STROGAR – STrengthening the Reporting Of Genetic Association studies
Sarah L. Kernsa,b,c, Dirk de Ruysscherd, Christian N. Andreassene, David Azriaf, Gillian C. Barnettg,
Jenny Chang-Claudeh, Susan Davidsoni, Joseph O. Deasyj, Alison M. Dunningk, Harry Ostrerb,c,
Barry S. Rosensteina, Catharine M.L. Westl, Søren M. Bentzenm,⇑
aDepartment of Radiation Oncology, Mount Sinai School of Medicine;bDepartment of Pathology, Albert Einstein College of Medicine;cDepartment of Genetics, Albert Einstein College
of Medicine, New York, USA;dDepartment of Radiation Oncology, University Hospitals Leuven/KU Leuven, Belgium;eDepartment of Experimental Clinical Oncology, Aarhus University
Hospital, Denmark;fMontpellier Cancer Institute, Montpellier University, France;gDepartment of Oncology, Cambridge University Hospital NHS Foundation Trust, UK;hDivision of
Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany;iDepartment of Clinical Oncology, Christie NHS Foundation Trust Hospital, Manchester, UK;
jDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA;kUniversity of Cambridge, Strangeways Research Laboratories;lInstitute of Cancer Sciences,
University of Manchester, UK;mDepartment of Human Oncology, University of Wisconsin, Madison, USA
a r t i c l ei n f o
Received 9 May 2013
Received in revised form 16 July 2013
Accepted 29 July 2013
Available online 27 August 2013
Normal tissue toxicity
a b s t r a c t
Despite publication of numerous radiogenomics studies to date, positive single nucleotide polymorphism
(SNP) associations have rarely been reproduced in independent validation studies. A major reason for
these inconsistencies is a high number of false positive findings because no adjustments were made
for multiple comparisons. It is also possible that some validation studies were false negatives due to
methodological shortcomings or a failure to reproduce relevant details of the original study. Transparent
reporting is needed to ensure these flaws do not hamper progress in radiogenomics. In response to the
need for improving the quality of research in the area, the Radiogenomics Consortium produced an
18-item checklist for reporting radiogenomics studies. It is recognised that not all studies will have
recorded all of the information included in the checklist. However, authors should report on all checklist
items and acknowledge any missing information. Use of STROGAR guidelines will advance the field of
radiogenomics by increasing the transparency and completeness of reporting.
? 2014 The Authors. Published by Elsevier Ireland Ltd. Radiotherapy and Oncology 110 (2014) 182–188
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-
Radiogenomics is a multi-disciplinary scientific research field
aiming to link human genomic variability to a cancer patient’s like-
lihood of developing toxicity following radiotherapy . Over 80
publications to date reported results of studies investigating corre-
lations between genetic markers and radiotherapy toxicity.
Although many reports published p-values that were nominally
statistically significant, findings have proved difficult to reproduce
[2–8]. One problem is that many studies carry out multiple com-
parisons without controlling the rate of false positive findings. As
the human genome contains around 11 million single nucleotide
polymorphisms (SNPs), the prior probability that a given SNP is
associated with the phenotype is low. Validation of significant
associations in independent cohorts is key to progress in radioge-
nomics, and requires greater standardisation of study designs, data
collection and data analysis. This can only be achieved by adopting
a common set of guidelines for reporting radiogenomics studies.
Radiogenomics shares many similarities with other genetic and
observational epidemiology studies but has unique challenges that
warrant a separate set of checklist items to complement the CON-
SORT, STROBE and STREGA guidelines [9–11]. STROBE guidelines
were used to develop checklist items for reporting information
on participants, methods and results . The STREGA extension
was drawn on for genetic epidemiology items, specifically for data
sources and measurement, and statistical methods . New items
were added to address radiotherapy aspects. An 18-item checklist
is presented for reporting radiogenomics studies (Table 1).
Explanation and elaboration of checklist items
The following sections explain briefly the relevance of each
checklist item for radiogenomics, and elaborate on the details re-
quired for manuscript reviewers and readers, referencing exem-
plary papers where available.
