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Calibration of credibility of agnostic genome-wide associations.

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 147B:964 –972 (2008)
Review Article
Calibration of Credibility of Agnostic
Genome-Wide Associations
John P.A. Ioannidis1,2,3*
1
Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine,
Ioannina, Greece
2
Biomedical Research Institute, Foundation for Research and Technology-Hellas, Ioannina, Greece
3
Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts
Genome-wide testing platforms are increasingly
used to promote ‘‘agnostic’’ approaches to the discovery of gene variants associated with the risk
of many common diseases and quantitative traits.
The early track record of genome-wide association (GWA) studies suggests that some proposed
associations are replicated quite consistently
with large-scale subsequent evidence from multiple studies, others have a more inconsistent
replication record, some have failed to be replicated by independent investigators and many
more early proposed associations await further
replication. An important question is how to
calibrate the credibility of these postulated associations. A simple Bayesian method is applied
here to achieve such calibration. The variability
of the estimated credibility is examined under
different assumptions. Empirical examples are
drawn from existing GWA studies. It is demonstrated that the credibility of different proposed
associations can cover a very wide range. The
credibility of specific associations usually
remains relatively robust when different plausible assumptions are made (within a reasonable
range) for the prior odds of an association being
true, or the magnitude of the anticipated effect
size for genetic associations. Heterogeneity and
bias assumptions can have a more major impact
on the credibility estimates and thus they need
very careful consideration in each case. Credibility calibration may be used in conjunction with
qualitative criteria for the appraisal of the cumulative evidence that take into consideration the
amount, consistency, and protection from bias
in the data.
ß 2008 Wiley-Liss, Inc.
KEY WORDS: genome; association; Bayes
Please cite this article as follows: Ioannidis JPA. 2008.
Calibration of Credibility of Agnostic Genome-Wide
Associations. Am J Med Genet Part B 147B:964–972.
*Correspondence to: John P.A. Ioannidis, Professor and Chairman, Department of Hygiene and Epidemiology, University of
Ioannina School of Medicine, Ioannina 45110, Greece.
E-mail: jioannid@cc.uoi.gr
Received 1 November 2007; Accepted 27 December 2007
DOI 10.1002/ajmg.b.30721
ß 2008 Wiley-Liss, Inc.
INTRODUCTION: AGNOSTIC
GENOME-WIDE ASSOCIATIONS
The advent of platforms that can measure common genetic
variation in massive scale has revolutionized the genetic
epidemiology of common diseases with complex etiology. Prior
approaches for the discovery of common gene variants of
importance for human diseases and quantitative traits had
met with frustrating hurdles. Candidate gene approaches
where genes were selected with some biological or other prior
rationale in mind were hindered by our incomplete knowledge
of the relevant biology. Candidate gene approaches resulted in
a few clear successes, but also a vast amount of non-replicated
postulated associations that caused a very confusing picture
in the literature [Ioannidis et al., 2001, 2003; Hirschhorn
et al., 2002; Cordell and Clayton, 2005]. Linkage studies
with genome-wide scans were also largely insensitive for
the underlying architecture of genetic effects. In fact, even the
scattered successes of prior approaches may come as a surprise
when we view them from the angle of complexity that genomewide association (GWA) studies can afford us.
GWA technology has evolved rapidly and the ability to
measure genetic variability en-mass is likely to continue to
improve in the foreseeable future. There are already a large
number of excellent technical, design, and statistical reviews
on GWA studies for the interested reader [Hirschhorn and
Daly, 2005; Marchini et al., 2005; Thomas et al., 2005; Wang
et al., 2005; Thomas, 2006]. Both the number of markers and
types of variation captured (e.g., copy number variation)
[Beckmann et al., 2007; McCarroll and Altshuler, 2007] in
routine applications may widen and whole genome sequencing
for large numbers of samples should soon become routine.
Given the evolution of the technological front, one can be even
more enthusiastic about the future. Nevertheless, currently
used commercial GWA platforms already allow for 65–75%
coverage [Barrett and Cardon, 2006] for most common human
genetic variation and with the potential to increase this
percentage with imputation of genotypes. Therefore, the
technology is probably already quite mature to address the
questions of whether and which common genetic variants
affect the risk of major human diseases.
An important question is how to interpret the results
obtained from such studies and their replication efforts,
especially when there is no other biological/external evidence
to strengthen or weaken the credibility of specific associations
that arise out of the mess of massive screening. A common
theme of GWA studies is the agnostic approach to gene
discovery. The genome is screened without any prior predilection for specific regions, genes, or variants thereof. Instead
of biological rationale, the selection of targets for study and
further replication is based on purely statistical, and thus
‘‘agnostic’’, criteria.
In this setting, the replication and the consistency of the
replication across different studies on the same variant and
Calibration of Credibility of Agnostic GWAs
phenotype assume prime importance [Ioannidis, 2007; NCINHGRI Working Group on Replication in Association Studies
et al., 2007; Ioannidis et al., 2008]. This article reviews briefly
the current track record of replication and consistency for
GWA investigations as of late 2007. To calibrate the credibility
of genome-wide-proposed associations, the article adopts a
simple method using Bayesian principles and the method is
applied illustratively in specific examples. Factors that affect
the credibility estimates are also discussed and credibility is
juxtaposed against existing consensus criteria for appraising
the epidemiological evidence in genetic associations.
EARLY REPLICATION RECORD
Early results from GWA studies are already available for a
few dozens of disease phenotypes and traits as of the writing of
this article (October 2007) and the pace of data production is
accelerating. Using a previously described classification
[Ioannidis, 2007], the early track record of these agnostic
approaches includes several major successes of consistent
replication, some inconsistent results, some more clear-cut
failures of replication, and many tentative or inconclusive
associations that still await further strengthening of the
evidence and replication from independent teams.
Replication
For several diseases, GWA approaches have already led to
the discovery of common gene variants that confer small effects
of susceptibility and that have been replicated also quite
consistently across several other independent studies. These
include, but are not limited to, Crohn’s disease [Duerr et al.,
2006; Hampe et al., 2007; Mathew, 2008; Parkes et al., 2007;
Raelson et al., 2007; Rioux et al., 2007; Wellcome Trust Case
Control Consortium, 2007], age-related macular degeneration
[Klein et al., 2005; Swaroop et al., 2007], type 2 diabetes
mellitus [Diabetes Genetics Initiative et al., 2007; Scott et al.,
2007; Zeggini et al., 2007], breast cancer [Easton et al., 2007],
and myocardial infarction [Helgadottir et al., 2007; McPherson
et al., 2007]. The number of markers identified and the
cumulative impact of these markers on the disease risk varies
a lot for different diseases. Thus, the identified variants
probably already explain a substantial proportion of the risk
variance for age-related macular degeneration and Crohn’s
disease. Conversely, each of 11 identified polymorphisms for
susceptibility to type 2 diabetes mellitus explain only 0.4–2%
of this variability [Zeggini et al., 2007].
Inconsistency
Very promising, but apparently inconsistent results have
been seen in some other cases. For example, while polymorphisms in the TRAF1/C5 locus have been implicated as
risk factors for rheumatoid arthritis by some large studies
(both GWA and candidate-approach) with several replication
investigations [Kurreeman et al., 2007; Plenge et al., 2007], not
all GWA studies find such an effect [Wellcome Trust Case
Control Consortium, 2007], and the results across studies seem
heterogeneous, even if the region definitely seems very
interesting. Also for type diabetes 2, some of the proposed
susceptibility variants [Zeggini et al., 2007] also show very large
inconsistency [Ioannidis et al., 2007a], as discussed below.
