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

A longitudinal approach to biological psychiatric research: The PsyCourse study

In current diagnostic systems, schizophrenia and bipolar disorder are still conceptualized as distinct categorical entities. Recently, both clinical and genomic evidence have challenged this Kraepelinian dichotomy. There are only few longitudinal studies addressing potential overlaps between these conditions. Here, we present design and first results of the PsyCourse study (N = 891 individuals at baseline), an ongoing transdiagnostic study of the affective-to-psychotic continuum that combines longitudinal deep phenotyping and dimensional assessment of psychopathology with an extensive collection of biomaterial. To provide an initial characterization of the PsyCourse study sample, we compare two broad diagnostic groups defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) classification system, that is, predominantly affective (n = 367 individuals) versus predominantly psychotic disorders (n = 524 individuals). Depressive, manic, and psychotic symptoms as well as global functioning over time were contrasted using linear mixed models. Furthermore, we explored the effects of polygenic risk scores for schizophrenia on diagnostic group membership and addressed their effects on nonparticipation in follow-up visits. While phenotypic results confirmed expected differences in current psychotic symptoms and global functioning, both manic and depressive symptoms did not vary between both groups after correction for multiple testing. Polygenic risk scores for schizophrenia significantly explained part of the variability of diagnostic group. The PsyCourse study presents a unique resource to research the complex relationships of psychopathology and biology in severe mental disorders not confined to traditional diagnostic boundaries and is open for collaborations.

Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders

Impulse control is becoming a critical survival skill for the twenty-first century. Impulsivity is implicated in virtually all externalizing behaviours and disorders, and figures prominently in the aetiology and long-term sequelae of substance use disorders (SUDs). Despite its robust clinical and predictive validity, the study of impulsivity is complicated by its multidimensional nature, characterized by a variety of trait-like personality dimensions, as well as by more state-dependent neurocognitive dimensions, with variable convergence across measures. This review provides a hierarchical framework for linking self-report and neurocognitive measures to latent constructs of impulsivity and, in turn, to different psychopathology vulnerabilities, including substance-specific addictions and comorbidities. Impulsivity dimensions are presented as novel behavioural targets for prevention and intervention. Novel treatment approaches addressing domains of impulsivity are reviewed and recommendations for future directions in research and clinical interventions for SUDs are offered. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.

Neuropsychological tests of the future: How do we get there from here?

OBJECTIVE

This article reviews current approaches to neuropsychological assessment, identifies opportunities for development of new methods using modern psychometric theory and advances in technology, and suggests a transition path that promotes application of novel methods without sacrificing validity.

METHODS

Theoretical/state-of-the-art review.

CONCLUSIONS

Clinical neuropsychological assessment today does not reflect advances in neuroscience, modern psychometrics, or technology. Major opportunities for improving practice include both psychometric and technological strategies. Modern psychometric approaches including item response theory (IRT) enable linking procedures that can place different measures on common scales; adaptive testing algorithms that can dramatically increase efficiency of assessment; examination of differential item functioning (DIF) to detect measures that behave differently in different groups; and person fit statistics to detect aberrant patterns of responding of high value for performance validity testing. Opportunities to introduce novel technologies include computerized adaptive testing, Web-based assessment, healthcare- and bio-informatics strategies, mobile platforms, wearables, and the 'internet-of-things'. To overcome inertia in current practices, new methods must satisfy requirements for back-compatibility with legacy instrumentation, enabling us to leverage the wealth of validity data already accrued for classic procedures. A path to achieve these goals involves creation of a global network to aggregate item-level data into a shared repository that will enable modern psychometric analyses to refine existing methods, and serve as a platform to evolve novel assessment strategies, which over time can revolutionize neuropsychological assessment practices world-wide.

From high definition precision healthcare to precision public oral health: opportunities and challenges

In anticipation of a major transformation in healthcare, this review provides highlights that anticipate the near future for oral public health (and beyond). Personalized or precision healthcare reflects the expectation that advances in genomics, imaging, and other domains will extend our risk assessment, diagnostic, and prognostic capabilities, and enables more effective prevention and therapeutic options for all Americans. Meanwhile, the current healthcare system does not meet cost, access, or quality criteria for all Americans. It is now an imperative that the success of "smart," quality, and cost-effective high definition precision healthcare requires a public health perspective for several reasons: a) to enhance generalizability, b) to assess methods of implementation, and c) to focus on both risk and prevention in large and small populations, thereby providing a balance between the generation of long-term knowledge and short-term health gains. Sensitivity and resolution, reasonable cost, access to all Americans, coordinated comprehensive care, and advances in whole genome sequencing (WGS) and big data analyses, coupled to other advances in biotechnology and digital/artificial intelligence/machine learning devices, and the behavioral, social, and environmental sciences, offer remarkable opportunities to improve the health and wellness of the American people [genotype + phenotype + environment + behavior = high definition healthcare]. The opportunity is to significantly improve the well-being and life expectancy of all people across the lifespan including the least-advantaged people in our society and potentially increase access, reduce the national costs, and improve health outcomes.

