Improving the Estimation of Celiac Disease Sibling Risk
by Non-HLA Genes
Valentina Izzo1,2, Michele Pinelli3, Nadia Tinto4,5, Maria Valeria Esposito4,5, Arturo Cola4,5, Maria Pia
Sperandeo1,2, Francesca Tucci1,2, Sergio Cocozza3, Luigi Greco1,2*, Lucia Sacchetti4,5
1Department of Pediatrics, University of Naples Federico II, Naples, Italy, 2European Laboratory for Food Induced Disease, University of Naples ‘‘Federico II’’, Naples, Italy,
3Department of Cellular and Molecular Biology and Pathology ‘‘L. Califano’’, University of Naples ‘‘Federico II’’, Naples, Italy, 4CEINGE Advanced Biotechnology, S.c.a.r.l.,
Naples, Italy, 5Department of Biochemistry and Medical Biotechnology, University of Naples ‘‘Federico II’’, Naples, Italy
Celiac Disease (CD) is a polygenic trait, and HLA genes explain less than half of the genetic variation. Through large GWAs
more than 40 associated non-HLA genes were identified, but they give a small contribution to the heritability of the disease.
The aim of this study is to improve the estimate of the CD risk in siblings, by adding to HLA a small set of non-HLA genes.
One-hundred fifty-seven Italian families with a confirmed CD case and at least one other sib and both parents were
recruited. Among 249 sibs, 29 developed CD in a 6 year follow-up period. All individuals were typed for HLA and 10 SNPs in
non-HLA genes: CCR1/CCR3 (rs6441961), IL12A/SCHIP1 and IL12A (rs17810546 and rs9811792), TAGAP (rs1738074), RGS1
(rs2816316), LPP (rs1464510), OLIG3 (rs2327832), REL (rs842647), IL2/IL21 (rs6822844), SH2B3 (rs3184504). Three associated
SNPs (in LPP, REL, and RGS1 genes) were identified through the Transmission Disequilibrium Test and a Bayesian approach
was used to assign a score (BS) to each detected HLA+SNPs genotype combination. We then classified CD sibs as at low or
at high risk if their BS was respectively , or $ median BS value within each HLA risk group. A larger number (72%) of CD
sibs showed a BS $ the median value and had a more than two fold higher OR than CD sibs with a BS value , the median
(O.R=2.53, p=0.047). Our HLA+SNPs genotype classification, showed both a higher predictive negative value (95% vs 91%)
and diagnostic sensitivity (79% vs 45%) than the HLA only. In conclusion, the estimate of the CD risk by HLA+SNPs
approach, even if not applicable to prevention, could be a precious tool to improve the prediction of the disease in a cohort
of first degree relatives, particularly in the low HLA risk groups.
Citation: Izzo V, Pinelli M, Tinto N, Esposito MV, Cola A, et al. (2011) Improving the Estimation of Celiac Disease Sibling Risk by Non-HLA Genes. PLoS ONE 6(11):
Editor: Yun Li, University of North Carolina, United States of America
Received July 4, 2011; Accepted October 6, 2011; Published November 7, 2011
Copyright: ? 2011 Izzo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by the PREVENT-CD project: EU-FP6-2005-FOOD4B-contract no. 036383 and by CEINGE Regione Campania (DGRC 1901/2009)
Italy. The authors thank Associazione Italiana Celiachia (AIC) for a supporting grant. The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Celiac disease (CD) is a chronic small-intestinal enteropathy,
triggered by gluten proteins contained in wheat, barley and rye
. The evidence of a strong genetic component is suggested by a
remarkable familiar aggregation: the prevalence of CD is, in fact,
10 times higher in first degree relatives (,10%) than in the whole
population (1%) [1–3] a and very high concordance (.80%) is
found in monozygotic twins . CD prevalence increased
significantly in the last 20 years, so becoming a major public
health problem, for this reason, in the near future the CD families
are predicted to be a major source of new cases and consequently
the CD risk prediction in these cohorts may be important.
At present Histocompatibility Leucocyte Antigens (HLA)
explains ,35%  of the genetic variance associated to CD.
We previously graded the HLA risk genotype in 5 risk groups
(from G1 to G5) and we were able to calculate the risk in each
group with very wide confidence intervals (from 0.1% to $20%)
. In particular the higher risk groups (.10%) were those
belonging to G1 and G2 groups . However, since HLA alone
can explain about 1/3rd of the genetic susceptibility to the disease,
other variants should be implicated.
