Phenomics: the systematic study of phenotypes on a genome-wide scale

AI in Neurology: Everything, Everywhere, all at Once PART 2: Speech, Sentience, Scruples, and Service

Artificial intelligence (AI) applications are finding use in real-world neurological settings. Whereas part 1 of this 3-part review series focused on the birth of AI and its foundational principles, this part 2 review shifts gears to explore more practical aspects of neurological care. The review details how large language models, generative AI, and robotics are supporting diagnostic accuracy, patient interaction, and treatment personalization. Special attention is given to ethical and philosophical facets of AI that nonetheless impact practical aspects of care and patient safety, such as accountability for AI-driven decisions and the "black box" nature of many algorithms. We will discuss whether AI systems can develop sentience, and the implications for human-AI collaboration. By examining human-robot interactions in neurology, this part 2 review highlights the profound impact AI could have on patient care and, as covered in the ensuing part 3, on global health care delivery and data analytics, while maintaining ethical oversight and human control. ANN NEUROL 2025.

Long-read sequencing of 945 Han individuals identifies structural variants associated with phenotypic diversity and disease susceptibility

Genomic structural variants (SVs) are a major source of genetic diversity in humans. Here, through long-read sequencing of 945 Han Chinese genomes, we identify 111,288 SVs, including 24.56% unreported variants, many with predicted functional importance. By integrating human population-level phenotypic and multi-omics data as well as two humanized mouse models, we demonstrate the causal roles of two SVs: one SV that emerges at the common ancestor of modern humans, Neanderthals, and Denisovans in GSDMD for bone mineral density and one modern-human-specific SV in WWP2 impacting height, weight, fat, craniofacial phenotypes and immunity. Our results suggest that the GSDMD SV could serve as a rapid and cost-effective biomarker for assessing the risk of cisplatin-induced acute kidney injury. The functional conservation from human to mouse and widespread signals of positive natural selection suggest that both SVs likely influence local adaptation, phenotypic diversity, and disease susceptibility across diverse human populations.

Convergent Validity of Experimental Cognitive Tests in a Large Community Sample

Experimental cognitive tests are designed to measure particular cognitive domains, although evidence supporting test validity is often limited. The Consortium for Neuropsychiatric Phenomics test battery administered 23 experimental and traditional neuropsychological tests to a large sample of community volunteers (n = 1,059) and patients with psychiatric diagnoses (n = 137), providing a unique opportunity to examine convergent validity with factor analysis. Traditional tests included subtests from the Wechsler and Delis-Kaplan batteries, while experimental tests included the Attention Networks Test, Balloon Analogue Risk Task, Delay Discounting Task, Remember-Know, Reversal Learning Task, Scene Recognition, Spatial and Verbal Capacity and Manipulation Tasks, Stop-Signal Task, and Task Switching. Several experimental cognitive measures were insufficiently related to other tests and were excluded from factor analyses. In the remaining 18 tests, exploratory factor analysis and subsequent multigroup confirmatory factor analysis supported a three-factor structure broadly corresponding to domains of verbal/working memory, inhibitory control, and memory. In sum, several experimental measures of inhibitory control had weak relationships with all other tests, while the convergent validity of most tests of working memory and memory was supported.

Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

Automated Phenotypic Trait Extraction for Rice Plant Using Terrestrial Laser Scanning Data

To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data

BACKGROUND

Heterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, to subclassify ASD. We will assess whether behavior-based subgrouping yields clinically, genetically, and neurologically distinct ASD groups.

METHODS

We propose a study involving 60 individuals with ASD recruited from a specialized psychiatric clinic to perform joint attention tasks. Through the examination of gaze patterns in social contexts, we will conduct a semi-supervised clustering analysis, yielding two primary clusters: good gaze response group and poor gaze response group. Subsequent comparison will occur across these clusters, scrutinizing neuroanatomical structure and connectivity using structural as well as functional brain imaging studies, genetic predisposition through single nucleotide polymorphism data, and assorted socio-demographic and clinical information.

