Multi-Analytic Approach Elucidates Significant Role of
Hormonal and Hepatocanalicular Transporter Genetic
Variants in Gallstone Disease in North Indian Population
Anshika Srivastava1, Avshesh Mishra1, Rajan Singh1, Rajani Rai1, Neena Srivastava2, Balraj Mittal1*
1 Department of Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGIMS) Lucknow, Uttar Pradesh, India, 2 Department of Physiology, King
George’s Medical University (KGMU) Lucknow, Uttar Pradesh, India
Abstract
Objective: Cholesterol gallstone disease (CGD) is a multifactorial and multistep disease. Apart from female gender and
increasing age being the documented non-modifiable risk factor for gallstones the pathobiological mechanisms underlying
the phenotypic expression of CGD appear to be rather complex, and one or more variations in genes could play critical roles
in the diverse pathways further progressing to cholesterol crystal formation. In the present study we performed genotyping
score, Multifactor dimensionality reduction (MDR) and Classification and Regression Tree analysis (CART) to identify
combinations of alleles among the hormonal, hepatocanalicular transporter and adipogenesis differentiation pathway
genes in modifying the risk for CGD.
Design: The present case-control study recruited total of 450 subjects, including 230 CGD patients and 220 controls. We
analyzed common ESR1, ESR2, PGR, ADRB3, ADRA2A, ABCG8, SLCO1B1, PPARc2, and SREBP2 gene polymorphisms to find out
combinations of genetic variants contributing to CGD risk, using multi-analytical approaches (G-score, MDR, and CART).
Results: Single locus analysis by logistic regression showed association of ESR1 IVS1-397C.T (rs2234693), IVS1-351A.G
(rs9340799) PGR ins/del (rs1042838) ADRB3-190 T.C (rs4994) ABCG8 D19H (rs11887534), SLCO1B1 Exon4 C.A (rs11045819)
and SREBP2 1784G.C (rs2228314) with CGD risk. However, the MDR and CART analysis revealed ESR1 IVS1-397C.T
(rs2234693) ADRB3-190 T.C (rs4994) and ABCG8 D19H (rs11887534) polymorphisms as the best polymorphic signature for
discriminating between cases and controls. The overall odds ratio for the applied multi-analytical approaches ranged from
4.33 to 10.05 showing an incremental risk for cholesterol crystal formation. In conclusion, our muti-analytical approach
suggests that, ESR1, ADRB3, in addition to ABCG8 genetic variants confer significant risk for cholesterol gallstone disease.
Citation: Srivastava A, Mishra A, Singh R, Rai R, Srivastava N, et al. (2013) Multi-Analytic Approach Elucidates Significant Role of Hormonal and Hepatocanalicular
Transporter Genetic Variants in Gallstone Disease in North Indian Population. PLoS ONE 8(4): e59173. doi:10.1371/journal.pone.0059173
Editor: Matias A. Avila, University of Navarra School of Medicine and Center for Applied Medical Research (CIMA), Spain
Received October 26, 2012; Accepted February 12, 2013; Published April 8, 2013
Copyright: ß 2013 Srivastava 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: The study was supported by research and fellowship grants from ICMR, DBT and DST. 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: bml_pgi@yahoo.com
genome-wide association studies (GWAS), in case-control cohorts,
and in family studies [7,8].
In human physiology, the gender disparity commences with
puberty and continues through the childbearing years [9,10,11,12]
which suggests that female sex hormones, could be an important
risk factor for the formation of cholesterol gallstones [13]. The
actions of these hormones such as estrogen, progesterone and
catecholamines are executed through one or more of their
respective receptors as estrogen receptors (ESRs), progesterone
receptor (PGR) and adrenergic receptor (ADR) Allelic variants of
ESR, PGR and ADR genes have been shown to be associated with
susceptibility or progression with various disorders such as
myocardial infarction [14,15], cholesterol gallstones and biliary
tract diseases [16].
Another area of interest is hepatocanalicular transporters
namely ATP-binding cassette transporters (ABC transporters)
and organic anion transporters both encoded by ABC and
SLCO1B1 genes respectively. Mutations in genes encoding these
Introduction
Cholesterol Gallstone disease (CGD) corresponds to one of the
most recurrent and costly gastroenterological disorder. It is worldwide health problem representing 10% to 15% of the adult
population in industrialised countries [1,2] whereas a prevalence
of 6% have been reported from North India [3]. The female
gender and increasing age are the documented non-modifiable risk
factors for gallstones [4], the pathobiological mechanisms underlying the phenotypic expression of CGD appear to be rather
complex, and one or more defects could occur in genes that play
critical roles in the diverse pathways leading to cholesterol
gallstone formation. The genetic determinants of gallstone
formation have only recently been dissected in humans [5]
Compelling evidence for familial clustering and an increased
concordance of the trait in monozygotic twins as compared to
dizygotic twins [6] further confirms the heritability of gallstones.
Thus ‘Gallstone genes’ are continuously being corroborated in
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Multi-Analytic Approach in Gallstone Disease
transporters have been implicated in cholesterol gallstones
formation owing to their ability to influence bile composition
and causing retention of substances normally secreted in bile.
