Genotyping of CFB genetic variants
A total of 10 CFB SNPs were selected and genotyped in 1,716 Korean subjects, comprised of 955 CHB patients and 761 population controls (Supplementary Table 1). Patients were divided into two subgroups, 659 HCC (-) CHB cases and 296 HCC (+) CHB cases. A gene map and LD among investigated SNPs are shown in Supplementary Figure 1A and 1B. Detailed information on the investigated SNPs, such as chromosome, position, allele, genotype distribution, heterozygosity, and HWE P, are presented in Supplementary Table 2.
Association of CFB genetic polymorphisms with CHB risk
In order to investigate the association between CFB genetic polymorphisms and risk of CHB, logistic regression analysis under an additive model was conducted. Analysis results indicated that rs12614 was significantly associated with risk of CHB even after applying Bonferroni correction for multiple testing (OR = 0.43, P = 5.91×10-10, Pcorr = 2.36×10-8; Table 1). In order to validate the genetic effects of rs12614, association analysis was conducted using the training and test sets from the subjects in this study (Supplementary Table 3). Additional subgroup analysis was performed to investigate the association between CFB SNPs and CHB-related HCC progression. Again, analysis results found that, rs12614 had significant associations with risk of CHB in both the HCC (-) CHB and the HCC (+) CHB subgroups (P = 6.60×10-8 and 3.10×10-6, respectively) even after Bonferroni correction was applied for multiple testing (Pcorr = 2.64×10-6 and 1.24×10-4, respectively). However, rs12614 did not show significant genetic effect on CHB-related HCC progression (P > 0.05).
Table 1. Association of CFB genetic polymorphisms with the risk of CHB and HCC
Marker
|
MAF
|
|
Comparing groups
|
Total (n=1,716)
|
CHB
|
PC (n=761)
|
|
CHB vs. PC
|
|
HCC (-) vs. PC
|
|
HCC (+) vs. PC
|
|
HCC (-) vs. HCC (+)
|
All CHB (n=955)
|
HCC (-) (n=659)
|
HCC (+) (n=f)
|
|
|
|
|
|
OR (95% CI)
|
P*
|
Pcorr**
|
|
OR (95% CI)
|
P*
|
Pcorr**
|
|
OR (95% CI)
|
P*
|
Pcorr**
|
|
OR (95% CI)
|
P*
|
rs4151667
|
0.018
|
0.019
|
0.018
|
0.022
|
0.016
|
|
1.23 (0.73-2.08)
|
0.42
|
-
|
|
1.16 (0.65-2.06)
|
0.61
|
-
|
|
1.41 (0.70-2.80)
|
0.34
|
-
|
|
0.82 (0.41-1.63)
|
0.58
|
rs12614
|
0.078
|
0.053
|
0.054
|
0.048
|
0.112
|
|
0.43 (0.33-0.57)
|
5.91×10-10
|
2.36×10-8
|
|
0.45 (0.33-0.61)
|
6.60×10-8
|
2.64×10-6
|
|
0.39 (0.25-0.60)
|
3.10×10-6
|
1.24×10-4
|
|
1.13 (0.72-1.78)
|
0.58
|
rs641153
|
0.082
|
0.083
|
0.086
|
0.076
|
0.080
|
|
1.04 (0.81-1.33)
|
0.74
|
-
|
|
1.08 (0.83-1.41)
|
0.54
|
-
|
|
0.94 (0.66-1.34)
|
0.75
|
-
|
|
1.14 (0.80-1.62)
|
0.45
|
rs117314762
|
0.015
|
0.013
|
0.014
|
0.010
|
0.018
|
|
0.67 (0.38-1.17)
|
0.16
|
-
|
|
0.73 (0.40-1.34)
|
0.31
|
-
|
|
0.54 (0.22-1.32)
|
0.15
|
-
|
|
1.35 (0.53-3.45)
|
0.51
|
rs1048709
|
0.279
|
0.269
|
0.265
|
0.277
|
0.293
|
|
0.88 (0.76-1.02)
|
0.11
|
-
|
|
0.86 (0.73-1.02)
|
0.09
|
-
|
|
0.92 (0.74-1.14)
|
0.47
|
-
|
|
0.94 (0.75-1.16)
|
0.58
|
rs537160
|
0.333
|
0.327
|
0.322
|
0.340
|
0.341
|
|
0.93 (0.81-1.08)
|
0.39
|
-
|
|
0.91 (0.77-1.07)
|
0.26
|
-
|
|
0.99 (0.81-1.21)
|
0.95
|
-
|
|
0.91 (0.74-1.13)
|
0.43
|
rs541862
|
0.082
|
0.083
|
0.086
|
0.076
|
0.080
|
|
1.04 (0.81-1.33)
|
0.74
|
-
|
|
1.08 (0.83-1.41)
|
0.54
|
-
|
|
0.94 (0.66-1.34)
|
0.75
|
-
|
|
1.14 (0.80-1.62)
|
0.45
|
rs4151657
|
0.323
|
0.335
|
0.341
|
0.321
|
0.307
|
|
1.13 (0.98-1.30)
|
0.09
|
-
|
|
1.16 (0.99-1.36)
|
0.06
|
-
|
|
1.06 (0.86-1.30)
|
0.55
|
-
|
|
1.09 (0.88-1.34)
|
0.40
|
rs45484591
|
0.001
|
0.001
|
0.002
|
0.000
|
0.001
|
|
0.79 (0.11-5.66)
|
0.82
|
-
|
|
1.15 (0.16-8.22)
|
0.89
|
-
|
|
-
|
-
|
-
|
|
-
|
-
|
rs2072633
|
0.477
|
0.468
|
0.467
|
0.471
|
0.488
|
|
0.92 (0.80-1.05)
|
0.25
|
-
|
|
0.91 (0.79-1.06)
|
0.26
|
-
|
|
0.93 (0.77-1.13)
|
0.50
|
-
|
|
0.98 (0.80-1.19)
|
0.85
|
* P-value of logistic regression analysis under additive model by adjusting for sex and age as covariates.
