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. Author manuscript; available in PMC: 2015 Nov 10.
Published in final edited form as: Vaccine. 2013 Sep 26;31(46):5381–5391. doi: 10.1016/j.vaccine.2013.09.026

Genetic variants within the MHC region are associated with immune responsiveness to childhood vaccinations☆

Berran Yucesoy a,*, Yerkebulan Talzhanov b, Victor J Johnson c, Nevin W Wilson d, Raymond E Biagini e, Wei Wang a, Bonnie Frye a, David N Weissman f, Dori R Germolec g, Michael I Luster h, Michael M Barmada b
PMCID: PMC4640212  NIHMSID: NIHMS732124  PMID: 24075919

Abstract

The influence of genetic variability within the major histocompatibility complex (MHC) region on variations in immune responses to childhood vaccination was investigated. The study group consisted of 135 healthy infants who had been immunized with hepatitis B (HBV), 7-valent pneumococcal conjugate (PCV7), and diphtheria, tetanus, acellular pertussis (DTaP) vaccines according to standard childhood immunization schedules. Genotype analysis was performed on genomic DNA using Illumina Goldengate MHC panels (Mapping and Exon Centric). At the 1 year post vaccination check-up total, isotypic, and antigen-specific serum antibody levels were measured using multiplex immunoassays. A number of single nucleotide polymorphisms (SNPs) within MHC Class I and II genes were found to be associated with variations in the vaccine specific antibody responses and serum levels of immunoglobulins (IgG, IgM) and IgG isotypes (IgG1, IgG4) (all at p< 0.001). Linkage disequilibrium patterns and functional annotations showed that significant SNPs were strongly correlated with other functional regulatory SNPs. These SNPs were found to regulate the expression of a group of genes involved in antigen processing and presentation including HLA-A, HLA-C, HLA-G, HLA-H, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DOB, and TAP-2. The results suggest that genetic variations within particular MHC genes can influence immune response to common childhood vaccinations, which in turn may influence vaccine efficacy.

Keywords: Major histocompatibility complex, Genetic polymorphism, Childhood vaccine, Immune response

1. Introduction

Even with uniform administration schemes, large inter-individual variability exists in vaccine responsiveness among vaccine recipients. For example, 5–20% and 2–10% of healthy individuals experience either hypo- or non-responsiveness to hepatitis B (HBV) or measles vaccination, respectively [1–4]. A strong genetic component has been demonstrated in the regulation of immune responses to the vaccines. A number of polymorphisms have been reported to be associated with vaccine responsiveness including variants of the major histocompatibility complex (MHC) [5–7] and cytokine and cytokine receptor genes [8–11].

The MHC spans ~4Mb and comprises over 180 protein-coding genes, many of which determine immune function, susceptibility to complex diseases, and transplant rejection. MHC class II molecules are involved in the presentation of MHC-peptide complexes on the surface of antigen presenting cells to CD4+ T cells. These molecules are highly polymorphic and this diversity helps determine immune recognition. Along with the HLA genes, several functionally important genes are located in this region including those that code for complement proteins C4, C2 and Factor B, the cytokines tumor necrosis factor α and β and TAP (antigen peptide transporter) that function in antigen processing [12,13].

The contribution of the MHC variants to the vaccine immune response was first observed by a significant excess of HLA-DR7 and a total absence of HLA-DR1 in individuals that failed to respond to hepatitis B vaccine [14]. Subsequent studies revealed associations between certain HLA class II (HLA-DR, HLA-DQ) alleles and poor or non-immune response to HBV [6,9,15–19]. In addition, poor responsiveness to hepatitis B vaccine was associated with extended MHC haplotypes such as B8-DR3-SC01, B44-DR7-FC31 and B18-DRB1*0301-DQB1*0201 [6,15,18–20]. Several studies have also demonstrated the influence of HLA allelic variation on immune response to measles, mumps, rubella, and influenza vaccines [21–24]. For example, HLA class I B*8, B*13, and B*44 alleles were associated with IgG seronegativity after a single dose of measles vaccine whereas the A*29-C*16-B*44 haplotype was associated with low IgG antibody levels after two doses of the same vaccine [21,25]. The DPA1*0201 and DPB1*0401 alleles were associated with low and high levels of rubella-induced antibodies in two separate cohorts, respectively [26]. Furthermore, the DRB1*04-DQB1*03-DPB1*03 and DRB1*15/16-DQB1*06-DPB1*03 haplotypes were associated with low levels of rubella-specific antibodies [23].

Although the HLA complex is one of the most extensively studied regions in the human genome, the other genes in the MHC region have not yet been well investigated with regard to vaccine responsiveness. In the present study, a focused approach has been taken to examine the association of SNPs within the MHC region with variation in childhood vaccine responses.

2. Materials and methods

2.1. Study population and vaccinations

Study procedures were approved by the Institutional Review Boards of all participating institutions. The subjects were infants seen in two University-affiliated general pediatrics clinics for routine 1 year old checkup examinations. These clinics routinely obtained blood by finger stick during the 1 year checkup to screen for anemia and lead poisoning. If parents gave informed consent, additional tubes of blood for genomic DNA were obtained. In addition, immunization records were reviewed to document history of immunization with HBV (Recombivax®, Merck&Co., Inc., White-house Station, NJ); DTaP (Daptacel®, Sanofi Pasteur, Ontario, CA); heptavalent pneumococcal conjugate vaccine (PCV7-serotypes 4, 6B, 9V, 14, 18C, 19F, 23F) (Prevnar®, Wyeth, Philadelphia, PA); inactivated polio vaccine (IPV); and Haemophilus influenza type b (Hib) conjugate vaccine in accordance with then-current guidelines for childhood immunization [27]. A total of 135 healthy infants, aged 11.5–14 months of age (mean: 12.6 months), were recruited into the study. The majority of children were non-Hispanic whites (121) and male (77). The demographics and immunological variables of the participants that were included in the analysis are given in Table 1.

Table 1.

Demographic characteristics and immunological variables of the study population.

N=135
Demographics
Age (mean months, range) 12.6 (11.5–14)
Gender (F/M) 58/77
Ethnicity (non-hispanic whites/others) 121/14
Antibody levels Median Mean CI (95%)
HBV(mIU/ml) 153 499.93 290.34,709.51
Diphteria(IU/ml) 0.32 0.56 0.45,0.66
Tetanus (IU/ml) 0.2 4.1 −1.43,9.63
PnPS4 (µg/ml) 1.3 2.7 2.02,3.39
PnPS6B (µg/ml) 3.17 10.77 5.48,16.06
PnPS9V (µg/ml) 3.3 18.04 9.02,27.06
PnPS14 (µg/ml) 2.45 5.5 3.42,7.57
PnPS18C (µg/ml) 2.24 5.67 3.74,7.6
PnPS19F(µg/ml) 2.37 24.8 2.27,47.33
PnPS23F(µg/ml) 4.01 52.02 19.42,84.62
IgG (mg/dl) 639.1 701.89 648.48,755.31
IgG1 (mg/dl) 653.1 710.36 651.19,769.52
IgG2 (mg/dl) 66.4 105.27 82.83,127.71
IgG3 (mg/dl) 34 40.96 36.68,45.24
IgG4 (mg/dl) 2 24.01 6.41,41.61
IgM (mg/dl) 141.6 150.68 139.45,161.91
IgA (mg/dl) 6 6.93 6.17,7.69
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2.2. Genotyping

Genomic DNA was extracted from whole blood samples using the QIAamp blood kit (QIAGEN Inc., Chatsworth, CA). Genotyping was performed according to the standard protocol provided by Illumina using the MHC Panel Set and Golden Gate protocol (Illumina Inc., San Diego, CA). The MHC SNP set consisted of two oligonucleotide pools, MHC Mapping Panel and MHC Exon-Centric Panel for 1228 and 1293 SNP loci, respectively. Both panels cover 2360 independent loci spaced at an average of 2.08 kb (range: 0.005–71.05 kb). Genotyping was performed in a 16-well format using universal BeadChips. A total of 250 ng to 1 µg DNA was used for each assay depending on the source. Genotypes were auto called using GenomeStudio software (Illumina, Inc., San Diego, CA).

