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ABO blood groups and hepatitis B virus infection: a systematic review and meta-analysis
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Abstract
Objective
Hepatitis B virus (HBV) infection is a major public health problem worldwide. Several studies have reported that ABO blood groups may be associated with HBV infection. However, its association is still controversial. We performed a meta-analysis to investigate whether ABO blood groups were associated with HBV infection.
Data sources
Relevant studies available before 1 December 2019 were identified by searching PubMed, EMBASE, Web of Science, ScienceDirect and the Cochrane Library.
Eligibility criteria
All cross-sectional or cohort studies from which the data of ABO blood group distribution and HBV infection could be extracted.
Data extraction and synthesis
Studies were identified and extracted by two reviewers independently. Risk ratios (RRs) and 95% CIs were pooled by random-effect models to quantify this association.
Results
Thirty-eight eligible articles including 241 868 HBV-infected subjects and 6 487 481 uninfected subjects were included. Overall, the risk of HBV infection had decreased by 8% in subjects with blood group B when compared with non-B blood group (RR=0.92, 95% CI 0.86 to 0.98). In the subgroup analyses, the inverse relationship between blood group B and HBV infection remained stable in higher endemic areas (HBV prevalence ≥5%), Asian people, larger sample size studies (≥2000), general population and blood donors, lower middle income group and studies published before the year 2010. Additionally, subjects with blood group O had a 12% increased risk of HBV infection (RR=1.12, 95% CI 1.01 to 1.24) in higher endemic areas. In the sensitivity analysis, the pooled risk estimates of blood group B and HBV infection were still stable.
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Most cited references50
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The relationship between blood groups and disease.
David J. Anstee (2010)
The relative contribution of founder effects and natural selection to the observed distribution of human blood groups has been debated since blood group frequencies were shown to differ between populations almost a century ago. Advances in our understanding of the migration patterns of early humans from Africa to populate the rest of the world obtained through the use of Y chromosome and mtDNA markers do much to inform this debate. There are clear examples of protection against infectious diseases from inheritance of polymorphisms in genes encoding and regulating the expression of ABH and Lewis antigens in bodily secretions particularly in respect of Helicobacter pylori, norovirus, and cholera infections. However, available evidence suggests surviving malaria is the most significant selective force affecting the expression of blood groups. Red cells lacking or having altered forms of blood group-active molecules are commonly found in regions of the world in which malaria is endemic, notably the Fy(a-b-) phenotype and the S-s- phenotype in Africa and the Ge- and SAO phenotypes in South East Asia. Founder effects provide a more convincing explanation for the distribution of the D- phenotype and the occurrence of hemolytic disease of the fetus and newborn in Europe and Central Asia.
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Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.
Xin Yuan, Dawn Waterworth, John R.B. Perry … (2008)
Plasma liver-enzyme tests are widely used in the clinic for the diagnosis of liver diseases and for monitoring the response to drug treatment. There is considerable evidence that human genetic variation influences plasma levels of liver enzymes. However, such genetic variation has not been systematically assessed. In the present study, we performed a genome-wide association study of plasma liver-enzyme levels in three populations (total n = 7715) with replication in three additional cohorts (total n = 4704). We identified two loci influencing plasma levels of alanine-aminotransferase (ALT) (CPN1-ERLIN1-CHUK on chromosome 10 and PNPLA3-SAMM50 on chromosome 22), one locus influencing gamma-glutamyl transferase (GGT) levels (HNF1A on chromosome 12), and three loci for alkaline phosphatase (ALP) levels (ALPL on chromosome 1, GPLD1 on chromosome 6, and JMJD1C-REEP3 on chromosome 10). In addition, we confirmed the associations between the GGT1 locus and GGT levels and between the ABO locus and ALP levels. None of the ALP-associated SNPs were associated with other liver tests, suggesting intestine and/or bone specificity. The mechanisms underlying the associations may involve cis- or trans-transcriptional effects (some of the identified variants were associated with mRNA transcription in human liver or lymphoblastoid cells), dysfunction of the encoded proteins (caused by missense variations at the functional domains), or other unknown pathways. These findings may help in the interpretation of liver-enzyme tests and provide candidate genes for liver diseases of viral, metabolic, autoimmune, or toxic origin. The specific associations with ALP levels may point to genes for bone or intestinal diseases.