Peroxisome proliferator-activated receptor c 2 (PPARc2)
orchestrate the adipocyte differentiation process whereas sterol
regulatory element binding protein 2 (SREBP-2) is involved in
adipocyte differentiation followed by cholesterol homeostasis.
Series of previous observations have suggested that regulatory
interactions between the SREBPs and PPARc2 can coordinate
cholesterol and fatty acid metabolism. Therefore, sequence
variation in these genes may further disrupt the cholesterol
homeostasis which in turn may nurture the development of CGD.
Previously, we have studied the role of some individual genetic
variants with CGD susceptibility in a North Indian population
[17,18,19]. Individual SNPs have little predictive value because of
their modest effect on risk, but combinations of gene variants may
improve the predictive ability and could be used to model
susceptibility to CGD. Therefore, the current study aimed to
search for gene-gene interactions in the selected pathways
(hormonal, hepatocanalicular and adipogenesis differentiation) as
a key contributory factor in the disease outcome.
The analysis of such interactions in case-control studies is
weighed down by one of the major problems, namely, the curse of
dimensionality. Recently, Multifactor-Dimensionality Reduction
(MDR) approach, tree-based techniques: classification and regression trees (CART), and genotyping score [20] have been used to
detect interactions in large-scale association studies [21]. The
strength of these methodologies is their ability to identify
association in cases of small sample sizes and low penetrance of
candidate single nucleotide polymorphisms (SNPs). Therefore, we
have extended our previous work on CGD susceptibility by jointly
investigating 13 SNP genotypes in 9 genes belonging to hormonal
pathway [ESR1 IVS1-397C.T (rs2234693), IVS1-351A.G
(rs9340799), Ex4-122C.G (rs1801132), ESR2 -789 A.C
(rs1271572), 1082 G.A (rs1256049) PGR ins/del (rs1042838)
ADRB3-190 T.C (rs4994) and ADRA2A (rs1800544)], hepatocanalicular transporter pathway [ABCG8 D19H (rs11887534),
SLCO1B1 Exon4 C.A (rs11045819), Ex6+40T.C (rs4149056)]
and adipogenesis differentiation pathway [PPAR c2 C.G
(rs1801282) SREBP2 1784G.C (rs2228314)], avoiding the problem of dimensionality and multiple comparisons.
Allelic Distribution of Studied Polymorphisms in Controls
The genotypic and allelic distribution of ESR1 IVS1-397C.T,
IVS1-351A.G, Ex4-122C.G, ESR2 -789 A.C, 1082 G.A
PGR ins/del ADRB3-190 T.C and ADRA2A -1291 C.G in
hormonal pathway, ABCG8 D19H, SLCO1B1 Exon4 C.A,
Ex6+40T.C in hepatocanalicular transporter pathway and PPAR
c2 C.G SREBP2 1784G.C in adipogenesis differentiation
pathway are shown in Table 2, 3 and 4. The details of the
selected genes have been shown in supplementary table 1 (Table
S1). The observed genotype frequencies of all the studied
polymorphisms in controls were in accordance with HardyWeinberg equilibrium (p,0.05).
Overall Frequency Distribution of Selected Hormonal,
Hepatocanalicular Transporter and Adipogenesis
Differentiation Gene Polymorphisms in GSD Patients and
Healthy Subjects
Association of hormonal pathway gene polymorphisms
with gallstone patients. Table 2 shows the risk of gallstones in
relation to each of the SNPs of ESR1, ESR2, PGR, and ADR in
hormonal pathway. On comparing the genotype frequency
distribution of our study groups i.e. gallstone patients with that
of healthy subjects (HS), the homozygous variant genotypes of
ESR1 IVS1-397C.T, IVS1-351A.G and ADRB3 -190 T.C
polymorphism showed statistically significant increased risk for
developing gallstone (p = ,0.001; [OR], 2.9: p = 0.002; [OR], 2.6:
p = ,0.001; [OR], 1.9.). On the contrary, no significant differences were observed in the distribution of Ex4-122C.G,
(ptrend = 0.605; MCS = 0.599), ESR2 -789 A.C (ptrend = 0.630;
MCS = 0.578), Ex6 1082 G.A (ptrend = 0.546; MCS = 0.435),
ADRA2A -1291 C.G (ptrend = 0.070; MCS = 0.065) polymorphisms in selected groups, both at genotypic and allelic levels. The
variant-containing genotypes (DI+II) of PGR ins/del showed low
risk in gallstone patients which was also significant (p = 0.004;
[OR], 0.4; p = 0.009; [OR], 0.4 Table 2) when compared with
homozygous wild-type DD genotype. Furthermore, on subdividing
the study groups on the basis of gender we observed that ESR1
IVS1-397C.T and ADRB3 -190 T.C conferred increased risk
for gallstones in female gender (Table S5).