CONCLUSIONS

The aim of the study is to investigate the discriminative properties and the validity of the joint attention-based subclassification of ASD using multi-modality data.

TRIAL REGISTRATION

Clinical trial, KCT0008530, Registered 16 June 2023, https://cris.nih.go.kr/cris/index/index.do .

Phenomics as an approach to Comparative Developmental Physiology

The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.

EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.

Functional genomic effects of indels using Bayesian genome-phenome wide association studies in sorghum

High-throughput genomic and phenomic data have enhanced the ability to detect genotype-to-phenotype associations that can resolve broad pleiotropic effects of mutations on plant phenotypes. As the scale of genotyping and phenotyping has advanced, rigorous methodologies have been developed to accommodate larger datasets and maintain statistical precision. However, determining the functional effects of associated genes/loci is expensive and limited due to the complexity associated with cloning and subsequent characterization. Here, we utilized phenomic imputation of a multi-year, multi-environment dataset using PHENIX which imputes missing data using kinship and correlated traits, and we screened insertions and deletions (InDels) from the recently whole-genome sequenced Sorghum Association Panel for putative loss-of-function effects. Candidate loci from genome-wide association results were screened for potential loss of function using a Bayesian Genome-Phenome Wide Association Study (BGPWAS) model across both functionally characterized and uncharacterized loci. Our approach is designed to facilitate in silico validation of associations beyond traditional candidate gene and literature-search approaches and to facilitate the identification of putative variants for functional analysis and reduce the incidence of false-positive candidates in current functional validation methods. Using this Bayesian GPWAS model, we identified associations for previously characterized genes with known loss-of-function alleles, specific genes falling within known quantitative trait loci, and genes without any previous genome-wide associations while additionally detecting putative pleiotropic effects. In particular, we were able to identify the major tannin haplotypes at the Tan1 locus and effects of InDels on the protein folding. Depending on the haplotype present, heterodimer formation with Tan2 was significantly affected. We also identified major effect InDels in Dw2 and Ma1, where proteins were truncated due to frameshift mutations that resulted in early stop codons. These truncated proteins also lost most of their functional domains, suggesting that these indels likely result in loss of function. Here, we show that the Bayesian GPWAS model is able to identify loss-of-function alleles that can have significant effects upon protein structure and folding as well as multimer formation. Our approach to characterize loss-of-function mutations and their functional repercussions will facilitate precision genomics and breeding by identifying key targets for gene editing and trait integration.

Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients

Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.

The application of zebrafish patient-derived xenograft tumor models in the development of antitumor agents

The cost of antitumor drug development is enormous, yet the clinical outcomes are less than satisfactory. Therefore, it is of great importance to develop effective drug screening methods that enable accurate, rapid, and high-throughput discovery of lead compounds in the process of preclinical antitumor drug research. An effective solution is to use the patient-derived xenograft (PDX) tumor animal models, which are applicable for the elucidation of tumor pathogenesis and the preclinical testing of novel antitumor compounds. As a promising screening model organism, zebrafish has been widely applied in the construction of the PDX tumor model and the discovery of antineoplastic agents. Herein, we systematically survey the recent cutting-edge advances in zebrafish PDX models (zPDX) for studies of pathogenesis mechanisms and drug screening. In addition, the techniques used in the construction of zPDX are summarized. The advantages and limitations of the zPDX are also discussed in detail. Finally, the prospects of zPDX in drug discovery, translational medicine, and clinical precision medicine treatment are well presented.

