcomes of selection on plasticity, could differ substantially between threshold traits and continuously distributed traits (as are typically considered). We thereby highlight theoretical developments that are required to rationalize and predict phenotypic and microevolutionary dynamics involving plastic threshold traits, and outline how intrinsic properties of such traits could provide relatively straightforward explanations for apparently idiosyncratic observed patterns of phenotypic variation. We summarize how key quantitative genetic parameters underlying threshold traits can be estimated, and thereby set the scene for embedding dynamic discrete traits into theoretical and empirical understanding of the role of plasticity in driving phenotypic, population, and evolutionary responses to environmental variation and change.
Topics: Adaptation, Physiological; Biological Evolution; Genetic Variation; Phenotype
PubMed: 34874068
DOI: 10.1111/evo.14408 -
PLoS Computational Biology Jan 2020Functional annotation of genes remains a challenge in fundamental biology and is a limiting factor for translational medicine. Computational approaches have been...
Functional annotation of genes remains a challenge in fundamental biology and is a limiting factor for translational medicine. Computational approaches have been developed to process heterogeneous data into meaningful metrics, but often do not address how findings might be updated when new evidence comes to light. To address this challenge, we describe requirements for a framework for incremental data integration and propose an implementation based on phenotype ontologies and Bayesian probability updates. We apply the framework to quantify similarities between gene annotations and disease profiles. Within this scope, we categorize human diseases according to how well they can be recapitulated by animal models and quantify similarities between human diseases and mouse models produced by the International Mouse Phenotyping Consortium. The flexibility of the approach allows us to incorporate negative phenotypic data to better prioritize candidate genes, and to stratify disease mapping using sex-dependent phenotypes. All our association scores can be updated and we exploit this feature to showcase integration with curated annotations from high-precision assays. Incremental integration is thus a suitable framework for tracking functional annotations and linking to complex human pathology.
Topics: Animals; Computational Biology; Disease Models, Animal; Genetic Predisposition to Disease; Genotype; Humans; Mice; Molecular Sequence Annotation; Phenotype
PubMed: 31986132
DOI: 10.1371/journal.pcbi.1007586 -
Scientific Reports Mar 2020To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study...
To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study was to present a large mouse phenotype-phenotype relationships dataset as a reference resource, alongside detailed evaluation of the resource. We used bias-minimized comprehensive mouse phenotype data and applied association rule mining to a dataset consisting of only binary (normal and abnormal phenotypes) data to determine relationships among phenotypes. We present 3,686 evidence-based significant associations, comprising 345 phenotypes covering 60 biological systems (functions), and evaluate their characteristics in detail. To evaluate the relationships, we defined a set of phenotype-phenotype association pairs (PPAPs) as a module of phenotypic expression for each of the 345 phenotypes. By analyzing each PPAP, we identified phenotype sub-networks consisting of the largest numbers of phenotypes and distinct biological systems. Furthermore, using hierarchical clustering based on phenotype similarities among the 345 PPAPs, we identified seven community types within a putative phenome-wide association network. Moreover, to promote leverage of these data, we developed and published web-application tools. These mouse phenome-wide phenotype-phenotype association data reveal general principles of relationships among mammalian phenotypes and provide a reference resource for biomedical analyses.
Topics: Animals; Genome-Wide Association Study; Mice; Phenotype
PubMed: 32127602
DOI: 10.1038/s41598-020-60891-w -
The European Journal of Neuroscience Jun 2021Microglia are the resident immune cells of the central nervous system (CNS) and are increasingly recognized as critical players in development, brain homeostasis, and... (Review)
Review
Microglia are the resident immune cells of the central nervous system (CNS) and are increasingly recognized as critical players in development, brain homeostasis, and disease pathogenesis. The lifespan, maintenance, proliferation, and turnover of microglia are important factors that regulate microglial behavior and affect their roles in the CNS. However, emerging evidence suggests that microglia are morphologically and phenotypically distinct in different brain areas, at different ages, and during disease. Ongoing research focuses on understanding how microglia acquire specific phenotypes in response to extrinsic cues in the environment and how phenotypes are specified by intrinsic properties of different populations of microglia. With the development of pharmacological and genetic tools that allow the investigation of microglia in vivo, there have been considerable advances in understanding molecular signatures of both homeostatic microglia and those reacting to injury and disease. Here, we review the master gene regulators that define microglia as well as discuss the evidence that microglia are heterogeneous and fall into distinct clusters that display specific intrinsic properties and perform unique tasks in different settings. Taken together, the information presented supports the idea that microglia morphology and transcriptional heterogeneity should be considered when studying the complex nature of microglia and their roles in brain health and disease.
Topics: Brain; Central Nervous System; Homeostasis; Microglia; Phenotype
PubMed: 33835613
DOI: 10.1111/ejn.15225 -
Scientific Reports Jan 2023Thoracic insufficiency syndromes are a genetically and phenotypically heterogeneous group of disorders characterized by congenital abnormalities or progressive...