Association of hepatocanalicular transporter pathway
gene polymorphisms with gallstone patients. Table 3 shows
the risk of gallstones in relation to each of the SNPs of ABCG8 and
SLCO1B1 in hepatocanalicular transporter pathway. We found
that in single locus analysis, the variant genotypes (GC+CC) of
ABCG8 145 G.C and (CA+AA) of SLCO1B1 463 C.A were
significantly associated and conferred increased risk of gallstone
disease (p = ,0.019; [OR], 2.4: p = 0.007; [OR], 2.6). On the
contrary, no significant difference were observed in the distribution of SLCO1B1 521 T.C (rs4149056) (ptrend = 0.416;
MCS = 0.298) polymorphism, both at genotypic and allelic levels
and therefore conferred no risk for developing gallstones.
Results
Population Characteristics
The demographic profile of gallstone patients with respect to
their age and gender matched controls are presented in Table 1.
Table 1. Demographic profile of controls and gallstone
patients.
Association of adipogenesis differentiation pathway gene
polymorphisms with gallstone patients. Table 4 shows the
Characteristic
Healthy subjects
Gallstone patients
Total
220
230
Age at interview
(years) Mean6 SD
49.069.8
48.6611.9
Male (n%)
77 (35.0)
83 (36.1)
Female (n%)
143 (65.0)
147 (63.9)
genotype and allele frequency distribution of sequence variants in
SREBP2 1784 G.C and PPAR c2 C.G. A borderline statistical
significance was observed when the homozygous variant genotypes
of SREBP2 1784 G.C (rs2228314) was compared i.e gallstone
patients with that of healthy subjects (HS) (p = 0.045; [OR], 4.8).
Furthermore, no significant difference was observed in the
distribution of PPAR c2 C.G (rs1801282) (ptrend = 0.256;
MCS = 0.218).
Sex
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Table 2. Hormonal pathway.
Genotypes/Alleles
Controls n (%)
Cases n (%)
p-value
OR (95% CI)
ESR 1 IVS1-397C.T
CC
91 (41.4)
64 (27.8)
2
1 (reference)
CT
110 (50.0)
128 (55.7)
0.019
1.66 (1.09–2.53)
TT
19 (8.6)
38 (16.5)
0.001
2.98 (1.56–5.70)
Ptrend
,0.001
*MCS
0.001
CT+TT
129 (58.6)
166 (72.2)
0.003
1.86 (1.23–2.80)
C
292 (66.4)
256 (55.7)
2
1 (reference)
T
148 (33.6)
204 (44.3)
0.001
1.59 (1.21–2.11)
0.142
1.37 (0.90–2.07)
0.002
2.65 (1.43–4.91)
ESR1 IVS1-351A.G
AA
90 (40.9)
69 (30.0)
AG
109 (49.5)
117 (50.9)
GG
21 (9.5)
44 (19.1)
1 (reference)
Ptrend
,0.001
*MCS
0.001
AG+GG
130 (59.1)
161 (70.0)
0.025
1.58 (1.06–2.35)
A
289 (65.7)
255 (55.5)
2
1 (reference)
G
151 (34.3)
205 (44.5)
0.005
1.49 (1.13–1.95)
ESR1 Ex4-122C.G
CC
106 (48.2)
120 (52.2)
2
1 (reference)
CG
104 (47.3)
97 (42.2)
0.487
0.87 (0.59–1.29)
GG
10 (4.5)
13 (5.7)
0.981
0.99 (0.41–2.40)
Ptrend
0.605
*MCS
0.599
CG+GG
114 (51.8)
110 (47.8)
0.518
0.88 (0.60–1.29)
C
318 (71.9)
337 (73.5)
2
1 (reference)
G
124 (28.1)
123 (26.5)
0.306
0.86 (0.63–1.15)
ESR2 -789 A.C
AA
94 (43.2)
105 (45.7)
2
1 (reference)
AC
109 (49.1)
107 (47.0)
0.596
0.90 (0.61–1.33)
CC
17 (7.7)
18 (7.4)
0.728
0.87 (0.41–1.85)
Ptrend
0.630
*MCS
0.578
AC+CC
126 (56.8)
125 (54.3)
0.571
0.90 (0.61–1.31)
A
297 (67.5)
317 (68.9)
2
1 (reference)
C
143 (32.5)
143 (31.1)
0.521
0.91 (0.68–1.21)
GG
206 (93.6)
212 (92.2)
2
1 (reference)
GA+AA
14 (6.4)
18 (7.8)
0.596
1.22 (0.58–2.56)
ESR2 1082 G.A
Ptrend
0.546
*MCS
0.435
G
428 (97.0)
442 (96.3)
2
1 (reference)
A
14 (3.0)
18 (3.7)
0.416
1.36 (0.65–2.87)
DD
181 (83.6)
208 (90.4)
2
1 (reference)
DI+II
39 (16.4)
22 (9.6)
0.009
0.46 (0.25–0.82)
PGR Ins/Del
Ptrend
0.011
*MCS
D
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0.015
401 (91.1)
438 (95.3)
2
3
1 (reference)
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Table 2. Cont.