2.3. Microsphere coupling

Pneumococcal polysaccharides (PnPS) were obtained from ATCC, Manassas, VA. Pneumococcal cell wall polysaccharide (CPS) was obtained from Staten Serum Institute (Copenhagen, Denmark). Diphtheria and tetanus toxoids were obtained from University of Massachusetts Biologics Laboratories, Jamaica Plain, MA. The PnPSs were conjugated to spectrally distinguishable microspheres (Luminex, Austin, TX) using 4-(4,6-dimethoxy[1,3,5]triazin-2-yl)-4-methyl-morpholinium [28]. Diphtheria and tetanus toxoids were conjugated to spectrally distinguishable microspheres using 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride and sulfo-N-hydroxysuccinimide [28].

2.4. Serum collection and analyses

Blood samples were collected at approximately 1 year of age (mean: 12.6 months). Serum was isolated and stored at −20 °C until analysis. Vaccine-specific antibody responses to PnPSs, diphtheria and tetanus toxoids; and total serum immunoglobulin levels (IgM, IgA, IgG and IgG subclasses) were measured by multiplex assay as previously described [29,30]. Briefly, microspheres coupled to PnPSs, diphtheria and tetanus toxoids were mixed and added to standards and samples diluted in PBS containing 1%BSA, 0.05%Tween 20, 10 µg/ml of CPS, and 100 µg/ml of PnPS 22. Measurement and data analysis were performed using the Bioplex multiplex testing platform (BioRad, Hercules, CA). Assays were performed in duplicate. Serum concentrations of IgG, IgA, IgM, and IgG subclasses were measured in duplicate using Beadlyte® assay according to the manufacturer’s instructions (Upstate, Lake Placid, NY).

Levels of specific serum antibody to HBsAg were determined using a commercially-available enzyme immunoassay according to the manufacturer’s instructions (ETI-AB-AUK PLUS, DiaSorin Inc., Stillwater, MN). All serum measurements were above the minimum detection limit of the assay.

2.5. Statistical analyses

SNP-specific deviations from the Hardy-Weinberg Equilibrium were tested using chi-squared goodness-of-fit tests. Antibody levels were transformed to their log values (base 2) before analysis to fit the normality assumptions. These variables were included in the analysis first as continuous variables then they were turned into binary variables at thresholds of 10% and 15% to examine the trend in data.

The genotype confidence score of the assay was set to 0.25 in GenomeStudio Genotyping module. Alleles that were not called in a sample were coded as missing in the analysis. For missing rates per individual and per SNP, a threshold of 2% was used. Datasets from exon-centric and mapping panels were merged using PLINK [31]. This dataset contained 1856 SNPs for 135 subjects including 77 males and 58 females. The initial two datasets had 111 markers in common and the concordance rate of these markers was 0.999. Initial datasets had 124 subjects in common thus, 11 subjects had set of markers either from exon-centric or mapping panels. The total genotype rate for the merged dataset was 0.96. Replicate sample comparisons within and across DNA genotyping plates also demonstrated high agreement (data not shown).

Statistical analysis was performed using PLINK version 1.07 [31]. Linear and logistic regression models, with adjustments for gender, age and ethnicity, were used to test for differences between antibody levels (as continuous and binary data) according to genotypes. Associations were tested individually and based on principal components determined from combinations of antibody levels. Linkage disequilibrium (LD) and haplotype blocks were assessed using default parameters in Haploview [32]. Pairwise LD was calculated only for SNPs within 200 kb. SNAP was used to find proxy SNPs within 500 kb based on LD and physical distance [33]. RegulomeDB was used to annotate SNPs with known and predicted regulatory elements [34].

3. Results

All analyses were conducted on both the quantitative antibody phenotype and the dichotomized serotype status (lowest 10% and 15%). Since discretization could result in arbitrary cut-off levels, the focus of this report will be on the results with the quantitative phenotypes. However, the results for both analyses were consistent with each other (overlaps with the binary analysis are marked in Tables 2 and 3).

Table 2.

Linear regression results for vaccine-specific antibody responses.

