Carol M. Hamilton

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.

Using the PhenX Toolkit to Add Standard Measures to a Study

The PhenX (consensus measures for Phenotypes and eXposures) Toolkit (https://www.phenxtoolkit.org/) offers high‐quality, well‐established measures of phenotypes and exposures for use by the scientific community. The goal is to promote the use of standard measures, enhance data interoperability, and help investigators identify opportunities for collaborative and translational research. The Toolkit contains 395 measures drawn from 22 research domains (fields of research), along with additional collections of measures for Substance Abuse and Addiction (SAA) research, Mental Health Research (MHR), and Tobacco Regulatory Research (TRR). Additional measures for TRR that are expected to be released in 2015 include Obesity, Eating Disorders, and Sickle Cell Disease. Measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The Toolkit provides a description of each PhenX measure, the rationale for including it in the Toolkit, protocol(s) for collecting the measure, and supporting documentation. Users can browse measures in the Toolkit or can search the Toolkit using the Smart Query Tool or a full text search. PhenX Toolkit users select measures of interest to add to their Toolkit. Registered Toolkit users can save their Toolkit and return to it later to revise or complete. They then have options to download a customized Data Collection Worksheet that specifies the data to be collected, and a Data Dictionary that describes each variable included in the Data Collection Worksheet. The Toolkit also has a Register Your Study feature that facilitates cross‐study collaboration by allowing users to find other investigators using the same PhenX measures.

Data compatibility in the addiction sciences: An examination of measure commonality

The need for comprehensive analysis to compare and combine data across multiple studies in order to validate and extend results is widely recognized. This paper aims to assess the extent of data compatibility in the substance abuse and addiction (SAA) sciences through an examination of measure commonality, defined as the use of similar measures, across grants funded by the National Institute on Drug Abuse (NIDA) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Data were extracted from applications of funded, active grants involving human-subjects research in four scientific areas (epidemiology, prevention, services, and treatment) and six frequently assessed scientific domains. A total of 548 distinct measures were cited across 141 randomly sampled applications. Commonality, as assessed by density (range of 0-1) of shared measurement, was examined. Results showed that commonality was low and varied by domain/area. Commonality was most prominent for (1) diagnostic interviews (structured and semi-structured) for substance use disorders and psychopathology (density of 0.88), followed by (2) scales to assess dimensions of substance use problems and disorders (0.70), (3) scales to assess dimensions of affect and psychopathology (0.69), (4) measures of substance use quantity and frequency (0.62), (5) measures of personality traits (0.40), and (6) assessments of cognitive/neurologic ability (0.22). The areas of prevention (density of 0.41) and treatment (0.42) had greater commonality than epidemiology (0.36) and services (0.32). To address the lack of measure commonality, NIDA and its scientific partners recommend and provide common measures for SAA researchers within the PhenX Toolkit.

Building a Biomedical Cyberinfrastructure for Collaborative Research

For the potential power of genome-wide association studies (GWAS) and translational medicine to be realized, the biomedical research community must adopt standard measures, vocabularies, and systems to establish an extensible biomedical cyberinfrastructure. Incorporating standard measures will greatly facilitate combining and comparing studies via meta-analysis. Incorporating consensus-based and well-established measures into various studies should reduce the variability across studies due to attributes of measurement, making findings across studies more comparable. This article describes two well-established consensus-based approaches to identifying standard measures and systems: PhenX (consensus measures for phenotypes and eXposures), and the Open Geospatial Consortium (OGC). NIH support for these efforts has produced the PhenX Toolkit, an assembled catalog of standard measures for use in GWAS and other large-scale genomic research efforts, and the RTI Spatial Impact Factor Database (SIFD), a comprehensive repository of geo-referenced variables and extensive meta-data that conforms to OGC standards. The need for coordinated development of cyberinfrastructure to support measures and systems that enhance collaboration and data interoperability is clear; this paper includes a discussion of standard protocols for ensuring data compatibility and interoperability. Adopting a cyberinfrastructure that includes standard measures and vocabularies, and open-source systems architecture, such as the two well-established systems discussed here, will enhance the potential of future biomedical and translational research. Establishing and maintaining the cyberinfrastructure will require a fundamental change in the way researchers think about study design, collaboration, and data storage and analysis.