Genotypes/Alleles
Controls n (%)
Cases n (%)
p-value
OR (95% CI)
I
39 (8.9)
22 (4.7)
0.002
0.41 (0.24–0.72)
TT
178 (80.9)
158 (68.7)
2
1 (reference)
TC+ CC
42 (19.1)
72 (31.3)
,0.001
1.96 (1.43–2.69)
ADRB3 -190 T.C
Ptrend
0.003
*MCS
0.002
T
398 (90.5)
388 (84.3)
2
1 (reference)
C
42 (9.5)
72 (15.7)
0.005
1.80 (1.19–2.73)
ADRA2A -1291 C.G
CC
61 (27.7)
53 (23.0)
2
1 (reference)
CG
117 (53.2)
117 (50.9)
0.678
1.1 (0.7–1.7)
GG
42 (19.1)
60 (26.1)
0.070
1.6 (1.0–2.9)
Ptrend
0.075
*MCS
0.065
CG+GG
159 (72.3)
177 (77.0)
0.317
C
239 (54.3)
223 (48.4)
2
1.2 (0.8–1.9)
1 (reference)
G
201 (45.6)
237 (51.5)
0.056
1.5 (1.0–2.5)
MCS = Monte Carlo Simulation; Significant values are in bold; For categorical data Cochrane Armitage trend test was used.
doi:10.1371/journal.pone.0059173.t002
Haplotype Analysis
Linkage disequilibrium and haplotypes analysis of ESR1
and ESR2 in case and control groups. On LD analysis, ESR1
Table 3. Hepatocanalicular transporter pathway.
Genotypes/
Alleles
Controls n Cases n
(%)
(%)
p-value
OR (95% CI)
GG
209 (95.0)
206 (89.6)
2
1 (reference)
GC+CC
11 (5.0)
24 (10.4)
0.019
2.47 (1.16–5.25)
rs2234693 and rs9340799 were found to be in strong linkage
disequilibrium (D’ = 0.575). Haplotypes were constructed for the
three polymorphisms in ESR1 gene including IVS1-397C.T,
IVS1-351A.G and Ex4-122C.G. The haplotypes comprising
ABCG8 145G.C
Ptrend
0.031
*MCS
0.022
Table 4. Adipogenesis differentiation pathway.
Genotypes/
Alleles
Controls n
(%)
Cases n (%) p-value
OR (95% CI)
1 (reference)
G
429 (97.5)
436 (94.9)
2
1 (reference)
SREBP2 1784G.C
C
11 (2.5)
24 (5.1)
0.025
2.41 (1.12–5.22)
GG
145 (65.9)
138 (60.0)
2
GC
73 (33.2)
82 (35.7)
0.475
1.16 (0.77–1.74)
2 (0.9)
10 (4.3)
0.045
4.87 (1.03–22.96)
SLCO1B1 Exon4 C.A
CC
205 (93.2)
200 (87.0)
2
1 (reference)
CC
CA+AA
15 (6.8)
30 (13.0)
0.007
2.63 (1.30–5.29)
Ptrend
0.067
*MCS
0.057
Ptrend
0.028
*MCS
0.020
C
425 (96.6)
430 (93.2)
2
1 (reference)
A
15 (3.4)
30 (6.8)
0.015
2.21 (1.16–4.21)
GC+CC
75 (34.1)
92 (40.0)
0.250
1.26 (0.85–1.87)
G
363 (82.5)
358 (77.8)
2
1 (reference)
C
77 (17.5)
102 (22.2)
0.165
1.27 (0.91–1.79)
PPARG c2 C.G
SLCO1B1 Ex6+40T.C
TT
212 (96.4)
218 (94.8)
2
1 (reference)
CC
178 (80.9)
176 (76.5)
2
1 (reference)
TC+CC
8 (3.6)
12 (5.2)
0.422
1.46 (0.57–3.72)
CG+GG
42 (19.1)
54 (23.5)
0.351
1.25 (0.78–1.98)
Ptrend
0.416
Ptrend
*MCS
0.298
*MCS
398 (90.5)
406 (88.4)
2
1 (reference)
42 (9.5)
54 (11.6)
0.652
1.11 (0.71–1.72)
T
432 (99.0)
448 (98.7)
2
1 (reference)
C
C
8 (1.0)
12 (1.3)
0.850
1.08 (0.46–2.52)
G
0.218
MCS = Monte Carlo Simulation; Significant values are in bold; For categorical
data Cochrane Armitage trend test was used.
doi:10.1371/journal.pone.0059173.t004
MCS = Monte Carlo Simulation; Significant values are in bold; For categorical
data Cochrane Armitage trend test was used.
doi:10.1371/journal.pone.0059173.t003
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of small numbers. The number of risk alleles ranged from 1 to 11
with a median of 4 among control subjects and 6 among cases
(Figure 1). The risk for gallstone disease was estimated for each
number of risk alleles, relative to the median number of risk alleles
of 4, and ranged from an OR of 2.27 (95% confidence interval
[CI], 1.0–4.6) for 5 risk alleles to an OR of 8.27 (95% CI, 0.90–
75.2) for 11 risk alleles. The average relative risk increase per risk
allele, when treated as an ordinal variable, however, could be
estimated with a high level of precision, and was 2.7 (95% CI,
1.12–1.16). This corresponded to several fold difference in risk
between the lowest and the highest number of risk alleles in our
population.