Non-Replication
For some proposed risk variants, much larger replication
studied have more conclusively failed to verify any effect on the
risk of the disease of interest. Examples include the LTA
variant for myocardial infarction that emerged from the first
published GWA study [Ozaki et al., 2002; Clarke et al., 2006];
965
the 13 polymorphisms proposed for Parkinson’s disease
[Maraganore et al., 2005; Elbaz et al., 2006]; the INSIG2neighboring variant that was proposed to be associated with
obesity risk [Herbert et al., 2006; Dina et al., 2007; Loos et al.,
2007; Rosskopf et al., 2007]; and even some of the gene variants
(such as EXT2-ALX4 variants) proposed for modulation of risk
of type 2 diabetes in the first-published GWA on this disease
[Sladek et al., 2007], but not replicated with much larger GWA
and replication studies [Zeggini et al., 2007].
Early Evidence
Finally, for many newly proposed associations, investigators
have presented early data clearly stating that the findings are
tentative and need further data before any association can be
claimed. Examples include the first published GWA data for
bipolar disorder [Wellcome Trust Case Control Consortium,
2007], ischemic stroke [Matarin et al., 2007], and Alzheimer’s
disease [Coon et al., 2007] where signals were either nondefinitive or already known (e.g., linked polymorphisms in the
APOE epsilon 4 polymorphism region for Alzheimer’s disease).
This classification of the status of the evidence is unavoidably subject to potential misclassification. For example, one
can never exclude completely the possibility that very small
effects below the threshold of resolution of epidemiological
methods exist for associations in the ‘‘non-replication’’ category. For the inconsistency category, it is likely that more data
may clarify whether inconsistency is due to bias or genuine
diversity underlying genuine associations. Finally, early
evidence evolves continuously as new data are generated.
CALIBRATION OF CREDIBILITY: A SIMPLE
BAYESIAN METHOD
I present here a simple method for calibrating the credibility
(the probability that it is true) for a proposed association that
has been derived from an agnostic genome-wide screening
approach. The method follows standard Bayesian principles
and has been previously applied to estimate Bayes factors
empirically for a large sample of non-genetic epidemiological
associations and associations of gene variants from the
candidate gene era [Ioannidis, 2008]. The presented approach
is not the only one that can be used. Some main alternatives
include the false discovery rate (FDR), the false-positive report
probability (FPRP) and other Bayesian extensions thereof;
these measures are related among themselves [Wacholder
et al., 2004; Wakefield, 2007].
In the Bayesian framework, the credibility of an association
depends on the pre-study odds and the Bayes factor conferred
by the study data. The pre-study odds Opre reflect what we
think about the association before running the study (or, more
generically, excluding the evidence conferred by the study
data). Then the data of the study correspond to a Bayes factor B
that modifies our prior belief. After the study, the credibility of
the association is C ¼ Opre/(B(1 þ Opre/B)). Given that for
massive testing approaches, Opre is very low, this is practically
equal to the probability of any association (among the ones
massively tested) being true before any data are collected, C0.
An advantage of using log-odds ratio as the metric of
association is that it is reasonable to assume normality for
this metric, unless small studies are involved. One could
consider modeling also other measures of association, for
example, the variance explained, but the distribution would
typically be more complex. Use of normal likelihoods simplifies
the calculations. The prior can be specified for convenience as a
‘‘lump and smear’’ where a lump of likelihood is placed at the
null hypothesis (no association) and the remaining is normally
distributed under the alternative centered on 0 (to allow for
bidirectionality of effects) and with variance var(yA). The
966
Ioannidis
observed effect size for a single polymorphism of interest is
considered to be an estimate of the true effect y with variance
var(y). Thus the observed data are represented by:
N½y; varðyÞ
and the alternative is represented by:
N½0; varðyA Þ
From this, it follows that the Bayes factor [Spiegelhalter et al.,
2004] is given by:
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
varðyA Þ
z2m
exp
B¼ 1þ
varðyÞ
2ð1 þ ðvarðyÞ=varðyA ÞÞÞ
where zm is the standardized test statistic for the null
hypothesis derived from the data. Let us call yA the expected
value of the effect under the alternative hypothesis, if there is
an effect
in the positive
direction (relative risk >1.00), then
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
yA ¼ 2p1 varðyA Þ. If we coin the genetic comparison for all
true associations so as to express the relative risks as >1.00,
then yA is the average expected effect. Then the ratio of the
two variances (alternative and observed) is py2A =2varðyÞ and
thus:
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
"
#
!ffi
u
2
u
py
z2m
A
B ¼ t1 þ
exp
2varðyÞ
2ð1 þ ð2varðyÞ=ðpy2A ÞÞÞ
that is, the Bayes factor can be estimated from the variance
of the observed genetic effect, its frequentist-derived P-value
(through the corresponding z-statistic in the normal distribution) and the pre-specified expected value of the effect
under the alternative hypothesis, if there is a susceptibility
effect.
Sensitivity analyses can examine whether conclusions are
affected appreciably by different prior assumptions regarding
the value of yA. Based on current evidence, the majority of
associations revealed through GWA approaches tend to have
small effect sizes corresponding to odds ratios of 1.10–1.40
[Ioannidis et al., 2006; Khoury et al., 2006]. Theoretical
considerations also suggest that there may be more associations of very small effects, fewer with small effects and very few
with larger effects [Diabetes Genetics Initiative of Broad
Institute of Harvard and MIT, 2007]. The modeling of the
alternative prior fits well to this scheme. We will consider here
a range of yA corresponding to log-odds ratio of 0.049, 0.140,
0.262, 0.405, and 0.588, that is, 1.05, 1.15, 1.30, 1.50, and 1.80
in the odds ratio scale.
EMPIRICAL DEMONSTRATION: BAYES FACTOR
Table I shows estimates of B under these different
assumptions for different postulated associations that have
emerged from GWA studies and have had a different
replication track record. To show the spectrum of possibilities,
I have selected an example with a single signal giving
consistent replication in several studies (periodic limb movements), one example with very extensive consistent replication of many signals and some inconsistency that may
reflect interesting heterogeneity in genetic effects (type 2
diabetes mellitus), and one example where none of the most
statistically significant signals were replicated with further
testing.
The association between the rs3923809 variant of the
BTBD9 gene and periodic limb movements [Stefansson et al.,
2007] has had a very consistent effect in the original GWA and
two replication studies. The estimated Bayes factor after
combining all data varies between 105.26 and 1011.44.
The joint effort from three teams of GWA investigators and
their replication studies reported 11 polymorphisms giving
strong signals of association for type 2 diabetes mellitus
[Zeggini et al., 2007]. Here I use summary estimates of genetic
effects that incorporate also between-study heterogeneity
in the calculations (random effects model) [Ioannidis et al.,
2007a]. As shown in Table I, there are considerable differences
in the estimated Bayes factors for these 11 variants. Different
assumptions for the alternative do not affect a lot the Bayes
estimate for a specific variant, with variability being less than 1
log10 scale and often less than 0.5 log10 scale for each variant.
However, across the 11 variants, the range of Bayes factors is
extreme, extending from 100.12 to <1030. Six variants have
Bayes factors of 105 or less, and for another one (SLC30A8
rs13266634) additional data from subsequent studies [Sladek
et al., 2007; Steinthorsdottir et al., 2007] would bring the Bayes
factor in the same range. Also for CDKAL1, at least one other
study has found a strong signal in the same gene, but different
marker [Steinthorsdottir et al., 2007]. Of the remaining
variants that have less impressive Bayes factors, one was
found to be associated to a different, correlated phenotype
(FTO, see below), one is still uncertain (rs9300039), and one
(PPARG rs1801282) is further supported by several previous
studies already performed in the candidate gene era, although
the inconsistency of effects across studies may be a hint that
the true culprit polymorphism linked with the rs1801282
marker is not yet identified.