The Role of Executive Function in Adolescent Adaptive Risk-Taking on the Balloon Analogue Risk Task

The present study examined the role of executive control functions (ECF) in adaptive risk-taking during adolescence. Healthy individuals aged 8-25 were administered ECF measures and the Balloon Analogue Risk Task (BART), a computerized measure of risk-taking propensity. Findings demonstrated that adolescents who executed a more consistent response strategy evidenced better performance on the BART. Greater working memory (WM) predicted lower response variability and WM capacity mediated the relationship between age and variability. Results suggest that intra-individual response variability may index adaptive risk-taking and that the development of ECF, specifically WM, may play an integral role in adaptive decision making during adolescence and young adulthood.

Cognitive deficit in methamphetamine users relative to childhood academic performance: link to cortical thickness

Individuals with cognitive problems may be predisposed to develop substance use disorders; therefore, differences in cognitive function between methamphetamine users and control participants may be attributable to premorbid factors rather than methamphetamine use. The goal of this study was to clarify the extent to which this is the case. Childhood academic transcripts were obtained for 37 methamphetamine-dependent adults and 41 control participants of similar educational level and premorbid IQ. Each participant completed a comprehensive cognitive battery and received a structural magnetic resonance imaging scan. Data from control participants and linear regression were used to develop a normative model to describe the relationship between childhood academic performance and scores on the cognitive battery. Using this model, cognitive performance of methamphetamine users was predicted from their premorbid academic scores. Results indicated that methamphetamine users' childhood grade point average was significantly lower than that of the control group (p < 0.05). Further, methamphetamine users' overall cognitive performance was lower than was predicted from their grade point average prior to methamphetamine use (p = 0.001), with specific deficits in attention/concentration and memory (ps < 0.01). Memory deficits were associated with lower whole-brain cortical thickness (p < 0.05). Thus, in addition to having an apparent premorbid weakness in cognition, methamphetamine users exhibit subsequent cognitive function that is significantly lower than premorbid estimates would predict. The results support the view that chronic methamphetamine use causes a decline in cognition and/or a failure to develop normative cognitive abilities, although aside from methamphetamine use per se, other drug use and unidentified factors likely contribute to the observed effects.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6–7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

The Myth of Optimality in Clinical Neuroscience

Clear evidence supports a dimensional view of psychiatric illness. Within this framework the expression of disorder-relevant phenotypes is often interpreted as a breakdown or departure from normal brain function. Conversely, health is reified, conceptualized as possessing a single ideal state. We challenge this concept here, arguing that there is no universally optimal profile of brain functioning. The evolutionary forces that shape our species select for a staggering diversity of human behaviors. To support our position we highlight pervasive population-level variability within large-scale functional networks and discrete circuits. We propose that, instead of examining behaviors in isolation, psychiatric illnesses can be best understood through the study of domains of functioning and associated multivariate patterns of variation across distributed brain systems.

MicroCT-based phenomics in the zebrafish skeleton reveals virtues of deep phenotyping in a distributed organ system

Phenomics, which ideally involves in-depth phenotyping at the whole-organism scale, may enhance our functional understanding of genetic variation. Here, we demonstrate methods to profile hundreds of phenotypic measures comprised of morphological and densitometric traits at a large number of sites within the axial skeleton of adult zebrafish. We show the potential for vertebral patterns to confer heightened sensitivity, with similar specificity, in discriminating mutant populations compared to analyzing individual vertebrae in isolation. We identify phenotypes associated with human brittle bone disease and thyroid stimulating hormone receptor hyperactivity. Finally, we develop allometric models and show their potential to aid in the discrimination of mutant phenotypes masked by alterations in growth. Our studies demonstrate virtues of deep phenotyping in a spatially distributed organ system. Analyzing phenotypic patterns may increase productivity in genetic screens, and facilitate the study of genetic variants associated with smaller effect sizes, such as those that underlie complex diseases.