0167-8140/? 2014 The Authors. Published by Elsevier Ireland Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
⇑Corresponding author. Address: Department of Human Oncology, University of
Wisconsin, School of Medicine and Public Health, K4/314 Clinical Sciences Center
(CSC), 600 Highland Avenue, Madison, WI 53792-4675, USA.
E-mail address: email@example.com (S.M. Bentzen).
Radiotherapy and Oncology 110 (2014) 182–188
Contents lists available at ScienceDirect
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STROGAR - 18-item checklist for reporting radiogenomics studies.
Title and abstract
Title and abstract1 Include the primary outcome(s) and type of study (whether GWAS or gene-specific); provide an informative summary of the
study including study design, whether discovery or validation, sample size, main endpoints, and major results.
Background/rationale2 Note if the study is a GWAS or a candidate gene/SNP study and, if candidate gene study, rationale for choice of genes/SNPs;
give a general description of the study setting.
Define the primary/main outcome(s) of interest; describe the overall/long-term goal of the study; note if it is a discovery,
validation, or multi-stage study. Use terminology and definitions from National Cancer Institute biomarker study guidelines
, where applicable.
Study design4 Specify the study design (case-control, cohort); whether data were collected under a controlled trial setting; whether data
were collected retrospectively or prospectively. Report power and sample size considerations.
Specify the source(s) of the patients and, if multiple sources, whether they are pooled or treated as separate cohorts; define
inclusion/exclusion criteria; report whether co-morbidities and medications were assessed by self-report or medical records;
define methods/system used for tumour staging; describe the larger patient population from which the study sample was
drawn; define how major changes in treatment protocol were handled in the analysis.
Specify details of radiation treatment parameters including: organ(s)-at-risk, dose–time-fractionation; dose-rate, target
volume selection [ex: breast + boost], dose to critical substructures, dose–volume metric used, the type of treatment and
treatment setting, radiation modality [ex: external beam vs. brachytherapy], whether single or combined treatment
modalities were used, whether primary treatment or salvage therapy, imaging & planning details, ICRU recommendations
followed and note relaxation of criteria, note any changes in dose or treatment protocol over the time course of enrolment
and whether there were any interruptions in treatment.
Specify how intra-patient or pre-treatment assessment was made and whether it is accounted for in defining phenotype(s);
note whether patient reported outcomes or physician-assessed outcomes are being used to define phenotype(s); note which
toxicity scoring system was used (if using a common/standard system); define the grading scales used and whether the
phenotype(s) is/are defined as continuous, dichotomous or categorical; describe frequency of follow-up scheduling and
diagnostic intensity; define the post-treatment timeframe for assessment of toxicity outcomes; describe whether outcome(s)
is/are based on a single time point or the maximum/worst time point out of a series of follow-up assessments; note if/how
competing risks were handled (such as non-radiation related manifestation of the phenotype); note any medical intervention
that may influence study outcome(s).
Specify DNA source and isolation methods; note the methods/platform used for genotyping; specify whether genotyping was
done in one stage or multiple stages; note whether genotyping was done in more than one lab or batch, and if so, how batch
effects were handled; describe methods for genotype calling and cite the algorithm used; note whether genotype calling was
done for the whole study sample together or in batches; describe quality control (QC) methods including concordance
between duplicates, control samples, and checks for cryptic relatedness; describe methods for assessing population
structure; describe SNP/CNP filtering methods including filtering on per-sample call rate, per-SNP call rate, minor allele
frequency and Hardy–Weinberg equilibrium; note whether imputation was used and, if so, describe methods.
Define the statistical methods and models used for association testing; cite the software and settings used; describe how
censoring was handled; define model selection methods used for multivariable models; describe whether all samples are
analysed together or sequentially if the study involves multiple cohorts; for multi-stage studies, define methods for selecting
variants to follow up in subsequent stages; describe how missing data were handled; if multiple cohorts were included,
describe data harmonisation methods; note whether gene–gene interaction or gene–environment interaction was
investigated; describe methods used to adjust for population structure; describe methods used to correct for multiple
comparisons and/or control for risk of false-positive findings.