Susceptible and resistant olive cultivars show differential physiological response to Xylella fastidiosa infections

Olive quick decline syndrome (OQDS) is a severe disease, first described in Italy in late 2013, caused by strains of Xylella fastidiosa subsp. pauca (Xfp) in susceptible olive cultivars. Conversely, resistant olive cultivars do not develop OQDS but present scattered branch dieback, which generally does not evolve to severe canopy decline. In the present study, we assessed the physiological responses of Xfp-infected olive trees of susceptible and resistant cultivars. Periodic measurements of stomatal conductance (gs) and stem water potential (Ψstem) were performed using a set of healthy and Xfp-infected plants of the susceptible "Cellina di Nardò" and resistant "Leccino" and "FS17" cultivars. Strong differences in Δgs and ΔΨstem among Xfp-infected trees of these cultivars were found, with higher values in Cellina di Nardò than in Leccino and FS17, while no differences were found among healthy plants of the different cultivars. Both resistant olive cultivars showed lower water stress upon Xfp infections, compared to the susceptible one, suggesting that measurements of gs and Ψstem may represent discriminating parameters to be exploited in screening programs of olive genotypes for resistance to X. fastidiosa.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

The field phenotyping platform's next darling: Dicotyledons

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups

Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.

Natterin-like depletion by CRISPR/Cas9 impairs zebrafish (Danio rerio) embryonic development

Background

The Natterin protein family was first discovered in the venom of the medically significant fish Thalassophryne nattereri, and over the last decade natterin-like genes have been identified in various organisms, notably performing immune-related functions. Previous findings support natterin-like genes as effector defense molecules able to activate multiprotein complexes driving the host innate immune response, notably due to the pore-forming function of the aerolysin superfamily members. Herein, employing a combination of the CRISPR/Cas9 depletion system, phenotype-based screening, and morphometric methods, we evaluated the role of one family member, LOC795232, in the embryonic development of zebrafish since it might be implicated in multiple roles and characterization of the null mutant is central for analysis of gene activity.

Results

Multiple sequence alignment revealed that the candidate natterin-like has the highest similarity to zebrafish aep1, a putative and better characterized fish-specific defense molecule from the same family. Compared to other species, zebrafish have many natterin-like copies. Whole-mount in situ hybridization confirmed the knockout and mutant embryos exhibited epiboly delay, growth retardation, yolk sac and heart edema, absent or diminished swim bladder, spinal defects, small eyes and head, heart dysfunction, and behavioral impairment. As previously demonstrated, ribonucleoproteins composed of Cas9 and duplex guide RNAs are effective at inducing mutations in the F0 zebrafish.

Conclusions

The considerably high natterin-like copies in zebrafish compared to other species might be due to the teleost-specific whole genome duplication and followed by subfunctionalization or neofunctionalization. In the present work, we described some of the natterin-like features in the zebrafish development and infer that natterin-like proteins potentially contribute to the embryonary development and immune response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08369-z.

The Natterin family was discovered in the venom of the fish Thalassophryne nattereri.

The zebrafish genome encodes eleven natterin-like genes.

Natterin-like might be a novel fish-specific defense molecule.

Natterin-like proteins are thought to be pore-forming molecules.

Reverse genetic study and phenotypic characterization suggests natterin-like genes may have roles in zebrafish development.

Compromised reactive but intact proactive inhibitory motor control in Tourette disorder

Tourette disorder (TD) is characterized by tics, which are sudden repetitive involuntary movements or vocalizations. Deficits in inhibitory control in TD patients remain inconclusive from the traditional method of estimating the ability to stop an impending action, which requires careful interpretation of a metric derived from race model. One possible explanation for these inconsistencies is that race model's assumptions of independent and stochastic rise of GO and STOP process to a fixed threshold are often violated, making the classical metric to assess inhibitory control less robust. Here, we used a pair of metrics derived from a recent alternative model to address why stopping performance in TD is unaffected despite atypical neural circuitry. These new metrics distinguish between proactive and reactive inhibitory control and estimate them separately. When these metrics in adult TD group were contrasted with healthy controls (HC), we identified robust deficits in reactive control, but not in proactive control in TD. The TD group exhibited difficulty in slowing down the speed of movement preparation, which they rectified by their intact ability to postpone the movement.

