Tools for standardized data collection: Speech, Language, and Hearing measurement protocols in the PhenX Toolkit.

The PhenX Toolkit (https://www.phenxtoolkit.org/) is an online catalog of recommended measurement protocols to facilitate cross-study analyses for biomedical research. An expert review panel (ERP) reviewed and updated the PhenX Toolkit Speech and Hearing domain to improve the precision and consistency of speech, language, and hearing disorder phenotypes. A three-member ERP convened in August 2018 to review the measurement protocols in the PhenX Speech and Hearing domain. Aided by three additional experts in voice assessment, vertigo, and stuttering, the ERP updated the 28 protocols to reflect the latest science and technology. ERP recommendations include six new protocols, five updated protocols (from the same source), and one retired protocol. New additions include two voice-related, three hearing-related, and two speech-related protocols. Additions reflect new phone/tablet applications for hearing and language, and clinical evaluations of voice. "Language" was added to the domain name, which is now "Speech, Language, and Hearing," to represent language-related protocols. These protocols can facilitate the assessment of speech, language, and hearing in clinical and population research. Common data elements (i.e., use of the same variables across studies) used by geneticists, otolaryngologists, audiologists, speech-language pathologists, and in other disciplines can lead to cross-study data integration and increased statistical power when studies are combined.

Genomic medicine implementation protocols in the PhenX Toolkit: tools for standardized data collection.

PURPOSE: The PhenX Toolkit ( www.phenxtoolkit.org ), an online catalog of recommended measurement protocols, facilitates cross-study analyses for research with human participants. The PhenX Steering Committee recommended genomic medicine implementation as a new research domain, with the following scope: genomic knowledge and education (both patients and providers); implementation science; changes in management and treatment; return of results; patient outcomes; and ethical, legal, and social issues (ELSI) associated with genomic research. METHODS: A seven-member expert Working Group convened in October 2019 to identify well-established measurement protocols for a new genomic medicine implementation domain and used the established PhenX consensus process to select measurement protocols for inclusion in the PhenX Toolkit. RESULTS: The Working Group recommended 15 measurement protocols for inclusion in the PhenX Toolkit, with priority given to those with empirical evidence supporting validity. Consortia funded by the National Institutes of Health, and particularly the National Human Genome Research Institute, proved critical in identifying protocols with established utility in this research domain, and identified protocols that were developed through a rigorous process for scope elements that lacked formally validated protocols. CONCLUSION: Use of these protocols, which were released in September 2020, can facilitate standard data collection for genomic medicine implementation research.

The Children's Respiratory and Environmental Workgroup (CREW) birth cohort consortium: design, methods, and study population.

BACKGROUND: Single birth cohort studies have been the basis for many discoveries about early life risk factors for childhood asthma but are limited in scope by sample size and characteristics of the local environment and population. The Children's Respiratory and Environmental Workgroup (CREW) was established to integrate multiple established asthma birth cohorts and to investigate asthma phenotypes and associated causal pathways (endotypes), focusing on how they are influenced by interactions between genetics, lifestyle, and environmental exposures during the prenatal period and early childhood. METHODS AND RESULTS: CREW is funded by the NIH Environmental influences on Child Health Outcomes (ECHO) program, and consists of 12 individual cohorts and three additional scientific centers. The CREW study population is diverse in terms of race, ethnicity, geographical distribution, and year of recruitment. We hypothesize that there are phenotypes in childhood asthma that differ based on clinical characteristics and underlying molecular mechanisms. Furthermore, we propose that asthma endotypes and their defining biomarkers can be identified based on personal and early life environmental risk factors. CREW has three phases: 1) to pool and harmonize existing data from each cohort, 2) to collect new data using standardized procedures, and 3) to enroll new families during the prenatal period to supplement and enrich extant data and enable unified systems approaches for identifying asthma phenotypes and endotypes. CONCLUSIONS: The overall goal of CREW program is to develop a better understanding of how early life environmental exposures and host factors interact to promote the development of specific asthma endotypes.