Genotyping strategy and
Data analysis and
Patient characteristics10 Report number of individuals at each stage of the study (e.g. numbers examined for eligibility, numbers confirmed eligible,
included in study, completed follow-up, successfully genotyped and analysed). Give reasons for nonparticipation at each
stage. Give description of the included patient sample regarding demographic (e.g. age at start of therapy, sex, race/ethnicity)
and clinical characteristics (e.g. site and stage of primary tumour, chemotherapy, hormone therapy), details of radiation
exposure, where appropriate (e.g. type, dose, boost) and potential confounders and effect modifiers (e.g. life-style related
factors, co-morbidities, and medications), including missing data; report length of follow-up and number of events and
number of patients at risk at various follow-up times e.g. yearly.
It is recommended to include a flow diagram of patients included/excluded from the study, as proposed by the CONSORT
Report baseline function (if relevant); report numbers of responders and non-responders for dichotomous outcomes,
descriptive statistics for quantitative outcome(s), or distributions for categorical outcomes.
Report call rates; numbers of samples and numbers of SNPs excluded on the basis of QC filters; if imputation was used, note
which variants are imputed and which are genotyped directly; report genetically determined racial/ethnic groups or other
population clusters; report genomic inflation factor as well as corrected genomic inflation factor after controlling for
For each SNP/CNP, report: common identifier (such as dbSNP rs number), minor allele identity and frequency, phenotype by
genotype category, effect size (with 95% confidence interval) and p-value; genetic inheritance model(s) used; for
multivariable analyses, report unadjusted and adjusted estimate and note which covariates were included in the model(s).
Report sub-group analyses and/or secondary outcomes of interest.
Primary associations 13
Summarise key results in the context of the study objectives given in the Introduction.
Discuss limitations of the study in the context of bias (noting both direction and size), confounding, sample size and power,
and representativeness of study population.
Provide an overall interpretation of the findings in the context of previous clinical studies, genetic association studies, and
biological studies of radiation response.
Comment on the potential clinical utility of the findings in the context of the patient populations to which the results may
S.L. Kerns et al./Radiotherapy and Oncology 110 (2014) 182–188
Items 1–3 – title and abstract; introduction: background/rationale and
See STROBE explanation and elaboration paper .
Item 4 – methods: study design
Samples and data can be collected in observational studies (co-
hort, case-control, cross-sectional) or within randomised con-
trolled trials. For recent examples of different study designs see
Kerns et al. and Barnett et al. [4,13]. An earlier paper by Bentzen
elaborates on the importance of improving study designs in radia-
tion oncology . Information should be given on the research
question or hypothesis being tested; primary and secondary end-
points; and statistical power and justification of patient numbers.
Reporting the statistical power of the study is important so that
definitive conclusions can be drawn about the strength of associa-
tion between toxicity and a genetic variant that the study was
powered to detect; for example SNPs with a minor allele frequency
of 25% or greater and genotype relative risk of 1.5 or greater. An
example description of a power calculation is in Barnett et al. .
Item 5 – methods: patient population
Detailed information on the process leading to participant
inclusion in a study is important because participants might differ
from the target population to which any findings will be applied.
Such information aids readers in understanding and potentially
trying to reproduce the methods in validation studies. For cohort,
case-control and cross-sectional studies, the population from
which the sample was selected and the method of recruitment
should be described. The number of individuals at each stage of
recruitment should be accounted for ideally using a flow diagram,
comparable to CONSORT . Depending on the type of study, this
may include the numbers of patients: examined for eligibility, con-
firmed as eligible, included in the study, with follow-up data, gen-
otyped, successfully genotyped and analysed. Matching may be
used in radiogenomics studies to make groups directly comparable
for potential confounders or effect modifiers and reduce the com-
plexity (as in cohort studies) or ensure the similarity in the distri-
bution of variables and potential confounders between cases and
controls (in case-control studies) [15,16]. For matched studies,
the matching criteria and number of exposed and unexposed pa-
tients should be given in cohort studies and for case-control stud-
ies the number of controls per case. Though replication studies
may not have the same matching information available, this will
at least allow for assessment of limitations and potential discrep-
ancies in the patient population that may account for lack of repli-
cation of SNP association. For examples of how to detail
information see Barnett et al. and Fig. 1 in Kerns et al. [4,13].