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Novel Association of ABO Histo-Blood Group Antigen with Soluble ICAM-1: Results of a Genome-Wide Association Study of 6,578 Women
Guillaume Pare, Daniel I. Chasman, Mark D Kellogg … (2008)
Introduction ICAM-1 is a member of the immunoglobulin superfamily of adhesion receptors and consists of 5 immunoglobulin-like extracellular domains, a transmembrane domain and a short cytoplasmic domain. ICAM-1, present on endothelial cells, serves as a receptor for the leukocyte integrins LFA-1 (lymphocyte function-associated antigen-1) and Mac-1 (CD11b/CD18), facilitating leukocyte adhesion and migration across the endothelium [1]. A soluble form of ICAM-1 (sICAM-1) is found in plasma and consists of the extra-cellular domains of ICAM-1. Although the process leading to the formation of sICAM-1 is not entirely clear, sICAM-1 is thought to be shed from the cell membrane via proteolytic cleavage of ICAM-1. Because sICAM-1 binds to LFA-1, it is capable of inhibiting lymphocyte attachment to endothelial cells [2]. Furthermore, sICAM-1 has been shown to bind human rhinoviruses, the etiologic agent of 40–50% of common colds, and to inhibit rhinovirus infection in vitro [3]. Likewise, a circulating fragment of sICAM-1 binds to erythrocytes infected with Plasmodium falciparum, the etiologic agent of malaria [4] (MIM 611162). Finally, plasma concentration of sICAM-1 has been shown to provide unique predictive value for the risk of myocardial infarction (MIM 608446), ischemic stroke (MIM 601367), peripheral arterial disease (MIM 606787) and noninsulin-dependent diabetes mellitus (MIM 125853) in epidemiological studies [5]–[7]. Despite relatively high heritability estimates (from 0.34 to 0.59) [8],[9] for sICAM-1, few genetic variants are known to influence its concentrations. Two recent linkage studies have shown evidence for genetic association at the ICAM1 (GeneID 3383) locus (19p13.3-p13.2) [8],[9] and two candidate SNPs within the extracellular domains of ICAM-1 itself, G241R (rs1799969) and K469E (rs5498), have been correlated with circulating sICAM-1 levels [10],[11]. By contrast, a recent genome wide association study (GWAS) from the Framingham investigators involving 1006 participants and 70,987 SNPs revealed no association reaching a genome-wide level of significance, including the ICAM1 locus itself, although this study had no genetic marker within 60 kb of the gene [12]. To more comprehensively explore this issue, we performed a larger GWAS, evaluating 336,108 SNPs in 6,578 apparently healthy women. Methods Study Sample and sICAM-1 Measurements All participants in this study were part of the Women's Genome Health Study (WGHS) [13]. Briefly, participants in the WGHS include American women from the Women's Health Study (WHS) with no prior history of cardiovascular disease, diabetes, cancer, or other major chronic illness who also provided a baseline blood sample at the time of study enrollment. The WHS is a recently completed 2×2 randomized clinical trial of low-dose aspirin and vitamin E in the primary prevention of cardiovascular disease and cancer. For all WGHS participants, EDTA anticoagulated plasma samples were collected at baseline and stored in vapor phase liquid nitrogen (−170°C). Circulating plasma sICAM-1 concentrations were determined using a commercial ELISA assay (R&D Systems, Minneapolis, Minn.); the assay used is known not to recognize the K56M (rs5491) variant of ICAM-1 [14] and the 22 carriers of this mutation were therefore excluded from further analysis. This study has been approved by the institutional review board of the Brigham and Women's Hospital. Additional clinical characteristics of these subsets are provided in Table S1. Genotyping Genotyping was performed in two stages, a first sample being used to discover new associated loci and the second sample being used to validate them by replication. These two samples were genotyped independently of one another in two batches. The first (WGHS-1) and second (WGHS-2) batches included 4,925 and 2,056 self-reported Caucasian WGHS participants, respectively. No related individuals were detected when tested with an identity by state analysis [15]. Samples were genotyped with the Infinium II technology from Illumina. Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips was used. In either case, the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism, inflammation or cardiovascular diseases. Genotyping at 318,237 HumanHap300 Duo SNPs and 45,571 custom content SNPs was attempted, for a total of 363,808 SNPs. Genetic context for all annotations are derived from human genome build 36.1 and dbSNP build 126. SNPs with call rates 1% in Caucasians were used for analysis. After quality control, 307,748 HumanHap300 Duo SNPs and 28,360 custom content SNPs were left, for a total of 336,108 SNPs. From the initial 4925 WGHS-1 and 2056 WGHS-2 individuals genotyped, 4582 WGHS-1 individuals and 2014 WGHS-2 individuals were kept for further analysis. Population Stratification Because population stratification can result in inflated type I error, a principal component analysis using 1443 ancestry informative SNPs was performed using PLINK [17] in order to confirm self-reported ancestry. Briefly, these SNPs were chosen based on Fst >0.4 in HapMap populations (YRB, CEU, CHB+JPT) and inter-SNP distance at least 500 kb in order to minimize linkage disequilibrium. Different ethnic groups were clearly distinguished with the two first components. Out of 4582 WGHS-1 and 2014 WGHS-2 self-identified Caucasians, 12 and 6 were removed from analysis because they did not cluster with other Caucasians, leaving 4570 (WGHS-1) and 2008 (WGHS-2) participants for analysis, respectively. Two more analyses were undertaken to rule out the possibility that residual stratification within Caucasians was responsible for the associations observed. First, association analysis was done with correction by genomic control. This method estimates the average effect of population substructure in the sample (based on median T values) and accordingly corrects the test statistics [18]. Second, a principal component analysis [19] was performed in Caucasians (only) using 124,931 SNPs chosen to have pair-wise linkage disequilibrium lower than r2 = 0.4. The first three components were then used as covariates in the association analysis. As adjustment by these covariates did not change the conclusions, we present analysis among the WGHS-1 and WGHS-2 Caucasian participants without further correction for sub-Caucasian ancestry unless stated otherwise. Association Analysis To identify common genetic variants influencing sICAM-1 levels, we first attempted to discover which loci significantly contributed to sICAM-1 concentrations in WGHS-1. Plasma concentrations of sICAM-1 were adjusted for age, smoking, menopause and body mass index using a linear regression model in R to reduce the impact of clinical covariates on sICAM-1 variance. The adjusted sICAM-1 values were then tested for association with SNP genotypes by linear regression in PLINK [17], assuming an additive contribution of each minor allele. A conservative P-value cut-off of 5×10−8 was used to correct for the roughly 1,000,000 independent statistical tests thought to correspond to all the common genetic variation of the human genome [20]. Replication of genome-wide significant associations was performed on adjusted sICAM-1 values from the replication sample (WGHS-2), using a Bonferroni correction to account for multiple hypothesis testing. Model Selection Algorithm To further define the extent of genetic associations, a forward selection linear multiple regression model was used at the previously identified loci. Briefly, all genotyped SNPs within 100 kb of the most significantly associated SNP at each replicated locus and passing quality control requirements were tested for possible incorporation into a multiple regression model. In stepwise fashion, a SNP was added to the model if its multiple regression P-value was less than 10−4 (to account for all the SNPs being considered) and if it had the smallest P-value among all the SNPs not yet included in the model. This analysis was done on WGHS-1 individuals using adjusted sICAM-1 values. We then proceeded to validate our multiple regression model in WGHS-2 samples. Using only the SNPs previously selected in WGHS-1, we added them in a multiple regression model in the same order as they were chosen in WGHS-1. We considered the model validated if each time a SNP was included in the model, its regression P-value was lower than 0.01 (to account for multiple testing) and the direction of effect consistent. Analytical Interference Assay Plasma from A blood group individuals was mixed 1∶1 or 1∶2 with a monoclonal anti-A antibody (Ortho-Clinical Diagnostics, Rochester NY), and allowed to incubate 10 minutes or 60 minutes at room temperature, or 60 minutes or 12 hours at 4°C before assaying sICAM-1 levels by the standard technique. To exclude the possibility that the antibody itself interfered with the assay, the same procedure was repeated with plasma from O blood group individuals. Finally, plasma from O group individuals, which is expected to contain both anti-A and anti-B polyclonal antibodies, was mixed with plasma from A group