Resting-state fMRI and social cognition: An opportunity to connect

Many psychiatric disorders are characterized by altered social cognition. The importance of social cognition has previously been recognized by the National Institute of Mental Health Research Domain Criteria project, in which it features as a core domain. Social task-based functional magnetic resonance imaging (fMRI) currently offers the most direct insight into how the brain processes social information; however, resting-state fMRI may be just as important in understanding the biology and network nature of social processing. Resting-state fMRI allows researchers to investigate the functional relationships between brain regions in a neutral state: so-called resting functional connectivity (RFC). There is evidence that RFC is predictive of how the brain processes information during social tasks. This is important because it shifts the focus from possibly context-dependent aberrations to context-independent aberrations in functional network architecture. Rather than being analysed in isolation, the study of resting-state brain networks shows promise in linking results of task-based fMRI results, structural connectivity, molecular imaging findings, and performance measures of social cognition-which may prove crucial in furthering our understanding of the social brain.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL).

Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.

Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Rare Copy Number Variants in NRXN1 and CNTN6 Increase Risk for Tourette Syndrome

Tourette syndrome (TS) is a model neuropsychiatric disorder thought to arise from abnormal development and/or maintenance of cortico-striato-thalamo-cortical circuits. TS is highly heritable, but its underlying genetic causes are still elusive, and no genome-wide significant loci have been discovered to date. We analyzed a European ancestry sample of 2,434 TS cases and 4,093 ancestry-matched controls for rare (< 1% frequency) copy-number variants (CNVs) using SNP microarray data. We observed an enrichment of global CNV burden that was prominent for large (> 1 Mb), singleton events (OR = 2.28, 95% CI [1.39-3.79], p = 1.2 × 10-3) and known, pathogenic CNVs (OR = 3.03 [1.85-5.07], p = 1.5 × 10-5). We also identified two individual, genome-wide significant loci, each conferring a substantial increase in TS risk (NRXN1 deletions, OR = 20.3, 95% CI [2.6-156.2]; CNTN6 duplications, OR = 10.1, 95% CI [2.3-45.4]). Approximately 1% of TS cases carry one of these CNVs, indicating that rare structural variation contributes significantly to the genetic architecture of TS.

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Whole-Organism Cellular Pathology: A Systems Approach to Phenomics

Phenotype is defined as the state of an organism resulting from interactions between genes, environment, disease, molecular mechanisms, and chance. The purpose of the emerging field of phenomics is to systematically determine and measure phenotypes across biology for the sake of understanding. Phenotypes can affect more than one cell type and life stage, so ideal phenotyping would include the state of every cell type within the context of both tissue architecture and the whole organism at each life stage. In medicine, high-resolution anatomic assessment of phenotype is obtained from histology. Histology's interpretative power, codified by Virchow as cellular pathology, is derived from its ability to discern diagnostic and characteristic cellular changes in diseased tissues. Cellular pathology is observed in every major human disease and relies on the ability of histology to detect cellular change in any cell type due to unbiased pan-cellular staining, even in optically opaque tissues. Our laboratory has shown that histology is far more sensitive than stereomicroscopy for detecting phenotypes in zebrafish mutants. Those studies have also shown that more complete sampling, greater consistency in sample orientation, and the inclusion of phenotypes extending over longer length scales would provide greater coverage of common phenotypes. We are developing technical approaches to achieve an ideal detection of cellular pathology using an improved form of X-ray microtomography that retains the strengths and addresses the weaknesses of histology as a screening tool. We are using zebrafish as a vertebrate model based on the overlaps between zebrafish and mammalian tissue architecture, and a body size small enough to allow whole-organism, volumetric imaging at cellular resolution. Automation of whole-organism phenotyping would greatly increase the value of phenomics. Potential societal benefits would include reduction in the cost of drug development, a reduction in the incidence of unexpected severe drug and environmental toxicity, and more rapid elucidation of the contributions of genes and the environment to phenotypes, including the validation of candidate disease alleles identified in population and personal genetics.

Phenomics of Vascular Disease: The Systematic Approach to the Combination Therapy