Vaccine Gene Position SNP Genotype N Median antibody
level (µg/ml)		 Mean antibody
level (µg/ml)		 CI (95%) P
HBV RPP21 −28937 rs3129820 AA 0 – – –
AG 24 63.5 281.97 84.74, 479.19
GG 101 191.3 587.49 316.12, 858.85 0.0004941
HBV RPP21 −38015 rs6939217 AA 0 – – –
AG 26 63.5 270.33 88.94, 451.72
GG 99 191.3 596.6 319.97, 873.23 0.0001285
HBV ZBTB12 −557 rs558702 AA 0 – – –
AG 18 43.5 151.11 −10.74, 312.95
GG 107 210 591.15 333.96, 848.33 0.0002137
HBV BF −61 rs1270942 AA 113 186.5 552.82 308.91, 796.72
AG 21 38.5 136.59 −7.98, 281.16
GG 0 – – – 7.88E–05
HBV STK19 −363 rs389884 AA 113 186.5 552.82 308.91, 796.72
AG 21 38.5 136.59 −7.98, 281.16
GG 0 – – – 7.88E–05
HBV TNXB −1025 rs1150758 CC 1 140.7 140.7 –
CG 27 46.6 167.1 42.87, 291.32
GG 106 189.25 573.07 313.6, 832.54 0.0005311
HBV TNXB −2739 rs1150753 AA 107 210 591.15 333.96, 848.33
AG 18 43.5 151.11 −10.74, 312.95
GG 0 – – – 0.0002137
HBV CREBL1 −2856 rs1269852 CC 0 – – –
CG 18 43.5 151.11 −10.74, 312.95
GG 107 210 591.15 333.96, 848.33 0.0002137
HBV NOTCH4 [163/12] rs3134942 AA 1 43.5 43.5 –
AC 25 74.84 275.66 92.79, 458.53
CC 99 212.44 597.6 320.82, 874.38 0.0009721
HBV NOTCH4 −827 rs313296 AA 1 43.5 43.5 –
AG 25 74.84 275.66 92.79, 458.53
GG 99 212.44 597.6 320.82, 874.38 0.0009721
HBV BTNL2 −4312 rs3129950 CC 4 19.25 25.5 −24.09, 75.09
CG 14 46.6 189.76 −23.89, 403.4
GG 107 210 591.15 333.96, 848.33 4.28E–05
HBV HLA-DRA −7559 rs984778 AA 62 106.2 209.42 133.79, 285.06
AG 55 183.1 540.01 317.94, 762.09
GG 17 583.54 1341.03 −166.48, 2848.55 0.0006534
HBV HLA-DRA −6430 rs3135338 AA 64 106.2 207.29 134.08, 280.5
AG 53 210 554.98 325.29, 784.67
GG 17 583.54 1341.03 −166.48, 2848.55 0.0005719
HBV HLA-DRA −2455 rs3135395 AA 17 583.54 1341.03 −166.48, 2848.55
AC 55 183.1 540.01 317.94,762.09
CC 62 106.2 209.42 133.79, 285.06 0.0006534
HBV HLA-DRA −2285 rs2395178 CC 62 106.2 209.42 133.79, 285.06
CG 55 183.1 540.01 317.94, 762.09
GG 17 583.54 1341.03 −166.48, 2848.55 0.0006534
HBV HLA-DQA1 −567 rs2187668 AA 1 0 0 –
AG 24 45.05 135.61 11.91, 259.32
GG 109 187.2 569.54 317.13, 821.95 3.35E–05
Tet PSORS1C1 −648 rs3130454 AA 56 0.24 3.43 −2.26, 9.11
AG 57 0.16 0.23 0.17,0.29
GG 12 0.13 0.2 0.05, 0.35 0.0005775
PnPS4 HLA-DOB −4135 rs2857130* AA 17 3.79 5.5 2.16, 8.84
AT 54 1.5 3.11 1.88, 4.34
TT 63 0.98 1.59 1.14, 2.04 0.0003583
PnPS4 HLA-DOB −3695 rs2857127 AA 16 3.9 5.66 2.1, 9.21
AG 55 1.55 3.11 1.9, 4.31
GG 63 0.98 1.59 1.14, 2.04 0.0004657
PnPS4 HLA-DOB −3409 rs6929716* AA 63 0.98 1.59 1.14, 2.04
AG 54 1.5 3.11 1.88, 4.34
GG 17 3.79 5.5 2.16, 8.84 0.0003583
PnPS4 HLA-DOB 2018 rs7383433* AA 17 3.79 5.5 2.16, 8.84
AG 54 1.5 3.11 1.88, 4.34
GG 63 0.98 1.59 1.14, 2.04 0.0003583
PnPS4 HLA-DOB −553 rs5009557* AA 61 0.98 1.56 1.11, 2.01
AG 56 1.5 3.09 1.9, 4.28
GG 17 3.79 5.5 2.16, 8.84 0.0003038
PnPS4 TAP2 −148 rs1015166 AA 13 0.58 0.9 0.31,1.49
AG 59 1.07 2.07 1.25, 2.9
GG 62 2.19 3.67 2.43, 4.91 0.0007788
PnPS9V HLA-DOB −553 rs5009557 AA 61 2.74 8.24 0.04, 16.45
AG 56 3.38 26.03 8.12, 43.94
GG 17 7.1 27.59 −3.24, 58.42 0.0007529
PnPS14 LEMD2 −4863 rs755495 AA 4 1.62 2.7 −2.78, 8.19
AG 34 1.25 3.6 1.46,5.74
GG 87 2.79 5.92 3.18, 8.65 0.00089
PnPS19F COL11A2 −82 rs9368758 AA 2 4.51 4.51 −34.68, 43.71
AG 16 7.94 140.74 −46.2, 327.68
GG 116 2.11 9.35 0.66, 18.05 0.0001841
PnPS19F COL11A2 −1038 rs2269346 AA 2 4.51 4.51 −34.68, 43.71
AG 16 7.94 140.74 −46.2, 327.68
GG 116 2.11 9.35 0.66, 18.05 0.0001841
PnPS19F HSD17B8 −45 rs383711 AA 2 4.51 4.51 −34.68, 43.71
AG 16 7.94 140.74 −46.2, 327.68
GG 116 2.11 9.35 0.66, 18.05 0.0001841
PnPS19F RING1 −462 rs213210 AA 117 2.09 9.29 0.67, 17.91
AG 16 7.94 140.74 −46.2, 327.68
GG 2 4.51 4.51 −34.68, 43.71 0.0001718
PnPS23F NOTCH4 −20 rs2071280 CC 67 6.15 99.82 35.38, 164.26
CG 57 2.68 4.98 2.89, 7.07
GG 10 4.41 4.99 2.42, 7.55 0.0003241
PnPS23F NOTCH4 −57 rs2071287 AA 28 2.81 3.63 2.56, 4.7
AG 72 4.36 28.48 0.29, 56.67
GG 34 7.87 143.22 29.57, 256.87 0.0006293
PnPS23F NOTCH4 −24 rs2071277 AA 34 7.87 143.22 29.57, 256.87
AG 72 4.36 28.48 0.29, 56.67
GG 28 2.81 3.63 2.56, 4.7 0.0006293
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*

Markers that had significant p-values in logistic regression with binary cut-off 10%.

Underlined markers were significant in PCA.

Bold markers are significant in haplotype analysis.

Table 3.

Linear regression results for immunoglobulin levels.