The table shows also the results of the first two-tier GWA
on Parkinson’s disease, where several polymorphisms were
proposed as susceptibility loci for the disease [Maraganore
et al., 2005], but none of them were replicated with subsequent
evidence from much larger replication studies [Elbaz et al.,
2006]. Variability in the estimated Bayes factors is not very
prominent, and no variant reached a Bayes factor more
extreme than 103.5 under any assumption; most values were
actually at least a log10 scale more conservative. Thus it should
not be surprising that none of these variants were eventually
replicated.
EMPIRICAL DEMONSTRATION: CREDIBILITY
There is some unavoidable subjectivity in the choice of C0. If
we set C0 ¼ 0, then under this extremely skeptical (nihilistic)
stance, no association is true and this cannot change,
regardless of what results we get. If the whole approach to
common genetic variants is irrelevant to genetic risk, no
matter what P-values we find, the associations simply reflect
error and bias. Leaving the nihilistic prior aside, it may be
reasonable to assume C0 ¼ 0.00001 approximately for platforms with 300–500 k markers that achieve 65–75% coverage
of the genome at r2 > 0.8 [Barrett and Cardon, 2006]. This
means that approximately 3–5 markers among those tested
are expected to reflect true associations. This is based on
considerations of how many common variants are likely to
underlie the genetic variability, given the typical observed
effects for single variants. However, it is unavoidable that this
will vary from disease to disease depending on the proportion of
the risk variance due to common genetic variants, the linkage
disequilibrium pattern of these variants, the exact magnitude
of their effects, and the presence or not of epistatic interactions.
Obviously, C0 would also depend on the testing platform
characteristics, including the number of markers, coverage
achieved in the populations under study, and possible
redundancy of the represented markers. Therefore, in most
circumstances, one may need to consider a range of C0
spanning at least two log10 scales (0.0001–0.000001).
Table II shows for different values of C0 the post-study
credibility for the associations whose Bayes factors were
calculated in Table I. Estimates are provided for Bayes factors
Calibration of Credibility of Agnostic GWAs
967
TABLE I. Estimated Bayes Factors for Selected Associations Proposed by GWA Studies According to Different Values of yA
(0.049, 0.140, 0.262, 0.405, and 0.588, Corresponding to Odds Ratios of 1.05, 1.15, 1.30, 1.50, and 1.80, Respectively)
Estimated log10(Bayes factor) under different
assumptions for the yA
Gene
Variant
OR (95% CI)
Periodic limb movements in sleep
BTBD9
rs3923809
1.72 (1.50–1.98)
Type 2 diabetes mellitus
—
rs9300039
1.25 (1.04–1.50)
FTO
rs8050136
1.13 (1.02–1.25)
PPARG
rs1801282
1.16 (1.07–1.25)
CDKAL1
rs10946398
1.12 (1.07–1.17)
SLC30A8
rs13266634
1.12 (1.07–1.18)
CDKN2B
rs564398
1.12 (1.07–1.17)
HHEX
rs5015480–
1.13 (1.08–1.17)
rs1111875
KCNJ11
rs5215
1.14 (1.10–1.19)
IGF2BP2
rs4402960
1.15 (1.10–1.19)
CDKN2B
rs10811661
1.20 (1.14–1.25)
TCF7L2
rs7901695
1.37 (1.31–1.43)
Parkinson’s disease
SEMA5A
rs7702187
1.74 (1.36–2.24)
—
rs10200894
1.84 (1.38–2.45)
—
rs2313982
2.01 (1.44–2.79)
—
rs17329669
1.71 (1.33–2.21)
—
rs7723605
1.78 (1.35–2.35)
—
ss46548856
1.88 (1.38–2.57)
GALNT3
rs16851009
1.84 (1.36–2.49)
PRDM2
rs2245218
1.67 (1.29–2.14)
PASD1
rs7878232
1.38 (1.17–1.62)
—
rs1509269
1.71 (1.30–2.26)
—
rs11737074
1.50 (1.21–1.86)
P-value
yA ¼ 0.049
yA ¼ 0.140
yA ¼ 0.262
yA ¼ 0.405
yA ¼ 0.588
3 1014
5.26
10.35
11.30
11.44
11.40
0.015
0.015
0.0003
3.2 106
8.7 106
1.2 107
5.7 1010
0.31
0.56
1.74
3.68
3.26
4.89
7.01
0.67
0.63
2.04
3.74
3.36
5.09
7.29
0.63
0.45
1.88
3.53
3.15
4.90
7.10
0.50
0.28
1.71
3.35
2.98
4.72
6.93
0.36
0.12
1.56
3.20
2.82
4.57
6.78
5 1011
6.5 1012
7.8 1015
1.0 1048
7.96
8.75
10.99
>30
8.31
9.17
12.00
>30
8.13
8.99
11.90
>30
7.96
8.82
11.75
>30
7.80
8.67
11.60
>30
0.78
0.57
0.44
0.67
0.56
0.45
0.47
0.62
1.12
0.49
0.68
2.62
2.17
1.92
2.29
2.07
1.86
1.88
2.11
2.44
1.81
1.96
3.34
2.96
2.82
2.93
2.75
2.64
2.62
2.69
2.62
2.40
2.30
3.48
3.15
3.10
3.05
2.90
2.85
2.80
2.78
2.56
2.51
2.29
3.45
3.15
3.15
3.01
2.89
2.86
2.80
2.73
2.45
2.48
2.21
7.62 106
1.70 105
1.79 105
2.30 105
3.30 105
3.65 105
4.17 105
4.61 105
6.87 105
9.21 105
1.55 104
For the polymorphism implicated in periodic limb movements in sleep, there is no between-study heterogeneity in the three datasets [Stefansson et al., 2007]
and fixed effects synthesis coincides with random effects. For the type 2 diabetes polymorphisms summary effects from three GWA studies and their
replication efforts [Zeggini et al., 2007] are obtained by random effects synthesis [as in Ioannidis et al., 2007a]. Note that some of the polymorphisms for type 2
diabetes are not really derived from agnostic approaches, but were already suggested or known from pre-GWA studies (notably PPARG, KCNJ11, IGF2BP2,
and TCF7L2), however all 11 polymorphisms are shown here for completeness. For Parkinson’s disease, genetic effects from the synthesis of data from the
two tiers of the first GWA are obtained directly from the GWA publication [Maraganore et al., 2005].
calculated for yA corresponding to odds ratios of 1.05, 1.30, and
1.80. As shown, for the rs3923809 variant of the BTBD9, the
credibility is always very high unless there is a combination of
very low pre-study credibility (0.000001) and genetic effects
are expected to be very small (averaging odds ratios around
1.05). With this combination of assumptions, the observed
genetic effect (odds ratio 1.72) is only 16% likely to be true. For
the type 2 diabetes associations, some variants have ubiquitously very high credibility regardless of the exact assumptions, while some others have a wide range of credibility
depending on the exact assumptions. Finally, for the polymorphisms that arise from the first GWA study of Parkinson’s
disease, credibility is always practically negligible, regardless
of the background assumptions.