Turning the Curve Into Straight: Phenogenetics of the Spine Morphology and Coordinate Maintenance in the Zebrafish

The vertebral column, or spine, provides mechanical support and determines body axis posture and motion. The most common malformation altering spine morphology and function is adolescent idiopathic scoliosis (AIS), a three-dimensional spinal deformity that affects approximately 4% of the population worldwide. Due to AIS genetic heterogenicity and the lack of suitable animal models for its study, the etiology of this condition remains unclear, thus limiting treatment options. We here review current advances in zebrafish phenogenetics concerning AIS-like models and highlight the recently discovered biological processes leading to spine malformations. First, we focus on gene functions and phenotypes controlling critical aspects of postembryonic aspects that prime in spine architecture development and straightening. Second, we summarize how primary cilia assembly and biomechanical stimulus transduction, cerebrospinal fluid components and flow driven by motile cilia have been implicated in the pathogenesis of AIS-like phenotypes. Third, we highlight the inflammatory responses associated with scoliosis. We finally discuss recent innovations and methodologies for morphometrically characterize and analyze the zebrafish spine. Ongoing phenotyping projects are expected to identify novel and unprecedented postembryonic gene functions controlling spine morphology and mutant models of AIS. Importantly, imaging and gene editing technologies are allowing deep phenotyping studies in the zebrafish, opening new experimental paradigms in the morphometric and three-dimensional assessment of spinal malformations. In the future, fully elucidating the phenogenetic underpinnings of AIS etiology in zebrafish and humans will undoubtedly lead to innovative pharmacological treatments against spinal deformities.

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

Phenotypes of Cardiovascular Diseases: Current Status and Future Perspectives

Cardiovascular diseases (CVDs) are a large group of diseases and have become the leading cause of morbidity and mortality worldwide. Although considerable progresses have been made in the diagnosis, treatment and prognosis of CVD, communication barriers between clinicians and researchers still exist because the phenotypes of CVD are complex and diverse in clinical practice and lack of unity. Therefore, it is particularly important to establish a standardized and unified terminology to describe CVD. In recent years, there have been several studies, such as the Human Phenotype Ontology, attempting to provide a standardized description of the disease phenotypes. In the present article, we outline recent advances in the classification of the major types of CVD to retrospectively review the current progresses of phenotypic studies in the cardiovascular field and provide a reference for future cardiovascular research.

Phenomics Approach to Investigate Behavioral Toxicity of Environmental or Occupational Toxicants in Adult Zebrafish (Danio rerio)

Over the last few years, environmental pollution, especially water pollution, has become a serious issue worldwide. Thus, methods that can help us understand the impact and effects of these pollutants, especially on aquatic animals, are needed. Behavioral assessment has emerged as a crucial tool in toxicology and pharmacology because many studies have shown, in multiple animal models, that various pharmacological compounds can alter behavior, with many of the findings being translatable to humans. Moreover, behavior study can also be used as a suitable indicator in the ecotoxicological risk assessment of pollutants. Several model organisms, especially rodent models, have been extensively employed for behavior studies. However, assessments using this model are generally time consuming, expensive, and require extensive facilities for housing experimental animals. Moreover, behavioral studies typically use different measurements and assessment tools, making comparisons difficult. In addition, even though behavioral phenomics has the potential to comprehensively illustrate the toxicities of chemicals, there is only a limited number of studies focusing on animal behavior using such a global approach. Here, we describe a phenomics approach that can be used to investigate the impact of pollutants using zebrafish. The approach consists of several behavioral tests, including response to a novel environment, mirror-reflection image, predator fish, and conspecifics, after exposure to a test chemical. Phenotype fingerprinting, a method for summarizing individual phenotypes based on the results of the behavioral tests, is then conducted to reduce data complexity and display the pattern of each compound on behavioral phenotypes in zebrafish. This approach may be useful to researchers studying the potential adverse effects of different pollutants. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Novel tank test Basic Protocol 2: Shoaling test Basic Protocol 3: Aggression test (mirror biting test) Basic Protocol 4: Social interaction test Basic Protocol 5: Fear response test Basic Protocol 6: PCA and heatmap clustering.

Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

A web-based survey on various symptoms of computer vision syndrome and the genetic understanding based on a multi-trait genome-wide association study

A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH₄ > 0.9) on retinal MYT1L, known to play an important role in neuronal differentiation. This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

[Neurogenetics of schizophrenia: findings from studies based on data sharing and global partnerships]

Schizophrenic psychoses are the result of a multifactorial process in which not only environmental influences but also genetic factors play an important role. These factors are based on a complex mode of inheritance that involves a large number of genetic variants. In the last three decades, biological psychiatric research has focused closely on molecular genetic aspects of the hereditary basis of schizophrenic psychoses. In particular, international consortia are combining cohorts from individual researchers, creating continuously increasing sample sizes and thus increased statistical power. As part of the Psychiatric Genomics Consortium (PGC), genome-wide association studies with tens of thousands of patients and controls have for the first time found robustly replicable markers for schizophrenic psychoses. Through intensive phenotyping, first approaches to a transdiagnostic clinical reclassification of severe mental illnesses have been established in the longitudinal PsyCourse study of the UMG Göttingen and the LMU Munich, allowing new biologically validated disease subgroups with prognostic value to be identified. For the first time environmental factors could even be examined in an African cohort that contribute to the development of the psychosis. In the coming years, the enormous technical progress in the area of genomic high-throughput technologies (next-generation sequencing) is expected to provide new knowledge not only about the influence of frequently occurring single nucleotide polymorphisms but also about rare variants. For the successful use of this technological revolution an exchange of data between research groups is essential.

Efficacy of inhibitory control depends on procrastination and deceleration in saccade planning

A goal-directed flexible behavior warrants our ability to timely inhibit impending movements deemed inappropriate due to an abrupt change in the context. Race model of countermanding rapid saccadic eye movement posits a competition between a preparatory GO process and an inhibitory STOP process rising to reach a fixed threshold. Stop-signal response time (SSRT), which is the average time STOP takes to rise to the threshold, is widely used as a metric to assess the ability to revoke a movement. A reliable estimation of SSRT critically depends on the assumption of independence between GO and STOP process, which has been violated in many studies. In addition, the physiological correlate of stochastic rise of STOP process to a threshold remains unsubstantiated thus far. Here, we introduce a method to estimate the efficacy of inhibitory control on the premise of an alternative model that assumes deceleration of GO process following the stop-signal onset. The average reaction time increased exponentially with the increase in the maximum duration available to attenuate GO process by the stop-signal. Our method estimates saccade procrastination in anticipation of the stop-signal, and the rate of increase in attenuation on GO process. Unlike SSRT, these new metrics are independent of how the stopping performance varies with the delay between go- and stop-signal onsets. We reckon that these metrics together qualify to be considered as an efficient alternative to SSRT for the estimation of individuals' ability to countermand saccades, especially in cases when the assumptions of race model are no longer valid.

Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations

The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull’s progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (r_a) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.

Big Data Solutions for Controversies in Breast Cancer Treatment

The digital world of data is expanding with an annual growth rate of 40%, and health care is among the fastest growing sector of the digital world with an annual growth rate of 48%. Rapid growth in technology has augmented data generation; for example, electronic health records produce huge amounts of patient-level data, whereas national registries capture information on numerous factors affecting health care delivery and patient outcomes. This big data can be utilized to improve health care outcomes. This review discusses relevant applications in breast cancer treatment.

Taking insights into phenomics of microbe-mineral interaction in bioleaching and acid mine drainage: Concepts and methodology

Phenomics is originally a biological concept. In the most recent years, the studies of plant and human phenomics have started, and show a strong momentum and trend of development. In this paper, based on the related research on bioleaching/acid mine drainage (AMD), we put forward the relevant concepts and methodology of phenomics of microbe-mineral interaction (MMI) in bioleaching/AMD environments. It refers to the systematic study on phenotypes of MMI on both levels of microbiome and mineralome under various environmental conditions, by which it gives the relationship between microbial/mineral genome and phenome of MMI responding to the varying environmental conditions. The pertinent methodology is of mainly (meta)-omics, synchrotron radiation-based techniques and supercomputing-based density function theory (DFT) calculation.

UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions

Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVIrededge), Simple Ratio (SR), green Chlorophyll Index (CIgreen), and Rededge Chlorophyll Index (CIrededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CIgreen, CIrededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates.

Toward Neurosubtypes in Autism

There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function-that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms' ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.

Mitochondrial disease in children

Mitochondrial disease presenting in childhood is characterized by clinical, biochemical and genetic complexity. Some children are affected by canonical syndromes, but the majority have nonclassical multisystemic disease presentations involving virtually any organ in the body. Each child has a unique constellation of clinical features and disease trajectory, leading to enormous challenges in diagnosis and management of these heterogeneous disorders. This review discusses the classical mitochondrial syndromes presenting most frequently in childhood and then presents an organ-based perspective including systems less frequently linked to mitochondrial disease, such as skin and hair abnormalities and immune dysfunction. An approach to diagnosis is then presented, encompassing clinical evaluation and biochemical, neuroimaging and genetic investigations, and emphasizing the problem of phenocopies. The impact of next-generation sequencing is discussed, together with the importance of functional validation of novel genetic variants never previously linked to mitochondrial disease. The review concludes with a brief discussion of currently available and emerging therapies. The field of mitochondrial medicine has made enormous strides in the last 30 years, with approaching 400 different genes across two genomes now linked to primary mitochondrial disease. However, many important questions remain unanswered, including the reasons for tissue specificity and variability of clinical presentation of individuals sharing identical gene defects, and a lack of disease-modifying therapies and biomarkers to monitor disease progression and/or response to treatment.

Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System

Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.

Neurocognitive and Psychiatric Markers for Addiction: Common vs. Specific Endophenotypes for Heroin and Amphetamine Dependence

BACKGROUND

The differential utility of neurocognitive impulsivity and externalizing/ internalizing traits as putative endophenotypes for dependence on heroin vs. amphetamine is unclear.

OBJECTIVE

This exploratory study aims to determine: (1) whether neurocognitive impulsivity dimensions and externalizing/internalizing traits are correlated between siblings discordant for heroin and amphetamine dependence; and (2) which of these associations are common across substances and which are substance- specific.

METHODS

Pearson correlations between individuals with 'pure' heroin and amphetamine dependence and their unaffected biological siblings (n = 37 heroin sibling pairs; n = 30 amphetamine sibling pairs) were run on 10 neurocognitive measures, 6 externalizing measures, and 5 internalizing measures. Sibling pair effects were further examined using regression.

RESULTS

Siblings discordant for heroin dependence were significantly correlated on delay aversion on the Cambridge Gambling Task, risk-taking on the Balloon Analogue Risk Task, sensation seeking, and hopelessness. Siblings discordant for amphetamine dependence were significantly correlated on the quality of decision-making on the Cambridge Gambling Task, discriminability on the Immediate Memory Task, commission errors on the Go/No Go Task, trait impulsivity, ADHD and anxiety sensitivity.

CONCLUSION

Dimensions of impulsivity and externalizing/internalizing traits appear to aggregate among siblings discordant for substance dependence. Risk-taking propensity, sensation seeking and hopelessness were specific for heroin sibling pairs. Motor/action impulsivity, trait impulsivity, and anxiety sensitivity were specific to amphetamine sibling pairs. Decisional/choice impulsivity was common across both heroin and amphetamine sibling pairs. These findings provide preliminary evidence for the utility of neurocognitive impulsivity and externalizing/ internalizing traits as candidate endophenotypes for substance dependence in general and for substance-specific dependencies.

Artificial Intelligence for Cardiac Imaging-Genetics Research

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies

Objective

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.