Variable Gene Position SNP Genotype N Median antibody
level (µg/ml)		 Mean antibody
level (µg/ml)		 CI (95%) P
IgG HLA-F −6607 rs2517911 AA 82 711.75 779.3 704.2, 854.41
AG 48 565.7 593.31 528.77, 657.85
GG 4 467.05 422.65 239.3, 606 3.55E–05
IgG HLA-F −1219 rs1628578 AA 79 712.1 781.93 704.55, 859.3
AC 51 565.8 600.19 537.27, 663.1
CC 4 467.05 422.65 239.3, 606 5.59E–05
IgG FLJ35429 −3984 rs1611350 AA 73 711.4 781.82 701.95, 861.69
AG 52 600.15 637.3 566.7, 707.91
GG 9 482.6 428.9 333.78, 524.02 2.18E–05
IgG FLJ35429 −2622 rs1610601 AA 4 467.05 422.65 239.3, 606
AC 46 574.35 602.82 537.73, 667.92
CC 84 705.6 769.67 694.93, 844.4 0.0002386
IgG FLJ35429 −76 rs1633088 AA 76 713 792.14 711.74, 872.53
AG 44 583.25 607.69 542.78, 672.59
GG 5 440.9 408.06 277.7, 538.42 5.69E–05
IgG1 HLA-F −6607 rs2517911* AA 82 698.85 771.48 692.1, 850.86
AG 48 595.9 639.54 550.28, 728.8
GG 4 331.2 317 240.95, 393.05 0.000619
IgG1 HLA-F −1219 rs1628578* AA 79 700.1 779.57 698.26, 860.87
AC 51 594 634.78 549.34, 720.22
CC 4 331.2 317 240.95, 393.05 0.0003055
IgG1 FLJ35429 −3984 rs1611350* AA 73 700.1 780.94 697.15, 864.72
AG 52 626.2 663.82 573.44, 754.2
GG 9 357.4 411.21 276.69, 545.74 0.0001011
IgG1 FLJ35429 −76 rs1633088* AA 76 735.55 792.73 709.36, 876.1
AG 44 595.9 637.62 545.94, 729.31
GG 5 324.7 310.28 255.6,364.96 8.80E–05
IgG4 C6orf10 −395 rs2050190 AA 72 1.3 9.28 3.6,14.95
AG 53 2.5 15.55 3.27, 27.82
GG 9 44.4 188.8 −86.14, 463.74 0.0007056
IgG4 BTNL2 −14748 rs3135363 AA 66 0.85 7.45 2.12,12.78
AG 54 3.35 45.19 1.7, 88.67
GG 5 8.5 30.36 −15.21, 75.93 0.0008295
IgM RFP −8611 rs381808 AA 42 120.8 779.3 112.77,157.81
AT 56 144.25 593.31 133.46,166.53
TT 27 193.2 422.65 158.51, 204.52 0.0005431
IgM RFP −4252 rs3130838 AA 106 149.6 781.93 147.11,172.65
AG 19 96.5 600.19 84.14,130.14
GG 0 – 422.65 – 0.0005307
IgM RFP −2162 rs2894066 AA 29 101 781.82 93.9,130.27
AG 58 147 637.3 136.9,174.9
GG 47 164.1 428.9 150.05, 185.03 5.55E–05
IgM RFP −2579 rs1237485† AA 44 117.35 422.65 110.86, 154.48
AG 53 145.2 602.82 135.54,169.56
GG 28 187.3 769.67 158.53, 202.91 0.0001627
IgM RFP −6522 rs3118361 AA 0 – 792.14 –
AG 19 96.5 607.69 84.14,130.14
GG 106 149.6 408.06 147.11,172.65 0.0005307
IgM RFP −13339 rs3135329*,† AA 27 98 779.3 92.26,131.5
AT 52 147 593.31 136.67,177.48
TT 46 161.95 422.65 152.26,186.62 4.12E–05
IgM RFP −16449 rs3130843 AA 29 108.1 781.93 95.38,149.25
AT 54 147 600.19 133.78,169.07
TT 42 171.5 422.65 154.52,191.14 6.55E–05
IgM RFP −23343 rs763009† AA 27 193.2 781.82 161.51, 205.6
AG 54 145.1 637.3 134.81,168.5
GG 44 117.35 428.9 110.86, 154.48 9.52E–05
IgM RFP −55155 rs3135322† AA 28 187.3 422.65 157.93, 202.51
AG 51 146.9 602.82 135.21,170.58
GG 46 120.8 769.67 112.59,154.33 0.0001742
IgM OR2W1 −4129 rs3130756† AA 42 117.35 792.14 108.58, 153.24
AG 54 147.75 607.69 137.05, 171.04
GG 29 181.4 408.06 156.26, 200.03 8.70E–05
IgM OR2W1 −18170 rs3117143 AA 0 – 779.3 –
AC 19 96.5 593.31 84.14,130.14
CC 106 149.6 422.65 147.11,172.65 0.0005307
IgM OR2J3 −6574 rs3131091† AA 42 117.35 781.93 108.58, 153.24
AG 54 147.75 600.19 137.05, 171.04
GG 29 181.4 422.65 156.26, 200.03 8.70E–05
IgM OR2J3 −6226 rs3130766l† AA 42 117.35 781.82 108.58, 153.24
AG 54 147.75 637.3 137.05, 171.04
GG 29 181.4 428.9 156.26, 200.03 8.70E–05
IgM OR2J2 −19782 rs3129126l† AA 29 181.4 422.65 156.26, 200.03
AG 53 146.9 602.82 136.83,171.47
GG 43 117.7 769.67 109.52, 153.12 0.0001131
IgM OR2J2 −17278 rs3129173 AA 106 149.6 792.14 147.11, 172.65
AC 19 96.5 607.69 84.14, 130.14
CC 0 – 408.06 – 0.0005307
IgM OR2J2 −21483 rs1977074 AA 29 164.1 779.3 149.27, 194.49
AG 53 150.6 593.31 140.22, 174.93
GG 43 117.7 422.65 109.52, 153.12 0.0004792
IgM OR2J2 −25224 rs3116830 AA 0 – 781.93 –
AG 19 96.5 600.19 84.14, 130.14
GG 106 149.6 422.65 147.11, 172.65 0.0005307
IgM OR5U1 −51756 rs12182511† CC 41 117.7 781.82 109.54, 155.15
CG 56 145.1 637.3 137.37, 171.18
GG 28 172.75 428.9 153.55, 197.7 0.000253
IgM OR5U1 −34089 rs3117326 AA 0 – 422.65 –
AG 19 96.5 602.82 84.14, 130.14
GG 106 149.6 769.67 147.11, 172.65 0.0005307
IgM OR5U1 −23067 rs6456942 AA 29 164.1 792.14 149.27, 194.49
AG 54 148.75 607.69 139.07, 173.5
GG 42 120.8 408.06 110.12, 154.6 0.000678
IgM OR10C1 −2701 rs1535039 AA 106 149.6 779.3 147.11, 172.65
AG 19 96.5 593.31 84.14, 130.14
GG 0 – 422.65 – 0.0005307
IgM OR2H1 −3258 rs2746149† AA 104 149.6 781.93 147.33, 173.09
AG 21 96.5 600.19 87.28, 133.78
GG 0 – 422.65 – 0.0004298
IgM OR2H1 −10604 rs2746150 AA 0 – 781.82 –
AG 19 96.5 637.3 84.14, 130.14
GG 106 149.6 428.9 147.11, 172.65 0.0005307
IgM MAS1L −7364 rs1233489 AA 0 – 422.65 –
AT 19 96.5 602.82 84.14, 130.14
TT 106 149.6 769.67 147.11, 172.65 0.0005307
IgM MAS1L −22142 rs1233478† AA 6 112.3 792.14 48.49, 184.34
AC 44 115.05 607.69 112.46, 149.42
CC 75 156.1 408.06 151.76, 182.19 9.24E–05
IgM FLJ35429 −3984 rs1611350 AA 73 159.3 779.3 148.59, 180.55
AG 52 134.3 593.31 124.18, 157.44
GG 9 77.5 422.65 60.74, 124.12 0.0003157
IgM FLJ35429 −2622 rs1610601 AA 4 110.45 781.93 49.58, 155.02
AC 46 122.25 600.19 112.39, 150.36
CC 84 158.45 422.65 149.06, 177.48 0.0008349
IgM HLA-G −19782 rs2734985 AA 83 159.8 781.82 151.82, 182.1
AG 40 125.3 637.3 108.8, 139.77
GG 2 76.8 428.9 –192.57,346.17 0.0001734
IgM HCG9 −33622 rs356971 AA 92 159.55 422.65 150.68,178.76
AC 33 117.7 602.82 99.27, 132.76
CC 0 – 769.67 – 0.0001766
IgM RNF39 [86/195] rs9261290 AA 105 150.6 792.14 147.63, 173.3
AG 20 97.25 607.69 84.92, 128.45
GG 0 – 408.06 – 0.0003493
IgM TRIM39 −15910 rs3130380 AA 0 – 779.3 –
AG 18 97.25 593.31 82.48, 130.33
GG 107 148.6 422.65 146.82, 172.2 0.00063
IgM HLA-C −4934 rs2524069 AA 105 150.6 781.93 147.36, 173.05
AT 27 117 600.19 98.84, 140.4
TT 2 58.1 422.65 12.36, 103.84 0.0001513
IgM HLA-DRA −8386 rs3135339 CC 15 107 781.82 85.73, 134.13
CG 44 128.15 637.3 118.03, 150.29
GG 75 164.1 428.9 151.82, 184.59 0.0004524
IgM HLA-DRA −7805 rs2395172 AA 75 164.1 422.65 151.82, 184.59
AG 44 128.15 602.82 118.03, 150.29
GG 15 107 769.67 85.73, 134.13 0.0004524
IgM HLA-DRA −6708 rs3129859 CC 71 164.1 792.14 149.78, 182.94
CG 45 129.6 607.69 124.19, 159.98
GG 18 104 408.06 87.67, 130.35 0.0007321
IgM HLA-DRA −3992 rs983561 AA 75 164.1 779.3 151.82, 184.59
AC 44 128.15 593.31 118.03, 150.29
CC 15 107 422.65 85.73, 134.13 0.0004524
IgM HLA-DRA −2571 rs2395177 CC 14 108.85 781.93 87.77, 138.19
CG 40 126.75 600.19 114.85, 147.64
GG 71 165.7 422.65 154.28, 188.01 0.0003933
IgM HLA-DRA −494 rs3129872 AA 15 107 781.82 85.73, 134.13
AT 44 128.15 637.3 118.03, 150.29
TT 75 164.1 428.9 151.82, 184.59 0.0004524
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*