In the last four years, several Genome Wide Association studies
(GWAs) identified about 40 genomic regions harboring 64
candidate genes, which are involved in adaptive and innate
immunity in CD and also linked to other autoimmune diseases
[6–13]. Unfortunately altogether non-HLA genes account for only
4% of the genetic variance .
In the field of complex diseases, particularly in CD, great
attention is now paid to use the available genetic data to predict
the risk of disease in asymptomatic individuals and to support the
diagnosis in difficult cases. Although it was described that non-
HLA genes improve the ability to identify individuals at high risk,
the increased predict ability by only genetics seems still modest in
the general population . However the use of non-HLA genes
in the disease risk prediction in CD sibs has not yet been
The Bayesian approach was shown to be useful in the managing
the GWAS results, for example, in predicting the susceptibility to
breast cancer . Applying the same approach in a CD family
cohort, we wish to improve the estimation of CD risk among
siblings of Coeliacs over the available risk HLA-based and thus
provide a better tool to evaluate the health status or to predict the
disease in these at risk individuals.
PLoS ONE | www.plosone.org1 November 2011 | Volume 6 | Issue 11 | e26920
SNPs evaluation and computation of the Bayesian Score
All individuals were typed for 10 CD previously associated SNPs
 and the TDT test was performed on results obtained from
157 trios of the training set (Fig. 1). Three out of ten investigated
SNPs (rs1464510 in LPP, rs842647 in REL and rs2816316 in
RGS1 genes) were significantly associated with CD (Table 1). In
particular: rs1464510 in LPP gene showed a strong association
(p,0.001) according to an additive model, whereas, both
rs2816316 in RGS1 and rs842647 in REL genes were also
significantly associated (respectively p=0.025 and p=0.034), by a
recessive model. For supporting information about allelic and
genotypic frequencies observed in the sample see Table S1 and S2.
In order to evaluate the occurrence of HLA-SNP interaction,
we stratified the training set (Fig. 1) according to the HLA risk
group of the proband. No statistically significant interaction was
found between HLA and non-HLA genes (data not shown).
To compute the Bayesian Score (BS) we compared the
frequency of each HLA+SNPs genotype combination detected in
probands and in controls (Training set, Fig. 1). Through this
approach we obtained a BS for each HLA+SNPs genotype
combination (data not shown).
Validation of the BS and testing of a classification model
The validation set (Fig. 1) was composed by the sibs of the
probands, both affected (n=29) and unaffected (n=220). In these
subjects we evaluated if HLA+SNPs genotyping could improve the
identification of CD risk in sibs better than HLA only.
We assigned to each sib, both affected and unaffected, the BS
value corresponding to his HLA+SNPs genotype combination as
previously calculated in the training set (Table 2). We observed an
increase of average BS values from HLA group 5 to HLA group 1
and, within all 5 HLA groups we found an increase of the BS
corresponding to an increase of ‘‘A’’ alleles in the haplotype
combination. In order to identify sibs at high risk to develop CD,
we distributed affected and unaffected sibs on the basis of their BS
(under or above median BS) within each HLA group from G1 to
G5 (Fig. 2). Considering the distribution of all the sibs, it is evident
that above the median BS within each HLA group, there was
always a larger number of affected sibs than under the median:
72% (21/29) versus 28% (8/29). Interestingly, 2/29 affected sibs
that being at lower HLA risk (HLA group 4 and 5) could be
misclassified, were correctly classified by their BS above the
median. We calculated the Odds Ratio (OR) of CD classification
based on the BS, above or under the median, within each HLA
group. CD sibs with a BS value above the median, had more than
two fold higher risk (OR) compared to CD sibs with a BS value
under the median (2.53, 95% C.I.: 1.68–3.39; p=0.047).
In our previous work, we estimated the HLA related risk to
develop CD, defining a risk range from 0.01 to 0.21 . To refine
the previous HLA based estimation of risk by this new approach
we set the HLA related mean risk as the a priori risk of the Bayesian
Model. Having set the HLA risk at the median level of the
Bayesian Score, we produced a picture of the variation of risk
given by the non-HLA genes for each HLA class (Fig. 3). It is
remarkable that the addition of the 3 SNPs does modify the HLA
only risk through the 5 HLA risk classes, also in subjects with no
DQ2 neither DQ8 haplotype, where the increase in risk is
significantly larger than that observed in HLA risk classes.