Thoracic insufficiency syndromes are a genetically and phenotypically heterogeneous group of disorders characterized by congenital abnormalities or progressive deformation of the chest wall and/or vertebrae that result in restrictive lung disease and compromised respiratory capacity. We performed whole exome sequencing on a cohort of 42 children with thoracic insufficiency to elucidate the underlying molecular etiologies of syndromic and non-syndromic thoracic insufficiency and predict extra-skeletal manifestations and disease progression. Molecular diagnosis was established in 24/42 probands (57%), with 18/24 (75%) probands having definitive diagnoses as defined by laboratory and clinical criteria and 6/24 (25%) probands having strong candidate genes. Gene identified in cohort patients most commonly encoded components of the primary cilium, connective tissue, and extracellular matrix. A novel association between KIF7 and USP9X variants and thoracic insufficiency was identified. We report and expand the genetic and phenotypic spectrum of a cohort of children with thoracic insufficiency, reinforce the prevalence of extra-skeletal manifestations in thoracic insufficiency syndromes, and expand the phenotype of KIF7 and USP9X-related disease to include thoracic insufficiency.
Topics: Phenotype; Spine
PubMed: 36653407
DOI: 10.1038/s41598-023-27641-0 -
Yi Chuan = Hereditas Aug 2022Since Darwin's time, elucidating the mechanism of adaptive evolution has been one of the most important scientific issues in evolutionary biology and ecology. Adaptive... (Review)
Review
Since Darwin's time, elucidating the mechanism of adaptive evolution has been one of the most important scientific issues in evolutionary biology and ecology. Adaptive evolution usually means that species evolve special phenotypic traits to increase fitness under selective pressures. Phenotypic adaptation can be observed at different hierarchical levels of morphology, physiology, biochemistry, histology, and behavior. With the breakthroughs of molecular biology and next-generation sequencing technologies, mounting evidence has uncovered the genetic architecture driving adaptive complex phenotypes. Studying the molecular genetic mechanisms of evolutionary adaption will enable us to understand the forces shaping biodiversity and set up genotype-phenotype-environment interactions. Genetic bases of adaptive evolution have been explained by multiple hypotheses, including major-effect genes, supergenes, polygenicity, noncoding regions, repeated regions, and introgression. The strong selection pressure exerted by high-altitude extreme environments greatly promotes the occurrence of phenotypic and genetic adaptation in species. Studies on multi-omics data provide new insights into adaptive evolution. In this review, we systematically summarize the genetic mechanism of adaptive evolution, research progress in adaptation to high-altitude environmental conditions, and existing challenges and discuss the future perspectives, thereby providing guidance for researchers in this field.
Topics: Biological Evolution; Altitude; Genetic Variation; Adaptation, Physiological; Phenotype
PubMed: 36384664
DOI: 10.16288/j.yczz.22-108 -
Journal of Biomedical Informatics Apr 2023Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups... (Review)
Review
Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.
Topics: Algorithms; Electronic Health Records; Machine Learning; Natural Language Processing; Phenotype
PubMed: 36933631
DOI: 10.1016/j.jbi.2023.104335 -
Beyond the behavioural phenotype: Uncovering mechanistic foundations in aquatic eco-neurotoxicology.The Science of the Total Environment Jul 2022During the last decade, there has been an increase in awareness of how anthropogenic pollution can alter behavioural traits of diverse aquatic organisms. Apart from... (Review)
Review
During the last decade, there has been an increase in awareness of how anthropogenic pollution can alter behavioural traits of diverse aquatic organisms. Apart from understanding profound ecological implications, alterations in neuro-behavioural indices have emerged as sensitive and physiologically integrative endpoints in chemical risk assessment. Accordingly, behavioural ecotoxicology and broader eco-neurotoxicology are becoming increasingly popular fields of research that span a plethora of fundamental laboratory experimentations as well as applied field-based studies. Despite mounting interest in aquatic behavioural ecotoxicology studies, there is, however, a considerable paucity in deciphering the mechanistic foundations underlying behavioural alterations upon exposure to pollutants. The behavioural phenotype is indeed the highest-level integrative neurobiological phenomenon, but at its core lie myriads of intertwined biochemical, cellular, and physiological processes. Therefore, the mechanisms that underlie changes in behavioural phenotypes can stem among others from dysregulation of neurotransmitter pathways, electrical signalling, and cell death of discrete cell populations in the central and peripheral nervous systems. They can, however, also be a result of toxicity to sensory organs and even metabolic dysfunctions. In this critical review, we outline why behavioural phenotyping should be the starting point that leads to actual discovery of fundamental mechanisms underlying actions of neurotoxic and neuromodulating contaminants. We highlight potential applications of the currently existing and emerging neurobiology and neurophysiology analytical strategies that should be embraced and more broadly adopted in behavioural ecotoxicology. Such strategies can provide new mechanistic discoveries instead of only observing the end sum phenotypic effects.
Topics: Aquatic Organisms; Ecotoxicology; Environmental Pollution; Phenotype; Water Pollutants, Chemical
PubMed: 35306067
DOI: 10.1016/j.scitotenv.2022.154584