individuals in 1∶1 ratio, again with incubation as above and measurement of sICAM-1 levels. Results/Discussion As shown in Table 1, 19 SNPs passed our stringent genome-wide significance threshold when tested in WGHS-1 individuals, clustering within two loci in the vicinity of the ICAM1 (19p13.2) and ABO (GeneID 28) (9q34.2) genes (Figure 1). The replication threshold in WGHS-2 was conservatively set at a 2-sided P-value of 0.002, applying a Bonferroni correction to account for 19 tests. Using this cutoff, we were able to replicate 17 of the 19 associated SNPs, including SNPs at both the ICAM1 and ABO loci. Only rs2116941 (19p13.2) and rs7256672 (19p13.2) did not replicate using this standard. Nevertheless, each of these SNPs had a P-value lower than 10−9 when tested on the combined sample (i.e. WGHS-1 and WGHS-2 pooled together). Among the replicated SNPs, only rs7258015 (19p13.2) deviated from Hardy-Weinberg equilibrium (p = 0.00007), but visual inspection of the raw genotyping signal for this SNP did not reveal any obvious artifact. Major and minor alleles are shown in Table S2. 10.1371/journal.pgen.1000118.g001 Figure 1 Genetic Context of Genome-Wide Significant Associations. Genomic context for each of two loci with genome-wide association with sICAM-1 levels. The ICAM1 locus (19p13.2) is shown in Figure 1-A and the ABO locus (9q34.2) in Figure 1-B. Upper panel: Genes from RefSeq release 25. Only one isoform is shown when multiple splicing variants are known. Middle Panel: SNPs are shown according to their physical location and P-values (red dots). Also shown is the genetic distance in cM from the lowest P-value SNP (light grey line) along with the position of recombination hotspots (light grey vertical bars). Recombination rates and hotspots are based on HapMap data, as described by McVean et al.[53] and Winckler et al. [54]. Lower panel: Pair wise linkage disequilibrium (D′ and R2) between SNPs based on WGHS data. 10.1371/journal.pgen.1000118.t001 Table 1 Genome-Wide Significant SNPs for sICAM1. SNP Locus Position(kb) Nearest Gene Function MAFa HWb WGHS-1 WGHS-2 Combined Median sICAM-1 umol/L (n) Beta P-Value Beta P-Value Beta P-Value A1/A1c A1/A2c A2/A2c rs687621 9q34.2 135126.9 ABO intron 0.34 0.08 −10.88 1.3E-11 −11.21 1.4E-06 −10.95 1.3E-16 343 (2856) 334 (2894) 328 (806) rs687289 9q34.2 135126.9 ABO intron 0.34 0.07 −11.00 8.2E-12 −10.81 3.5E-06 −10.91 1.7E-16 343 (2859) 334 (2887) 328 (804) rs657152 9q34.2 135129.1 ABO intron 0.37 0.13 −10.17 1.7E-10 −9.93 1.5E-05 −10.06 1.5E-14 343 (2652) 334 (2992) 331 (905) rs500498 9q34.2 135138.5 ABO intron 0.45 0.10 9.53 5.0E-10 9.53 2.4E-05 9.52 6.1E-14 332 (2017) 337 (3160) 347 (1358) rs505922 9q34.2 135139.0 ABO intron 0.34 0.05 −11.15 4.5E-12 −10.83 3.2E-06 −11.02 9.2E-17 343 (2876) 334 (2871) 328 (798) rs507666 9q34.2 135139.2 ABO intron 0.20 0.42 −18.12 1.1E-20 −16.93 5.9E-10 −17.73 5.1E-29 343 (4221) 328 (2055) 314 (268) rs10409243 19p13.2 10194.0 EDG5 3′ UTRd 0.41 0.96 −10.93 4.4E-12 −8.22 2.6E-04 −10.11 5.4E-15 344 (2313) 335 (3158) 329 (1083) rs2116941 19p13.2 10195.4 EDG5 3′ UTRd 0.20 0.76 −12.05 7.9E-10 −7.47 7.5E-03 −10.67 3.1E-11 341 (4209) 333 (2076) 320 (249) rs8111930 19p13.2 10229.0 MRPL4 intron 0.12 0.27 18.88 4.4E-15 13.35 4.3E-05 17.10 1.3E-18 335 (5031) 346 (1369) 377 (102) rs1799969 19p13.2 10255.8 ICAM1 NSe 0.12 0.34 −26.11 2.1E-28 −33.18 1.3E-21 −28.19 3.6E-47 343 (5117) 319 (1336) 304 (99) rs5498 19p13.2 10256.7 ICAM1 NSe 0.43 0.15 13.88 6.2E-19 11.78 1.2E-07 13.22 4.8E-25 329 (2117) 338 (3123) 355 (1235) rs923366 19p13.2 10258.2 ICAM1 3′ UTRd 0.44 0.71 13.07 5.7E-17 11.05 7.9E-07 12.46 2.5E-22 329 (2088) 338 (3202) 353 (1243) rs3093030 19p13.2 10258.4 ICAM1 - 0.43 0.59 13.14 3.7E-17 11.41 2.7E-07 12.61 5.9E-23 329 (2109) 338 (3204) 353 (1243) rs281440 19p13.2 10261.3 ICAM5 - 0.22 0.69 −13.39 7.2E-13 −10.99 4.9E-05 −12.69 1.5E-16 343 (4021) 331 (2223) 322 (314) rs2075741 19p13.2 10262.1 ICAM5 intron 0.43 0.69 13.27 1.9E-17 11.40 3.2E-07 12.69 3.6E-23 329 (2104) 338 (3208) 353 (1240) rs2278442 19p13.2 10305.8 ICAM3 intron 0.35 0.65 −9.44 1.0E-08 −7.66 7.7E-04 −8.88 3.1E-11 344 (2727) 334 (3007) 332 (815) rs2304237 19p13.2 10307.6 ICAM3 NSe 0.22 0.49 10.83 1.2E-08 12.23 5.2E-06 11.26 3.9E-13 334 (3919) 342 (2129) 356 (313) rs7258015 19p13.2 10310.4 ICAM3 NSe 0.22 0.00007 10.60 3.2E-08 12.73 8.6E-06 11.19 2.1E-12 334 (3873) 342 (2275) 356 (255) rs7256672 19p13.2 10440.5 PDE4A 3′ UTRd 0.36 0.67 −9.06 2.7E-08 −6.36 5.8E-03 −8.24 6.3E-10 343 (2655) 337 (3049) 324 (845) a MAF: Minor allele frequency based on the combined samples. b HW: Deviation from Hardy-Weinberg equilibrium P-value based on the combined samples. c A1: Major