Strengthening the reporting of genetic risk prediction studies: the GRIPS statement.
ABSTRACT The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of genetic risk prediction studies (the GRIPS statement), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published at http://www.plosmedicine.org.
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ABSTRACT: A growing body of evidence suggests that environmental pollutants, such as heavy metals, persistent organic pollutants and plasticizers play an important role in the development of chronic diseases. Most epidemiologic studies have examined environmental pollutants individually, but in real life, we are exposed to multi-pollutants and pollution mixtures, not single pollutants. Although multi-pollutant approaches have been recognized recently, challenges exist such as how to estimate the risk of adverse health responses from multi-pollutants. We propose an "Environmental Risk Score (ERS)" as a new simple tool to examine the risk of exposure to multi-pollutants in epidemiologic research.PLoS ONE 06/2014; 9(6):e98632. · 3.53 Impact Factor
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ABSTRACT: Increasing numbers of research reporting guidelines are being published. These guidelines facilitate rigorous and complete reporting, and presentation of published studies. However, current reporting guidelines do not address issues related to costs of research methods. We propose to publish costs of research in order to increase transparency, efficiency, quality and ultimately reproducibility of scientific studies.Methods of information in medicine. 07/2014; 53(4).
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ABSTRACT: Background Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.Materials and methodsThe Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors.ResultsThe resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document.Conclusions To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).European Journal of Clinical Investigation 01/2015; 32(2). · 3.37 Impact Factor
The recent successes of genome-wide association studies
and the promises of whole genome sequencing fuel
interest in the translation of this new wave of basic
genetic knowledge to health care practice. Knowledge
about genetic risk factors may be used to target
diagnostic, preventive, and therapeutic interventions for
com plex disorders based on a person’s genetic risk, or to
complement existing risk models based on classical non-
genetic factors, such as the Framingham risk score for
cardiovascular disease. Implementation of genetic risk
prediction in health care requires a series of studies that
encompass all phases of translational research [1,2],
starting with a comprehensive evaluation of genetic risk
With increasing numbers of discovered genetic
markers that can be used in future genetic risk prediction
studies, it is crucial to enhance the quality of the
reporting of these studies, since valid interpretation
could be compromised by the lack of reporting of key
information. Information that is often missing includes
details in the description of how the study was designed
and conducted (for example, how genetic variants were
selected and coded, how risk models or genetic risk
scores were constructed, and how risk categories were
chosen), or how the results should be interpreted. An
appropriate assessment of the study’s strengths and
weak nesses is not possible without this information.
There is ample evidence that prediction research often
suffers from poor design and bias, and these may also
have an impact on the results of the studies and on
models of disease outcomes based on these studies [3-5].
Although most prognostic studies published to date
claim significant results [6,7], very few translate to
clinically useful applications. Just as for observational
epidemiological studies , poor reporting complicates
the use of the specific study for research, clinical, or
public health purposes and hampers the synthesis of
evidence across studies.
Reporting guidelines have been published for various
research designs , and these contain many items that
are also relevant to genetic risk prediction studies. In
particular, the guidelines for genetic association studies
(STrenghtening the REporting of Genetic Association
studies - STREGA) have relevant items on the assessment
of genetic variants, and the guidelines for observational
studies (Strengthening the Reporting of OBservational
studies in Epidemiology - STROBE) have relevant items
about the reporting of study design. The guidelines for
diagnostic studies (STAndards for Reporting Diagnostic
accuracy - STARD) and those for tumor marker prog-
nostic studies (Reporting of tumor MARKer studies -
REMARK) include relevant items about test evaluation;
The rapid and continuing progress in gene discovery
for complex diseases is fueling interest in the potential
application of genetic risk models for clinical and
public health practice. The number of studies assessing
the predictive ability is steadily increasing, but the
quality and completeness of reporting varies. A
multidisciplinary workshop sponsored by the Human
Genome Epidemiology Network developed a checklist
of 25 items recommended for strengthening the
reporting of genetic risk prediction studies (the GRIPS
statement), building on the principles established by
prior reporting guidelines. These recommendations
aim to enhance the transparency of study reporting,
and thereby to improve the synthesis and application
of information from multiple studies that might
differ in design, conduct, or analysis. A detailed
Explanation and Elaboration document is published at
Strengthening the reporting of genetic risk
prediction studies: the GRIPS statement
A Cecile JW Janssens1*, John PA Ioannidis2,3,4,5,6, Cornelia M van Duijn1, Julian Little7 and Muin J Khoury8;
for the GRIPS Group
*Correspondence: A Cecile JW Janssens. Email: firstname.lastname@example.org
1Department of Epidemiology, Erasmus University Medical Center, PO Box 2040,
Rotterdam 3000 CA, The Netherlands
Full list of author information is available at the end of the article
© 2011 Janssens et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Janssens et al. Genome Medicine 2011, 3:16
the REMARK guidelines also have relevant items about
risk prediction [10-13]. However, none of these guidelines
are fully suited to genetic risk prediction studies, an
emerging field of investigation with specific methodo-
logical issues that need to be addressed, such as the
handling of large numbers of genetic variants (from tens
to tens of thousands) and flexibility in handling such
large numbers in analyses. We organized a two-day
workshop with an international group of risk prediction
researchers, epidemiologists, geneticists, methodologists,
statisticians, and journal editors to develop recom-
mendations for the reporting of genetic risk prediction
studies - the GRIPS statement.