Markers that had significant p-values in logistic regression with binary cut-off 10%.

†

Markers that had significant p-values in logistic regression with binary cut-off 15.

Underlined markers were significant in PCA.

Bold markers are significant in haplotype analysis.

3.1. Association between vaccine specific antibody responses and SNPs

1856 SNPs in 154 genes were studied for their influence on vaccine induced serum antibody levels. All genotype frequencies were in Hardy Weinberg Equilibrium. After adjusting for gender, age and ethnicity, several SNPs were significantly associated with vaccine specific antibody responses. Table 2 summarizes these associations and provides p values calculated in an additive manner. The RPP21 (rs3129820 and rs6936217), ZBTB12 (rs558702), BF (rs1270942), STK19 (rs389884), TNXB (rs1150758, 1150753), CREBL1 (rs1269852), NOTCH4 (rs3134942 and rs3131296), BTNL2 (rs3129950), HLA-DRA (rs984778, rs3135338, rs3135395 and rs2395178) and HLA-DQA1 (rs2187668) SNPs were significantly associated with variations in median anti-HBsAg antibody levels (p< 0.001). In addition, the GG genotype of the PSORS1C1 rs3130454 SNP was associated with a lower serum antibody levels to tetanus (p< 0.001). Regarding antibody response to PCV7, the HLA-DOB (rs2857130, rs2857127, rs6929716, rs7383433, rs5009557) and TAP2 (rs1015166) SNPs were associated with significant variations in PnPS4 (p< 0.001) serotype specific antibody titers. The HLA-DOB rs5009557 SNP was also associated with variation in PnPS9V serotype level. The GG genotype of LEMD2 rs755495 SNP was associated with higher serum antibody levels to serotype PnPS14 (p<0.001). SNPs in the COL11A2 (rs9368758, rs2269346), HSD17B8 (rs383711), RING1 (rs213210) were associated with variations in PnPS19F serotype-specific antibody titers (p < 0.001) while the NOTCH4 SNPs (rs2071280, rs2071287, rs2071277) were associated with altered response to PnPS23F serotype. Manhattan plots showing the association signals for each vaccine (and serotype) are provided in Supplementary Fig. 1. None of the other polymorphisms that were examined showed any significant association with immune responses to vaccine antigens.

Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.vaccine.2013.09.026.

3.2. Association between immunoglobulin levels and SNPs

Significant associations (p < 0.001) were observed between certain SNPs and serum concentrations of IgM, IgG, and IgG subclasses (Table 3). Total IgG and IgG1 subclass levels significantly effected in subjects with HLA-F and FLJ35429 SNPs while IgG4 levels varied significantly by the BTNL2 and C6orf10 SNPs (<0.001). The HLA-G, HLA-C, HLA-DRA, HCG9, FLJ35429, MAS1L, OR2W1, OR2J3, OR2J2, OR2H1, OR5U1, OR10C1, RFP, RFN39 and TRIM39 SNPs were significantly associated with variations in IgM levels (p< 0.001). Manhattan plots showing the association signals for each Ig class and subclass are given in Supplementary Fig. 2. There was no significant association between serum IgA levels and any of the tested SNPs.

Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.vaccine.2013.09.026.

3.3. Association between antibody levels and principal components

In order to reduce data dimensionality, we performed principal component analysis (PCA). Immune responses were categorized into two groups. The first group consisted of vaccine specific antibody responses (10 variables) and the second group included Ig levels (7 variables). A number of significant associations were identified between individual principal components and genotypes (Supplementary Table 1). SNPs that were significant in both linear regression and PCA are marked in Tables 2 and 3.

Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.vaccine.2013.09.026.

3.4. Association between antibody levels and haplotypes

A number of significant associations were identified between inferred haplotypes and vaccine-induced immune responses both in continuous and binary analyses. Table 4 shows haplotype frequencies and significant associations. Variation in median anti-HBsAg levels was significantly associated with ten haplotypes. The first haplotype included 11 SNPs that were mapped to the RPP21 gene. Two of these SNPs (rs3129820 and rs6936217) were also significant in linear regression analysis. Four of the other haplotypes also included SNPs that showed individual associations with immune response to hepatitis B vaccine. These haplotypes consisted of SNPs in the ZBTB12, BF, TNXB, and NOTCH4 genes. Variation in PnPS4 antibody level was associated with the haplotype that contained five SNPs also identified in the linear regression analysis. All these SNPs were mapped to the HLA-DOB gene. The haplotype correlated with variability in PnPS19F level was constructed by SNPs that were mapped to COL11A2, RXRB, SLC39A7, HSD17B8 and RING1 genes. However, only SNPs mapped to COL11A2, HSD17B8 and RING1 genes were individually associated with PnPS19F levels. Both NOTCH4 SNP and haplotype that included this SNP were associated with variation in PnPS23F serotype level. Haplotypes containing SNPs that showed individual associations are displayed in bold in Table 4.

Table 4.

Haplotype associations.