Methods

The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.

Results

Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.

Conclusions

PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.

The current state of omics technologies in the clinical management of asthma and allergic diseases

OBJECTIVE

To review the state of omics science specific to asthma and allergic diseases and discuss the current and potential applicability of omics in clinical disease prediction, treatment, and management.

DATA SOURCES

Studies and reviews focused on the use of omics technologies in asthma and allergic disease research and clinical management were identified using PubMed.

STUDY SELECTIONS

Publications were included based on relevance, with emphasis placed on the most recent findings.

RESULTS

Omics-based research is increasingly being used to differentiate asthma and allergic disease subtypes, identify biomarkers and pathological mediators, predict patient responsiveness to specific therapies, and monitor disease control. Although most studies have focused on genomics and transcriptomics approaches, increasing attention is being placed on omics technologies that assess the effect of environmental exposures on disease initiation and progression. Studies using omics data to identify biological targets and pathways involved in asthma and allergic disease pathogenesis have primarily focused on a specific omics subtype, providing only a partial view of the disease process.

CONCLUSION

Although omics technologies have advanced our understanding of the molecular mechanisms underlying asthma and allergic disease pathology, omics testing for these diseases are not standard of care at this point. Several important factors need to be addressed before these technologies can be used effectively in clinical practice. Use of clinical decision support systems and integration of these systems within electronic medical records will become increasingly important as omics technologies become more widely used in the clinical setting.

Blocking and re-arrangement of pots in greenhouse experiments: which approach is more effective?

BACKGROUND

Observations measured in field and greenhouse experiments always contain errors. These errors can arise from measurement error, local or positional conditions of the experimental units, or from the randomization of experimental units. In statistical analysis errors can be modelled as independent effects or as spatially correlated effects with an appropriate variance-covariance structure. Using a suitable experimental design, a part of the variance can be captured through blocking of the experimental units. If experimental units (e.g. pots within a greenhouse) are mobile, they can be re-arranged during the experiment. This re-arrangement enables a separation of variation due to time-invariant position effects and variation due to the experimental units. If re-arrangement is successful, the time-invariant positional effect can average out for experimental units moved between different positions during the experiment. While re-arrangement is commonly done in greenhouse experiments, data to quantify its usefulness is limited.

RESULTS

A uniformity greenhouse experiment with barley (Hordeum vulgare L.) to compare re-arrangement of pots with a range of designs under fixed-position arrangement showed that both methods can reduce the residual variance and the average standard error of a difference. All designs with fixed-position arrangement, which accounted for the known north-south gradient in the greenhouse, outperformed re-arrangement. An α-design with block size four performed best across seven plant growth traits.

CONCLUSION

Blocking with a fixed-position arrangement was more efficient in improving precision of greenhouse experiments than re-arrangement of pots and hence can be recommended for comparable greenhouse experiments.

Reproducible phenotype alteration due to prolonged cooling of the pupae of Polyommatus icarus butterflies

The phenotypic changes induced by prolonged cooling (2-12 weeks at 5 °C in the dark) of freshly formed Polyommatus icarus pupae were investigated. Cooling halted the imaginal development of pupae collected shortly after transformation from the larval stage. After cooling, the pupae were allowed to continue their developmental cycle. The wings of the eclosed specimens were investigated by optical microscopy, scanning and cross-sectional transmission electron microscopy, UV-VIS spectroscopy and microspectroscopy. The eclosed adults presented phenotypic alterations that reproduced results that we published previously for smaller groups of individuals remarkably well; these changes included i) a linear increase in the magnitude of quantified deviation from normal ventral wing patterns with increasing cooling time; ii) slight alteration of the blue coloration of males; and iii) an increasing number of blue scales on the dorsal wing surface of females with increasing cooling time. Several independent factors, including disordering of regular scale rows in males, the number of blue scales in females, eclosion probability and the probability of defect-free eclosion, showed that the cooling time can be divided into three periods: 0-4 weeks, 4-8 weeks, and 8-12 weeks, each of which is characterized by specific changes. The shift from brown female scales to first blue scales with a female-specific shape and then to blue scales with a male-specific shape with longer cooling times suggests slow decomposition of a substance governing scale formation.