Allele; A2: Minor Allele; Median sICAM-1 values were determined on the combined samples. d 3′ UTR: 3′ Untranslated Region. e NS: Non-Synonymous Coding SNP. We then applied our model selection algorithm in WGHS-1 individuals (see Methods) using 54 SNPs at 19p13.2 (ICAM1 locus) and 68 SNPs at 9q34.2 (ABO locus). As can be seen in Table 2, 3 out of 54 SNPs at 19p13.2 were selected by our algorithm and 1 out 68 SNPs at 9q34.2 was selected. All four SNPs selected in WGHS-1 were validated in WGHS-2. Pairwise linkage disequilibrium between these SNPs was low. For instance, r2 was lower than 0.35 between ICAM1 SNPs while it was lower than 0.002 between the ABO SNP rs507666 and the ICAM1 SNPs. Among these SNPs, there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an additional degree of freedom. Interestingly, one of the four selected SNPs (rs281437) was non-significant in univariate analysis, illustrating that its inclusion in the model and significant association are conditional on the genotypes at rs5498 and rs281437. No gene-gene interaction was observed between ICAM1 and ABO SNPs. The 3 SNPs at 19q13.2 (ICAM1) collectively explained 6.9% of the total variance in sICAM-1 concentrations (pooling WGHS-1 and WGHS-2 together), whereas the ABO SNP rs507666 explained 1.5%. In comparison, clinical covariates accounted for 18.8% of the variance (Table 3), and together the candidate loci and the clinical variables accounted for 27.3% of total variance. It should be noted that the estimated effect sizes of the ICAM1 and ABO loci are minimums since the genotyped variants might not be the actual functional variants. 10.1371/journal.pgen.1000118.t002 Table 2 Multiple Linear Regression Statistics of SNPs Retained by the Forward Model Selection Algorithm. Locus SNP Nearest Gene Function MAFa HWb WGHS-1c WGHS-2c Combinedc Beta P-Value Beta P-Value Beta P-Value 19p13.2 rs1799969 ICAM1 NSd 0.12 0.34 −41.2 <2.0E-16 −49.3 <2.0E-16 −43.5 <2.0E-16 rs5498 ICAM1 NSd 0.43 0.15 30.4 <2.0E-16 28.3 <2.0E-16 29.7 <2.0E-16 rs281437 ICAM1 3′ UTRe 0.30 0.47 11.2 1.3E-08 7.9 5.1E-03 10.1 3.2E-10 9q34.2 rs507666 ABO intron 0.20 0.42 −16.8 <2.0E-16 −16.8 1.9E-10 −16.7 <2.0E-16 a MAF: Minor allele frequency based on the combined samples. b HW: Deviation from Hardy-Weinberg equilibrium P-value based on the combined samples. c All analyses were performed using adjusted sICAM1 values (see text for details). Beta coefficients and P-values were derived form a multiple linear model that included all 4 SNPs. d NS: Non-Synonymous Coding SNP. e 3′ UTR: 3′ Untranslated Region. 10.1371/journal.pgen.1000118.t003 Table 3 Partition of sICAM-1 Variance According to Genetic and Clinical Variables. Category Variable Variable R2 Category R2 Clinical Covariates Age 0.012 0.188 Body Mass Index 0.035 Menopause Status 0.006 Smoking 0.135 9q34.2 (ABO) Locus rs507666 0.015 0.015 19p13.2 (ICAM1) Locus rs1799969 0.026 0.069 rs5498 0.039 rs281437 0.005 TOTAL 0.273 The 3 SNPs at the 19p13.2 (ICAM1) locus selected by our algorithm were also used in haplotype analysis using WHAP [21], as implemented in PLINK [17] (Table 4). The estimate of the proportion of variance attributable to haplotypes, as well as their regression coefficients, is consistent with the linear model of these same SNPs, reinforcing the adequacy of a strictly additive model to explain the association. 10.1371/journal.pgen.1000118.t004 Table 4 Haplotype Analysis of rs1799969, rs5498 and rs281437 (19p13.2; ICAM1 Locus). Haplotype Frequency Beta P-Value rs1799969 rs5498 rs281437 A G G 0.12 −43.91 1.7E-95 G A G 0.27 −30.16 3.0E-78 G A A 0.30 −19.98 5.3E-37 G G G 0.32 Reference - Omnibus (3 df) p-value = 1.0E-124. WGHS-1 and WGHS-2 were combined for this analysis. The ABO histo-blood group antigen is the most important blood group system in transfusion medicine. Using data from Seattle SNPs (http://pga.mbt.washington.edu) as well as from the Blood Group Antigen Mutation Database (www.ncbi.nlm.nih.gov), it can be demonstrated that rs507666 is a perfect surrogate for type A1 histo-blood group antigen. Moreover, using rs687289 as a marker for the O allele, rs8176746 for the B allele and rs8176704 for the A2 allele, complete blood group antigen phenotype can be re-constructed by haplotype analysis (no serotype data is available in WGHS). Imputed haplotypes perfectly fitted the pattern expected from the literature and their association with sICAM-1 is shown in Tables 5 and 6. The A1 allele is associated with the lowest sICAM-1 concentrations while the A2 allele is associated with low concentrations, intermediate between the A1 and O allele. In comparison, the B allele is associated with slightly higher concentrations than the O allele. 