Genetic risk prediction studies typically develop or
validate models that predict the risk of disease, but they
are also being investigated for use in predicting prog nostic
outcome, treatment response, or treatment-related harms.
Risk prediction models are statistical algorithms, which
may be simple genetic risk scores (for example, risk allele
counts), may be based on regression analyses (for example,
weighted risk scores or predicted risks), or may be based
on more complex analytic approaches, such as support
vector machine learning or classification trees. The risk
models may be based on genetic variants only, or include
both genetic and non-genetic risk factors .
The 25 items of the GRIPS statement are intended to
maximize the transparency, quality, and completeness of
reporting on research methodology and findings in a
particular study. It is important to emphasize that these
recommendations are guidelines only for how to report
research and do not prescribe how to perform genetic
risk prediction studies. The guidelines do not support or
oppose the choice of any particular study design or
method; for example, the guidelines recommend that the
study population should be described, but do not specify
which population is preferred in a particular study.
The intended audience for the reporting guidelines is
broad and includes epidemiologists, geneticists, statis-
ticians, clinician scientists, and laboratory-based investi-
gators who undertake genetic risk prediction studies, as
well as journal editors and reviewers who have to appraise
the design, conduct and analysis of such studies. In
addition, it includes ‘users’ of such studies who wish to
understand the basic premise, design, and limitations of
genetic prediction studies in order to interpret the results
for their potential application in health care. These
guidelines are also intended to ensure that essential data
from future genetic risk prediction studies are presented in
standardized form, which will facilitate information
synthesis as part of systematic reviews and meta-analyses.
Items presented in the checklist are relevant for a wide
array of risk prediction studies, because GRIPS focuses
on the main aspects of the design and analysis of risk
prediction studies. GRIPS does not address randomized
trials that may be performed to test risk models, nor does
it specifically address decision analyses, cost-effectiveness
analyses, assessment of health care needs, or assessment
of barriers to health care implementation . Once the
performance of a risk model has been established, these
next steps toward implementation require further
evaluation [10,16]. For the reporting of these studies,
which go beyond the assessment of genetic risk models
as such, additional requirements apply. However, proper
documentation of genetic predictive research according
to GRIPS might facilitate the translation of research
findings into clinical and public health practice.
The GRIPS statement was developed by a multidiscipli-
nary panel of 25 risk prediction researchers, epidemio-
logists, geneticists, methodologists, statisticians, and
journal editors, seven of whom were also part of the
STREGA initiative . They attended a two-day meeting
in Atlanta, Georgia (US) in December 2009 that was
sponsored by the US Centers for Disease Control and
Prevention on behalf of the Human Genome Epidemio-
logy Network (HuGENet) . Participants discussed a
draft version of the guidelines that was prepared and
distributed before the meeting. This draft version was
developed on the basis of existing reporting guidelines,
namely STREGA , REMARK , and STARD .
These were selected out of all available guidelines 
because of their focus on observational study designs and
genetic factors (STREGA), prediction models (REMARK),
and test evaluation (REMARK and STARD). During the
meeting, methodological issues pertinent to risk predic-
tion studies were addressed in presentations. Workshop
participants were asked to change, combine, or delete
proposed items and add additional items if necessary.