Association of High-Order Interactions with GSD Risk by
MDR Analysis
Table 6 shows the best interaction model by MDR analysis. The
one-factor model for predicting GS risk was ABCG8 145 G.C
SNP (testing accuracy = 0.515, CVC = 7/10, permutation
p = 0.027). The two-factor model of ESR1 IVS1 351A.G and
ADRB3 -190 T.C had an improved testing accuracy of 0.578
(permutation p = ,0.001) however, the CVC was increased (10/
10). The three factor model was the three-factor model including
ESR1 IVS1-397C.T ESR1 IVS1 351A.G and ADRB3 190T.C SNPs, which yielded the testing accuracy of 0.605 and
the CVC of 08/10 (permutation p = ,0.001). The best four-factor
interaction model consisted of ESR1 IVS1-397C.T, ESR1 IVS1
351A.G, ADRB3 -190T.C and ABCG8 145 G.C with highest
testing accuracy compared with the one-factor model (CVC = 10/
10 permutation p = ,0.001).
Figure 1. The 13-SNP G-score distribution in patients with
gallstones and control subjects.
doi:10.1371/journal.pone.0059173.g001
the homozygous wild alleles were taken as reference and the
difference in the frequencies of haplotypes between patients and
controls were tested using chi-square test.
The results of the studied three polymorphisms of ESR1
revealed that distribution of T,G,C haplotypes was significantly
higher in gallstone patients (25.1% v/s 13.7) in comparison to
controls and was conferring high risk for gallstone disease
(p = 0.0012; [OR], 2.2). Global haplotypes analysis indicated a
statistically significant difference between cases and controls
based on the distribution pattern of the ESR1 haplotypes
(p = ,0.001). Furthermore, none of the ESR2 haplotypes
conferred risk for gallstones presenting the global haplotypes
association p-value = 0.65 (Table S2; S3).
Association of High-Order Interactions with GSD Risk by
CART Analysis
Table 7 shows the CART, which included all investigated
genetic variants of the selected pathways. The final tree structure
contained seven terminal nodes as defined by single-nucleotide
polymorphisms of the hormonal, hepatocanalicular transporter
and adipogenesis differentiation pathway genes. Consistent with
the MDR best one-factor model, the initial split of the root node
on the decision tree was ESR1 IVS1-397C.T, suggesting that this
SNP is the strongest risk factor for GSD among the polymorphisms examined. Individuals carrying ESR1 IVS1 -397CC,
ADRB3-190 TT, ABCG8 145 GG and ADRA2A GG genotypes
had the lowest case rate of 17.2%, considered as reference. Further
inspection of the tree structure revealed distinct interaction
patterns between individuals carrying the ESR1 IVS1-397 variant
and those with the ADRB3 variant and SLCO1B1 463 C.A wild
genotypes. Using the terminal node with lowest case rate as
reference, individuals carrying the combination of ESR1 IVS1397TT, SLCO1B1 Exon4CC, ESR2 1082GG, ESR1 IVS1-351AA
and ESR1 Ex4-122GG exhibited a significantly higher risk for
Linkage disequilibrium and haplotypes analysis of
SLCO1B1 in case and control groups. SLCO1B1 Exon4
C.A and Ex6+40T.C were found to be in strong linkage
disequilibrium (D’ = 0.8916). Haplotypes analysis of these two
polymorphisms gave rise to three haplotypes, of which C, T was
the most common haplotypes in control population. On comparing the haplotypes frequencies in controls and gallstone cases, A, T
haplotypes was more commonly distributed in gallstone patients
and was imposing risk for the disease (p = 0.017; OR = 2.21)
(Table S4).
G-score. For each individual, we counted the number of riskincreasing alleles. The number of risk alleles ranged from 1 to 11
in overall 450 subjects (Figure 1). The mean (6SD) G-score was
5.4361.96 in gallstones subjects and 4.6361.95 in controls (pvalue = ,0.001) (Table 5). At the more extreme ends of the risk
distribution, CIs around risk estimates became very wide because
Table 5. Mean G-Scores in the Selected Pathway and their Corresponding p-values.
Selected Pathways
Cases
Controls
p-value
Hormonal
4.4661.68
3.9261.76
0.001
Hepatocanalicular Transporter
0.2960.54
0.16860.37
0.004
Adipogenesis Differentiation
0.6760.84
0.5460.70
0.062
Overall Genotyping Score Mean for all pathways
5.4361.96
4.6361.95
,0.001
Significant values are in bold.
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Table 6. Association of High-Order Interactions with GSD Risk by MDR Analysis.
No. of interacting
loci
Best Interaction Model
Testing
Accuracy
#
1
ABCG8 145 G.C
0.5156
7/10
0.0027
2
ESR1 IVS1 351A.G ADRB3 -190T.C
0.5784
10/10
,0.001
3
ESR1 IVS1-397C.T ESR1 IVS1 351A.G, ADRB3 -190T.C
0.6050
8/10
,0.001
4
a
0.6212
10/10
,0.001
ESR1 IVS1-397C.T, ESR1 IVS1 351A.G, ADRB3 -190T.C
ABCG8 145 G.C
P for permutation Testing
CVC
#
CVC: Cross Validation Consistency.