PhenX measures for phenotyping rare genetic conditions.

INTRODUCTION: The PhenX Toolkit, an online resource of well-established measures of phenotypes and exposures, now has 16 new measures recommended for assessing rare genetic conditions. MATERIALS AND METHODS: These measures and their protocols were selected by a working group of domain experts with input from the scientific community. RESULTS: The measures, which cover life stages from birth through adulthood, include clinical scales, characterization of rare genetic conditions, bioassays, and questionnaires. Most are broadly applicable to rare genetic conditions (e.g., family history, growth charts, bone age, and body proportions). Some protocols (e.g., sweat chloride test) target specific conditions. DISCUSSION: The rare genetic condition measures complement the existing measures in the PhenX Toolkit that cover anthropometrics, demographics, mental health, and reproductive history. They are directed at research pertaining to common and complex diseases. PhenX measures are publicly available and are recommended to help standardize assessments across a range of biomedical study designs. To facilitate incorporation of measures into human subjects' research, the Toolkit offers data collection worksheets and compatible data dictionaries. CONCLUSION: Widespread use of standard PhenX measures in clinical, translational, and epidemiological research will enable more uniform cross-study comparisons and increase statistical power with the potential for enhancing scientific discovery.Genet Med advance online publication 12 January 2017.

Research standardization tools: pregnancy measures in the PhenX Toolkit.

Only through concerted and well-executed research endeavors can we gain the requisite knowledge to advance pregnancy care and have a positive impact on maternal and newborn health. Yet the heterogeneity inherent in individual studies limits our ability to compare and synthesize study results, thus impeding the capacity to draw meaningful conclusions that can be trusted to inform clinical care. The PhenX Toolkit (http://www.phenxtoolkit.org), supported since 2007 by the National Institutes of Health, is a web-based catalog of standardized protocols for measuring phenotypes and exposures relevant for clinical research. In 2016, a working group of pregnancy experts recommended 15 measures for the PhenX Toolkit that are highly relevant to pregnancy research. The working group followed the established PhenX consensus process to recommend protocols that are broadly validated, well established, nonproprietary, and have a relatively low burden for investigators and participants. The working group considered input from the pregnancy experts and the broader research community and included measures addressing the mode of conception, gestational age, fetal growth assessment, prenatal care, the mode of delivery, gestational diabetes, behavioral and mental health, and environmental exposure biomarkers. These pregnancy measures complement the existing measures for other established domains in the PhenX Toolkit, including reproductive health, anthropometrics, demographic characteristics, and alcohol, tobacco, and other substances. The preceding domains influence a woman's health during pregnancy. For each measure, the PhenX Toolkit includes data dictionaries and data collection worksheets that facilitate incorporation of the protocol into new or existing studies. The measures within the pregnancy domain offer a valuable resource to investigators and clinicians and are well poised to facilitate collaborative pregnancy research with the goal to improve patient care. To achieve this aim, investigators whose work includes the perinatal population are encouraged to utilize the PhenX Toolkit in the design and implementation of their studies, thus potentially reducing heterogeneity in data measures across studies. Such an effort will enhance the overall impact of individual studies, increasing the ability to draw more meaningful conclusions that can then be translated into clinical practice.

The PhenX Toolkit: Recommended Measurement Protocols for Social Determinants of Health Research.