Variable BP1 BP2 SNP1 SNP2 Haplotype F Beta T P Genes
HBV 30443533 30471924 rs3129808 rs3130113 AGCAAAGAAGA 0.096 −1.32 12.8 5.00E–04 RPP21
HBV 31920017 31956199 rs9267576 rs589428 CGGTCGGGACA 0.0813 −1.66 20.3 1.00E–04 C6orf48|NEU1|C6orf29|BAT8
HBV 31959213 31978305 rs652888 rs558702 GGAGA 0.072 −1.55 14.6 5.00E–04 BAT8|ZBTB12
HBV 31980530 32002605 rs2763982 rs3020644 GAAGA 0.156 −1.06 14.4 5.00E–04 ZBTB12|C2
HBV 32024379 32036778 rs537160 rs419788 AGGAGA 0.0784 −1.54 16.7 3.00E–04 BF|RDBP|SKIV2L
HBV 32134085 32174155 rs1009382 rs17421624 AGCGAGGA 0.0761 −1.55 16.4 3.00E–04 TNXB
HBV 32274362 32290737 rs8192575 rs206015 CAAGGACGTGG 0.111 −1.1 11.8 0.0009999 NOTCH4
HBV 32298006 32302832 rs3132946 rs3096691 GGGGAG 0.216 −0.819 12.7 4.00E–04 NOTCH4
HBV 32303966 32306914 rs365053 rs2849015 AAAAA 0.116 −1.37 19.1 1.00E–04 NOTCH4
HBV 32315371 32319295 rs416352 rs411326 AAAAG 0.0728 −1.56 14.7 5.00E–04 NOTCH4
PnPS4 32871088 32888702 rs2621377 rs11244 GCACGAAAGGAGG 0.325 0.691 12.9 0.0007999 HLA-DOB
PnPS18C 29816201 29836188 rs1610603 rs1610628 GGAAAA 0.012 −3.22 13 0.0005999 FLJ35429
PnPS19F 33252221 33293896 rs1799908 rs213212 AAGGGCAAACGCGAGAGAGGGGA 0.0701 1.71 16.4 0.0006999 COL11A2|RXRB|SLC39A7|HSD17B8|RING1
PnPS23F 32263559 32272847 rs204993 rs2071280 AAACGG 0.287 −1.12 13.6 3.00E–04 PBX2|GPSM3|NOTCH4
IgG 29807135 29811241 rs2523402 rs2394160 AGGAGGGAA 0.201 −0.34 14.3 0.0005999 FLJ35429
IgG 29816201 29836188 rs1610603 rs1610628 GGGGAA 0.216 −0.376 17.4 2.00E–04 FLJ35429
IgG 31133030 31183094 rs2523849 rs1064190 AGGGAAGAGGGCAAGCA 0.164 0.343 11.7 0.0009999 C6orf15
IgG 31601867 31630648 rs2734574 rs6929796 TCACAAGAGGCAAGTGGCG 0.0224 0.84 12.3 0.0009999 BAT1|ATP6V1G2|NFKBIL1
IgG1 29816201 29836188 rs1610603 rs1610628 GGGGAA 0.216 −0.425 16.5 5.00E–04 FLJ35429
IgG2 30253719 30320795 rs7774730 rs3094635 CCGGGACAGGGGAAGGGACGAAGGGCGAT 0.0243 1.69 15.7 2.00E–04 TRIM15|TRIM26|FLJ45422
IgG4 31133030 31183094 rs2523849 rs1064190 AGGGAAGAGAGCGAGCA 0.068 2.2 12.7 4.00E–04 C6orf15
IgG4 32491419 32497626 rs3135382 rs3135363 AAA 0.736 −1.43 11.8 5.00E–04 BTNL2
IgM 28983481 29023087 rs209122 rs763009 GGACAGGGGGTTGA 0.426 0.333 17.3 1.00E–04 RFP
IgM 28983481 29023087 rs209122 rs763009 AGGCTAGAAGAAGG 0.0766 −0.581 12.5 0.0007999 RFP
IgM 29054899 29207558 rs3135322 rs3130778 GGAGCAGAAACGAAAAAAGCAGGAAAACAAGATAGAGAAAAG 0.076 −0.59 13.2 0.0007999 RFP|OR2W1|OR2B3|OR2J3
IgM 29216270 29364399 rs9393945 rs1884123 GCCAAAGAACAGGAAGAGAAGGAACGGAGAGAGG 0.419 0.294 12.4 4.00E–04 OR2J3|OR2J2|OR5U1
IgM 29216270 29364399 rs9393945 rs1884123 GGAGGAACGGGAACGAAGGGAACCACAGGGAGGA 0.076 −0.584 12.7 0.0007999 OR2J3|OR2J2|OR5U1
IgM 29390330 29443516 rs9393954 rs9380120 AATGAACAAGA 0.115 −0.436 11.2 0.0009999 OR5U1|OR5V1|OR12D3
IgM 29444033 29455023 rs4713211 rs238880 AAGGGA 0.444 0.274 11.7 0.0008999 OR12D3
IgM 29519411 29576788 rs1535039 rs1233487 GAGGGGGAGAGGAAGACAAAG 0.0717 −0.571 11.5 0.0009999 OR10C1|OR2H1|MAS1L
IgM 29580895 29591947 rs757256 rs1592410 AAAGA 0.212 −0.41 16 2.00E–04 MAS1L
IgM 29807135 29811241 rs2523402 rs2394160 AGGAGGGAA 0.201 −0.349 11.7 5.00E–04 FLJ35429
IgM 29926641 29931006 rs2734985 rs2428510 GAAA 0.175 −0.387 12.3 0.0005999 HLA-G
IgM 30017786 30028444 rs3094141 rs1655912 CGCAAC 0.194 −0.352 12.3 0.0007999 HLA-A
IgM 30059085 30119560 rs2735067 rs259939 GCAGAAACCGCACG 0.132 −0.498 15 2.00E–04 HCG9|ZNRD1
IgM 30344733 30424718 rs2844762 rs9380174 GACAGGTGCAACGGTAATGGGAAAAAAATG 0.072 −0.592 12.3 2.00E–04 FLJ45422|TRIM39|RPP21
IgM 31133030 31183094 rs2523849 rs1064190 AAGGGGAGAGAAAAGCA 0.092 −0.529 13.7 5.00E–04 C6orf15
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BP1: physical position of left-most (5’) SNP (base-pair). BP2: Physical position of right-most (3’) SNP (base-pair). SNP1: left-most (5’) SNP. SNP2: left-most (3’) SNP. F: frequency. BETA (OR): regression coefficient (estimated odds ratio). T: test statistic (T from Wald test). P: empirical p-values from permutation procedures (10,000 permutations). Haplotypes containing SNPs that are individually associated with immune responses are shown in bold.

Significant associations were also observed between certain haplotypes and serum immunoglobulin levels (Table 4). Many SNPs constructing these haplotypes were also identified in the linear regression analysis. Haplotypes consisting of FLJ35429 and BTNL2 SNPs were significantly associated with variation in IgG, IgG1 and IgG4 levels. Variability in serum IgM levels was associated with 15 haplotypes. Only four of them did not include SNPs that were identified in the linear regression analysis.

3.5. Regulatory information for significant associations

The 76 unique significant SNPs identified from initial analyses were used as inputs to the SNP Annotation and Proxy Search (SNAP) tool [33] to find additional SNPs in complete linkage disequilibrium (using an r2 of 1). This led to the identification of an additional 149 perfectly correlated SNPs using data from the International HapMap Project [35]. The total set of 225 SNPs was then used as inputs to the RegulomeDB [34] web resource, which integrates data from the ENCODE projects and other data sources regarding various types of functional assays including DNaseI-seq, ChIP-seq, RNAseq, and eQTL analyses. Coordinates of both significant and correlated SNPs were derived from hg19 to ensure that they match the locations of variants in RegulomeDB. SNPs with RegulomeDB scores between 1 and 3 (inclusive, where scoring refers to available datatypes supporting a functional role for the variant) and related genes are listed in Table 5.

Table 5.

Regulatory potential of associated/correlated SNPs and affected genes.