Item 6 – methods: radiation exposure
The absorbed dose distribution across relevant normal tissues
be considered in radiogenomics studies, as toxicity depends on the
distributionof dose inspace and time(dose–volume effectanddose
fractionation) . Accurate dosimetry and appropriate quality
assurance is needed to reduce the non-genetically related variation
in toxicity so that replication studies can be compared with original
reports of SNP-phenotype association. Authors should report meth-
ods for dosimetry or state if the information is not available.
If the critical normal tissue structure is known and relatively
mate of absorbed dose at that location (or mean dose to the con-
toured structure). Larger structures require a more extensive
analysis of dose–volume relationships. In some cases, patient co-
eters have little predictive value. In other cases, especially when
inter-institutional data are used, or when the prescription dose var-
ies within a cohort, variation in dose–volume parameters will cause
variability in the incidence of toxicity and should be included in any
predictive model or as a covariate in multivariable analysis .
Dosimetric variability thatis unaccounted forwillreducethe ability
to detect any genetic component to risk. A more detailed discussion
on reporting dose–volume-toxicity studies is given by Jackson et al.
how it was collected and used in the analysis. There are currently
severalexamplesof analysesthatincorporatebothdose andgenetic
risk factors [4,7,20,21]). Studies are increasingly involving multiple
cohorts, and EQD2 can be calculated to quantify prescribed doses
and differences in dose per fraction across studies, as illustrated in
the Radiogenomics Consortium meta-analysis of TGFB1 studies .
Item 7 – methods: phenotype(s)
The phenotype is radiotherapy toxicity, which can occur early
(during or within weeks of treatment) or late (3 months to many
years later). Second cancer induction is a very late toxicity .
The time when toxicity is assessed is important for radiogenomics
because late effects can manifest many years after irradiation and
can progress in severity . The intensity with which follow-up
information is sought and obtained influences incidence and preva-
lence estimates . Cultural or socioeconomic differences in com-
pliance to planned follow-up visits could potentially become
confounding factors in radiogenomics studies unless they are ac-
counted for in the analysis. Studies should specify whether toxicity
was recorded at a single time point or the maximum grade from a
For some tumour types baseline symptoms are correlated with tox-
icity after radiotherapy. For example, late toxicity following radio-
therapy to the prostate can be similar to the symptoms of prostate
cancer, benign prostate disease and bladder disorders. Change in
function from baseline may be calculated, or baseline function in-
the start of radiotherapy and, if so, provide a clear explanation of
methods to account for these in the analysis.
There are multiple endpoints of toxicity both for the different
tissues irradiated (e.g. skin telangiectasia, bowel obstruction or
lung pneumonitis) and also within a tissue or organ (e.g. breast
shrinkage, oedema, pigmentation, telangiectasia and pain) . Sev-
eral normal tissues may be irradiated, such as bowel, bladder and
reproductive organs following radiotherapy for tumours in the pel-
vis. As some SNPs identified are likely to be endpoint specific, the
endpoints studied should be carefully defined.
There are multiple scales for grading toxicity, e.g. the RTOG
(Radiation Therapy Oncology Group)/EORTC (European Organisa-
tion for Research and Treatment of Cancer) late effects scale ;
the LENT SOMA (Late Effects Normal Tissues: Subjective, Objective,
Management and Analytic) system [26,27], now largely super-
seded by the NCI Common Terminology Criteria for Adverse Effects
version 4.0 (CTCAEv4.0) . Both physician- and patient-reported
outcomes (PROs) can be obtained [24,29,30]. Authors should report
on the instruments used for recording toxicity to provide readers
with a clear understanding of how phenotypes were defined.