10.1371/journal.pgen.1000118.t005 Table 5 Association of sICAM-1 Concentrations (µmol/L) with Histo-Blood Group Antigen Alleles (9q34.2; ABO Locus). Allele Genotype Frequency Beta rs8176746 rs8176704 rs687289 rs507666 A1 C G A A 0.20 −14.3 A2 C A A G 0.07 −2.2 B A G A G 0.08 4.7 O C G G G 0.66 3.9 Omnibus (3 df) p-value = 2.0E-28. WGHS-1 and WGHS-2 were combined for this analysis. 10.1371/journal.pgen.1000118.t006 Table 6 Mean (SD) Level of sICAM-1 (µmol/L) According to Predicted ABO Alleles. First Allele A1 A2 O B Second Allele A1 326 (71) N = 268 332 (71) N = 185 338 (77) N = 1656 352 (89) N = 182 A2 - 346 (55) N = 33 352 (80) N = 537 366 (91) N = 56 O - - 358 (81) N = 2859 356 (80) N = 592 B - - - 342 (79) N = 51 WGHS-1 and WGHS-2 were combined for this analysis. Mean (SD) values were derived from unadjusted sICAM-1 concentrations. Because ABO histo-blood group antigens are known to vary in frequency among Caucasian sub-populations, we sought to investigate the potential effect of population stratification on the observed association even though adjustment of sICAM-1 values for the top ten components of our principal component analysis did not change our conclusions (see Methods). Visual inspection of the clustering pattern from the top two components confirmed a match with previously published work of sub-Caucasian stratification [22] (data not shown). Since these two components were reproducibly shown to correspond to a Northwest-Southeast European gradient [22] and the A1 allele follows such a gradient [23], we hypothesized that they would be tightly linked to A1 allele frequencies. Indeed, the second component showed evidence of association with A1 allelic frequencies (p = 2.5×10−6), while the first component was only weakly associated (p = 0.08). Nevertheless, neither the first nor second component was very tightly linked to sICAM-1 values (p = 0.69 and 0.0006 respectively with corresponding R2 of 3.8×10−5 and 0.0019), implying that stratification has no major effect on the sICAM-1 association. Furthermore, the weak association with the second component could be partially explained by the correlation with A1 alleles, with corrected P-value of 0.004 and R2 of 0.0013. Adjustment of sICAM-1 values for the first and second components did not substantially change the association between the A1 allele and sICAM-1 (unadjusted p = 5.1×10−29 and adjusted p = 5.5×10−28), demonstrating that stratification on a Northwest-Southeast European axis is not responsible for the association. We conclude that the data does not support the hypothesis that Northwest-Southeast sub-Caucasian stratification is responsible for the association of ABO variants with sICAM-1 concentrations since the A1 allele varies in frequency according to a Northwest-Southeast European axis while the slight variation in sICAM-1 among this same axis is at least partially dependent on the A1 allele. Indeed, there is no evidence in the literature that mean sICAM-1 concentrations vary at all among Caucasian sub-populations, and this lack of evidence is supported by an overall R2 of 0.005 (P-value of 0.0007) for the association between sICAM-1 concentrations and the top 10 principal components. The Secretor phenotype (as defined by rs601338 on chromosome 19q13.33) and the Lewis antigen phenotype (as defined by rs812936 on chromosome 19p13.3) are additional important members of the histo-blood group antigen system. These were therefore tested for association with sICAM-1 levels as well as for interaction with rs507666. No significant effect was observed. Although the sICAM-1 molecule itself is not known to bear the ABO histo-blood group antigen, this possibility could not be ruled out, especially given its extensive glycosylation [24],[25]. We therefore sought to exclude the remote chance that the association between A histo-blood group antigen and lower sICAM-1 values was the consequence of a lower affinity of the antibodies used in the sICAM-1 assay for sICAM-1 carrying the A antigen. In other words, if sICAM-1 does carry ABO histo-blood group antigen, then the allelic composition at the ABO locus could dictate the glycosylation status of the sICAM-1 molecule and possibly interfere with the immunoassay used. While there is no evidence that the two plasma proteins known to contain ABO histo-blood group antigen (von Willebrand factor and alpha 2-macroglobulin) [26] suffer from such analytical interference, immunoassays are potentially susceptible to differential glycosylation of their target protein [27]. We thus hypothesized that blocking the A antigen sites with either polyclonal or monoclonal