SNP Gene Vaccine/Ig Correlated SNPs Distance (bp) Affected genes
rs3131296 NOTCH4 HBV rs3131296 0 HLA-DQA1
rs3134942 NOTCH4 HBV rs3134942 0 HLA-DQA1
rs3129820 RPP21 HBV rs3129822 2639 HLA-A
rs3094035 19567 HLA-A|BTN3A2|HLA-C|HLA-DQA1|HLA-DQB1|HLA-DRB1|HLA-G|HLA-H
rs6936217 RPP21 HBV rs3129822 6439 HLA-A
rs3094035 10489 HLA-A|BTN3A2|HLA-C|HLA-DQA1|HLA-DQB1|HLA-DRB1|HLA-G|HLA-H
rs1150753 TNXB HBV rs1150752 4859 HLA-DQA1
rs3135338 HLA-DRA HBV rs3135338 0 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA|ERG
rs984778 1129 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA
rs3135395 3975 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA
rs3135395 HLA-DRA HBV rs3135395 0 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA
rs3135338 3975 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA|ERG
rs984778 5104 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA
rs984778 HLA-DRA HBV rs984778 0 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA
rs3135338 1129 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA|ERG
rs3135395 5104 HLA-DQA1|HLA-DRB1|HLA-DRB5|HLA-DRA
rs2187668 HLA-DQA1 HBV rs2187668 0 HLA-DQA1|BTN3A2|HLA-A|HLA-DPB1|HLA-DQB1|HLA-DRB1|HLA-DRB5
rs9273327 17339 HLA-DQA1
rs3129716 51552 HLA-DQA1|BTN3A2|HLA-A|HLA-DPB1|HLA-DQB1|HLA-DRB1|HLA-DRB5
rs5009557 HLA-DOB PnPS4|PnPS9V rs2067577 17512 HLA-DOB|HLA-DRB5|TAP2
rs2857127 HLA-DOB PnPS4 rs2067577 14370 HLA-DOB|HLA-DRB5|TAP2
rs2857130 HLA-DOB PnPS4 rs2067577 13930 HLA-DOB|HLA-DRB5|TAP2
rs6929716 HLA-DOB PnPS4 rs2067577 14656 HLA-DOB|HLA-DRB5|TAP2
rs7383433 HLA-DOB PnPS4 rs2067577 16047 HLA-DOB|HLA-DRB5|TAP2
rs2071287 NOTCH4 PnPS23F rs2071287 0 HLA-DQA1|HLA-DQB1
rs2071277 NOTCH4 PnPS23F rs2071287 1250 HLA-DQA1|HLA-DQB1
rs1611350 FLJ35429 IgG1|IgG|IgM rs1611350 0 BTN3A2|HLA-A|ZFP57
rs1628578 HLA-F IgG1|IgG rs1632957 6835 HLA-G
rs2517911 HLA-F IgG1|IgG rs1632957 4033 HLA-G
rs2734985 HLA-G IgM rs5013088 1727 HLA-A|Hs.519979|BTN3A2|HCG4|HLA-G|HLA-H|KIT|NDUFS1|TPD52L2|ZFP57
rs2395172 HLA-DRA IgM rs2395172 0 HLA-DRB5
rs5000563 4293 HLA-DRB5|HLA-DQB1
rs983561 HLA-DRA IgM rs5000563 480 HLA-DRB5|HLA-DQB1
rs2395172 3813 HLA-DRB5
rs3129872 HLA-DRA IgM rs3129872 0 HLA-DRB5|HLA-DQB1
rs3129876 859 HLA-DRB5|HLA-DQA1
rs3129877 1444 HLA-DRB5|HLA-DQB1
rs2395181 HLA-DRA IgM rs2395181 0 HLA-DRB5|HLA-DQA1
rs3129881 HLA-DRA IgM rs3129881 0 HLA-DRB5|HLA-DQA1
rs2395177 HLA-DRA IgM rs2395177 0 HLA-DRB5
rs3116830 OR2J2 IgM rs3130893 186868 HLA-A
rs3129791 213282 HLA-A
rs3130837 219483 HLA-A
rs3130845 244208 HLA-A
rs3131073 246603 HLA-A
rs3118361 RFP IgM rs3130893 82420 HLA-A
rs3129791 56006 HLA-A
rs3130837 49805 HLA-A
rs3130845 25080 HLA-A
rs3131073 22685 HLA-A
rs9261290 RNF39 IgM rs9261290 0 HLA-A
rs3130380 TRIM39 IgM rs3130380 0 HLA-A
rs3094064 17123 HLA-A
rs3130377 44263 HLA-A
rs3130350 48709 HLA-A|BTN3A2|HLA-C|HLA-DQA1|HLA-DQB1|HLA-DRB1|HLA-G|HLA-H|ZFP57
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4. Discussion

Consistent with previous studies, the present study demonstrates that MHC region variants significantly contribute to vaccine-specific antibody responses in a relatively specific and predictable manner. The MHC region exhibits high levels of allelic diversity and extensive patterns of LD encompassing multiple genes involved in immunity. The MHC class II molecules were extensively studied in relation to vaccine induced immune responses and their variations were found to be associated with altered antibody production against the presented antigen [36–38]. However, very little is published on vaccine-induced immunogenicity regarding genetic variation in other parts of the MHC region [7]. Since the significant association signals may tag nearby functional SNPs due to high levels of LD within the region, we identified highly correlated SNPs within 500 kb and assessed their regulatory potential. This analysis showed that significant signals from different regions of the MHC are correlated with SNPs that control the expression of a small number of genes involved in antigen processing and presentation.

Previous reports demonstrated that the immune response to hepatitis B vaccine is largely determined by HLA-DR and HLA-DQ alleles [9,17,39]. Certain HLA class II alleles were associated with high (DRB1*01, DRB1*11, DRB1*15, DQB1*0501, DPB1*0401) and poor or non-response to HBV (DRB1*03, DRB1*07, DQB1*02, DPB1*1101). In line with these observations, we found HLA-DR and HLA-DQ SNPs to be significantly associated with the variations in median anti-HBsAg antibody levels. HLA-DRA rs3135395, rs3135338, and rs984778 SNPs were in strong LD. Functional annotation of SNPs using RegulomeDB showed that these SNPs regulate the expression level of genes including HLA-DRA, HLA-DQA1, HLA-DRB1, HLA-DRB5 and ERG. Similarly, HLA-DQA1 rs2187668 SNP had a regulatory effect on HLA-DQA1, BTN3A2, HLA-A, HLA-DPB1, HLA-DQB1, HLA-DRB1 and HLA-DRB5 genes. Interestingly, the HLA-DQA1 gene was also regulated by NOTCH4 SNPs (rs3131296 and rs3134942) and two highly correlated TNXB (rs1150752, rs1150753) SNPs. We were not able to find regulatory information for RPP21 rs3129820 and rs6936217 SNPs, but both of them were highly correlated with two SNPs (rs3129822 and rs3094035) that affect the expression of HLA-A, BNTN3A2, HLA-C, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-G and HLA-H genes. The role of other genes has not been extensively characterized in the context of vaccine immunity.