IMPACT OF HETEROGENEITY
When several datasets are combined to obtain a summary
genetic effect for the variant of interest, the results may
sometimes differ substantially depending on whether the
summary estimates takes into account or not the possibility
of between-study heterogeneity. Statistical tests for betweenstudy heterogeneity such as the Cochran’s Q statistic have
negligible power when few studies are involved [Higgins and
Thompson, 2002]. This is almost the rule when a new
association is proposed and an effort is made to replicate it in
a few other populations. Therefore, a non-significant test of
heterogeneity is no proof of homogeneity. Similarly, the I2
metric which can measure the extent of variability that is
beyond chance can have very wide confidence intervals in the
presence of only few datasets [Huedo-Medina et al., 2006;
Ioannidis et al., 2007b], thus an I2 ¼ 0% is not necessarily
reassuring that there is no heterogeneity in the strength of a
genetic association between different settings.
Statistical heterogeneity is only a surrogate of clinical and
biological heterogeneity. The interested reader is referred
elsewhere for a more detailed list of causes that may result in
heterogeneity of genetic effects across different populations in
the GWA setting [Ioannidis, 2007]. Besides bias, genuine
diversity may be seen at the level of the genetic structure (e.g.,
differential linkage disequilibrium with the real culprit
marker); or at the level of phenotype structure (e.g., differential correlation with some other correlated phenotype). In
the agnostic setting of GWA screening of markers, it is more
likely to hit linked markers rather than the true culprit
markers that mediate the functional biological effects. This has
implications on whether fine mapping should precede replication efforts or vice versa, and theoretical simulations suggest
that this depends on a number of assumptions [Clarke et al.,
2007]. Moreover, most common disease definitions have been
developed based on operational (clinically functional) criteria
and may reflect a very high level of underlying biological
diversity. Common diseases are also highly correlated with
each other [Rzhetsky et al., 2007]. We already have examples
where the originally identified association that emerged from a
GWA probably reflected an association with a correlated
968
Ioannidis
TABLE II. Credibility Estimates for the Associations of Table I
With prior credibility,
C0 ¼ 0.0001
Gene
Variant
With prior credibility,
C0 ¼ 0.00001
With prior credibility,
C0 ¼ 0.000001
yA ¼ 0.049 yA ¼ 0.262 yA ¼ 0.588 yA ¼ 0.049 yA ¼ 0.262 yA ¼ 0.588 yA ¼ 0.049 yA ¼ 0.262 yA ¼ 0.588
Periodic limb movements in sleep
BTBD9
rs3923809
0.948
Type 2 diabetes mellitus
—
rs9300039
0.000
FTO
rs8050136
0.000
PPARG
rs1801282
0.005
CDKAL1
rs10946398
0.325
SLC30A8
rs13266634
0.154
CDKN2B
rs564398
0.886
HHEX
rs5015480–
0.999
rs1111875
KCNJ11
rs5215
1.000
IGF2BP2
rs4402960
1.000
CDKN2B
rs10811661
1.000
TCF7L2
rs7901695
1.000
Parkinson’s disease
SEMA5A
rs7702187
0.001
—
rs10200894
0.000
—
rs2313982
0.000
—
rs17329669
0.000
—
rs7723605
0.000
—
ss46548856
0.000
GALNT3
rs16851009
0.000
PRDM2
rs2245218
0.000
PASD1
rs7878232
0.001
—
rs1509269
0.000
—
rs11737074
0.000
1.000
1.000
0.645
1.000
1.000
0.154
1.000
1.000
0.000
0.000
0.007
0.252
0.124
0.887
0.999
0.000
0.000
0.004
0.136
0.062
0.788
0.998
0.000
0.000
0.001
0.046
0.018
0.437
0.990
0.000
0.000
0.001
0.033
0.014
0.440
0.992
0.000
0.000
0.000
0.015
0.007
0.270
0.984
0.000
0.000
0.000
0.005
0.002
0.072
0.911
0.000
0.000
0.000
0.003
0.001
0.073
0.927
0.000
0.000
0.000
0.002
0.001
0.036
0.857
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.999
1.000
1.000
1.000
0.999
1.000
1.000
1.000
0.998
1.000
1.000
1.000
0.989
0.998
1.000
1.000
0.993
0.999
1.000
1.000
0.985
0.998
1.000
1.000
0.179
0.084
0.062
0.079
0.054
0.042
0.040
0.046
0.040
0.024
0.019
0.222
0.125
0.123
0.094
0.071
0.068
0.060
0.051
0.027
0.029
0.016
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.021
0.009
0.007
0.008
0.006
0.004
0.004
0.005
0.004
0.003
0.002
0.028
0.014
0.014
0.010
0.008
0.007
0.006
0.005
0.003
0.003
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
The value 0.000 corresponds to estimated credibility <0.001. Only the data of Table I are considered here, but some associations have additional evidence
from other studies as well (see text for details).
phenotype. Thus for an FTO variant, while there was large
between-study heterogeneity in the strength of the association
with type 2 diabetes, the associations had consistent effects
when obesity and body mass index were examined as
phenotypes [Frayling et al., 2007].
Searching for correlated phenotypes when heterogeneity is
detected in an original association is an interesting prospect. If
an association is inconsistent, one may search for a correlated
phenotype where the association is consistent across populations. However, one has to be cautious that for some disease
phenotypes, there exist a very large number of correlated
phenotypes. This introduces a new layer of multiplicity of analyses. If many such correlated phenotypes are probed, I would
argue that the new, seemingly consistent association needs to
be further replicated in new, independent datasets. Otherwise,
it may be spurious and may have risen simply out of ‘‘moving
the goalpost,’’ a well-known bias in the literature of selective
outcome analysis and reporting [Chan and Altman, 2005].
Heterogeneity should not be ignored in the calculations of
summary genetic effects and the respective credibility.
Unfortunately, it is common practice to combine data from
GWA and replication investigations with fixed effects
approaches that do not take any such heterogeneity into
account. The estimated credibility levels can change a lot if
heterogeneity is not factored in the calculations. Table III
shows such examples for five variants that were implicated to
be associated with type 2 diabetes, but on closer inspection they
have modest or larger heterogeneity (I2 point estimates >25%).
The Bayes factor is far more impressive when heterogeneity is
ignored, but assumption of homogeneous effect sizes misses the
opportunity to use the heterogeneity in an informative fashion
and to pursue correlated phenotypes, linked polymorphisms
and other reasons that may underlie the diversity of effects.
In the presence of unexplained heterogeneity that exceeds a
certain threshold, it has been shown that associations become
non-replicable (the power to replicate them cannot exceed a
TABLE III. Bayes factors Ignoring Heterogeneity for Associations With Estimated I2 > 25% (Assuming yA ¼ 0.140, i.e., OR ¼ 1.15)
Gene
—
FTO
PPARG
CDKAL1
SLC30A8
Polymorphism
I2 (95% CI)
Random effects,
P-value
log10
(Bayes factor)
Fixed effects OR
(95% CI)
Fixed effects,
P-value
log10
(Bayes factor)
rs9300039
rs8050136
rs1801282
rs10946398
rs13266634
75% (0–90)
77% (0–91)
47% (0–84)
46% (0–84)
32% (0–81)
0.015
0.015
0.0003
3.2 106
8.7 106
0.67
0.63
2.04
3.74
3.36
1.25 (1.15–1.37)
1.17 (1.12–1.22)
1.14 (1.08–1.20)
1.12 (1.08–1.16)
1.12 (1.07–1.16)
4.3 107
1.3 1012
1.7 106
4.1 1011
5.3 108
4.60
9.85
4.04
8.37
5.41
Based on data from three GWA studies, as synthesized by Zeggini et al. [2007] and Ioannidis et al. [2007a]. For PPARG, the association was already proposed
and replicated in several studies before the GWA studies. Moreover, additional data have accumulated on several of these polymorphisms. The Bayes factors
are calculated strictly based on the data from the three GWA investigations for illustrative purposes.