The addition of only 3 SNPs to the HLA significantly improved
the prediction of CD risk in sibs, identifying, within a specific HLA
group, those individuals which are more likely to become celiacs.
In fact, considering HLA 1 and 2 as the highest risk groups , we
HLA+SNPs genotype combination obtaining both an higher
predictive negative value (NPV) and an higher diagnostic
sensitivity (DS) than HLA only, respectively 95% vs 91% (NPV)
and 79% vs 45% (DS) (Table 3). Although the discovery of 39
polymorphism associated to CD improved the estimation of the
of our proposed
Figure 1. Study design of CD families. The family set was splitted
in a Training set, that is 157 trios composed by the 157 probands and
their unaffected parents and in a Validation set, that is 249 sibs of the
Table 1. Genotypic Transmission Disequilibrium Test (TDT)
SNP GeneModel Risk Allele OR (95% CI)p value
rs1464510 LPP AdditiveA 2.36 (1.64–3.41) ,0.001
rs2816316RGS1 RecessiveA 1.75 (1.07–2.86) 0.025
rs842647REL RecessiveA 1.66 (1.04–2.65) 0.034
rs2327832OLIG3 AdditiveG 1.35 (0.90–2.03) 0.150
rs6441961CCR1/CCR3AdditiveA 1.24 (0.89–1.72) 0.189
rs6822844IL2/IL21 AdditiveC 1.43 (0.82–2.49) 0.210
rs1738074TAGAP Dominant A 1.31 (0.79–2.16) 0.293
rs3184504 SH2B3 AdditiveA 1.19 (0.86–1.63) 0.294
rs17810546 IL12A AdditiveG 1.10 (0.80–1.51) 0.572
rs9811792IL12A/SCHIP1Dominant G 1.10 (0.59–2.05) 0.753
For supporting information about allelic frequencies observed in Trios
(Probands and unaffected parents) and in sibs (affected and unaffected) see
Role of Non-HLA Genes in Sibling CD Risk
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heritability of only 4–5% , in this Bayesian model the addition
of only three SNPs of associated genes improves the sensitivity of
risk prediction of 34% compared to the HLA only model.
The computation of the AUC of the ROC curve output a C
statistic equal to 0.70 for HLA and 0.73 for HLA+SNPs
classifications, showing that the inclusion of SNPs moderately
improved the prediction ability.
In our previous work, we considered in CD families the risk to
develop the disease according to a specific HLA haplotypes,
obtaining a risk range from 0.01 to $0.20 . In the present study
we evaluated the role of 3 non-HLA genetic markers to influence
the CD risk in first relatives of CD affected children.
We collected data on families with at least one CD-affected
among offspring. This family set helped to evaluate the association
between SNPs and CD (TDT on parents-offspring trios) and to
estimate the risk of CD in the other sibs. The TDT design provides
robustness to population stratification and mitigation of the
possible confounding effect of environmental factors, because all
family members share the same environment .
Ten SNPs, selected from those previously found to be associated
with CD by GWAS , were successfully genotyped. In our
Figure 2. Distribution of CD sibs based on their BS. Affected (n=29) and unaffected (n=220) CD sibs were classified on the basis of their BS
value , or $ median BS within each HLA group. Horizontal lines correspond to the median BS of all sibs in each HLA risk-group (HLA Group 1=0.90,
HLA Group 2=0.86, HLA Group 3=0.62, HLA Group 4=0.60, HLA Group 5=0.13).
Table 2. Bayesian Score (BS) assigned to each HLA-SNPs genotype combination.
Associated SNPs BS
(rs2816316) HLA Group 1HLA Group 2 HLA Group 3HLA Group 4HLA Group 5
CC AG|GGAC|CC0.66 0.63 0.320.30 0.04
CCAG|GGAA 0.740.73 0.420.39 0.06
CC AAAC|CC 0.710.690.38 0.35 0.05
CCAA AA 0.790.770.480.46 0.08
AC AG|GG AC|CC0.810.80 0.52 0.490.09
AC AG|GG AA 0.87 0.860.62 0.60 0.13
AC AAAC|CC 0.850.840.58 0.550.11
ACAA AA 0.900.89 0.680.66 0.16
AAAG|GG AC|CC0.91 0.90* 0.69 0.18
AA AG|GG AA0.94 0.930.79 0.77 0.25
AAAA AC|CC0.930.920.76 0.740.22
AAAA AA 0.950.950.830.81 0.30
*The combination LPP*AA - REL*AG|GG - RGS1*AC|CC for HLA group 3 was not found among our sibs’ cohort.