Participants had extensive post-meeting electronic corres-
pondence. To harmonize our recommendations for
genetic risk prediction studies with previous guidelines,
we chose the same wording for the items wherever
possible. Finally, we tried to create consistency with
previous guidelines for the evaluation of risk prediction
studies of cardiovascular diseases and cancer [2,19]. The
final version of the checklist is presented in Table 1.
Accompanying this GRIPS statement, an Explanation
and Elaboration document has been written ,
modeled after those developed for other reporting
guidelines [21-24]. The Explanation and Elaboration docu-
ment illustrates each item with at least one published
Janssens et al. Genome Medicine 2011, 3:16
Page 2 of 5
Title and abstract
(a) Identify the article as a study of risk prediction using genetic factors. (b) Use recommended keywords in the
abstract: genetic or genomic, risk, prediction
Background and rationale 2 Explain the scientific background and rationale for the prediction study
Specify the study objectives and state the specific model(s) that is/are investigated. State if the study concerns
the development of the model(s), a validation effort, or both
Study design and setting
Specify the key elements of the study design and describe the setting, locations, and relevant dates, including
periods of recruitment, follow-up, and data collection
Participants 5* Describe eligibility criteria for participants, and sources and methods of selection of participants
Clearly define all participant characteristics, risk factors and outcomes. Clearly define genetic variants using a
widely used nomenclature system
(a) Describe sources of data and details of methods of assessment (measurement) for each variable. (b) Give a
detailed description of genotyping and other laboratory methods
(a) Describe how genetic variants were handled in the analyses. (b) Explain how other quantitative variables were
handled in the analyses. If applicable, describe which groupings were chosen, and why
Analysis: risk model
Specify the procedure and data used for the derivation of the risk model. Specify which candidate variables
were initially examined or considered for inclusion in models. Include details of any variable selection procedures
and other model-building issues. Specify the horizon of risk prediction (for example, 5-year risk)
Analysis: validation 10 Specify the procedure and data used for the validation of the risk model
Analysis: missing data 11 Specify how missing data were handled
Analysis: statistical methods
Specify all measures used for the evaluation of the risk model, including, but not limited to, measures of model fit
and predictive ability
Analysis: other 13 Describe all subgroups, interactions, and exploratory analyses that were examined
Report the numbers of individuals at each stage of the study. Give reasons for non-participation at each stage.
Report the number of participants not genotyped, and reasons why they were not genotyped
Report demographic and clinical characteristics of the study population, including risk factors used in the risk
Descriptives: model estimates 16
Report unadjusted associations between the variables in the risk model(s) and the outcome. Report adjusted
estimates and their precision from the full risk model(s) for each variable
Risk distributions 17* Report distributions of predicted risks and/or risk scores
Assessment 18 Report measures of model fit and predictive ability, and any other performance measures, if pertinent
Validation 19 Report any validation of the risk model(s)
Other analyses 20 Present results of any subgroup, interaction, or exploratory analyses, whenever pertinent
Discuss limitations and assumptions of the study, particularly those concerning study design, selection of
participants, and measurements and analyses, and discuss their impact on the results of the study
Give an overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from
similar studies, and other relevant evidence
Generalizability 23 Discuss the generalizability and, if pertinent, the health care relevance of the study results
State whether databases for the analyzed data, risk models, and/or protocols are or will become publicly
available and, if so, how they can be accessed
Give the source of funding and the role of the funders for the present study. State whether there are any conflicts
Items marked with an asterisk should be reported for every population in the study.
Janssens et al. Genome Medicine 2011, 3:16
Page 3 of 5
example that we consider transparent in reporting,
explains the rationale for its inclusion in the checklist,
and presents details of the items that need to be
addressed to ensure transparent reporting. The Expla na-
tion and Elaboration document was produced after the
meeting. The document was prepared by a small
subgroup and shared with all workshop participants for
additional revisions and final approval.
High-quality reporting reveals the strengths and weak-
nesses of empirical studies, facilitates the interpretation
of the scientific and health care relevance of the results -
especially within the framework of systematic reviews
and meta-analyses - and helps build a solid evidence base
for moving genomic discoveries into applications in
health care practice. The GRIPS guidelines were developed
to improve the transparency, quality and completeness of
the reporting of genetic risk prediction studies. As
outlined in the introduction, GRIPS does not prescribe
how studies should be designed, conducted, or analyzed,
and therefore the guidelines should not be used to assess
the quality of empirical studies . The guidelines
should be used only to check whether all essential items
are adequately reported.