Calibration of Credibility of Agnostic GWAs
certain level), regardless of how large are the conducted studies
[Mooneshinghe et al., 2008].
IMPACT OF BIAS
Following a previous formulation [Ioannidis, 2005], I define
bias as any reason, beyond random chance, that may yield a
nominally statistically significant association when no such
association exists. An extensive number of checklists exist for
biases in observational epidemiology [Sanderson et al., 2007].
For genetic associations, main considerations [Ioannidis et al.,
2008] include biases in genotype and phenotype measurements, confounding, and selective reporting and have been
extensively discussed in the genetic epidemiology literature
already from the candidate gene era [Cordell and Clayton,
2005; Hattersley and McCarthy, 2005; Newton-Cheh and
Hirschhorn, 2005; Pan et al., 2005; Pompanon et al., 2005;
Calnan et al., 2006; Wang et al., 2006]. Here I will only mention
briefly a few issues that may be more relevant to GWA efforts.
Differential (according to phenotype) genotyping errors are a
special concern when case and control samples have been
collected, saved, or processed separately. Unless special care is
taken to make the whole process similar for case and control
samples, spurious systematic differences may arise. Differential (according to genotype) misclassification is less likely to
occur at the phenotype level. Confounding due to population
stratification remains a threat. Even though the available
evidence suggests that stratification is not a big concern for
carefully geographically/ethnically defined populations (e.g.,
the UK population in the Wellcome Trust Case Control
Consortium [2007]), strict control for stratification with
appropriate methods such as principal component analysis is
indicated [Price et al., 2006]. Even with negligible average
stratification, a few specific emerging associations per scan
may still reflect stratification. Finally, in the GWA setting,
variants of selective reporting bias may arise, for example,
selective presentation of only the most promising, most
statistically significant results for specific variants, analyses
and choice of genetic model or adjustment. The importance of
making all GWA databases publicly available with detailed,
non-selected information, cannot be overstressed. Efforts such
as GAIN [The GAIN Collaborative Research Group et al., 2007]
are critical, but public availability of datasets may be difficult
to make a ubiquitous mandate worldwide. For replication
studies, one may still face some of the same selective reporting
forces that existed in the candidate gene era. A collateral
damage from the otherwise very useful advent of large-scale
evidence with powerful consortia [Ioannidis et al., 2005;
Seminara et al., 2007] may ensue, if the results of the current
generation of studies with massive testing and large sample
sizes are considered definitive and smaller replication studies
find difficulty publishing results disagreeing with or being
inconclusive against findings from powerful consortia. Independent replication needs to be safeguarded in an era of global
research networking.
One has to scrutinize each proposed association that stems
from GWA or any other study very carefully for the presence of
any visible biases. When bias is known or is revealed, its effect
can be properly factored. However, bias often remains latent.
One may simulate the impact of different amounts of latent
bias on the credibility of the associations.
Let us consider that bias can cause an x proportion of
variants pass a given P-value threshold for a specific phenotype
association. If k variants have been tested, then the expected
number of variants that pass the threshold due to bias is xk. If
n variants have passed this threshold, then xk out of n are
expected to reflect bias. By default, we do not know which
these specific ‘‘biased’’ variants are. However, we can correct
the credibility of each of the n variants for bias on average,
969
multiplying by (n xk)/n. For variants with uncorrected
credibility estimates exceeding 50%, the corrected for bias
credibility will remain above 50% if xk < (C 0.5)n/C.
For example, assuming a C0 ¼ 0.00001, for periodic limb
movements in sleep we have already estimated the credibility
of the association with the rs3923809 polymorphism of BTBD9
to be 0.645, if the average genetic effects are expected to have
odds ratio of 1.05; and 1.000, if the average genetic effects are
expected to have odds ratio of 1.30–1.80. Here n ¼ 1 (only this
variant had such extreme statistical significance) and to retain
credibility of at least 50% for this association, we need xk < 0.22
or xk < 1, respectively. This means that, if the average genetic
effects are expected to have an odds ratio of 1.05, then bias
would not be able to decrease the credibility of this association
below 50%, unless it can produce associations with P ¼ 1014 at
least once in every five diseases screened with similar GWA
approaches with half a million markers. If the average genetic
effects are expected to have an odds ratio of 1.30–1.80, then
bias would not be able to decrease the credibility of this
association below 50%, unless it can produce associations with
P ¼ 1014 at least once in every single disease screened with a
similar GWA approach with half a million markers.
One may also calculate the corrected-for-bias credibility
allowing xk to vary according to the threshold of statistical
significance, assuming it is more difficult for bias alone to
produce lower levels of statistical significance. However, even
for extreme levels of statistical significance, bias cannot be
completely excluded.
P-VALUES VERSUS CREDIBILITY
P-values correlate with the Bayes factors and derived
credibility estimates, but correlation is not perfect. Big versus
small studies and different assumptions about the prior and
the extent of bias can affect this correlation. P-values only deal
with the possibility of random chance being responsible for a
false refutation of the null hypothesis under conditions of
perfection (no bias). They do not account for the possible
distribution of genetic effects and they offer no reassurance
against bias. For example, consistent replication across many
studies will decrease the P-value of the association in an
overarching meta-analysis of all data. The credibility will also
often increase with such consistent replication. However,
credibility may not decrease in parallel to increasing P-values,
if the emerging effects are considered atypical in magnitude.
For example, accumulation of very large sample sizes from
many studies may create highly statistically significant results
for associations with very small effects, for example, odds ratio
1.02. Depending on the prior, such effects may be considered
more compatible with the null rather than the alternative
hypothesis. Moreover, credibility will decrease, even with
decreasing P-values, if replication is consistent simply because
the same biases occur repeatedly across studies. The Bayesian
approach is thus not just a test of the data, but can also
incorporate explicitly our assumptions about the exact genetic
architecture (e.g., relative balance of rare variation, heterogeneity, epistasis, common variants with very small effects)
and the validity of our models. One can evaluate the credibility
of an association under different assumptions about the
genetic architecture, models and biases. A simple Excel
calculator of Bayes factor and credibility estimates is available
at www.dhe.med.uoi.gr/software.htm.
CONCLUDING COMMENTS
Interim guidelines, the Venice criteria, have been proposed
recently on assessing the cumulative evidence on genetic
associations [Ioannidis et al., 2008]. They focus on evaluation
of the amount of evidence, consistency of replication and
protection from bias. These three axes are used in a semi-
970
Ioannidis
quantitative approach and they fit to the description of
credibility ranking process described above. For more information on their operationalization, the reader is referred to the
guidelines publication [Ioannidis et al., 2008]. The Bayesian
approach that was described here is one of several variants
that can be adopted to calibrate credibility. It is simple to use
and hopefully can provide some useful insights.
One should also caution that while the illustrative examples
presented here span a very wide range of credibility estimates
and a very wide range of supporting epidemiological evidence,
perhaps calibration of credibility would make more sense to
apply only on associations that have a large amount of data,
consistent replication with lack of demonstrable betweenstudy heterogeneity in the datasets where the association has
been probed, and also the evidence seems to be probably
adequately protected from obvious sources of bias (AAA
categorization in the Venice criteria). For small studies, those
without consistent replication, and those with clear presence of
bias, the credibility is very low by default.