Vascular diseases are usually caused by multifactorial pathogeneses involving genetic and environmental factors. Our current understanding of vascular disease is, however, based on the focused genotype/phenotype studies driven by the “one-gene/one-phenotype” hypothesis. Drugs with “pure target” at individual molecules involved in the pathophysiological pathways are the mainstream of current clinical treatments and the basis of combination therapy of vascular diseases. Recently, the combination of genomics, proteomics, and metabolomics has unraveled the etiology and pathophysiology of vascular disease in a big-data fashion and also revealed unmatched relationships between the omic variability and the much narrower definition of various clinical phenotypes of vascular disease in individual patients. Here, we introduce the phenomics strategy that will change the conventional focused phenotype/genotype/genome study to a new systematic phenome/genome/proteome approach to the understanding of pathophysiology and combination therapy of vascular disease. A phenome is the sum total of an organism’s phenotypic traits that signify the expression of genome and specific environmental influence. Phenomics is the study of phenome to quantitatively correlate complex traits to variability not only in genome, but also in transcriptome, proteome, metabolome, interactome, and environmental factors by exploring the systems biology that links the genomic and phenomic spaces. The application of phenomics and the phenome-wide associated study (PheWAS) will not only identify a systemically-integrated set of biomarkers for diagnosis and prognosis of vascular disease but also provide novel treatment targets for combination therapy and thus make a revolutionary paradigm shift in the clinical treatment of these devastating diseases.

From Genome-Wide Association Study to Phenome-Wide Association Study: New Paradigms in Obesity Research

Obesity is a condition in which excess body fat has accumulated over an extent that increases the risk of many chronic diseases. The current clinical classification of obesity is based on measurement of body mass index (BMI), waist-hip ratio, and body fat percentage. However, these measurements do not account for the wide individual variations in fat distribution, degree of fatness or health risks, and genetic variants identified in the genome-wide association studies (GWAS). In this review, we will address this important issue with the introduction of phenome, phenomics, and phenome-wide association study (PheWAS). We will discuss the new paradigm shift from GWAS to PheWAS in obesity research. In the era of precision medicine, phenomics and PheWAS provide the required approaches to better definition and classification of obesity according to the association of obese phenome with their unique molecular makeup, lifestyle, and environmental impact.

How can we harness quantitative genetic variation in crop root systems for agricultural improvement?

Root systems are a black box obscuring a comprehensive understanding of plant function, from the ecosystem scale down to the individual. In particular, a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them, how we can bridge knowledge gaps and accelerate the derivation of structure-function relationships for roots, and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.

Automated Detection of Retinal Cell Nuclei in 3D Micro-CT Images of Zebrafish using Support Vector Machine Classification

Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The long-term goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

Common Polymorphisms in the Age of Research Domain Criteria (RDoC): Integration and Translation

The value of common polymorphisms in guiding clinical psychiatry is limited by the complex polygenic architecture of psychiatric disorders. Common polymorphisms have too small an effect on risk for psychiatric disorders as defined by clinical phenomenology to guide clinical practice. To identify polymorphic effects that are large and reliable enough to serve as biomarkers requires detailed analysis of a polymorphism's biology across levels of complexity from molecule to cell to circuit and behavior. Emphasis on behavioral domains rather than clinical diagnosis, as proposed in the Research Domain Criteria framework, facilitates the use of mouse models that recapitulate human polymorphisms because effects on equivalent phenotypes can be translated across species and integrated across levels of analysis. A knockin mouse model of a common polymorphism in the brain-derived neurotrophic factor gene (BDNF) provides examples of how such a vertically integrated translational approach can identify robust genotype-phenotype relationships that have relevance to psychiatric practice.

Long-term neural and physiological phenotyping of a single human

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

Standardization efforts enabling next-generation sequencing and microarray based biomarkers for precision medicine

Microarrays and next-generation sequencing technologies have been increasingly employed in biomedical research. However, before they can be reliably used as clinical biomarker tests, standardization and quality control measures need to be developed to ensure their analytical validity. This review summarizes community-wide efforts such as the MicroArray and Sequencing Quality Control (MAQC/SEQC) project which have identified factors influencing the performance of these technologies. Consequently, consensus-based standards and well-documented best practices have been developed to improve the quality of scientific research, and reference materials and reference datasets have been made available for evaluating the technical proficiency in future studies. These efforts have built the foundation on which the translational application of genomics based technologies can help realize precision medicine.

"Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.

Cerebrospinal fluid monoamine metabolite concentrations as intermediate phenotypes between glutamate-related genes and psychosis

Glutamate-related genes have been associated with schizophrenia, but the results have been ambiguous and difficult to replicate. Homovanillic acid (HVA), 5-hydroxyindoleacetic acid (5-HIAA) and 3-methoxy-4-hydroxyphenylglycol (MHPG) are the major degradation products of the monoamines dopamine, serotonin and noradrenaline, respectively, and their concentrations in the cerebrospinal fluid (CSF), mainly HVA, have been associated with schizophrenia. In the present study, we hypothesized that CSF HVA, 5-HIAA and MHPG concentrations represent intermediate phenotypes in the association between glutamate-related genes and psychosis. To test this hypothesis, we searched for association between 238 single nucleotide polymorphisms (SNPs) in ten genes shown to be directly or indirectly implicated in glutamate transmission and CSF HVA, 5-HIAA and MHPG concentrations in 74 patients with psychotic disease. Thirty-eight nominally significant associations were found. Further analyses in 111 healthy controls showed that 87% of the nominal associations were restricted to the patients with psychosis. Some of the psychosis-only-associated SNPs found in the d-amino acid oxidase activator (DAOA) and the kynurenine 3-monooxygenase (KMO) genes have previously been reported to be associated with schizophrenia. The present results suggest that CSF monoamine metabolite concentrations may represent intermediate phenotypes in the association between glutamate-related genes and psychosis.