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

BACKGROUND

There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain.

METHODS

A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components.

RESULTS

Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks.

CONCLUSIONS

We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

Cooperativity of co-factor NR2F2 with Pioneer Factors GATA3, FOXA1 in promoting ERα function

Estrogen receptor α (ERα) drives growth in the majority of human breast cancers by binding to regulatory elements and inducing transcriptional events that promote tumor growth. ERα binding activity largely depends on access to binding sites on chromatin, which is facilitated in part by Pioneer Factors (PFs). Transcription factors operate in complexes through thousands of genomic binding sites in a combinatorial fashion to control the expression of genes. However, the extent of crosstalk and cooperation between ERα pioneer factors and more collaborative transcription factors in breast cancer still remains to be elucidated systematically. Methods: Here, we determined the genomic binding information of 40 transcription-related factors and histone modifications with ChIP-seq in ENCODE and integrated it with other genomic information (RNA-seq, ATAC-seq, Gene microarray, 450k methylation chip, GRO-seq), forming a multi-dimension network to illuminate ERα associated transcription. Results: We show that transcription factor, NR2F2 binds to most sites independently of estrogen. Perturbation of NR2F2 expression decreases ERα DNA binding, chromatin openning, and estrogen-dependent cell growth. In the genome-wide analysis, we show that most binding events of NR2F2 and known pioneer factors FOXA1, GATA3 occur together, covering 85% of the ERα binding sites. Regions bound by all the three TFs appeared to be the most active, to have the strongest ERα binding and to be enriched for the super enhancers. Conclusions: The ERα binds to pre-accessible sites containing ERE elements bound by the three transcription factors (NR2F2, FOXA1 and GATA3).The three genes were also identified to correlate with decreased metastatic potential in patient cohorts and co-regulate each other. Together, our results suggest that NR2F2 is a cofactor with FOXA1 and GATA3 in ERα-mediated transcription.

Closing the 'phenotype gap' in precision medicine: improving what we measure to understand complex disease mechanisms

The central concept underlying precision medicine is a mechanistic understanding of each disease and its response to therapy sufficient to direct a specific intervention. To execute on this vision requires parsing incompletely defined disease syndromes into discrete mechanistic subsets and developing interventions to precisely address each of these etiologically distinct entities. This will require substantial adjustment of traditional paradigms which have tended to aggregate high-level phenotypes with very different etiologies. In the current environment, where diagnoses are not mechanistic, drug development has become so expensive that it is now impractical to imagine the cost-effective creation of new interventions for many prevalent chronic conditions. The vision of precision medicine also argues for a much more seamless integration of research and development with clinical care, where shared taxonomies will enable every clinical interaction to inform our collective understanding of disease mechanisms and drug responses. Ideally, this would be executed in ways that drive real-time and real-world discovery, innovation, translation, and implementation. Only in oncology, where at least some of the biology is accessible through surgical excision of the diseased tissue or liquid biopsy, has "co-clinical" modeling proven feasible. In most common germline disorders, while genetics often reveal the causal mutations, there still remain substantial barriers to efficient disease modeling. Aggregation of similar disorders under single diagnostic labels has directly contributed to the paucity of etiologic and mechanistic understanding by directly reducing the resolution of any subsequent studies. Existing clinical phenotypes are typically anatomic, physiologic, or histologic, and result in a substantial mismatch in information content between the phenomes in humans or in animal 'models' and the variation in the genome. This lack of one-to-one mapping of discrete mechanisms between disease and animal models causes a failure of translation and is one form of 'phenotype gap.' In this review, we will focus on the origins of the phenotyping deficit and approaches that may be considered to bridge the gap, creating shared taxonomies between human diseases and relevant models, using cardiovascular examples.