**P-value after Bonferroni correction for multiple testing.
Significant associations are shown in bold face (P < 0.05).
MAF, minor allele frequency; CHB, chronic hepatitis B; HCC, hepatocellular carcinoma; OR, odds ratio; CI, confidence interval.
Independent genetic effect of rs12614 on CHB risk
In order to understand the association between rs12614 and CHB risk, particularly with respect to independent genetic effect on CHB susceptibility, this study conducted LD calculations and conditional analysis on 10 previously identified CHB susceptibility markers (rs9277535 of HLA-DPB1; rs3077 of HLA-DPA1; rs2856718 of HLA-DQB1; rs7453920 of HLA-DQB2; rs1419881 of TCF19; rs1265163 of OCT4; rs652888 and rs35875104 of EHMT2; rs9394021 and rs2517459 of VARS2-SFTA). Supplementary Figure 2 shows the LD plot of rs12614 and the 10 CHB susceptibility markers. The results show that CFB rs12614 did not display tight LDs with any known, nearby CHB-susceptible loci (pairwise r2 ≤ 0.15; Supplementary Figure 2). In addition, when adjusting for previously identified CHB markers, rs12614 maintained significant association with CHB risk (P < 0.05; Table 2), indicating that rs12614 had independent genetic effect on CHB susceptibility to previously identified CHB risk markers.
Table 2. Results of conditional analysis with the previously identified CHB marker
Marker
|
Allele
|
OR (95% CI)
|
P-value*
|
Conditional P-value by
|
HLA-DPB1
rs9277535**
|
HLA-DPA1
rs3077**
|
HLA-DQB1
rs2856718**
|
HLA-DQB2
rs7453920**
|
TCF19
rs1419881**
|
OCT4
rs1265163**
|
EHMT2
rs652888**
|
EHMT2
rs35875104**
|
VARS2-SFTA
rs9394021**
|
VARS2-SFTA
rs2517459**
|
rs12614
|
C>T
|
0.42 (0.31-0.56)
|
5.91×10-10
|
5.94×10-6
|
3.52×10-5
|
4.46×10-6
|
1.57×10-4
|
4.73×10-8
|
6.97×10-8
|
5.87×10-8
|
7.80×10-7
|
2.07×10-8
|
5.29×10-7
|
*P-value of logistic regression analysis by adjusting for sex and age as covariates.
**Previously identified CHB marker.
Significant associations are shown in bold face (P < 0.05).
OR, odds ratio; CI, confidence interval.
Cumulative genetic effects of CHB susceptible loci
In order to examine the detailed genetic effects of all 11 CHB susceptible loci including rs12614 (rs12614 of CFB; rs9277535 of HLA-DPB1; rs3077 of HLA-DPA1; rs2856718 of HLA-DQB1; rs7453920 of HLA-DQB2; rs1419881 of TCF19; rs1265163 of OCT4; rs652888 and rs35875104 of EHMT2; rs9394021 and rs2517459 of VARS2-SFTA) in a Korean population, an allele test was conducted for each SNP. The GRSs of the genotypes were calculated using the ORs from allele test (Table 3).