Can functional hologenomics aid tackling current challenges in plant breeding?

Molecular plant breeding usually overlooks the genetic variability that arises from the association of plants with endophytic microorganisms, when looking at agronomic interesting target traits. This source of variability can have crucial effects on the functionality of the organism considered as a whole (the holobiont), and therefore can be selectable in breeding programs. However, seeing the holobiont as a unit for selection and improvement in breeding programs requires novel approaches for genotyping and phenotyping. These should not focus just at the plant level, but also include the associated endophytes and their functional effects on the plant, to make effective desirable trait screenings. The present review intends to draw attention to a new research field on functional hologenomics that if associated with adequate phenotyping tools could greatly increase the efficiency of breeding programs.

Striatal D1- and D2-type dopamine receptors are linked to motor response inhibition in human subjects

Motor response inhibition is mediated by neural circuits involving dopaminergic transmission; however, the relative contributions of dopaminergic signaling via D1- and D2-type receptors are unclear. Although evidence supports dissociable contributions of D1- and D2-type receptors to response inhibition in rats and associations of D2-type receptors to response inhibition in humans, the relationship between D1-type receptors and response inhibition has not been evaluated in humans. Here, we tested whether individual differences in striatal D1- and D2-type receptors are related to response inhibition in human subjects, possibly in opposing ways. Thirty-one volunteers participated. Response inhibition was indexed by stop-signal reaction time on the stop-signal task and commission errors on the continuous performance task, and tested for association with striatal D1- and D2-type receptor availability [binding potential referred to nondisplaceable uptake (BPND)], measured using positron emission tomography with [(11)C]NNC-112 and [(18)F]fallypride, respectively. Stop-signal reaction time was negatively correlated with D1- and D2-type BPND in whole striatum, with significant relationships involving the dorsal striatum, but not the ventral striatum, and no significant correlations involving the continuous performance task. The results indicate that dopamine D1- and D2-type receptors are associated with response inhibition, and identify the dorsal striatum as an important locus of dopaminergic control in stopping. Moreover, the similar contribution of both receptor subtypes suggests the importance of a relative balance between phasic and tonic dopaminergic activity subserved by D1- and D2-type receptors, respectively, in support of response inhibition. The results also suggest that the stop-signal task and the continuous performance task use different neurochemical mechanisms subserving motor response inhibition.

Applying high-throughput phenotyping to plant-insect interactions: picturing more resistant crops

Through automated image collection and analysis, high-throughput phenotyping (HTP) systems non-destructively quantify a diversity of traits in large plant populations. Some platforms collect data in greenhouses or growth chambers while others are field-based. Platforms also vary in the number and type of sensors, including visible, fluorescence, infrared, hyperspectral, and three-dimensional cameras that can detect traits within and beyond the visible spectrum. These systems could be applied to quantify the impact of herbivores on plant health, to monitor herbivores in choice or no-choice bioassays, or to estimate plant properties such as defensive allelochemicals. By increasing the throughput, precision, and dimensionality of these measures, HTP has the potential to revolutionize the field of plant-insect interactions, including breeding programs for resistance and tolerance.

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study (GWAS) approach, which has been used to identify single nucleotide polymorphisms (SNPs) associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype-phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

Towards an ontology of cognitive control

The goal of cognitive neuroscience is to map mental functions onto their neural substrates. We argue here that this goal requires a formal approach to the characterization of mental processes, and we present one such approach by using ontologies to describe cognitive processes and their relations. Using a classifier analysis of data from the BrainMap database, we examine the concept of "cognitive control" to determine whether the proposed component processes in this domain are mapped to independent neural systems. These results show that some subcomponents can be uniquely classified, whereas others cannot, suggesting that these different components may vary in their ontological reality. We relate these concepts to the broader emerging field of phenomics, which aims to characterize cognitive phenotypes on a global scale.