Finally, any effort for calibrating the credibility of associations derived from GWA investigations needs to be corroborated eventually by the accumulating evidence on the longerterm replication history of GWA-proposed associations. Additional lines of evidence, such as various sources of experimental information or other data pointing to biological plausibility
may be useful to incorporate in the credibility estimation down
the road. However, we still have a lot to learn about the interface of epidemiological and biological credibility, especially for
otherwise agnostic associations. The important question is: In
the evolution of evidence over time, do associations that are
graded as having ‘‘strong evidence’’ (AAA Venice categorization) and high credibility survive upon further replication
testing? This may not necessarily be a perfect gold standard,
but we have to accept that a meticulous examination of the
accumulated evidence-to-date is the best we can achieve.
In this regard, genetic epidemiology offers a situation where
continuation of replication ad infinitum with continuous
accumulation of evidence and clarification of associations is
not necessarily bad or unjustified, if the required resources can
be met.
REFERENCES
Barrett JC, Cardon LR. 2006. Evaluating coverage of genome-wide
association studies. Nat Genet 38:659–662.
Beckmann JS, Estivill X, Antonarakis SE. 2007. Copy number variants and
genetic traits: Closer to the resolution of phenotypic to genotypic
variability. Nat Rev Genet 8:639–646.
Calnan M, Smith GD, Sterne JA. 2006. The publication process itself was the
major cause of publication bias in genetic epidemiology. J Clin Epidemiol
59:1312–1318.
Chan AW, Altman DG. 2005. Identifying outcome reporting bias in
randomised trials on PubMed: Review of publications and survey of
authors. BMJ 330:753.
Clarke R, Xu P, Bennett D, Lewington S, Zondervan K, Parish S, Palmer A,
Clark S, Cardon L, Peto R, Lathrop M, Collins R. 2006. Lymphotoxinalpha gene and risk of myocardial infarction in 6,928 cases and 2,712
controls in the ISIS case-control study. PLoS Genet 2:e107.
Clarke GM, Carter KW, Palmer LJ, Morris AP, Cardon LR. 2007. Fine
mapping versus replication in whole-genome association studies. Am J
Hum Genet 81:995–1005.
Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ,
Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler
D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson Bostrom K,
Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C,
Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR,
Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R,
Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson
M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry
R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B,
Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB,
Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S, 2007.
Genome-wide association analysis identifies loci for type 2 diabetes and
triglyceride levels. Science 316:1331–1336.
Dina C, Meyre D, Samson C, Tichet J, Marre M, Jouret B, Charles MA,
Balkau B, Froguel P. 2007. Comment on ‘‘A common genetic variant is
associated with adult and childhood obesity’’. Science 315:187.
Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ,
Steinhart AH, Abraham C, Regueiro M, Griffiths A, Dassopoulos T,
Bitton A, Yang H, Targan S, Datta LW, Kistner EO, Schumm LP, Lee
AT, Gregersen PK, Barmada MM, Rotter JI, Nicolae DL, Cho JH. 2006. A
genome-wide association study identifies IL23R as an inflammatory
bowel disease gene. Science 314:1461–1463.
Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger
DG, Struewing JP, Morrison J, Field H, Luben R, Wareham N, Ahmed S,
Healey CS, Bowman R; SEARCH collaborators, Meyer KB, Haiman CA,
Kolonel LK, Henderson BE, Le Marchand L, Brennan P, Sangrajrang S,
Gaborieau V, Odefrey F, Shen CY, Wu PE, Wang HC, Eccles D, Evans
DG, Peto J, Fletcher O, Johnson N, Seal S, Stratton MR, Rahman N,
Chenevix-Trench G, Bojesen SE, Nordestgaard BG, Axelsson CK,
Garcia-Closas M, Brinton L, Chanock S, Lissowska J, Peplonska B,
Nevanlinna H, Fagerholm R, Eerola H, Kang D, Yoo KY, Noh DY, Ahn
SH, Hunter DJ, Hankinson SE, Cox DG, Hall P, Wedren S, Liu J, Low
YL, Bogdanova N, Schurmann P, Dork T, Tollenaar RA, Jacobi CE,
Devilee P, Klijn JG, Sigurdson AJ, Doody MM, Alexander BH, Zhang J,
Cox A, Brock IW, MacPherson G, Reed MW, Couch FJ, Goode EL, Olson
JE, Meijers-Heijboer H, van den Ouweland A, Uitterlinden A, Rivadeneira F, Milne RL, Ribas G, Gonzalez-Neira A, Benitez J, Hopper JL,
McCredie M, Southey M, Giles GG, Schroen C, Justenhoven C, Brauch
H, Hamann U, Ko YD, Spurdle AB, Beesley J, Chen X, kConFab; AOCS
Management Group, Mannermaa A, Kosma VM, Kataja V, Hartikainen
J, Day NE, Cox DR, Ponder BA, 2007. Genome-wide association study
identifies novel breast cancer susceptibility loci. Nature 447:1087–1093.
Elbaz A, Nelson LM, Payami H, Ioannidis JP, Fiske BK, Annesi G, Carmine
Belin A, Factor SA, Ferrarese C, Hadjigeorgiou GM, Higgins DS,
Kawakami H, Kruger R, Marder KS, Mayeux RP, Mellick GD, Nutt JG,
Ritz B, Samii A, Tanner CM, Van Broeckhoven C, Van Den Eeden SK,
Wirdefeldt K, Zabetian CP, Dehem M, Montimurro JS, Southwick A,
Myers RM, Trikalinos TA. 2006. Lack of replication of thirteen singlenucleotide polymorphisms implicated in Parkinson’s disease: A largescale international study. Lancet Neurol 5:917–923.
Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren
CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW,
Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim
S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett
AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F,
Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS,
Morris AD, Smith GD, Hattersley AT, McCarthy MI. 2007. A common
variant in the FTO gene is associated with body mass index and
predisposes to childhood and adult obesity. Science 316:889–894.
Hampe J, Franke A, Rosenstiel P, Till A, Teuber M, Huse K, Albrecht M,
Mayr G, De La Vega FM, Briggs J, Günther S, Prescott NJ, Onnie CM,
Häsler R, Sipos B, Fölsch UR, Lengauer T, Platzer M, Mathew CG,
Krawczak M, Schreiber S. 2007. A genome-wide association scan of
nonsynonymous SNPs identifies a susceptibility variant for Crohn
disease in ATG16 L1. Nat Genet 2007. 39:207–211.
Hattersley AT, McCarthy MI. 2005. What makes a good genetic association
study? Lancet 366:1315–1323.
Cordell HJ, Clayton DG. 2005. Genetic association studies. Lancet 366:
1121–1131.
Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T,
Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson
G, Gudbjartsson DF, Magnusson KP, Andersen K, Levey AI, Backman
VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H,
Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP,
Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR,
Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K. 2007. A
common variant on chromosome 9p21 affects the risk of myocardial
infarction. Science 316:1491–1493.
Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund
University, and Novartis Institutes of BioMedical Research, Saxena R,
Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T, Wichmann
HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A, Hebebrand J,
Coon KD, Myers AJ, Craig DW, Webster JA, Pearson JV, Lince DH, Zismann
VL, Beach TG, Leung D, Bryden L, Halperin RF, Marlowe L, Kaleem M,
Walker DG, Ravid R, Heward CB, Rogers J, Papassotiropoulos A,
Reiman EM, Hardy J, Stephan DA. 2007. A high-density whole-genome
association study reveals that APOE is the major susceptibility gene for
sporadic late-onset Alzheimer’s disease. J Clin Psychiatry 68:613–618.
Calibration of Credibility of Agnostic GWAs
971
Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H, Hirschhorn JN, Laird
NM, Lenburg ME, Lange C, Christman MF. 2006. A common genetic
variant is associated with adult and childhood obesity. Science 312:
279–283.