SNPs mapped to the HLA-DOB, TAP2, COL11A2, LEMD2, HSD17B8, RING1 and NOTCH4 genes were associated with the variations in immune response to PnPS serotypes. Five SNPs mapping to the HLA-DOB gene were associated with PnPS4 serotype and interestingly, they were correlated with the same SNP (rs2067577) that affects the regulation of HLA-DOB, HLA-DRB5 and TAP-2 genes. HLA-DR plays a central role in the presentation of peptides on the cell surface for T-cell recognition. HLA-DOB is an important modulator in the HLA class II restricted antigen presentation pathway by interaction with the HLA-DM molecule in B-cells. The transporter associated with antigen processing (TAP) gene, a member of the ATP-binding cassette transporter super family, is involved in the processing of endogenous peptides that bind to MHC class I molecules [40]. It has been suggested that TAP polymorphisms may cause differential antigen processing and thereby influence antigen presentation by MHC molecules. TAP2 allelic variants have also been found associated with measles antibody response [41,42]. Based on their role in the process of the immunogenic peptides, it is plausible that genetic variability within HLA-DOB, HLA-DR and TAP-2 genes may affect the efficiency of antigen presentation and thereby vaccine immunogenicity. Two highly linked NOTCH4 SNPs (rs2071287 and rs2071277) showed significant association with PnPS23F serotype in linear regression analysis. Notably, these two SNPs were also associated with the third principal component of vaccine specific antibody group. The RegulomeDB database showed that the rs2071287 SNP has a regulatory effect on HLA-DQA1 and HLA-DQB1 genes. There are no previous studies reporting association between MHC SNPs and antibody levels to diphtheria. In line with this, we did not find any association between SNPs and diphtheria induced immune responses in our population. This could be related to the nature of the vaccine, as toxoid vaccines tend not to be highly immunogenic.

A large number of SNPs were associated with total serum Ig levels and mapped to BNTL2, RFP, OR2W1, OR2J3, OR2J2, OR5U1, OR10C1, OR2H1, MAS1L, FLJ35429, HLA-F, HLA-G, HCG9, RFN39, TRIM39, HLA-C and HLA-DRA genes. In the association analysis, the variations in median IgM, IgG and IgG1 levels were significantly associated with FLJ35429 and HLA-F linked SNPs. RegulomeDB showed that the FLJ35429 rs1611350 SNP affects the regulation of BTN3A2, HLA-A and ZFP57 genes. The function of the BTN3A2 and ZFP57 genes in immune response is yet unknown. Although no regulatory information exists for the HLA-F SNPs (rs1628578 and rs2517911), both variants were in strong LD with another HLA-F SNP rs1632967, which affects the expression of HLA-G. HLA-A and HLA-G are non-classical class I proteins that play a central role in antigen presentation and immunomodulation [43,44]. HLA-G and its polymorphic sites have been associated with susceptibility to viral infections and autoimmune diseases [45,46]. These three SNPs (rs1611350, rs1628578 and rs2517911) were associated with IgG1, IgG and IgM levels suggesting that genetic variability at these loci may play role in the quantitative regulation of other antibody responses and represent plausible candidate genetic modifiers of vaccine immunity.

Two SNPs mapped to the OR2J2 (rs3116830) and RFP (rs3118361) genes were associated with IgM levels and found to be correlated with the same five SNPs (rs3130893, rs3129791, rs3130837, rs3130845 and rs3131073) that affect HLA-A gene expression. The TRIM39 (rs3130380) and RNF39 (rs9261290) SNPs that were associated with IgM levels were also found to influence HLA-A gene regulation. Six IgM-associated SNPs mapped to the HLA-DRA gene and their correlated SNPs were found to be affecting the regulation of HLA-DRB5, HLA-DQB1 and HLA-DQA1 genes. Results from PCA analysis showed that the second principal component of Ig group was associated with SNPs mapped to the RFP, OR2W1, OR2J3 and OR2J2 genes. Some of the SNPs that overlapped with the SNPs identified in the linear regression analysis (except rs381808) were also significant in logistic regression analysis with binary cut-off 15%. This might be explained by the fact that some SNPs were mapped to the same genes or haplotypes or they were physically close to each other to show association as a group.

The majority of the haplotype associations were related to the variations in median anti-HBsAg and IgM levels. Although some SNPs within the blocks mapped to genes involved in antigen presentation (HLA-A, HLA-G, HLA-DOB, TAP-2), most mapped to genes with unidentified immune functions (e.g., FLJ35429, C6orf15, RFP, OR5U1). Some of the haplotypes included SNPs that were also identified in the linear regression analysis. This might be due to a strong correlation between SNPs that construct the haplotype. Either high LD between SNPs caused the association to spread across the haplotype or strong association with the haplotypes let individual SNPs to be significant. SNPs identified only in linear regression analysis were possibly not included in any of the haplotypes or haplotypes including these SNPs did not reach statistical significance (p< 0.001). This is also true for the identified haplotypes that did not contain any significant SNPs from the regression analysis. Overall, strong association with certain haplotypes and individual SNPs that construct these haplotypes suggest that genetic variability in this region is strongly correlated with altered immune responses.

This is the first study reporting associations between SNPs within the entire MHC and immune response to childhood vaccines and suggests that this region is likely to contain a number of genes that affect vaccine responsiveness. The results were not corrected for multiple comparisons since our analysis was based on the well-defined role of the MHC in immune responses. Instead, we reported all tests that reached a p< 0.001 level of significance and focused on the functional relevance of SNPs associated with vaccine specific responses. Although functional annotation of SNPs was supported by experimental regulatory data, the significance of these findings requires further validation. Replication in independent samples, fine mapping and functional studies may reveal the genetic mechanisms underlying these associations. More importantly, pathways/allelic variants identified through genetic studies may help the development of more uniformly effective next-generation vaccine formulations that could improve vaccine immunogenicity and efficacy.

Acknowledgements

We thank the parents and children who participated in this study. We acknowledge the efforts of Debbie Velickoff from the West Virginia University, Pediatrics Department. We thank Dr. Don Beezhold for his critical review.

This work was supported in part by an Interagency Agreement with the Intramural Research Program of the NIEHS (Y1-ES-0001).

Abbreviations

CPS

pneumococcal cell wall polysaccharide

DTaP

diphtheria, tetanus, and pertussis

HBsAg

surface antigen of hepatitis B virus

HBV

hepatitis B virus

Hib

haemophilus influenza type b

HLA

human leukocyte antigen

Ig

immunoglobulin

IPV

inactivated polio vaccine

LD

linkage disequilibrium

MHC

major histocompatibility complex

OR

odds ratio

PnPS

pneumococcal polysaccharides

PCV7

heptavalent pneumococcal conjugate

SNP

single nucleotide polymorphism

Footnotes

☆

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health. This article may, in part, be the work product of an employee or group of employees of the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), however, the statements, opinions or conclusions contained therein do not necessarily represent the statements, opinions or conclusions of NIEHS, NIH or the United States government.

Conflict of interest statement: None declared.

Contributor Information

Berran Yucesoy, Email: byucesoy@cdc.gov.

Yerkebulan Talzhanov, Email: yet5@pitt.edu.

Victor J. Johnson, Email: vjohnson@brt-labs.com.

Nevin W. Wilson, Email: nwilson@medicine.nevada.edu.

Raymond E. Biagini, Email: RBiagini@cdc.gov.

Wei Wang, Email: WWang1@cdc.gov.

Bonnie Frye, Email: BFrye@cdc.gov.

David N. Weissman, Email: DWeissman@cdc.gov.

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Michael M. Barmada, Email: barmada@pitt.edu.

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