Revealing plant cryptotypes: defining meaningful phenotypes among infinite traits

The plant phenotype is infinite. Plants vary morphologically and molecularly over developmental time, in response to the environment, and genetically. Exhaustive phenotyping remains not only out of reach, but is also the limiting factor to interpreting the wealth of genetic information currently available. Although phenotyping methods are always improving, an impasse remains: even if we could measure the entirety of phenotype, how would we interpret it? We propose the concept of cryptotype to describe latent, multivariate phenotypes that maximize the separation of a priori classes. Whether the infinite points comprising a leaf outline or shape descriptors defining root architecture, statistical methods to discern the quantitative essence of an organism will be required as we approach measuring the totality of phenotype.

POEAS: Automated Plant Phenomic Analysis Using Plant Ontology

Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas.

From genes to behavior: placing cognitive models in the context of biological pathways

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior.

Improved drug therapy: triangulating phenomics with genomics and metabolomics

Embracing the complexity of biological systems has a greater likelihood to improve prediction of clinical drug response. Here we discuss limitations of a singular focus on genomics, epigenomics, proteomics, transcriptomics, metabolomics, or phenomics-highlighting the strengths and weaknesses of each individual technique. In contrast, 'systems biology' is proposed to allow clinicians and scientists to extract benefits from each technique, while limiting associated weaknesses by supplementing with other techniques when appropriate. Perfect predictive modeling is not possible, whereas modeling of intertwined phenomic responses using genomic stratification with metabolomic modifications may greatly improve predictive values for drug therapy. We thus propose a novel-integrated approach to personalized medicine that begins with phenomic data, is stratified by genomics, and ultimately refined by metabolomic pathway data. Whereas perfect prediction of efficacy and safety of drug therapy is not possible, improvements can be achieved by embracing the complexity of the biological system. Starting with phenomics, the combination of linking metabolomics to identify common biologic pathways and then stratifying by genomic architecture, might increase predictive values. This systems biology approach has the potential, in specific subsets of patients, to avoid drug therapy that will be either ineffective or unsafe.

Hypothesis exploration with visualization of variance

Background

The Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes—to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics—wide-scale, systematic study of phenotypes—to neuropsychiatry research.

Results

This paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles—patterns of values across phenotypes—that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.

Conclusions

The ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports ‘natural selection’ on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics.

A non-linear dynamical approach to belief revision in cognitive behavioral therapy

Belief revision is the key change mechanism underlying the psychological intervention known as cognitive behavioral therapy (CBT). It both motivates and reinforces new behavior. In this review we analyze and apply a novel approach to this process based on AGM theory of belief revision, named after its proponents, Carlos Alchourrón, Peter Gärdenfors and David Makinson. AGM is a set-theoretical model. We reconceptualize it as describing a non-linear, dynamical system that occurs within a semantic space, which can be represented as a phase plane comprising all of the brain's attentional, cognitive, affective and physiological resources. Triggering events, such as anxiety-producing or depressing situations in the real world, or their imaginal equivalents, mobilize these assets so they converge on an equilibrium point. A preference function then evaluates and integrates evidentiary data associated with individual beliefs, selecting some of them and comprising them into a belief set, which is a metastable state. Belief sets evolve in time from one metastable state to another. In the phase space, this evolution creates a heteroclinic channel. AGM regulates this process and characterizes the outcome at each equilibrium point. Its objective is to define the necessary and sufficient conditions for belief revision by simultaneously minimizing the set of new beliefs that have to be adopted, and the set of old beliefs that have to be discarded or reformulated. Using AGM, belief revision can be modeled using three (and only three) fundamental syntactical operations performed on belief sets, which are expansion; revision; and contraction. Expansion is like adding a new belief without changing any old ones. Revision is like adding a new belief and changing old, inconsistent ones. Contraction is like changing an old belief without adding any new ones. We provide operationalized examples of this process in action.

dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text

BACKGROUND

Discerning the genetic contributions to complex human diseases is a challenging mandate that demands new types of data and calls for new avenues for advancing the state-of-the-art in computational approaches to uncovering disease etiology. Systems approaches to studying observable phenotypic relationships among diseases are emerging as an active area of research for both novel disease gene discovery and drug repositioning. Currently, systematic study of disease relationships on a phenome-wide scale is limited due to the lack of large-scale machine understandable disease phenotype relationship knowledge bases. Our study innovates a semi-supervised iterative pattern learning approach that is used to build an precise, large-scale disease-disease risk relationship (D1 → D2) knowledge base (dRiskKB) from a vast corpus of free-text published biomedical literature.