Item 8 – methods: genotyping strategy and quality control
Authors should report the steps taken to ensure the high quality
of genotyping data. For example, authors should report processes
STROGAR – STrengthening the Reporting Of Genetic Association studies in Radiogenomics
to prevent sample mix-up, such as ‘barcoding’ with a set of highly
polymorphic SNPs that are present on the genotyping platform and
genotyped separately to compare with array results. To ensure
readers are confident that results are not biased by sample mix-
up, authors should report whether duplicate samples were assayed
in multiple experiments and concordant results obtained. Authors
should also report whether pair-wise comparisons were performed
to check for cryptic relatedness (i.e. unknown kinship). It is impor-
tant to report samples excluded from analyses on the basis of ques-
tion of identity or low call rate, as substantial differences in call
rates between cases and controls can lead to spurious results. For
transparency, authors should report the numbers of patients in
whom genotyping was attempted and was successful.
chosen. If discordant findings are reported between studies, it is
and lower error rate than the other, or whether one study ran all
ferencesinresults. For the samereasons,authorsshould reporthow
moved, and whether poorly performing or monomorphic and rare
SNPs were filtered out. Some useful data checks that can inform
the following: (i) checks for batch or study centre effects or for unu-
sual patterns of missing data, including marked differences in the
call rates between the cases and controls; (ii) a Hardy–Weinberg
equilibrium (HWE) check to determine whether deviations from
HWE are systematic from inbreeding, population stratification or
subject selection as opposed to being limited to a discrete number
of SNPs and possibly an indication of phenotype association; and
(iii) check of SNP association distribution with the log quantile–
quantile (QQ) p-value plot .
It is also important that authors report methods used for han-
dling missing data. Authors should report if, and how, they inves-
tigated whether missingness is systematic between cases and
controls. A few missing genotypes should not introduce bias; how-
ever, for multipoint analyses, many individuals might be missing
one or a few genotypes, which could be a compounding effect. If
data imputation is used to address missingness, this must be stated
and the approach used reported.
Item 9 – methods: data analysis and statistical methods
The analysis of data from radiogenomics studies is dominated
by three major issues: (1) high dimensionality of the data set, (2)
confounding factors and, for late toxicity, (3) censoring.
High dimensionality data sets in radiogenomics
Three main factors may contribute to the high dimensionality of
items often evaluated repeatedly over time; (ii) the availability of a
large number of dose–volume parameters for each individual in
ered in each study. In a study addressing the impact of just seven
SNPs upon two different normal tissue endpoints (corresponding
by chance – even under the null hypothesis – is >50% assuming the
should interpret their results in the context of the experiment wide
false positive rate, study-specific Q–Q plots, and/or genome-wide
sets for testing the statistical significance of the top findings of the
discovery study sample. Along similar lines, when reporting multi-
ple-comparisons corrected data in radiogenomics, it is important
to provide details on dose–volume measures included in multivari-
able models. A classical dose–volume histogram (DVH) contains a
tive associations is due to the number of not only SNPs tested, but
also the DVH parameters used.
Confounding in radiogenomics
A confounding factor or confounder is a variable that correlates
(positively or negatively) with both the dependent variable (radio-
therapy toxicity) and the independent variable (genotype) thus
causing a spurious relationship between the two. A factor affecting
the risk of radiotherapy toxicity is not a true confounder, unless it
is also associated with genotype, typically through ancestry. Popu-
lation stratification results in differences in allele frequencies be-
tween cases and controls because of systematic differences in
ancestry rather than association of genes with disease. This is a sig-
nificant confounder in all genetic association studies, and can make
comparisons across studies with differing ancestry difficult. Radi-
ogenomics studies can involve multi-ethnic cohorts because of
the difficulty in obtaining large sample sizes with detailed clinical
data from ethnically uniform populations. Authors should report
methods used for assessing and correcting for population stratifi-
cation, and corrected and non-corrected data should be compared.