antibodies would result in spuriously low sICAM-1 values if sICAM-1 does indeed carry ABO histo-blood group antigen and if the A antigen is located in the vicinity of one of the two antibody binding sites used by the immunoassay. No differential effects of the mixing procedures (see Methods) were observed suggesting that the A blood group antigen was not interfering with measurement of sICAM-1 levels. We therefore conclude that the genetic association of the ABO variant is not due to analytic interference. However, we can not exclude that sICAM-1 bears the ABO histo-blood group antigen. Finally, we sought to assess the presence of other associations that did not pass our stringent genome-wide P-value cut-off. We therefore repeated the whole-genome association analysis on the combined sample (i.e. WGHS-1 and WGHS-2 pooled together). While no new locus was associated at a genome-wide level, rs9889486 had the lowest p-value (outside of 9q34.2 and19p13.2; p = 3.2×10−6) with a false discovery rate [28] of 0.03. This SNP is intronic to CCDC46 (GeneID 201134) (17q24.1), a gene whose function is not well characterized. Among other low p-value SNPs, we note rs1049728 (p = 1.3×10−5) with a false discovery rate of 0.08 and the 51st most strongly associated SNP overall. This SNP is located in the 3′ untranslated region of RELA (GeneID 5970) (11q13.1), which is part of the NFKB signaling complex, arguably the most important known regulator of ICAM1 expression [29]. The non-synonymous coding ICAM1 SNPs rs1799969 (G241R) and rs5498 (K469E) were previously described as being associated with sICAM-1 levels[10],[11] whereas the association involving rs281437 is unreported. The later SNP is in the 3′ untranslated region of ICAM-1. Of interest, the minor allele of rs1799969 (arginine) is correlated with lower sICAM-1 and has been associated with lower risk of type I diabetes[30], while the minor allele of rs5498 (glutamic acid) is correlated with higher sICAM-1 levels and has been associated with lower risk of asthma [11] (MIM 600807), inflammatory bowel disease [31] (MIM 266600) and type I diabetes [32] (MIM 222100). Furthermore, it has been demonstrated in vitro that this SNP affects ICAM-1 mRNA splicing pattern and apoptosis in human peripheral blood mononuclear cells [33]. It is also noteworthy that sICAM-1 has been shown to inhibit insulitis and onset of autoimmune diabetes in a mouse model of type I diabetes [34] whereas ICAM1 itself was proven to be crucial to the priming of T cells against beta cells [35]. The most striking result of this report is the association between sICAM-1 levels and rs507666, a SNP intronic to the ABO gene. The ABO gene encodes glycosyltransferase enzymes which transfer specific sugar residues to a precursor substance, the H antigen. There are three major alleles at the ABO locus: A, B and O. Variation at the ABO locus is remarkable in that these alleles encode enzymes with different specificities as well as activities. The A allele encodes the enzyme alpha1→3 N-acetylgalactosamyl-transferase which forms the A antigen from the H antigen. The A allele (as well as the B and O alleles) is itself heterogeneous and comprises several subgroups, of which A1 and A2 are the most important. As compared to A1, the A2 allele has 30–50 fold less A transferase activity [36]. The B allele encodes the enzyme alpha1→3 galactosyltransferase which forms the B antigen from the H antigen. The O allele does not produce an active enzyme [37]. Consistent with the A antigen being associated with lower sICAM-1 concentrations and with the A1 allele having 30–50 fold more A transferase activity than the A2 allele, the A1 allele is associated with the lowest sICAM-1 concentrations while the A2 allele is associated with low concentrations as well, but still higher than the A1 allele (Table 5). Although we excluded the possibility of an analytical interference to explain the association, the exact mechanism linking histo-blood group antigen to sICAM-1 concentrations remains elusive. Among the different hypotheses, it remains possible that sICAM-1 bears the A antigen, a modification that might increase its clearance by increasing its affinity for its receptor(s) and/or decrease its secretion, perhaps by decreasing its affinity for the protease(s) producing sICAM-1 from membrane-bound ICAM1. Alternatively, lower sICAM-1 concentrations might be the result of the presence of the A antigen on its receptor(s) and/or protease(s). ABO histo-blood group phenotype has been linked to a plethora of diseases, including infectious diseases, cancers and vascular diseases [38]. Particularly interesting is the association