Mathew CG. 2008. Links to the pathogenesis of Crohn disease provided by
genome-wide association scans. Nat Rev Genet 9:9–14.
Higgins JP, Thompson SG. 2002. Quantifying heterogeneity in a metaanalysis. Stat Med 21:1539–1558.
McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR,
Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, Boerwinkle
E, Hobbs HH, Cohen JC. 2007. A common allele on chromosome 9
associated with coronary heart disease. Science 316:1488–1491.
Hirschhorn JN, Daly MJ. 2005. Genome-wide association studies for
common diseases and complex traits. Nat Rev Genet 6:95–108.
Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. 2002. A comprehensive review of genetic association studies. Genet Med 4:45–61.
Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J. 2006.
Assessing heterogeneity in meta-analysis: Q statistic or I2 index?
Psychol Methods 11:193–206.
McCarroll SA, Altshuler DM. 2007. Copy-number variation and association
studies of human disease. Nat Genet 39(7 Suppl):S37–S42.
Mooneshinghe R, Khoury MJ, Liu T, Ioannidis JP. 2008. Required sample
size and nonreplicability thresholds for heterogeneous genetic associations. Proc Natl Acad Sci USA 105:617–622.
Ioannidis JP. 2008. Effect of formal statistical significance on the credibility
of observational associations. Am J Epidemiol (in press).
NCI-NHGRI Working Group on Replication in Association Studies,
Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas
G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks
LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF Jr, Freimer NB,
Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J,
Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister
EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S,
Wijsman EM, Winn DM, Collins FS, 2007. Replicating genotypephenotype associations. Nature 447:655–660.
Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG. 2001.
Replication validity of genetic association studies. Nat Genet 29:306–309.
Newton-Cheh C, Hirschhorn JN. 2005. Genetic association studies of
complex traits: Design and analysis issues. Mutat Res 573:54–69.
Ioannidis JP, Trikalinos TA, Ntzani EE, Contopoulos-Ioannidis DG. 2003.
Genetic associations in large versus small studies: An empirical
assessment. Lancet 361:567–571.
Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, Sato H, Sato H,
Hori M, Nakamura Y, Tanaka T. 2002. Functional SNPs in the
lymphotoxin-alpha gene that are associated with susceptibility to
myocardial infarction. Nat Genet 32:650–654.
Ioannidis JP. 2005. Why most published research findings are false. PLoS
Med 2:e124.
Ioannidis JP. 2007. Non-replication and inconsistency in the genome-wide
association setting. Hum Hered 64:203–213.
Ioannidis JP, Bernstein J, Boffetta P, Danesh J, Dolan S, Hartge P, Hunter
D, Inskip P, Jarvelin MR, Little J, Maraganore DM, Bishop JA, O’Brien
TR, Petersen G, Riboli E, Seminara D, Taioli E, Uitterlinden AG, Vineis
P, Winn DM, Salanti G, Higgins JP, Khoury MJ. 2005. A network of
investigator networks in human genome epidemiology. Am J Epidemiol
162:302–304.
Ioannidis JP, Trikalinos TA, Khoury MJ. 2006. Implications of small effect
sizes of individual genetic variants on the design and interpretation of
genetic association studies of complex diseases. Am J Epidemiol
164:609–614.
Ioannidis JP, Patsopoulos NA, Evangelou E. 2007a. Heterogeneity in metaanalyses of genome-wide association investigations. PLoS ONE 2:e841.
Ioannidis JP, Patsopoulos NA, Evangelou E. 2007b. Uncertainty in
heterogeneity estimates in meta-analysis. BMJ 335:914–916.
Ioannidis JP, Boffetta P, Little J, O’brien TR, Uitterlinden AG, Vineis P,
Balding DJ, Chokkalingam A, Dolan SM, Flanders WD, Higgins JP,
McCarthy MI, McDermott DH, Page GP, Rebbeck TR, Seminara D,
Khoury MJ. 2008. Assessment of cumulative evidence on genetic
associations: Interim guidelines. Int J Epidemiol 37:120–132.
Khoury MJ, Little J, Gwinn M, Ioannidis JP. 2006. On the synthesis
and interpretation of consistent but weak gene-disease associations in
the era of genome-wide association studies. Int J Epidemiol 36:439–445.
Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK,
SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J,
Barnstable C, Hoh J. 2005. Complement factor H polymorphism in agerelated macular degeneration. Science 308:385–389.
Kurreeman FA, Padyukov L, Marques RB, Schrodi SJ, Seddighzadeh M,
Stoeken-Rijsbergen G, van der Helm-van Mil AH, Allaart CF, Verduyn
W, Houwing-Duistermaat J, Alfredsson L, Begovich AB, Klareskog L,
Huizinga TW, Toes RE. 2007. A candidate gene approach identifies the
TRAF1/C5 region as a risk factor for rheumatoid arthritis. PLoS Med
4:e278.
Loos RJ, Barroso I, O’rahilly S, Wareham NJ. 2007. Comment on ‘‘A common
genetic variant is associated with adult and childhood obesity’’. Science
315:187.
Maraganore DM, de Andrade M, Lesnick TG, Strain KJ, Farrer MJ, Rocca
WA, Pant PV, Frazer KA, Cox DR, Ballinger DG. 2005. High-resolution
whole-genome association study of Parkinson disease. Am J Hum Genet
77:685–693.
Marchini J, Donnelly P, Cardon LR. 2005. Genome-wide strategies for
detecting multiple loci that influence complex diseases. Nat Genet
37:413–417.
Matarin M, Brown WM, Scholz S, Simon-Sanchez J, Fung HC, Hernandez D,
Gibbs JR, De Vrieze FW, Crews C, Britton A, Langefeld CD, Brott TG,
Brown RD Jr, Worrall BB, Frankel M, Silliman S, Case LD, Singleton A,
Hardy JA, Rich SS, Meschia JF. 2007. A genome-wide genotyping study
in patients with ischaemic stroke: Initial analysis and data release.
Lancet Neurol 6:414–420.
Pan Z, Trikalinos TA, Kavvoura FK, Lau J, Ioannidis JP. 2005. Local
literature bias in genetic epidemiology: An empirical evaluation of the
Chinese literature. PLoS Med 2:e334.
Parkes M, Barrett JC, Prescott NJ, Tremelling M, Anderson CA, Fisher SA,
Roberts RG, Nimmo ER, Cummings FR, Soars D, Drummond H, Lees
CW, Khawaja SA, Bagnall R, Burke DA, Todhunter CE, Ahmad T, Onnie
CM, McArdle W, Strachan D, Bethel G, Bryan C, Lewis CM, Deloukas P,
Forbes A, Sanderson J, Jewell DP, Satsangi J, Mansfield JC; Wellcome
Trust Case Control Consortium, Cardon L, Mathew CG, 2007. Sequence
variants in the autophagy gene IRGM and multiple other replicating loci
contribute to Crohn’s disease susceptibility. Nat Genet 39:830–832.
Plenge RM, Seielstad M, Padyukov L, Lee AT, Remmers EF, Ding B, Liew A,
Khalili H, Chandrasekaran A, Davies LR, Li W, Tan AK, Bonnard C, Ong
RT, Thalamuthu A, Pettersson S, Liu C, Tian C, Chen WV, Carulli JP,
Beckman EM, Altshuler D, Alfredsson L, Criswell LA, Amos CI, Seldin
MF, Kastner DL, Klareskog L, Gregersen PK. 2007. TRAF1-C5 as a risk
locus for rheumatoid arthritis—A genomewide study. N Engl J Med 357:
1199–1209.