Table 3. Determination of Genetic Risk Score based on Allele test of CHB susceptible loci
Gene/ Marker
|
Genotype distribution
|
|
Allele test
|
GRS**
|
CHB (n=955)
|
PC (n=761)
|
|
Allele*
|
OR (95% CI)
|
P-value
|
CFB/ rs12614
|
CC(829)
|
CC(550)
|
|
C
|
1
|
-
|
1
|
CT(91)
|
CT(142)
|
|
T
|
0.44 (0.33-0.57)
|
6.46×10-10
|
0.44
|
TT(3)
|
TT(7)
|
|
|
|
|
0.22
|
|
|
|
|
|
|
|
|
HLA-DPB1/ rs9277535***
|
GG(420)
|
GG(184)
|
|
G
|
1
|
-
|
1
|
GA(428)
|
GA(384)
|
|
A
|
0.49 (0.43-0.56)
|
9.47×10-24
|
0.49
|
AA(107)
|
AA(193)
|
|
|
|
|
0.25
|
|
|
|
|
|
|
|
|
HLA-DPA1/ rs3077***
|
GG(489)
|
GG(221)
|
|
G
|
1
|
-
|
1
|
GA(382)
|
GA(385)
|
|
A
|
0.48 (0.41-0.55)
|
1.84×10-24
|
0.48
|
AA(84)
|
AA(155)
|
|
|
|
|
0.24
|
|
|
|
|
|
|
|
|
HLA-DQB1/ rs2856718***
|
CC(226)
|
CC(263)
|
|
C
|
1
|
-
|
1
|
CT(418)
|
CT(373)
|
|
T
|
1.72 (1.50-1.97)
|
3.10×10-15
|
1.72
|
TT(311)
|
TT(125)
|
|
|
|
|
3.44
|
|
|
|
|
|
|
|
|
HLA-DQB2/ rs7453920***
|
GG(743)
|
GG(461)
|
|
G
|
1
|
-
|
1
|
GA(196)
|
GA(275)
|
|
A
|
0.49 (0.41-0.60)
|
1.07×10-13
|
0.49
|
AA(16)
|
AA(25)
|
|
|
|
|
0.25
|
|
|
|
|
|
|
|
|
TCF19/ rs1419881***
|
TT(419)
|
TT(270)
|
|
T
|
1
|
-
|
1
|
TC(424)
|
TC(361)
|
|
C
|
0.74 (0.64-0.85)
|
3.46×10-5
|
0.74
|
CC(112)
|
CC(130)
|
|
|
|
|
0.37
|
|
|
|
|
|
|
|
|
EHMT2/ rs652888***
|
TT(610)
|
TT(565)
|
|
T
|
1
|
-
|
1
|
TC(300)
|
TC(188)
|
|
C
|
1.65 (1.37-1.99)
|
5.14×10-8
|
1.65
|
CC(45)
|
CC(8)
|
|
|
|
|
3.3
|
|
|
|
|
|
|
|
|
OCT4/ rs1265163***
|
GG(401)
|
GG(392)
|
|
G
|
1
|
-
|
1
|
GC(439)
|
GC(299)
|
|
C
|
1.32 (1.14-1.53)
|
1.36×10-4
|
1.32
|
CC(114)
|
CC(70)
|
|
|
|
|
2.64
|
|
|
|
|
|
|
|
|
EHMT2/ rs35875104***
|
TT(878)
|
TT(650)
|
|
T
|
1
|
-
|
1
|
TC(75)
|
TC(108)
|
|
C
|
0.53 (0.39-0.71)
|
2.42×10-5
|
0.53
|
CC(2)
|
CC(3)
|
|
|
|
|
0.27
|
|
|
|
|
|
|
|
|
VARS2-SFTA/ rs9394021***
|
AA(303)
|
AA(189)
|
|
A
|
1
|
-
|
1
|
AG(471)
|
AG(368)
|
|
G
|
0.73 (0.64-0.84)
|
1.10×10-5
|
0.73
|
GG(177)
|
GG(203)
|
|
|
|
|
0.37
|
|
|
|
|
|
|
|
|
VARS2-SFTA/ rs2517459***
|
GG(809)
|
GG(560)
|
|
G
|
1
|
-
|
1
|
GA(139)
|
GA(193)
|
|
A
|
0.53 (0.43-0.67)
|
3.71×10-8
|
0.53
|
AA(6)
|
AA(8)
|
|
|
|
|
0.27
|
*Major and minor alleles were determined in all study subjects
**GRS was calculated by multiplying the number of minor alleles by effect size (OR) of the SNP
***Previously identified CHB marker in Korean population
Significant associations are shown in bold face (P < 0.05).
CHB, chronic hepatitis B; PC, population control; OR, odds ratio; CI, confidence interval; GRS, genetic risk score.
To elucidate the cumulative genetic effects of all 11 CHB loci in the study subjects, the cumulative GRSs were evaluated. The cumulative GRSs ranged from 5.24 (most protected group) to 17.38 (most susceptible group), and CHB patients showed significantly higher cumulative GRSs than did the healthy control subjects (Supplementary Table 4 and Figure 1A). It was found that as cumulative GRSs increased, ORs significantly increased as well. In particular, individuals with GRSs of less than 7 showed an OR of 0.17 (log10 OR = -0.77), while individuals with GRSs of over 14 showed an OR of 3.42 (log10 OR = 0.53) (Figure 1B).