Finally, the methodology for designing and assessing
genetic risk prediction models is still developing. For
example, newer measures of reclassification were first
intro duced in 2007 , and several alternative reclassi fi-
ca tion measures have been proposed . Which
measures to apply and when to use measures of re-
classification are still subject to ongoing evaluation and
discussion . Furthermore, alternative strategies for
constructing risk models other than simple regression
analyses are being explored, and these may add increased
complexity to the reporting. In formulating the items of
the GRIPS statement, these methodological advances
were anticipated. It is for this reason that the GRIPS
statement recommends how a study should be reported
and not how a study should be conducted or analyzed.
Therefore, methodological and analytical developments
will not immediately impact the validity and relevance of
the items, but the GRIPS statement will be updated when
this is warranted by essential new developments in the
construction and evaluation of genetic risk models.
In order to encourage dissemination of the GRIPS state-
ment, this article will also be published by PLoS Medicine,
Annals of Internal Medicine, BMJ, Circulation: Cardio
vascular Genetics, European Journal of Clinical Investiga
tion, European Journal of Epidemiology, European Journal
of Human Genetics, Genetics in Medicine, Genome
Medicine, and Journal of Clinical Epidemiology.
GRIPS, Genetic RIsk Prediction Studies; REMARK, Reporting of tumor MARKer
studies; STARD, STAndards for Reporting Diagnostic accuracy; STREGA,
STrenghtening the REporting of Genetic Association studies.
The authors declare that they have no competing interests.
The members of the GRIPS group are: A Cecile JW Janssens, John PA Ioannidis,
Sara Bedrosian, Paolo Boffetta, Siobhan M Dolan, Nicole Dowling, Isabel
Fortier, Andrew N Freedman, Jeremy M Grimshaw, Jeffrey Gulcher, Marta
Gwinn, Mark A Hlatky, Holly Janes, Peter Kraft, Stephanie Melillo, Christopher
J O’Donnell, Michael J Pencina, David Ransohoff, Sheri D Schully, Daniela
Seminara, Deborah M Winn, Caroline F Wright, Cornelia M van Duijn, Julian
Little, and Muin J Khoury. Funding: the workshop was sponsored by the
Centers for Disease Control and Prevention on behalf of the Human Genome
Epidemiology Network (HuGENet). The findings and conclusions in this
report are those of the authors and do not necessarily reflect the views of the
Department of Health and Human Services. A Cecile JW Janssens is financially
supported by grants from the Erasmus University Medical Center Rotterdam,
the Center for Medical Systems Biology in the framework of the Netherlands
Genomics Initiative (NGI) and the VIDI grant of the Netherlands Organisation
for Scientific Research (NWO). John PA Ioannidis: Tufts CTSI is supported by
the National Institutes of Health/National Center for Research Resources
(UL1 RR025752). Opinions in this paper are those of the authors and do not
necessarily represent the official position or policies of the Tufts CTSI. Julian
Little holds a Canada Research Chair in Human Genome Epidemiology. The
funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
1Department of Epidemiology, Erasmus University Medical Center, PO
Box 2040, Rotterdam 3000 CA, The Netherlands. 2Department of Hygiene
and Epidemiology, University of Ioannina School of Medicine, University
Campus, Ioannina 45110, Greece. 3Biomedical Research Institute, Foundation
for Research and Technology-Hellas, University Campus, Ioannina 45110,
Greece. 4Department of Medicine, Tufts University School of Medicine,
750 Washington St, Boston, MA 02111, USA. 5Center for Genetic Epidemiology
and Modeling and Tufts CTSI, Institute for Clinical Research and Health Policy
Studies, Tufts Medical Center, 750 Washington St, Boston, MA 02111, USA.
6Stanford Prevention Research Center, Stanford University School of Medicine,
251 Campus Drive, Stanford, CA 94305, USA. 7Department of Epidemiology
and Community Medicine, University of Ottawa, 451 Smyth Rd, Ottawa,
Ontario K1H 8M5, Canada. 8Office of Public Health Genomics, Centers for
Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333, USA.
Submitted: 18 January 2011 Accepted: 16 March 2011
Published: 16 March 2011
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Cite this article as: Janssens ACJW, et al.: Strengthening the reporting of
genetic risk prediction studies: the GRIPS statement. Genome Medicine 2011,
Janssens et al. Genome Medicine 2011, 3:16
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