RESULTS

21,354,075 MEDLINE records comprised the text corpus under study. First, we used one typical disease risk-specific syntactic pattern (i.e. "D1 due to D2") as a seed to automatically discover other patterns specifying similar semantic relationships among diseases. We then extracted D1 → D2 risk pairs from MEDLINE using the learned patterns. We manually evaluated the precisions of the learned patterns and extracted pairs. Finally, we analyzed the correlations between disease-disease risk pairs and their associated genes and drugs. The newly created dRiskKB consists of a total of 34,448 unique D1 → D2 pairs, representing the risk-specific semantic relationships among 12,981 diseases with each disease linked to its associated genes and drugs. The identified patterns are highly precise (average precision of 0.99) in specifying the risk-specific relationships among diseases. The precisions of extracted pairs are 0.919 for those that are exactly matched and 0.988 for those that are partially matched. By comparing the iterative pattern approach starting from different seeds, we demonstrated that our algorithm is robust in terms of seed choice. We show that diseases and their risk diseases as well as diseases with similar risk profiles tend to share both genes and drugs.

CONCLUSIONS

This unique dRiskKB, when combined with existing phenotypic, genetic, and genomic datasets, can have profound implications in our deeper understanding of disease etiology and in drug repositioning.

Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid

Studying genetic determinants of intermediate phenotypes is a powerful tool to increase our understanding of genotype-phenotype correlations. Metabolic traits pertinent to the central nervous system (CNS) constitute a potentially informative target for genetic studies of intermediate phenotypes as their genetic underpinnings may elucidate etiological mechanisms. We therefore conducted a genome-wide association study (GWAS) of monoamine metabolite (MM) levels in cerebrospinal fluid (CSF) of 414 human subjects from the general population. In a linear model correcting for covariates, we identified one locus associated with MMs at a genome-wide significant level (standardized β=0.32, P=4.92 × 10(-8)), located 20 kb from SSTR1, a gene involved with brain signal transduction and glutamate receptor signaling. By subsequent whole-genome expression quantitative trait locus (eQTL) analysis, we provide evidence that this variant controls expression of PDE9A (β=0.21; P unadjusted=5.6 × 10(-7); P corrected=0.014), a gene previously implicated in monoaminergic transmission, major depressive disorder and antidepressant response. A post hoc analysis of loci significantly associated with psychiatric disorders suggested that genetic variation at CSMD1, a schizophrenia susceptibility locus, plays a role in the ratio between dopamine and serotonin metabolites in CSF. The presented DNA and mRNA analyses yielded genome-wide and suggestive associations in biologically plausible genes, two of which encode proteins involved with glutamate receptor functionality. These findings will hopefully contribute to an exploration of the functional impact of the highlighted genes on monoaminergic transmission and neuropsychiatric phenotypes.

Phenomics of cardiac chloride channels

Forward genetic studies have identified several chloride (Cl-) channel genes, including CFTR, ClC-2, ClC-3, CLCA, Bestrophin, and Ano1, in the heart. Recent reverse genetic studies using gene targeting and transgenic techniques to delineate the functional role of cardiac Cl- channels have shown that Cl- channels may contribute to cardiac arrhythmogenesis, myocardial hypertrophy and heart failure, and cardioprotection against ischemia reperfusion. The study of physiological or pathophysiological phenotypes of cardiac Cl- channels, however, is complicated by the compensatory changes in the animals in response to the targeted genetic manipulation. Alternatively, tissue-specific conditional or inducible knockout or knockin animal models may be more valuable in the phenotypic studies of specific Cl- channels by limiting the effect of compensation on the phenotype. The integrated function of Cl- channels may involve multiprotein complexes of the Cl- channel subproteome. Similar phenotypes can be attained from alternative protein pathways within cellular networks, which are influenced by genetic and environmental factors. The phenomics approach, which characterizes phenotypes as a whole phenome and systematically studies the molecular changes that give rise to particular phenotypes achieved by modifying the genotype under the scope of genome/proteome/phenome, may provide more complete understanding of the integrated function of each cardiac Cl- channel in the context of health and disease.

Human intellectual disability genes form conserved functional modules in Drosophila

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules.

Intellectual Disability (ID) affects 2% of our population and is associated with many different disorders. Although more than 400 causative genes (‘ID genes’) have been identified, their function remains poorly understood and the degree to which these disorders share a common molecular basis is unknown. Here, we systematically characterized behavioral and morphological phenotypes associated with 270 conserved ID genes, using the Drosophila eye and photoreceptor neurons as a model. These and follow up approaches generated previously undescribed genotype-phenotype associations for the majority (180) of ID gene orthologs, and identified, among others, 16 novel regulators of basal neurotransmission. Importantly, groups of genes that show the same phenotype in Drosophila are highly enriched in known connectivity, also share increased phenotypic similarity in humans and successfully predicted novel gene functions. In total, we mapped 26 conserved functional modules that together comprise 100 ID gene orthologs. Our findings provide unbiased evidence for the long suspected but never experimentally demonstrated functional coherence among ID disorders. The identified conserved functional modules may aid to develop therapeutic strategies that target genetically heterogeneous ID patients with a common treatment.