An example is erectile dysfunction after brachytherapy for pros-
tate cancer where a recent study showed that African American
race/ethnicity is significantly associated with increasing log-odds
for better erectile function even after adjustment for pre-treatment
sexual health-related quality of life score and age . This would
mean that any genotype with significantly higher (or lower) prev-
alence in African Americans than Whites could show a spurious
association with erectile dysfunction in a mixed patient sample
of African Americans and Whites. In other words, race/ethnicity
is a confounder for this endpoint. The link between ancestry and
radiotherapy toxicity could also be due to variations in life-style
factors; smoking for example lowers the risk of radiation pneumo-
nitis , and the prevalence of smoking varies considerably in the
United States according to race/ethnicity , again making smok-
ing a potential confounder in radiogenomics studies.
Technique-dependent variation in radiation dose distribution –
with a resulting effect on radiotherapy toxicity – and/or in time–
dose-fractionation schedules could become a confounding factor
especially in multi-centre radiogenomics studies where demo-
graphics vary between centres or where the use of a specific tech-
nique was related to race/ancestry, perhaps through socio-
economic status. An example is a SEER-MEDICARE based study
showing that the use of intensity-modulated radiotherapy for head
and neck cancer from 2000 through 2005 ranged from 11.3% of
cases in Kentucky to 40.4% of cases in Hawaii, creating a possible
association with genotypes due to differences in ancestry in the
two populations .
Any report of a radiogenomics study should carefully consider
possible confounding factors and describe attempts at adjusting
for these in the data analysis.
Censoring and the analysis of late effects
(Right-) censoring occurs when an endpoint requires prolonged
observation of a patient. An example is skin telangiectasia, which
can appear 10 years or more following irradiation . For a pa-
tient without telangiectasia 5 years after radiotherapy, it is con-
ceivable that the patient will never develop telangiectasia or,
alternatively, the time to development of telangiectasia exceeds
5 years, i.e. the observation is censored. Special statistical methods
are required to adjust for censoring and should be reported, see for
example Bentzen et al. .
In some matched case control studies, controls (i.e. patients
who had not reached the endpoint when last seen) are only
S.L. Kerns et al./Radiotherapy and Oncology 110 (2014) 182–188
included if they have a prescribed minimum follow-up (e.g.
5 years). This creates an asymmetry between cases and controls,
as it appears unreasonable to disregard events that occur early
after radiotherapy, i.e. in this case before 5 years. The problem in
a radiogenomics context is that 5-year survivors are likely to have
more favourable prognostic factors compared to the whole popula-
tion of patients, e.g., having less advanced cancer or less likely to
develop intercurrent disease, for example related to smoking. This
again can lead to issues with confounding (see Section ‘‘Confound-
ing in radiogenomics’’).
Item 10 – results: patient characteristics
Description of patient characteristics and their exposures helps
readers to assess the generalisability of the study findings. Infor-
mation about potential confounders and effect modifiers, including
whether and how they were measured and accounted for in the
analysis (described in Methods, Item 9) influences judgments
about study validity and relevance of findings. Authors should pro-
vide the description for the overall patient sample as well as for
subgroups, such as patients presenting with events. Continuous
variables can be summarised using the mean and standard devia-
tion, or the median and inter-quartile range. Ordinal and categori-
cal variables should be presented as frequency distributions.
Genotype distribution of patients with and without the event of
interest can be compared for potential effect modifiers, such as age,
body mass index, and co-morbidity (e.g. diabetes, collagen vascular
disease). Information on the amount of missing data for relevant
parameters (in tables or figures) should be provided for assessment
of potential bias or generalisation of results. This also applies to the
extent of loss to follow-up. Duration and extent of follow-up for
the available outcome data can be provided as a summary with
either the median or mean follow-up time, where appropriate as
well as the minimum and maximum follow-up times.