The model with the maximum testing accuracy and maximum CVC cross was considered as the best model. The present study calculated, the best interaction model as
the four-factor model including aESR1 IVS1-397C.T, ESR1 IVS1 351A.G, ADRB3-190T.C, ABCG8 145 G.C polymorphisms.
doi:10.1371/journal.pone.0059173.t006
a
397T, IVS1-351G, Ex4-122C and SLCO1B1 haplotypes Exon4A,
Ex6+40T conferred increased risk for gallstones.
Based on the candidate SNPs in genes involved in the gallstone
pathway. We created a consolidated Genotype Score (G-score)
from the number of risk alleles as previously reported for risk
assessment of cardiovascular events and diabetes [25,26]. Our
assumption was that individuals with a high G-score might have a
higher probability of gallstone development as compared to those
with the low G-score. The overall G-scores for the three selected
pathways obtained were highly significant and conferred increased
risk for gallstone development. Further calculating the G-score
individually in respective pathways we found both hormonal and
hepatocanalicular transporter pathway conferred increase risk.
These results suggest significant role of hormonal receptor and
hepatocanalicular transporters in gallstone disease.
For the higher order gene-gene interaction analysis, we
employed statistical approaches namely MDR and CART analysis
to find out the particular combinations of genetic variants
contributing to CGD risk. In MDR analysis, we observed the
best four-factor interaction model consisting of ESR1 IVS1397C.T, ESR1 IVS1 351A.G, ADRB3 -190T.C and ABCG8
145 G.C with highest testing accuracy compared with the onefactor model.
In CART analysis, which is a non-parametric statistical
approach for conducting regression and classification analyses by
recursive partitioning. [17], study subjects were grouped according
to different risk levels on the basis of the different gene
polymorphisms. From this analysis, we found that development
GSD (adjusted OR 5.083; 95% CI, 1.3–18.48), whereas individuals with the combined genotypes of ESR1 IVS1-397TT, ABCG8
145 GC+CC, ESR2 1082GG+ ESR1 IVS1-351GG and ADRB3
TC+CC had the highest risk for CGD (adjusted OR 6.48; 95%
CI, 1.9–22.08). (Table 7).
Discussion
In order to achieve a more comprehensive evaluation of CGD
risk, present analysis was performed in order to identify high and
low intrinsic risk sets of sequence variants. Of the included 13
polymorphisms, some of them were found to be significantly
associated with CGD risk in our previous studies [17,18,19] while
others showed little or no influence on the risk for CGD
development. Moreover, accumulating evidence supports the
importance of adipogenesis differentiation and adrenergic receptor
pathways in cholesterol associated diseases [22,23,24]. Therefore,
we further extended our work by incorporating these two
pathways.
In the single-locus analysis, genetic variants of hormonal
pathway, ESR1 IVS1-397C.T, IVS1-351A.G and ADRB3 190
T.C were significantly associated with GSD risk [17]. However,
Alu insertion polymorphism of progesterone receptors (PGR)
conferred lower risk with gallstones. In hepatocanalicular transporter pathway ABCG8 D19H and SLCO1B1 Exon4 C.A
conferred increased risk for CGD At haplotypes level, we found
that the gallstones subjects who carry ESR1 haplotypes IVS1-
Table 7. Risk Estimates of CART Terminal Nodes.
Case ratea (%)
p-value
ORb
Nodes
Genotypes
1
ESR1IVS1397W+ADRB3W+ABCG8W+ADRA2A W
17.2
2
Reference
2
ESR1IVS1-397V+SLCO1B1 463 W+ESR2 1082W+ ESR1IVS1-351V +ESRHinf1 W
21.7
0.352
0.429 (0.07–2.55)
1.576 (.453–5.479)
2
ESR1IVS1-397W+ ADRB3W+ABCG8W +ADRA2AV+ESR2Bsa1 V
29.3
0.475
3
ESR1IVS1-397W+ ADRB3W+ABCG8W +ADRA2AV+ESR2Bsa1W
47.2
0.018
4.33 (1.29–14.59)
4
ESR1IVS1-397V+SLCO1B1463 W+ESR21082W+ESR1IVS1-351V +ESRHinf1 V
56.2
0.014
5.083 (1.39–18.48)
5
ESR1IVS1397V+ABCG8V+ESR21082W+ESR1IVS1-351V+ADRB3V
63.0
0.003
6.48 (1.90–22.08)
6
ESR1IVS1-397V+ ADRB3V
76.9
0.002
10.05 (2.33–43.29)
7
ESR1IVS1-397W+ADRB3V+ ESRHinf1W
78.6
0.002
24.554 (3.24–185.84)
W = wild genotype. V = variant genotype.
a
Case rate is the percentage of gallstone patients among all individuals in each node.
b
ORs of terminal nodes were calculated by LR analysis adjusted for age and gender.