Health disparities are driven by unequal conditions in the environments in which people are born, live, learn, work, play, worship, and age, commonly termed the Social Determinants of Health (SDoH). The availability of recommended measurement protocols for SDoH will enable investigators to consistently collect data for SDoH constructs. The PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a web-based catalog of recommended measurement protocols for use in research studies with human participants. Using standard protocols from the PhenX Toolkit makes it easier to compare and combine studies, potentially increasing the impact of individual studies, and aids in comparability across literature. In 2018, the National Institute on Minority Health and Health Disparities provided support for an initial expert Working Group to identify and recommend established SDoH protocols for inclusion in the PhenX Toolkit. In 2022, a second expert Working Group was convened to build on the work of the first SDoH Working Group and address gaps in the SDoH Toolkit Collections. The SDoH Collections consist of a Core Collection and Individual and Structural Specialty Collections. This article describes a Basic Protocol for using the PhenX Toolkit to select and implement SDoH measurement protocols for use in research studies. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol: Using the PhenX Toolkit to select and implement SDoH protocols.

Getting ready for the Human Phenome Project: the 2012 forum of the Human Variome Project.

A forum of the Human Variome Project (HVP) was held as a satellite to the 2012 Annual Meeting of the American Society of Human Genetics in San Francisco, California. The theme of this meeting was "Getting Ready for the Human Phenome Project." Understanding the genetic contribution to both rare single-gene "Mendelian" disorders and more complex common diseases will require integration of research efforts among many fields and better defined phenotypes. The HVP is dedicated to bringing together researchers and research populations throughout the world to provide the resources to investigate the impact of genetic variation on disease. To this end, there needs to be a greater sharing of phenotype and genotype data. For this to occur, many databases that currently exist will need to become interoperable to allow for the combining of cohorts with similar phenotypes to increase statistical power for studies attempting to identify novel disease genes or causative genetic variants. Improved systems and tools that enhance the collection of phenotype data from clinicians are urgently needed. This meeting begins the HVP's effort toward this important goal.

Linking complex disease and exposure data-insights from an environmental and occupational health study.

The disparate measurement protocols used to collect study data are an intrinsic barrier to combining information from environmental health studies. Using standardized measurement protocols and data standards for environmental exposures addresses this gap by improving data collection quality and consistency. To assess the prevalence of environmental exposures in National Institutes of Health (NIH) public data repositories and resources and to assess the commonality of the data elements, we analyzed clinical measures and exposure assays by comparing the Caribbean Consortium for Research in Environmental and Occupational Health study with selected NIH environmental health resources and studies. Our assessment revealed that (1) environmental assessments are widely collected in these resources, (2) biological assessments are less prevalent, and (3) NIH resources can help identify common data for meta-analysis. We highlight resources to help link environmental exposure data across studies to support data sharing. Including NIH data standards in environmental health research facilitates comparing and combining study data, and the use of NIH resources and adoption of standard measures will allow integration of multiple studies and increase the scientific impact of individual studies.

T-CLEARE: A Pilot Community-Driven Tissue-Clearing Protocol Repository.

Selecting and implementing a tissue-clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue-clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that many articles do not provide sufficient detail to replicate or reproduce experimental results. To help address this issue, we have developed a novel, freely available repository of tissue-clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue-clearing protocols did not perform well (negative results). The goal of T-CLEARE is to provide a forum for the community to share evaluations and modifications of tissue-clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.

The PhenX Toolkit: standard measurement resources for cancer outcomes and survivorship research.

This brief report introduces the Cancer Outcomes and Survivorship domain in the PhenX Toolkit (consensus measures for Phenotypes and eXposures), which includes 15 validated measurement protocols for cancer survivorship research that were recommended for inclusion in this publicly available resource. Developed with input from the scientific community, the domain provides researchers with well-established measurement protocols for evaluating physical and psychological effects, financial toxicity, and patient experiences with cancer care. The PhenX Toolkit, funded by the National Human Genome Research Institute since 2007, is an online resource that provides high-quality standard measurement protocols for a wide range of research areas (eg, smoking cessation, harm reduction and biomarkers, and social determinants of health). Use of the PhenX Cancer Survivorship Outcomes and Survivorship domain can simplify the selection of measurement protocols, data sharing, and comparisons across studies investigating the cancer survivorship experience.

Smoking cessation, harm reduction, and biomarkers protocols in the PhenX Toolkit: Tools for standardized data collection.