Role of Non-HLA Genes in Sibling CD Risk
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population three SNPs resulted significantly associated with CD
(those in LPP, RGS1 and REL genes) and the other seven
investigated SNPs, even if not statistically associated with CD,
showed always an higher frequency of the previously reported risk
alleles  in affected subjects than in controls.
The three genes selected appear to be appealing for the
pathogenesis of CD. LPP (OR=2.36; p,0.001) was reported to
be highly expressed in small intestinal mucosa and may have a
structural role at sites of cell adhesion in maintaining cell shape
and motility . RGS1 (OR=1.75; p=0.025) belongs to a family
of RGS genes. It attenuates the signaling activity of G-proteins,
blocking the homing of Intra Epithelial Lymphocytes (IELs), and it
is specifically expressed both in human small intestinal mucosa and
in murine IELs, key players in the development of human CD
villous atrophy [7,17]. REL (OR=1.66; p=0.034) is a subunit of
NF-kB complex, implicated in T cell differentiation  and it
appears to be a key molecule regulating inflammation and the
switch from tolerance to autoimmunity . It is interesting to
note that our data confirm previous pathogenetic implications
reported in literature of these SNPs with CD as well as with other
autoimmune diseases .
By the Bayesian approach we calculated a ranking score (BS)
among the sibs. However, it should be considered that BS is not a
plain disease risk, rather a method to rank different genotypes
according to their contribution to make an individual susceptible
to CD. For instance, some of our BS are very near to 1,
nevertheless none of the considered genotypes could give a 100%
risk to develop the disease. In other terms, we considered the BS as
a ranking measure, only stating that a given genotype could assign
a higher risk than another genotype but does not allow a
quantitative measure of the risk difference (2-fold, 3-fold, etc).
However, even if the addition of only 3 SNPs to HLA could be
considered at ‘‘minor effect’’ , we demonstrated that they
could significantly improve the prediction of CD risk in sibs, in
terms of diagnostic sensitivity and negative predictive value. So, in
a cohort of CD families, our data confirm that non-HLA SNPs
evaluation is an usefull diagnostic tool in CD risk evaluation as a
previous study showed in CD unrelated subjects .
CD, on the basis of the actual knowledge, cannot be exactly
predicted by genetic testing, but a reliable probabilistic method
might be associated to careful surveillance of infants carrying the
higher risk. This will help to significantly reduce the heavy load of
anxiety and pain associated with the appearance of symptoms of
CD, by anticipating, with simple serological tests, the clinical
appearance of the disease.
To improve the possibility to identify high risk patients in CD
families we propose in alternative to the classical HLA
classification (Fig. 4, panel A) a slight improved flow-chart
(Fig. 4, panel B): 1) HLA genotyping: subjects belonging to the
HLA risk groups 1 and 2 will be classified as at high CD risk; 2)
subjects belonging to the HLA risk groups 3 and 4, will be further
investigated for our SNPs combination (LPP, REL, RGS1) in
order to calculate their BS (Fig. 4, panel B). Among these latter
subjects those with a BS $ the median value will be classified at
high risk; 3) subjects belonging to the HLA risk group 5 will be
considered at low CD risk. All CD familials belonging to the above
high risk groups (HLA group 1–2 and HLA group 3–4 with BS $
median) will be undergo a strict surveillance.