Item 11 – results: phenotype(s)
As in any epidemiologic study, details of the numbers of cases
(and controls if used) with quantitative outcomes such as the
mean, median, and range should be given and how these have been
obtained. However, some aspects of phenotype reporting are spe-
cific to radiation oncology. For example, there is under-reporting
in clinical trials of toxicity deemed to be less important or not
requiring surgical correction . Some radiogenomics studies in-
clude only relatively high-grade toxicity in analyses (i.e. cases and
controls), since it is sometimes difficult to capture low grade toxic-
ity. It may be clinically relevant to account for the full spectrum of
toxicity in the study population. If data are missing then the way in
which this is handled should be reported (linked with Item 9).
It is important for many endpoints that baseline function is re-
ported in radiogenomics studies since the aim is to identify genetic
variants associated with toxicity specifically attributable to radio-
therapy (see Item 7). If available and relevant, summary statistics
for baseline function should be reported. Other co-morbid condi-
tions, previous treatments such as surgery, and obstetric history
can give rise to patient symptoms which should not be attributed
to radiotherapy (linked with Item 7). This applies to treatment sites
such as the pelvis where pre-existing bladder and bowel symptoms
are common, as are co-morbid conditions in an ageing population.
For example, pre-treatment sexual potency correlates strongly
with post-treatment sexual potency . Given this strong associ-
ation, it would be important to know whether (and how) pre-treat-
ment sexual potency is accounted for in studies of genetic
predictors, so that comparisons in SNP effect size(s) could be
drawn between studies.
Items 12 and 13 – results: genotypes (12) and primary associations
Little et al. discuss reporting of genotype and primary associa-
tion results in the STREGA guidelines . We defer to their expla-
nation for the main aspects of these checklist items. Two points are
particularly relevant to radiogenomics.
First, radiogenomics association results should be reported in
the context of clinical exposures (radiation dose, volume and type;
see Item 6) and effect modifiers (e.g. use of chemotherapy, smoking
history). Investigators should report whether they sought associa-
tions between radiation exposures and toxicity. Similarly, it is
important to report whether results are adjusted or controlled
for co-morbidity and surgical procedures, which may cause non-
radiation related manifestation of the phenotype. This in turn, in-
creases the variance of the dependent variable thus increasing
the risk of false negative findings in a study of a given size. For
example the use of clinical photographs immediately after surgery
and before radiotherapy for breast cancer in the United Kingdom
START trials enabled assessment of breast shrinkage due to radio-
therapy [39,40]. However, much of the clinically assessed breast in
duration and shrinkage at 2 years is due to surgery rather than
radiotherapy . In the case of significant associations, it should
be reported whether genotype–phenotype association results pre-
sented are adjusted for significant non-genetic factors.
Second, as highlighted above for Items 8 and 9, the large sample
size required in radiogenomics studies makes it difficult to obtain
sufficiently large cohorts from ethnically uniform populations, and
so cohorts are often multi-ethnic. Previous candidate gene studies
largely ignored ethnicity in genotype–phenotype associations, and
this undoubtedly has contributed at least in part to their lack of
reproducibility. Therefore it is important that investigators report
whether ethnicity was controlled for in reporting of genotype–
phenotype associations and downstream predictive modelling. If
there is evidence of association, ethnicity-adjusted genotype–phe-
notype results should be reported.
Item 14 – results: secondary analyses
In the STROBE Explanation & Elaboration paper, Vandenbroucke
et al. discuss the reporting of secondary analyses, e.g., analyses of
additional endpoints, sub-groups, interactions and sensitivity
. The problems associated with carrying out multiple analyses
are particularly relevant with the phenotype of radiotherapy toxic-
ity, where multiple toxicities are often studied in a single cohort.
Due to the danger of chance findings, un-planned secondary anal-
yses must be reported as such.
Item 16 – discussion: limitations
Given the numerous study design, endpoint and sample size
challenges described above, authors should report on the limita-
tions of their studies. It is unlikely that a study will have informa-
tion on all of the potential confounders and modifiers that could
influence association between genotype and toxicity phenotypes.