Common Measures for National Institute of Mental Health Funded Research

One of the most encouraging, but also the most challenging, aspects of current research on psychopathology is the diversity of measures used to assess constructs across research studies and programs. Clearly, this diversity reflects the creativity and generativity of our field and the continual growth of our science. At the same time, however, this diversity also makes data harmonization across studies difficult, if not sometimes impossible. The National Human Genome Research Institute recognized this conundrum in the field of genetics and started an initiative referred to as consensus measures for Phenotypes and eXposures (PhenX) to identify and recommend a small number of measures for each of 21 broad research domains that could be used as common assessments to facilitate integration across genomewide association studies (1–4). These measures are made available to the scientific community, at no cost, in the PhenX Toolkit (https://www.phenxtoolkit.org). Subsequently, the PhenX consensus process was used to identify measures in support of substance abuse and addiction (SAA) research, adding depth to the toolkit in this area. This project was funded by the National Institute on Drug Abuse (NIDA) with the participation of the National Institute on Alcohol Abuse and Alcoholism. Perhaps due to a growing awareness of the need to share data across studies to increase statistical power and study impact, a number of other common data element programs have been underway, including the Patient

UNIT 1.21 Using the PhenX Toolkit to Add Standard Measures to a Study

The PhenX (consensus measures for Phenotypes and eXposures) Toolkit (https://www.phenxtoolkit.org/) offers high‐quality, well‐established measures of phenotypes and exposures for use by the scientific community. The Toolkit contains 295 measures drawn from 21 research domains (fields of research). The measures were selected by Working Groups of domain experts using a consensus process that included input from the scientific community. The Toolkit provides a description of each PhenX measure, the rationale for including it in the Toolkit, protocol(s) for collecting the measure, and supporting documentation. Users can browse by measures, domains, or collections, or can search the Toolkit using the Smart Query Tool. Once users have selected some measures, they can download a customized Data Collection Worksheet that specifies what information needs to be collected, and a Data Dictionary that describes each variable included in their Data Collection Worksheet. To help researchers find studies with comparable data, PhenX measures and variables are being mapped to studies in the database of Genotypes and Phenotypes (dbGaP). Curr. Protoc. Hum. Genet. 71:1.21.1‐1.21.18

Standard measures for sickle cell disease research: the PhenX Toolkit sickle cell disease collections

Standard measures and common data elements for sickle cell disease (SCD) will improve the data quality and comparability necessary for cross-study analyses and the development of guidelines that support effective treatments and interventions. In 2014, the National Institutes of Health, National Heart, Lung, and Blood Institute (NHLBI) funded an Administrative Supplement to the PhenX Toolkit (consensus measures for Phenotypes and eXposures; https://www.phenxtoolkit.org/) to identify common measures to promote data comparability across SCD research. An 11-member Sickle Cell Disease Research and Scientific Panel provided guidance to the project, establishing a core collection of SCD-related measures and defining the scope of 2 specialty collections: (1) cardiovascular, pulmonary, and renal complications, and (2) neurology, quality-of-life, and health services. For each specialty collection, a working group of SCD experts selected high-priority measures using a consensus process that included scientific community input. The SCD measures were released into the Toolkit in August 2015. The 25 measures included in the core collection are recommended for use by all NHLBI-funded investigators performing human-subject SCD research. The 10 neurology, quality-of-life, and health services measures and 14 cardiovascular, pulmonary, and renal measures are recommended for use within these specialized research areas. For SCD and other researchers, PhenX measures will promote collaborations with clinicians and patients, facilitate cross-study analysis, accelerate translational research, and lead to greater understanding of SCD phenotypes and epigenetics. For clinicians, using PhenX measures will help elucidate the etiology, progression, and treatment of SCD, leading to improved patient care and quality of life.

Proceedings of a Sickle Cell Disease Ontology workshop — Towards the first comprehensive ontology for Sickle Cell Disease

Sickle cell disease (SCD) is a debilitating single gene disorder caused by a single point mutation that results in physical deformation (i.e. sickling) of erythrocytes at reduced oxygen tensions. Up to 75% of SCD in newborns world-wide occurs in sub-Saharan Africa, where neonatal and childhood mortality from sickle cell related complications is high. While SCD research across the globe is tackling the disease on multiple fronts, advances have yet to significantly impact on the health and quality of life of SCD patients, due to lack of coordination of these disparate efforts. Ensuring data across studies is directly comparable through standardization is a necessary step towards realizing this goal. Such a standardization requires the development and implementation of a disease-specific ontology for SCD that is applicable globally. Ontology development is best achieved by bringing together experts in the domain to contribute their knowledge. The SCD community and H3ABioNet members joined forces at a recent SCD Ontology workshop to develop an ontology covering aspects of SCD under the classes: phenotype, diagnostics, therapeutics, quality of life, disease modifiers and disease stage. The aim of the workshop was for participants to contribute their expertise to development of the structure and contents of the SCD ontology. Here we describe the proceedings of the Sickle Cell Disease Ontology Workshop held in Cape Town South Africa in February 2016 and its outcomes. The objective of the workshop was to bring together experts in SCD from around the world to contribute their expertise to the development of various aspects of the SCD ontology.

Research standardization tools: pregnancy measures in the PhenX Toolkit

&NA; 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.