One of the limitation of our cohort family study could be the
sample size, which may have not allowed to explore genes at
smaller effect, so explaining the lack of association between SNPs
in TAGAP, IL2/IL21, OLIG3, CCR, SH2B3, IL12A and
IL12A/SCHIP1 genes with CD although the trend observed in
previous studies in unrelated CD patients was confirmed . In
the main time the homogeneity of the genetic and environmental
domains in the tested families allows to explore risk factors within a
controlled cohort. A second limit of the study is the relatively short
(6 years) follow up of the sibship, which could cause an
underestimation of the disease development at later ages. Our
aim is to go on with the monitoring of these families in the next
In conclusion, the estimate of the CD risk by HLA+SNPs
approach, even if not applicable to prevention, could be a precious
tool improving CD diagnosis respect to the only HLA (NPV: 95%
vs 91%, and DS: 79% vs 45%), in the cohort of first degree
relatives. In fact in clinical practice the absence of HLA risk groups
1 or 2, allows to exclude the disease with high probability, while
testing the three SNPs in HLA groups 3 or 4 could represent a
further tool to identify less frequent CD cases. So, an infant with
high HLA+SNPs score even if belonging to HLA low risk groups,
shall undergo a simple surveillance system to allow proper
diagnosis and treatment before the full blow disease appears.
Materials and Methods
The written informed consent was obtained from all partici-
pants and from both parents for children. The research was
approved by the Ethics Committee of the School of Medicine,
University of Naples ‘‘Federico II’’ and was according to principles
of the Helsinki II declaration.
Figure 3. Refining the CD risk estimate. The picture shows the
modification of the a priori HLA related risk by the number of the at risk
‘‘A’’ alleles of the LPP, REL and RGS1 SNPs. From top to bottom lines
correspond to HLA group 1 to 5.
iagnostic characteristics of HLA and HLA-SNPs
HLA risk groups 1–2 HLA-SNPs genotype combination
Sensitivity 0.45 (0.27–0.64) 0.79 (0.73–0.84)
Specificity 0.71 (0.65–0.77)0.54 (0.48–0.60)
NPV0.91 (0.87–0.96) 0.95 (0.92–0.98)
PPV 0.17 (0.12–0.22)0.19 (0.14–0.24)
Role of Non-HLA Genes in Sibling CD Risk
PLoS ONE | www.plosone.org4 November 2011 | Volume 6 | Issue 11 | e26920
A cohort of CD families was recruited as previously described
. Families included a symptomatic CD patient (hereafter
referred as the proband), both parents and at least one sib (for a
total of 183 probands, 366 parents and 249 sibs); all probands, as
well as the new cases, were diagnosed according to the European
Society of Paediatrics Gastroenterology and Nutrition (ESP-
GHAN) criteria . Among the 249 sibs, 29 resulted to be
affected over a 6 years follow up program .
All individuals were grouped into five decreasing risk classes
according to their HLA genotype: very high (.20% with two
copies of DQ2.5, or DQ2.5/DQ2.2 Group 1), high (15–20% with
DQ2.2/DQA105, Group 2), intermediate (10–15% with one copy
of the DQ2.5 heterodimer, Group 3), moderate (1–10% with a
double copy of DQ8 or DQ2.2/DQ8, or double copy of DQ2.2,
Group 4) or negligible (,1%, with other genotype, Group 5)
(Table S3) .
Non-HLA Single Nucleotide Polymorphisms (SNP) typing
The 798 patients were genotyped for 10 non-HLA SNPs
associated with CD: rs6441961 on 3p21 (Chemokine C-C motif receptor
1 and 3 – CCR1/CCR3), rs17810546 and rs9811792 on 3q25-26
(SCHIP1 – Schwannomin interacting protein 1 – and IL12A – Interleukin
12A), rs1738074 on 6q25 (T cell activation GTPase activating protein –
TAGAP), rs2816316 on 1q31 (Regulator of G-protein signaling 1 –
RGS1), rs1464510 on 3q28 (Lipoma Preferred Partner – LPP),
rs2327832 on 6q23.3 (Oligodendrocyte transcription factor 3 – OLIG3),
rs842647 on 2p16.1 (Reticuloendotheliosis viral oncogene homolog – REL)
, rs6822844 on 4q27 (Interleukin 2 and 21 – IL2/IL21), rs3184504
on 12q24 (SH2B adaptor protein 3 – SH2B3). Genotyping reactions
were performed using TaqManHSNP Genotyping Assays on a
7900HT Fast Real-Time PCR System (Applied Biosystems, Foster
City, CA, USA); the final volume was 5 mL, containing master
mix, TaqMan assays and about 60 ng of genomic DNA template.
All 384 well plates were filled using BiomekH FX (Beckman
Coulter, Indianapolis, IN, USA). Allelic Discrimination results
were analyzed through the SDS software ver. 2.3.