Significant values are in bold.
doi:10.1371/journal.pone.0059173.t007
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Multi-Analytic Approach in Gallstone Disease
reasonable power as compared to single strategy employed in
calculating risk allele for disease prediction.
Also a prominent significant role of hormonal pathway was
elucidated when the means of genotyping scores of selected
pathways was calculated separately or all together. Therefore,
exhaustive analysis of multi-analytic approaches as MDR, CART
and G-scores are well recognized methods in understanding
complex traits, such as disease susceptibility and also the etiology
of complex diseases.
In summary, this is the first comprehensive study to use a
multigenic analysis for cholesterol gallstone disease, and the data
suggest that individuals with a higher number of genetic variations
in hormonal and hepatocanalicular transporter pathway genes are
at an increased risk for cholesterol gallstone disease, confirming
the importance of taking a multigenic pathway based approach to
risk assessment. The finding also indicates that the development of
gallstone involves complex genetic interactions and follows
different pathways depending on the specific genetic background
of the subjects. The present study provided evidence supporting
the cholesterol supersaturation contribution of hormonal, hepatocanalicular transporter and adipogenesis differentiation pathway
genes, of which interaction between ESR1, ADRB3 and ABCG8
genes were the most important.
Thus, our results support the concept that genetic polymorphisms can be used as cholesterol gallstone risk predictors and
multiple polymorphisms allow more precise delineation of risk
groups and suggest the future direction of association studies.
However, the present study included only North Indian individuals, therefore the results need to be replicated in other ethnic
groups.
of CGD involves complex genetic interactions among the
hormonal and hepatocanalicular transporter genetic variants. As
our results from CART analyses consistently suggested that
ESR1IVS1-397TT, ABCG8GC+CC, ESR1IVS1-351GG and
ADRB3 TC+CC polymorphisms are the most important single
susceptibility factor for CGD development.
The association between hormonal receptor gene polymorphisms and risk of gallstones are biologically convincing. It has
been assumed that the gallbladder is a female sex hormone
responsive organ, and these hormones might be involved in the
pathogenesis of gallbladder diseases. Elaborating on estrogen
receptor the animal studies have shown that ESRs are present in
the hepato-pancreatic-biliary tree [27,28,29] including bile duct
epithelial cells and gallbladder. In addition, immunohistochemical
and quantitative RT PCR studies have also revealed that the
expression level of ESR1 gene is approximately 50 fold higher
compared to ESR2. In animal models, 17beta estradiol promoted
gallstone formation which further involves the upregulation of
hepatic expression of ERalpha but not ERbeta. These studies
show that ESR-1 is key player and findings may offer a new
approach to treat gallstones by inhibiting hepatic ER activity with
a liver-specific, ERalpha-selective antagonists.
The literature regarding the ADRB3 confirms that it is localized
in the smooth muscles of the vasculature and the muscularis
propria of the gallbladder [30] where it is thought to mediate
relaxation and increase mucosal blood flow. The T.C polymorphism results in lowered responsiveness to potent agonists
including endogenous catecholamines. [31] The mutated receptor
had less ability to stimulate adenylyl cyclase and therefore less
accumulation of cAMP. [31] Activation of ADRB3 also results in
smooth muscle relaxation in the guinea-pig common bile duct,
[32] and since the ductal smooth muscle appear to be more
sensitive to activation of the ß3-adrenoceptor, there is the
possibility that these receptors may be involved in the regulation
of tone in the ductal smooth muscle and hence the outflow of bile.
Thus the inhibiting variant C in ADRB3 might result in gallstone
formation by impairing the relaxation of the gallbladder and
probably the biliary tree too, setting the stage for crystal formation.
In the selected hepatocanalicular transporters ABCG8 145G.C
conferred increased risk both individually and in combination to
hormonal receptors. A genome wide scan carried out by Buch
et al., [33] identified a variant D19H in the hepatic cholesterol
transporter (ABCG8) as major susceptibility factor for human
gallstone disease. Subsequently, this association has been replicated in various populations [9,19,34,35].
The phenomenon that a combination of polymorphisms within
genes of unrelated pathways may elevate the risk for CGD could
be explained by two hypotheses. One possibility is that some
connection between these genes or proteins exists but still remains
to be discovered. Another hypothesis, more credible in our
opinion, is that the genes influencing risk for CGD may as well
comprise a set of alterations located within genes not related to
each other.
Our multi-analytic approach revealed that the combination of
genotypes of respective polymorphisms as ESR1 IVS1-397 variant,
ABCG8 145 variant, ESR1 IVS1-351 variant and ADRB3 190
variant pose a significant risk for developing gallstone. Comparing
to the results of single locus analysis the role of SLCO1B1 Exon4
C.A SREBP2 1784 G.C and PGR ins/del was diminished when
the overall analysis of 13 selected polymorphisms was performed.
