The PhenX Toolkit: Get the Most from Your Measures

Overview

Journal Am J Epidemiol

Publisher Oxford University Press

Specialty Public Health

Date 2011 Jul 14

PMID 21749974

Citations 418

Authors

Carol M Hamilton

Lisa C Strader

Joseph G Pratt

Deborah Maiese

Tabitha Hendershot

Richard K Kwok

Jane A Hammond

Wayne Huggins

Dean Jackman

Huaqin Pan

Destiney S Nettles

Terri H Beaty

Lindsay A Farrer

Peter Kraft

Mary L Marazita

Jose M Ordovas

Carlos N Pato

Margaret R Spitz

Diane Wagener

Michelle Williams

Heather A Junkins

William R Harlan

Erin M Ramos

Jonathan Haines

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

The Relationship Between Fiber Intake and Gut Bacterial Diversity and Composition During the Third Trimester of Pregnancy.

Schwartz L, Ladouceur J, Russell M, Xie S, Bu S, Kerver J Nutrients. 2025; 17(5).

PMID: 40077643 PMC: 11901921. DOI: 10.3390/nu17050773.

Associations between social-network characteristics and postpartum health behaviors and weight among a sample of women who were overweight/obese pre-pregnancy.

Kent-Marvick J, Wong B, Simonsen S, Cloyes K, Debbink M, Creal C Womens Health (Lond). 2025; 21:17455057251321872.

PMID: 40071980 PMC: 11905029. DOI: 10.1177/17455057251321872.

Harmonization of SDQ and ASEBA Phenotypes: Measurement Variance Across Cohorts.

Jovic M, Amir-Haeri M, Rimfeld K, Ensink J, Lindauer R, Vrijkotte T J Psychopathol Behav Assess. 2025; 47(1):27.

PMID: 40062209 PMC: 11889055. DOI: 10.1007/s10862-025-10204-0.

Exploring the Unique Therapeutic Potential of Psychedelics to Reduce Chronic Shame Among Sexual and Gender Minority Adults.

Carlisle N, Dourron H, MacCarthy S, Zarrabi A, Hendricks P Psychedelic Med (New Rochelle). 2025; 1(4):210-217.

PMID: 40046860 PMC: 11658658. DOI: 10.1089/psymed.2022.0018.

Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study.

Bhavnani S, Zhang W, Bao D, Raji M, Ajewole V, Hunter R J Med Internet Res. 2025; 27:e48775.

PMID: 39932771 PMC: 11862773. DOI: 10.2196/48775.

References

Emilsson V, Thorleifsson G, Zhang B, Leonardson A, Zink F, Zhu J . Genetics of gene expression and its effect on disease. Nature. 2008; 452(7186):423-8. DOI: 10.1038/nature06758. View

Risch N, Herrell R, Lehner T, Liang K, Eaves L, Hoh J . Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis. JAMA. 2009; 301(23):2462-71. PMC: 2938776. DOI: 10.1001/jama.2009.878. View

Hindorff L, Sethupathy P, Junkins H, Ramos E, Mehta J, Collins F . Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009; 106(23):9362-7. PMC: 2687147. DOI: 10.1073/pnas.0903103106. View

Schad P, Mobley L, Hamilton C . Building a biomedical cyberinfrastructure for collaborative research. Am J Prev Med. 2011; 40(5 Suppl 2):S144-50. PMC: 5817638. DOI: 10.1016/j.amepre.2011.01.018. View

Barrett J, Hansoul S, Nicolae D, Cho J, Duerr R, Rioux J . Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat Genet. 2008; 40(8):955-62. PMC: 2574810. DOI: 10.1038/ng.175. View

Forrey A, McDonald C, DeMoor G, Huff S, Leavelle D, Leland D . Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. Clin Chem. 1996; 42(1):81-90. View

Hunter D, Kraft P . Drinking from the fire hose--statistical issues in genomewide association studies. N Engl J Med. 2007; 357(5):436-9. DOI: 10.1056/NEJMp078120. View

Yu W, Gwinn M, Clyne M, Yesupriya A, Khoury M . A navigator for human genome epidemiology. Nat Genet. 2008; 40(2):124-5. DOI: 10.1038/ng0208-124. View

Cimino J, Hayamizu T, Bodenreider O, Davis B, Stafford G, Ringwald M . The caBIG terminology review process. J Biomed Inform. 2009; 42(3):571-80. PMC: 2729758. DOI: 10.1016/j.jbi.2008.12.003. View

10.

Thorisson G, Muilu J, Brookes A . Genotype-phenotype databases: challenges and solutions for the post-genomic era. Nat Rev Genet. 2008; 10(1):9-18. DOI: 10.1038/nrg2483. View

11.

Kraft P, Hunter D . Genetic risk prediction--are we there yet?. N Engl J Med. 2009; 360(17):1701-3. DOI: 10.1056/NEJMp0810107. View

12.

McDonald C, Huff S, Suico J, Hill G, Leavelle D, Aller R . LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem. 2003; 49(4):624-33. DOI: 10.1373/49.4.624. View

13.

Fortier I, Burton P, Robson P, Ferretti V, Little J, LHeureux F . Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies. Int J Epidemiol. 2010; 39(5):1383-93. PMC: 2972444. DOI: 10.1093/ije/dyq139. View

14.

Stover P, Harlan W, Hammond J, Hendershot T, Hamilton C . PhenX: a toolkit for interdisciplinary genetics research. Curr Opin Lipidol. 2010; 21(2):136-40. PMC: 8038872. DOI: 10.1097/MOL.0b013e3283377395. View

15.

Barrett J, Clayton D, Concannon P, Akolkar B, Cooper J, Erlich H . Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet. 2009; 41(6):703-7. PMC: 2889014. DOI: 10.1038/ng.381. View

16.

Mailman M, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R . The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007; 39(10):1181-6. PMC: 2031016. DOI: 10.1038/ng1007-1181. View

17.

Pearson T, Manolio T . How to interpret a genome-wide association study. JAMA. 2008; 299(11):1335-44. DOI: 10.1001/jama.299.11.1335. View

18.

Hirschhorn J . Genomewide association studies--illuminating biologic pathways. N Engl J Med. 2009; 360(17):1699-701. DOI: 10.1056/NEJMp0808934. View

19.

Knoppers B, Fortier I, Legault D, Burton P . The Public Population Project in Genomics (P3G): a proof of concept?. Eur J Hum Genet. 2008; 16(6):664-5. DOI: 10.1038/ejhg.2008.55. View

20.

Zeggini E, Scott L, Saxena R, Voight B, Marchini J, Hu T . Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008; 40(5):638-45. PMC: 2672416. DOI: 10.1038/ng.120. View