Analysis strategy and statistics
In order to develop the model we splitted the sample into a
training and a validation set (Fig. 1). In the training set, composed by
probands and their unaffected parents, called trios, we evaluated
which SNP was associated to the disease, independently from the
HLA haplotype. Twenty-six CD families were excluded because
they did not meet the requirements for the analysis (e.g. there was
an affected parent or patients had an incomplete genotyping), so
the training set was composed by 157 trios (Fig. 1). As control
haplotypes we considered the haplotypes carried by parents and
not transmitted to the affected probands, as they could be
representative of the haplotypes in the general population, from
which cases are originated. The frequencies of HLA and non HLA
haplotypes in controls were estimated by the AFBAC (Affected
Family Based Controls) method implemented in the MASC
software tool . To evaluate the association of the SNPs with
the disease, we performed a Transmission Disequilibrium Test (TDT)
 by using the trio package ver. 1.1.15 for R statistical
computing software ver. 2.11.2. This method is based on
multivariate logistic regression. We considered: dominant, the
heterozygote genotype when it had the same risk effect of the high
risk homozygote genotype; recessive, the heterozygote genotype
when it had the same risk effect of the low risk homozygote
genotype; additive, the heterozygote genotype when it had a risk
effect intermediate between the two homozygote genotypes.
Firstly, we evaluated monovariate association between each SNP
and the disease. Secondly, among those resulted to be significantly
associated with CD, we evaluated for SNP-SNP interactions by
logistic regression model. Finally, the interaction between
significant SNPs and HLA groups was verified. In details, we
repeated association statistics between CD risk and SNPs on
different strata of HLA groups and, thus, computed the interaction
To evaluate the join effect of HLA and SNPs to influence the
risk to develop CD in sibs, we implied a Bayesian approach,
focusing only on those SNPs resulted significantly associated with
CD at TDT analysis. Usually Bayes’ revision of probabilities
Figure 4. Classification flow-chart. In panel A the classical HLA-based classification. In panel B the proposed BS-based classification considering
the genotypes of HLA plus LPP, RGS1 and REL SNPs.
Role of Non-HLA Genes in Sibling CD Risk
PLoS ONE | www.plosone.org5 November 2011 | Volume 6 | Issue 11 | e26920
allows the computation of an individual probability of an event Download full-text
given the a priori data from the general population where the
patient comes from. We arbitrarily selected an a priori probability
of 0.5, because in our situation we could difficulty apply the Bayes’
revision of probabilities and obtain an a posteriori risk of CD in
sibs. Indeed the estimated 10% of CD risk in first grade relatives
also account for the role of the genetics, therefore it cannot be used
as an a priori probability to calculate the a posteriori probability,
after considering the risk conferred by genetic factors (SNPs).
Firstly we considered the different frequencies of HLA
genotypes in probands and controls and secondly the non-HLA
SNPs genotypes frequencies. According to the Bayesian approach,
in each run we used the score obtained in the previous step as the
new a priori value for the following step (see Text S1). By this
approach, it was possible to assign to each combination of HLA
plus LPP, RGS1 and REL SNPs genotypes a BS. Secondly, we
assigned to each sib (validation set) a BS in dependence of the
specific HLA+SNPs genotype combination found. We arbitrarily
established the median BS as discrimination threshold between
low and high risk sibs and evaluated how affected and unaffected
sibs were distributed. We performed C statistic by using the R
‘‘ROCR’’ statistic package to strength the interpretation of the
In order to produce a more realistic Bayesian Risk Score, we
then considered the mean HLA related risk  as the a priori risk
(instead of 0.5) to be fit in the first step of the Bayesian equation.
The corresponding scores (higher or lower than the median) were
standardized by the starting HLA related risk.
in Validation set (sibs).
Allelic frequencies observed in Training set (Trios) and
frequencies observed in Training set (Trios) and in Validation set
Non-HLA genotypic frequencies and HLA groups
Classification according to the HLA genotype .
Conceived and designed the experiments: LG LS. Performed the
experiments: VI NT MVE AC MPS FT. Analyzed the data: VI MP SC
LG. Contributed reagents/materials/analysis tools: LG LS SC. Wrote the
paper: LG LS VI MP NT.
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Role of Non-HLA Genes in Sibling CD Risk
PLoS ONE | www.plosone.org6 November 2011 | Volume 6 | Issue 11 | e26920