It also suggests that ESR1, ADRB3 and ABCG8 have significant
incremental risk factors for gallstone disease. Thus, the application
of these multi analytical approaches allowed creating a decision
that has more sensitivity or specificity and was more accurate with
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Patients and Methods
Ethics Statement
The institutional ethical committee of Sanjay Gandhi Post
Graduate Institute of Medical Sciences (SGPGIMS) approved the
present study protocol and the authors followed the norms of
World’s Association Declaration of Helsinki. All the participants
provided written informed consent.
Study Population
The case control study recruited a total of 450 subjects,
including 230 cholesterol gallstone patients (GS) and 220 healthy
subjects. From the year June 2006 to September 2011 symptomatic cholesterol GS patients attending the Department of Gastrosurgery, Sanjay Gandhi Post Graduate Institute of Medical
Sciences and Department of Surgical Oncology Lucknow India,
were approached for participation in the present study. All subjects
were unrelated and confirmed to North Indian ethnicity.
Phenotype data. For each individual, ultrasound examinations were conducted at the Department of Radio-diagnosis and
Imaging SGPGIMS, Lucknow. Participants were considered as
having gallstones when one of the subsequent diagnostic criteria
was satisfied: (1) Gallbladder lumen with mobile nodular or
dependent layering echoes that exhibited posterior acoustic
shadowing, or (2) Gallbladder with hyperechoic shadowing
material filling the gallbladder lumen with an appearance of the
WES triad (i.e., the gallbladder wall, the echo of the stone, and the
acoustic shadow–a specific ultrasonographic sign of gallstones used
to make a reliable diagnosis of cholelithiasis [36]. The healthy
controls were randomly selected from a pool of healthy volunteers
that visited the general health check-up center at SGPGIMS
Lucknow, during the same period. In addition to a self-reported
gallstone history, transabdominal ultrasound was performed to
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April 2013 | Volume 8 | Issue 4 | e59173
Multi-Analytic Approach in Gallstone Disease
validate gallstone status and to identify silent gallstones. Inclusion
criteria for controls also included absence of asthma, coronary
artery disease, diabetes mellitus determined through maternal and
paternal family history. At recruitment, informed consent was
obtained from each subject and the information on demographic
characteristics, such as sex and age was collected by questionnaire.
Both patients and controls had similar ethnicity. The blood sample
and the clinical details were collected from each participant at
recruitment.
was used to estimate odds ratios [ORs] and their 95% confidence
intervals [CIs] adjusting for age and sex. A two-tailed p-value of
less than 0.05 was considered a statistical significant result. All
statistical analyses were performed using SPSS software version
16.0 (SPSS, Chicago, IL, USA). Ptrend and Monte Carlo
Simulation (MCS) were calculated through Cochrane Armitage
trend using XLstats whereas haplotype analysis was performed
using SNPstats (http://bioinfo.iconcologia.net/SNPstats).
Furthermore, higher-order gene-gene interactions associated
with CGD risk were determined through multifactor dimensionality reduction (MDR) using software version 2.0 beta8 and
classification regression tree analysis (CRT) using SPSS software
version 16.0.
DNA Samples and Genotyping
Genomic DNA was isolated from peripheral blood leukocytes
using salting out method [37]. The polymorphisms were
genotyped using the PCR or PCR restriction fragment length
polymorphism method. The details of genotyping for studied
polymorphisms are shown in Table S1. Ten percent of masked,
random sample of cases and controls were tested twice by different
laboratory personnel and the reproducibility was 100%.
Supporting Information
Table S1 The genes and SNPs investigated.
(DOC)
Haplotypes association of ESR1 gene (age and
gender adjusted).
(DOC)
Table S2
Genotype Score Calculation (G-score)
A Genotype score (G-score) was defined as the cumulative
number that counts the total number of risk-increasing alleles in
individuals. Genotyping of 13 selected SNPs in candidate genes
involved in hormonal, hepatocanalicular and adipogenesis differentiation pathways was performed and G-score was computed
from the number of variant alleles. A value of 2, 1 and 0 was
allotted to homozygous variant, heterozygous and homozygous
wild type genotypes respectively. Variant genotype was considered
as risk conferring. Using these 13 SNPs a Genotype Score (Gscore) was constructed ranging from 0 to 26 on the basis of the
number of risk alleles. For each sample a consolidated G-score was
calculated by adding the values from all 13 SNPs together.
Table S3 Haplotypes analysis of ESR2 gene (age and
gender adjusted).
(DOC)
Table S4 Haplotypes analysis of SLCO1B1 gene (age and
gender adjusted).
(DOC)
Table S5 Odds Ratios and 95% CI for Gallstones in
Relation to Polymorphisms of Hormonal Pathway after
Subdividing on the Basis of Gender.
(DOC)
Statistical Analysis
Author Contributions
Descriptive statistics were presented as mean and standard
deviation [SD] for continuous measures while absolute value and
percentages were used for categorical measures. Differences in
genotype and allele frequencies between study groups were
estimated by chi-square test. Unconditional logistic regression
Conceived and designed the experiments: BM AS. Performed the
experiments: AS RS RR. Analyzed the data: AS AM. Contributed
reagents/materials/analysis tools: BM AS. Wrote the paper: AS AM BM
NS.
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