Pompanon F, Bonin A, Bellemain E, Taberlet P. 2005. Genotyping errors:
Causes, consequences and solutions. Nat Rev Genet 6:847–859.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D.
2006. Principal components analysis corrects for stratification in
genome-wide association studies. Nat Genet 38:904–909.
Raelson JV, Little RD, Ruether A, Fournier H, Paquin B, Van Eerdewegh P,
Bradley WE, Croteau P, Nguyen-Huu Q, Segal J, Debrus S, Allard R,
Rosenstiel P, Franke A, Jacobs G, Nikolaus S, Vidal JM, Szego P,
Laplante N, Clark HF, Paulussen RJ, Hooper JW, Keith TP, Belouchi A,
Schreiber S. 2007. Genome-wide association study for Crohn’s disease in
the Quebec Founder Population identifies multiple validated disease
loci. Proc Natl Acad Sci USA 104:14747–14752.
Rioux JD, Xavier RJ, Taylor KD, Silverberg MS, Goyette P, Huett A, Green
T, Kuballa P, Barmada MM, Datta LW, Shugart YY, Griffiths AM,
Targan SR, Ippoliti AF, Bernard EJ, Mei L, Nicolae DL, Regueiro M,
Schumm LP, Steinhart AH, Rotter JI, Duerr RH, Cho JH, Daly MJ,
Brant SR. 2007. Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease
pathogenesis. Nat Genet 39:596–604.
Rosskopf D, Bornhorst A, Rimmbach C, Schwahn C, Kayser A, Kruger A,
Tessmann G, Geissler I, Kroemer HK, Volzke H. 2007. Comment on ‘‘A
common genetic variant is associated with adult and childhood obesity’’.
Science 315:187.
Rzhetsky A, Wajngurt D, Park N, Zheng T. 2007. Probing genetic overlap
among complex human phenotypes. Proc Natl Acad Sci USA 104:11694–
11699.
Sanderson S, Tatt ID, Higgins JP. 2007. Tools for assessing quality and
susceptibility to bias in observational studies in epidemiology: A
systematic review and annotated bibliography. Int J Epidemiol 36:
666–676.
972
Ioannidis
Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR,
Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ,
Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow
NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW,
Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe
RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF,
Bergman RN, Tuomilehto J, Collins FS, Boehnke M. 2007. A genomewide association study of type 2 diabetes in Finns detects multiple
susceptibility variants. Science 316:1341–1345.
Seminara D, Khoury MJ, O’Brien TR, Manolio T, Gwinn ML, Little J,
Higgins JP, Bernstein JL, Boffetta P, Bondy M, Bray MS, Brenchley PE,
Buffler PA, Casas JP, Chokkalingam AP, Danesh J, Davey Smith G,
Dolan S, Duncan R, Gruis NA, Hashibe M, Hunter D, Jarvelin MR,
Malmer B, Maraganore DM, Newton-Bishop JA, Riboli E, Salanti G,
Taioli E, Timpson N, Uitterlinden AG, Vineis P, Wareham N, Winn DM,
Zimmern R, Ioannidis JP. Human Genome Epidemiology Network, the
Network of Investigator Networks. 2007. The emergence of networks
in human genome epidemiology: Challenges and opportunities.
Epidemiology 18:1–8.
Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent
D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ,
Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre
D, Polychronakos C, Froguel P. 2007. A genome-wide association study
identifies novel risk loci for type 2 diabetes. Nature 445:881–885.
Swaroop A, Branham KE, Chen W, Abecasis G. 2007. Genetic susceptibility
to age-related macular degeneration: A paradigm for dissecting complex
disease traits. Hum Mol Genet 16(Spec No. 2):R174–R182.
The GAIN Collaborative Research Group, Manolio TA, Rodriguez LL,
Brooks L, Abecasis G; The Collaborative Association Study of Psoriasis,
Ballinger D, Daly M, Donnelly P, Faraone SV; The International MultiCenter ADHD Genetics Project, Frazer K, Gabriel S, Gejman P; The
Molecular Genetics of Schizophrenia Collaboration, Guttmacher A,
Harris EL, Insel T, Kelsoe JR; The Bipolar Genome Study, Lander E,
McCowin N, Mailman MD, Nabel E, Ostell J, Pugh E, Sherry S, Sullivan
PF; The Major Depression Stage 1 Genomewide Association in
Population-Based Samples Study, Thompson JF, Warram J; The
Genetics of Kidneys in Diabetes (GoKinD) Study, Wholley D, Milos
PM, Collins FS, 2007. New models of collaboration in genome-wide
association studies: The Genetic Association Information Network. Nat
Genet 39:1045–1051.
Thomas DC. 2006. Are we ready for genome-wide association studies?
Cancer Epidemiol Biomarkers Prev 15:595–598.
Thomas DC, Haile RW, Duggan D. 2005. Recent developments in genomewide association scans: A workshop summary and review. Am J Hum
Genet 77:337–345.
Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. 2004.
Assessing the probability that a positive report is false: An approach for
molecular epidemiology studies. J Natl Cancer Inst 96:434–442.
Spiegelhalter DJ, Abrams KR, Myles JP. 2004. Bayesian approaches to
clinical trials and health-care evaluation. Wiley: Chichester.
Wakefield J. 2007. A Bayesian measure of the probability of false discovery
in genetic epidemiology studies. Am J Hum Genet 81:208–227.
Stefansson H, Rye DB, Hicks A, Petursson H, Ingason A, Thorgeirsson TE,
Palsson S, Sigmundsson T, Sigurdsson AP, Eiriksdottir I, Soebech E,
Bliwise D, Beck JM, Rosen A, Waddy S, Trotti LM, Iranzo A,
Thambisetty M, Hardarson GA, Kristjansson K, Gudmundsson LJ,
Thorsteinsdottir U, Kong A, Gulcher JR, Gudbjartsson D, Stefansson K.
2007. A genetic risk factor for periodic limb movements in sleep. N Engl J
Med 357:639–647.
Wang WY, Barratt BJ, Clayton DG, Todd JA. 2005. Genome-wide
association studies: Theoretical and practical concerns. Nat Rev Genet
6:109–118.
Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R,
Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson
V, Ghosh S, Baker A, Snorradottir S, Bjarnason H, Ng MC, Hansen T,
Bagger Y, Wilensky RL, Reilly MP, Adeyemo A, Chen Y, Zhou J,
Gudnason V, Chen G, Huang H, Lashley K, Doumatey A, So WY, Ma RC,
Andersen G, Borch-Johnsen K, Jorgensen T, van Vliet-Ostaptchouk JV,
Hofker MH, Wijmenga C, Christiansen C, Rader DJ, Rotimi C, Gurney
M, Chan JC, Pedersen O, Sigurdsson G, Gulcher JR, Thorsteinsdottir U,
Kong A, Stefansson K. 2007. A variant in CDKAL1 influences insulin
response and risk of type 2 diabetes. Nat Genet 39:770–775.
Wellcome Trust Case Control Consortium. 2007. Genome-wide association
study of 14,000 cases of seven common diseases and 3,000 shared
controls. Nature 447:661–678.
Wang Y, Localio R, Rebbeck TR. 2006. Evaluating bias due to population
stratification in epidemiologic studies of gene-gene or gene-environment
interactions. Cancer Epidemiol Biomarkers Prev 15:124–132.
Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H,
Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B,
Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR,
Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS;
Wellcome Trust Case Control Consortium (WTCCC), McCarthy MI,
Hattersley AT, 2007. Replication of genome-wide association signals in
UK samples reveals risk loci for type 2 diabetes. Science 316:1336–1341.
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