The use of standard protocols in studies supports consistent data collection, improves data quality, and facilitates cross-study analyses. Funded by the National Institutes of Health, the PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a catalog of recommended measurement protocols that address a wide range of research topics and are suitable for inclusion in a variety of study designs. In 2020, a PhenX Working Group of smoking cessation experts followed a well-established consensus process to identify and recommend measurement protocols suitable for inclusion in smoking cessation and smoking harm reduction studies. The broader scientific community was invited to review and provide feedback on the preliminary recommendation of the Working Group. Fourteen selected protocols for measuring smoking cessation, harm reduction, and biomarkers research associated with smoking cessation were released in the PhenX Toolkit ( https://www.phenxtoolkit.org) in February 2021. These protocols complement existing PhenX Toolkit content related to tobacco regulatory research, substance use and addiction research, and other measures of smoking-related health outcomes. Adopting well-established protocols enables consistent data collection and facilitates comparing and combining data across studies, potentially increasing the scientific impact of individual studies.

The PhenX Toolkit: Measurement Protocols for Assessment of Social Determinants of Health.

INTRODUCTION: Social determinants are structures and conditions in the biological, physical, built, and social environments that affect health, social and physical functioning, health risk, quality of life, and health outcomes. The adoption of recommended, standard measurement protocols for social determinants of health will advance the science of minority health and health disparities research and provide standard social determinants of health protocols for inclusion in all studies with human participants. METHODS: A PhenX (consensus measures for Phenotypes and eXposures) Working Group of social determinants of health experts was convened from October 2018 to May 2020 and followed a well-established consensus process to identify and recommend social determinants of health measurement protocols. The PhenX Toolkit contains data collection protocols suitable for inclusion in a wide range of research studies. The recommended social determinants of health protocols were shared with the broader scientific community to invite review and feedback before being added to the Toolkit. RESULTS: Nineteen social determinants of health protocols were released in the PhenX Toolkit (https://www.phenxtoolkit.org) in May 2020 to provide measures at the individual and structural levels for built and natural environments, structural racism, economic resources, employment status, occupational health and safety, education, environmental exposures, food environment, health and health care, and sociocultural community context. CONCLUSIONS: Promoting the adoption of well-established social determinants of health protocols can enable consistent data collection and facilitate comparing and combining studies, with the potential to increase their scientific impact.

Using PhenX measures to identify opportunities for cross-study analysis.

The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.

Standard metadata for 3D microscopy.

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.

Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.

The PhenX Toolkit: get the most from your measures.

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.

PhenX: a toolkit for interdisciplinary genetics research.

PURPOSE OF REVIEW: To highlight standard PhenX (consensus measures for Phenotypes and eXposures) measures for nutrition, dietary supplements, and cardiovascular disease research and to demonstrate how these and other PhenX measures can be used to further interdisciplinary genetics research. RECENT FINDINGS: PhenX addresses the need for standard measures in large-scale genomic research studies by providing investigators with high-priority, well established, low-burden measurement protocols in a web-based toolkit (https://www.phenxtoolkit.org). Cardiovascular and Nutrition and Dietary Supplements are just 2 of 21 research domains and accompanying measures included in the PhenX Toolkit. SUMMARY: Genome-wide association studies (GWAS) provide promise for the identification of genomic markers associated with different disease phenotypes, but require replication to validate results. Cross-study comparisons typically increase statistical power and are required to understand the roles of comorbid conditions and environmental factors in the progression of disease. However, the lack of comparable phenotypic, environmental, and risk factor data forces investigators to infer and to compare metadata rather than directly combining data from different studies. PhenX measures provide a common currency for collecting data, thereby greatly facilitating cross-study analysis and increasing statistical power for identification of associations between genotypes, phenotypes, and exposures.

The PhenX Toolkit: Establishing Standard Measures for COVID-19 Research.