The Barratt Impulsiveness Scale-11: reassessment of its structure in a community sample

The Barratt Impulsiveness Scale (Version 11; BIS-11; Patton, Stanford, & Barratt, 1995) is a gold-standard measure that has been influential in shaping current theories of impulse control, and has played a key role in studies of impulsivity and its biological, psychological, and behavioral correlates. Psychometric research on the structure of the BIS-11, however, has been scant. We therefore applied exploratory and confirmatory factor analyses to data collected using the BIS-11 in a community sample (N = 691). Our goal was to test 4 theories of the BIS-11 structure: (a) a unidimensional model, (b) a 6 correlated first-order factor model, (c) a 3 second-order factor model, and (d) a bifactor model. Among the problems identified were (a) low or near-zero correlations of some items with others; (b) highly redundant content of numerous item pairs; (c) items with salient cross-loadings in multidimensional solutions; and, ultimately, (d) poor fit to confirmatory models. We conclude that use of the BIS-11 total score as reflecting individual differences on a common dimension of impulsivity presents challenges in interpretation. Also, the theory that the BIS-11 measures 3 subdomains of impulsivity (attention, motor, and nonplanning) was not empirically supported. A 2-factor model is offered as an alternative multidimensional structural representation.

Future scenarios for plant phenotyping

With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.

Phenotype refinement for comorbid attention deficit hyperactivity disorder and reading disability

Comorbidity between Attention Deficit Hyperactivity Disorder (ADHD) and reading disability (RD) is common; however, the heritability of this comorbidity is not well understood. This may be due to the complexity and heterogeneity of ADHD and RD phenotypes. Using alternative ADHD-RD sub-phenotypes instead of those arising from the DSM-IV may lead to greater success in the search for comorbid ADHD-RD susceptibility genes. Therefore, this study aims to refine ADHD-RD phenotypes into homogenous informative sub-phenotypes using latent class analysis (LCA). LCA was performed on 2,610 Australian twin families (6,535 individuals) in order to generate probabilistic genetically distinct classes that define ADHD-RD subtypes, including comorbidity, based on related symptom clusters. The LCA separated the phenotypes for ADHD and RD into nine classes. One class was unaffected; three classes demonstrated the three DSM-IV subtypes of ADHD, three subtypes showed different severities of RD, and two classes expressed a combination of RD and ADHD subtypes. LCA proved effective in refining the phenotypes of ADHD alone, RD alone, and ADHD-RD comorbidity, and its ability to classify them into homogenous groups based on clusters of symptoms, suggesting that the latent classes may be robust enough to use in molecular genetic studies.

Effectiveness of a web-based protocol for the screening and phenotyping of individuals with Tourette syndrome for genetic studies

Genome-wide association studies (GWAS) and other emerging technologies offer great promise for the identification of genetic risk factors for complex psychiatric disorders, yet such studies are constrained by the need for large sample sizes. Web-based collection offers a relatively untapped resource for increasing participant recruitment. Therefore, we developed and implemented a novel web-based screening and phenotyping protocol for genetic studies of Tourette syndrome (TS), a childhood-onset neuropsychiatric disorder characterized by motor and vocal tics. Participants were recruited over a 13-month period through the membership of the Tourette Syndrome Association (TSA; n = 28,878). Of the TSA members contacted, 4.3% (1,242) initiated the questionnaire, and 79.5% (987) of these were enrollment eligible. 63.9% (631) of enrolled participants completed the study by submitting phenotypic data and blood specimens. Age was the only variable that predicted study completion; children and young adults were significantly less likely to be study completers than adults 26 and older. Compared to a clinic-based study conducted over the same time period, the web-based method yielded a 60% larger sample. Web-based participants were older and more often female; otherwise, the sample characteristics did not differ significantly. TS diagnoses based on the web-screen demonstrated 100% accuracy compared to those derived from in-depth clinical interviews. Our results suggest that a web-based approach is effective for increasing the sample size for genetic studies of a relatively rare disorder and that our web-based screen is valid for diagnosing TS. Findings from this study should aid in the development of web-based protocols for other disorders.