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PhenX Project: Consensus Measures for Phenotypes & Exposures

The PhenX Project aims to provide an online resource of standardized phenotypic and environmental exposure measures. It includes protocols, data element dictionaries, and data collections worksheets.

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PhenX Project: Consensus Measures for Phenotypes & Exposures

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  1. The PhenX Project: Consensus Measures for Phenotypes & Exposures Erin M. Ramos, PhD MPH Division of Genomic Medicine NHGRI 9/8/15: NIH CDE Initiatives – Overview Webinar

  2. Standard Measures Needed – c2006 Type 2 Diabetes GWAS (>380K SNPs) • Combining studies increases ability to detect loci with moderate effect size (G x G; G x E interactions) • Replication of GWAS results • Potential for cross-study analysis and replication limited by lack of standardized measures (www.broad.mit.edu/diabetes/scandinavs/type2.html)

  3. The PhenX Project • Goal: Produce an online resource of standard phenotypic & environmental exposure measures • Cooperative Agreement (2007 U01  2013 U41) • ~ 450 measures addressing 21 domains • Protocols, data element dictionaries, data collections worksheets (www.phenxtoolkit.org)

  4. PhenX Definitions • DOMAIN: Topical area with unifying theme (organ system, complex disease) Substance Use • MEASURE: Certain characteristic of, or relating to, a study subject Nicotine Dependence • PROTOCOL: Standard procedure recommended collecting and record a PhenX measure Fagerstrom Test • COLLECTION: A set of measures with a shared characteristic, target population or topic. The measures may cut across research Domains. Mental Health Research

  5. NHGRI RTI NIH IC Liaisons External Scientific Panel Steering Committee Alcohol, Tobacco, Other Substances Anthropometrics Cancer Rare Genetic Conditions Pregnancy and Pediatrics Cardiovascular Demographics Diabetes Obesity TBD Environmental Exposures Gastrointestinal Infectious Disease & Immunity Neurology Nutrition/Dietary Supplements Ocular Oral Health Physical Activity & Fitness Psychiatric Psychosocial Reproductive Health Respiratory Skin, Bone, Muscle & Joint Social Environments Speech and Hearing

  6. Selecting PhenX Measures • Criteria for selecting measures include: • Well-established and broadly validated • Low burden to participant & investigator • Applicable across population groups • Freely available protocols • Useful to investigators who are not domain experts SC defines scope WG refines scope WG selects prelim measures Community Outreach WG selects final measures

  7. Searching the Toolkit

  8. My Toolkit

  9. NIH Collaborations PhenX Domains (N=25) Kevin Conway Greg Farber Ellen Werner Kay Wanke

  10. PhenX CDEs and variables • 21 Domains (demographics, diabetes, etc.) • 389 CDEs (CDEs are created for each protocol) • 11,961 variables • Substance Abuse and Addiction Collections • 121 CDEs • 3,009 variables • Mental Health Research Collections • 39 CDEs to date • 893 variables • Tobacco Regulatory Research Collections • 21 CDEs to date • 200 variables (Slide provided by C. Hamilton, PhenX PI)

  11. Example of PhenX CDEs and Variables 1 Protocol, 4 CDEs, 8 variables (Slide provided by C. Hamilton, PhenX PI)

  12. Linking to relevant efforts

  13. Adding Value by Crowdsourcing

  14. Closing thoughts • Lessons learned • Identifying NIH IC Liaisons early was important • Personalized emails are most effective for outreach • Evaluating update/adoption of PhenX is not easy • 2,000 Registered Users, 97 FOAs • Science changes, re-evaluate content often (PhenX Expert Review Panels) and have strong versioning system • Looking ahead • Protocol translations, education & outreach • dbGaP mapping, EHR integration, participate in trans-NIH CDE efforts

  15. Acknowledgments • NHGRI • Brenda Iglesias (Program Analyst) • PhenX Steering Committee • Mary Marazita, Co-Chair • Cathy McCarty, Co-Chair • Sharon Terry, Member • WG Chairs / Members • NIH Liaisons • Other Liaisons: HHS, DoD, CDC, FDA, VA • NIDA lead – Kevin Conway • TRSP lead – Kay Wanke • NIMH lead – Greg Farber • NHLBI lead – Ellen Werner • RTI Team • Carol Hamilton (Principal Investigator) • Tabitha Hendershot (Co-Investigator) • Amanda Riley (Project Manager) • Darigg Brown • Wayne Huggins • Debbie Maiese • Destiney Nettles • Helen Pan • Mike Phillips • Toolkit team • Communications team • Logistics team

  16. Contact Information Erin M. Ramos, PhD MPH Program Director, Division of Genomic Medicine National Human Genome Research Institute 5635 Fishers Lane, Suite 4076, MSC 9305 Bethesda, MD 20892-9305 Tel: 301.451.3706   RAMOSER@MAIL.NIH.GOV

  17. Appendix

  18. Toolkit Stats (July 2015) • Number of Registered Users: 1,949 • Number of Countries that Accessed PhenX : 155 • Number of Visits: 713,446 • Number of Unique Visits: 118,695 • Average Visits Per Day: 301 • Report Downloads: 4,830 • Register Your Study: 16 • Publications Citing PhenX: 94 • Number of Funding Opportunities: 97

  19. PhenX at ASHG Annual Meeting 2015 • ASHG Invited Sessions on October 7, 2015 • “Human Phenotypes for Researchers, Clinicians, and Patients” • Moderator: Jonathan Haines • Speakers: C. McCarty, A. Kho, H. Rehm, B. Patrick-Lake

  20. Add Value by PhenX/dbGaP Mapping • Huge # of variables in dbGaP (160,188) • Fewer # of variables in PhenX (16,112) • Mapping Comparable Variables Only • PhenX var => dbGAP var (harmonizable) same concept • dbGAP var => PhenX var (harmonizable) same concept • Tools • In-house web-based wrapper using NCBI search API • Drill down using dbGaP search pages • Evaluating effectiveness of two mapping procedures • Establish a schedule based on results (Slide provided by C. Hamilton, PhenX PI)

  21. PhenX Phase II • Improve Toolkit Content • 4 new domains • Review/Update PhenX domains • Extend Capabilities • Chinese and Spanish translations • Web-based protocols, Explore EHR Integration • Mapping PhenX Measures to studies in dbGaP • Identify opportunities to combine studies • Collaborations and Outreach “A complete understanding of disease also requires…high-quality phenotypic data. Obtaining phenotypic data that are both thorough and accurate enough to be analyzed in conjunction with genomic and environ­mental data requires meticulous application of phenotyping methods, improved definitions of phenotypes, new technologies, and the consist­ent use of data standards (http://www.phenx.org).” Green and Guyer, 2011

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