The PhenX (consensus measures for Phenotypes and eXposures) Toolkit (https://www.phenxtoolkit.org/) is a publicly available, web-based catalog of recommended, well-established measurement protocols of phenotypes and exposures. The goal of PhenX is to facilitate the use of standard measures, enhance data interoperability, and promote collaborative and translational research. PhenX is driven by the scientific community and historically has depended on working groups of experts to recommend measures for release in the PhenX Toolkit. The urgent need for recommended, standard measures for COVID-19 research triggered the development of a "rapid release" process for releasing new content in the PhenX Toolkit. Initially, PhenX collaborated with the National Institutes of Health (NIH) Office of Behavioral and Social Sciences Research, the National Human Genome Research Institute, and the NIH Disaster Research Response (DR2) program to create a library of COVID-19 measurement protocols. With additional support from NIH, PhenX adapted crowdsourcing techniques to accelerate prioritization and recommendation of protocols for release in the PhenX Toolkit. Prioritized COVID-19-specific protocols were used to anchor and define specialty collections of protocols that were subject to review and approval by the PhenX Steering Committee. In addition to the COVID-19-specific protocols, the specialty collections include existing, well-established PhenX protocols, use of which will further enhance data interoperability and cross-study analysis. The COVID-19 specialty collections are Behaviors and Risks; Ethnicity, Race and Demographics; History, Treatment and Outcomes; Information Resources; Psychosocial and Mental Health; and Socioeconomic. The development and usage of PhenX COVID-19 specialty collections are described in this article. © 2021 The Authors. Basic Protocol: Selecting COVID-19 protocols.

Using the PhenX Toolkit to Select Standard Measurement Protocols for Your Research Study.

The goals of PhenX (consensus measures for Phenotypes and eXposures) are to promote the use of standard measurement protocols and to help investigators identify opportunities for collaborative research and cross-study analysis, thus increasing the impact of individual studies. The PhenX Toolkit (https://www.phenxtoolkit.org/) offers high-quality, well-established measurement protocols to assess phenotypes and exposures in studies with human participants. The Toolkit contains protocols representing 29 research domains and 6 specialty collections of protocols that add depth to the Toolkit in specific research areas (e.g., COVID-19, Social Determinants of Health [SDoH], Blood Sciences Research [BSR], Mental Health Research [MHR], Tobacco Regulatory Research [TRR], and Substance Abuse and Addiction [SAA]). Protocols are recommended for inclusion in the PhenX Toolkit by Working Groups of domain experts using a consensus process that includes input from the scientific community. For each PhenX protocol, the Toolkit provides a detailed description, the rationale for inclusion, and supporting documentation. Users can browse protocols in the Toolkit, search the Toolkit using keywords, or use Browse Protocols Tree to identify protocols of interest. The PhenX Toolkit provides data dictionaries compatible with the database of Genotypes and Phenotypes (dbGaP), Research Electronic Data Capture (REDCap) data submission compatibility, and data collection worksheets to help investigators incorporate PhenX protocols into their study design. The PhenX Toolkit provides resources to help users identify published studies that used PhenX protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using the PhenX Toolkit to support or extend study design.

An Assessment of Environmental Health Measures in the Deepwater Horizon Research Consortia.

Research consortia play a key role in our understanding of how environmental exposures influence health and wellbeing, especially in the case of catastrophic events such as the Deepwater Horizon oil spill. A common challenge that prevents the optimal use of these data is the difficulty of harmonizing data regarding the environmental exposures and health effects across the studies within and among consortia. A review of the measures used by members of the Deepwater Horizon Research Consortia highlights the challenges associated with balancing timely implementation of a study to support disaster relief with optimizing the long-term value of the data. The inclusion of common, standard measures at the study design phase and a priori discussions regarding harmonization of study-specific measures among consortia members are key to overcoming this challenge. As more resources become available to support the use of standard measures, researchers now have the tools needed to rapidly coordinate their studies without compromising research focus or timely completion of the original study goals.