Using the PhenX Toolkit to Add Standard Measures to a Study

Tabitha Hendershot1, Huaqin Pan1, Jonathan Haines2, William R. Harlan3, Mary L. Marazita4, Catherine A. McCarty5, Erin M. Ramos6, Carol M. Hamilton1

1 RTI International, Research Triangle Park, North Carolina, 2 Case Western Reserve University, Cleveland, Ohio, 3 Retired, Office of the Director, National Institutes of Health, Bethesda, Maryland, 4 University of Pittsburgh, Pittsburgh, Pennsylvania, 5 Essentia Institute of Rural Health, Duluth, Minnesota, 6 National Human Genome Research Institute, Bethesda, Maryland
Publication Name:  Current Protocols in Human Genetics
Unit Number:  Unit 1.21
DOI:  10.1002/0471142905.hg0121s86
Online Posting Date:  July, 2015
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The PhenX (consensus measures for Phenotypes and eXposures) Toolkit ( 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. © 2015 by John Wiley & Sons, Inc.

Keywords: genome‐wide association studies (GWAS); PhenX; phenotypes; environmental exposures; epidemiology; research; standard measures

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Table of Contents

  • Introduction
  • Using the PhenX Toolkit
  • Commentary
  • Literature Cited
  • Figures
  • Tables
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Literature Cited

Literature Cited
  Bennett, S.N., Caporaso, N., Fitzpatrick, A.L., Agrawal, A., Barnes, K., Boyd, H.A., Cornelis, M.C., Hansel, N.N., Heis, G., Heit, J.A., Kang, J.H., Kittner, S.J., Kraft, P., Lowe, W., Marazita, M.L., Monroe, K.R., Pasquale, L.R., Ramos, E.M., van Dam, R.M., Udren, J., and Williams, K.; for the GENEVA Consortium. 2011. Phenotype harmonization and cross‐study collaboration in GWAS consortia: The GENEVA experience. Genet. Epidemiol. 35:159‐173.
  Conway, K.P., Vullo, G.C., Kennedy, A.P., Finger, M.S., Arpana, A., Bjork, J.M., Farrer, L.A., Hancock, D.B., Hussong, A., Wakim, P., Huggins, W., Hendershot, T., Nettles, D.S., Pratt, J., Maiese D., Junkins, H.A., Ramos, E.M., Strader, L.C., Hamilton, C.M., and Sher, K.J. 2014. Data compatibility in the addiction sciences: An examination of measure commonality. Drug Alcohol Depend. 141:153‐158.
  Maiese, D.R., Hendershot, T.P., Strader, L.C., Wagener, D.K., Hammond, J.A., Huggins, W., Kwok, R.K., Hancock, D.B., Whitehead, N.S., Nettles, D.S., Pratt, J.G., Scott, M.S., Conway, K.P., Junkins, H.A., Ramos, E.M., and Hamilton, C.M. 2013. PhenX—Establishing a Consensus Process to Select Common Measures for Collaborative Research. RTI Press publication no. MR‐0027‐1310. RTI Press, Research Triangle Park, N.C.
  Mailman, M.D., Feolo, M., Jin, Y., Kimura, M., Tryka, K., Bagoutdinov, R., Hao, L., Kiang, A., Paschall, J., Phan, L., Popova, N., Pretel, S., Ziyabari, L., Lee, M., Shao, Y., Wang, Z.Y., Sirotkin, K., Ward, M., Kholodov, M., Zbicz, K., Beck, J., Kimelman, M., Shevelev, S., Preuss, D., Yaschenko, E., Graeff, A., Ostell, J., and Sherry, S.T. 2007. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 39:1181‐1186.
  McCarty, C.A., Huggins, W., Aiello, A.E., Bilder, R.M., Hariri, A., Jernigan, T.L., Newman, E., Sanghera, D.K., Strauman, T.J., Zeng, Y., Ramos, E.M., and Junkins, H.A. 2014. PhenX RISING: Real world implementation and sharing of PhenX measures. BMC Med. Genom. 7:16 (doi: 10.1186/1755‐8794‐7‐16.
  Stover, P.J., Harlan, W.R., Hammond, J.A., Hendershot, T., and Hamilton, C.M. 2010. PhenX: A toolkit for interdisciplinary genetics research. Curr. Opin. Lipidol. 21:136‐140.
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