of non-O histo-blood groups — and group A in particular [39],[40] — with a higher risk of myocardial infarction, peripheral vascular disease, strokes and venous thromboembolism [41] (MIM 188050). While this phenomenon is partially explained by higher concentrations of the coagulation factors vonWillebrand and VIII (presumably because of decreased clearance) [42], the exact mechanism is not entirely understood. Underlining the complex nature of the biological processes involved, the A1 group (rs507666) is associated with lower levels of sICAM-1, a (positive) predictor of vascular diseases in epidemiological studies [5], [6], [43]–[46]. Among potential explanations as to this apparent disparity, it is possible that decreased sICAM-1 leads to increased adhesion of leukocytes on endothelial surface and therefore increased vascular inflammation, an important component of atherosclerosis [47]. Moreover, because group A individuals have been shown to have higher blood cholesterol [48] and coagulability [42], the decrease in sICAM-1 seen in these individuals could be offset by the increased susceptibility to vascular diseases conferred by these risk factors, even if sICAM-1 mechanistically causes these diseases. Alternatively, sICAM-1 might merely be a marker of increased inflammation and coagulation [49], both risk factors for vascular diseases. Also of special interest, group A antigen carriers have been recognized as having a higher risk of suffering from severe malaria when infected by Plasmodium falciparum [50]. Plamodium infected erythrocytes express a receptor (PfEMP-1) that binds specifically to cell-surface group A and B antigen as well as ICAM-1 [51], a major step in the sequestration of infected erythrocytes leading to the clinical complications of severe and cerebral malaria. The lower concentrations of sICAM-1 found in A1 group carriers could therefore be hypothesized to contribute to this higher risk either directly, if sICAM-1 can inhibit the sequestration process, or indirectly, if sICAM-1 levels reflect differences in the processing of the ICAM1 receptor itself. Several limitations warrant discussion. First, this study was conducted in Caucasian women. It is therefore difficult to generalize our results to other ethnicities or to men. Second, effect estimates derived from this study might be higher than in other populations as these are initial findings and because of the winner's curse [52]. Third, although we were able to rule out a technical artifact as the cause of our results, no mechanistic link is identified to explain the association between ABO histo-blood groups and sICAM-1. In particular, one pending question is whether or not ICAM-1 bears any ABO antigen at all. In this report, we demonstrate that sICAM-1 concentrations are associated with genetic variation at the ABO and ICAM1 loci in women. To our knowledge, this represents the first published genetic evidence that ABO may have a regulatory role on an inflammatory mediator, a finding with potential implication on a diverse array of immune-mediated disorders. Especially interesting is the fact that both ABO and ICAM1 have been previously related to vascular disease and malaria, two major causes of mortality and morbidity worldwide. The current study indicates a genetic link between histo-blood group antigen and inflammatory adhesion processes, providing the basis for physiological studies of this interaction. Supporting Information Table S1 Clinical Characteristics of the Samples Used. (0.04 MB DOC) Click here for additional data file. Table S2 Major and Minor Alleles. (0.04 MB DOC) Click here for additional data file.
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Journal ID (nlm-ta): BMJ Open
Journal ID (iso-abbrev): BMJ Open
Journal ID (hwp): bmjopen
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BMJ Open
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Publication date Collection: 2020
Publication date
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2
February
2020
Volume: 10
Issue: 1
Electronic Location Identifier: e034114
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Publisher ID: bmjopen-2019-034114DOI: 10.1136/bmjopen-2019-034114
PMC ID: 7045238
PubMed ID: 32014878
SO-VID: 191e77a9-f059-4c70-aea7-c4f1179a37a8
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History
Date
received
: 07
September
2019
Date
revision received
: 10
January
2020
Date
accepted
: 13
January
2020
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Subject:
Epidemiology
Subject:
Original Research
Subject:
1506
Subject:
1692
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ScienceOpen disciplines: Medicine