Similarly, few studies will have complete follow-up for toxicity
at regular intervals on every patient. Authors should report on
the variables unavailable for the study and discuss how omitting
these variables from analysis might affect their results.
In any genetic association study, the sample size and population
affect the type of genetic factors identified. A smaller study will be
likely to miss variants associated with very modest effect sizes or
variants that have very low minor allele frequency. A study carried
out in a Northern European population could potentially miss vari-
ants that are prevalent in Asian or African populations. Authors
should report on the limitations of their findings with respect to
STROGAR – STrengthening the Reporting Of Genetic Association studies in Radiogenomics
whether there are likely to be clinically relevant variants yet to be
identified in larger studies, with different genotyping coverage,
and/or in ethnically different populations.
Item 17 – discussion: interpretation
Most radiogenomics studies aim (1) to establish predictors of
treatment response and/or (2) to explore the mechanisms underly-
ing radiation effects. If prediction is the primary purpose of the
study, the discussion should, if possible, provide an estimation of
the clinical utility of a test based on the reported genetic associa-
tion. If exploration of biological mechanisms is the primary aim,
the interpretation should include possible mechanistic implica-
tions of the findings including the possibility that the investigated
SNP/s may be in linkage disequilibrium with other SNP/s and
therefore may not be the causative variants. Although some com-
mon genetic alterations may affect radiosensitivity across tissue
types/endpoints, others may only be relevant for individual end-
points. This distinction should also be considered in the interpreta-
tion of the results.
Findings should also be discussed in the context of other studies
addressing the impact of the same SNP/s. If possible, a formal
meta-analysis of the new and previous results could be considered
as part of the discussion. In addition, methods to reveal potential
publication bias (e.g. a funnel plot) should be considered whenever
Item 18 – discussion: generalisation and clinical utility
Authors should discuss the clinical utility of identifying a popu-
lation with higher/lower risk of developing toxicity in relation to
the effect sizes found. For example, radio resistant patients might
be offered a higher dose with modern techniques or radiotherapy
in combination with systemic therapies with the aim of improving
local control. In some cases it may be considered to avoid radio-
therapy completely in individuals with a high risk of developing
toxicity, provided that an effective alternative exists. For example,
for prostate cancer, surgery could be offered instead of radiother-
apy if a high risk of rectal bleeding or urinary discomfort is reliably
identified by a test before starting treatment. Alternately, active
surveillance could be considered for cancers with a very low risk
of progression. In cases where therapeutic alternatives to radio-
therapy are not available, a high risk of toxicity could lead to the
patient being considered for new radiotherapy techniques like pro-
tons. This part of the discussion is therefore important to consider
the translation of biological results to the clinic in terms of imple-
mentation and utility. If possible, specificity and sensitivity of a po-
tential test should be discussed.
Although numerous radiogenomics studies have been pub-
lished, positive SNP associations have rarely been reproduced in
independent validation studies. The inconsistent findings might
in part be due to a high number of false positive findings because
adjustments for multiple comparisons were not made. The incon-
sistent findings may also be due, in part, to underpowered discov-
ery studies. It is, however, also possible that some validation
studies have been false negatives due to methodological shortcom-
ings or a failure to reproduce relevant details of the original study.
Without complete and transparent reporting, these flaws will con-
tinue to hamper progress in radiogenomics. The guidelines out-
lined in this paper aim to correct this shortcoming. Like other
reporting guidelines, STROGAR is intended as a guideline only
rather than a prescription for study design and conduct – though
investigators may find the guideline of some help when designing
a prospective study protocol. It is hoped that the STROGAR guide-
lines will help researchers improve their design and reporting of
new radiogenomics studies, interpret published research, and facil-
itate the discovery of SNPs that are genuinely associated with
Conflict of interest
All authors declare no conflict of interest.
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Luft-und der Speisewege.
STROGAR – STrengthening the Reporting Of Genetic Association studies in Radiogenomics