Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines

Abstract

Background: Humoral immune responses to infectious agents or vaccination vary substantially among individuals, and many of the factors responsible for this variability remain to be defined. Current evidence suggests that human genetic variation influences (i) serum immunoglobulin levels, (ii) seroconversion rates, and (iii) intensity of antigen-specific immune responses. Here, we evaluated the impact of intrinsic (age and sex), environmental, and genetic factors on the variability of humoral response to common pathogens and vaccines.

Methods: We characterized the serological response to 15 antigens from common human pathogens or vaccines, in an age- and sex-stratified cohort of 1000 healthy individuals (Milieu Intérieur cohort). Using clinical-grade serological assays, we measured total IgA, IgE, IgG, and IgM levels, as well as qualitative (serostatus) and quantitative IgG responses to cytomegalovirus, Epstein-Barr virus, herpes simplex virus 1 and 2, varicella zoster virus, Helicobacter pylori, Toxoplasma gondii, influenza A virus, measles, mumps, rubella, and hepatitis B virus. Following genome-wide genotyping of single nucleotide polymorphisms and imputation, we examined associations between ~ 5 million genetic variants and antibody responses using single marker and gene burden tests.

Results: We identified age and sex as important determinants of humoral immunity, with older individuals and women having higher rates of seropositivity for most antigens. Genome-wide association studies revealed significant associations between variants in the human leukocyte antigen (HLA) class II region on chromosome 6 and anti-EBV and anti-rubella IgG levels. We used HLA imputation to fine map these associations to amino acid variants in the peptide-binding groove of HLA-DRβ1 and HLA-DPβ1, respectively. We also observed significant associations for total IgA levels with two loci on chromosome 2 and with specific KIR-HLA combinations.

Conclusions: Using extensive serological testing and genome-wide association analyses in a well-characterized cohort of healthy individuals, we demonstrated that age, sex, and specific human genetic variants contribute to inter-individual variability in humoral immunity. By highlighting genes and pathways implicated in the normal antibody response to frequently encountered antigens, these findings provide a basis to better understand disease pathogenesis.

Trials registration: ClinicalTrials.gov , NCT01699893.

Keywords: Age; GWAS; HLA; Human genomics; Humoral immunity; Immunoglobulins; Infection; Serology; Sex; Vaccination.

Fig. 1

Age and sex impact on serostatus. a Effect sizes of significant linear associations (adjusted P values (adj. P < 0.05)) between age and serostatus as determined based on clinical-grade serologies in the 1000 healthy individuals from the Milieu Intérieur cohort. Effect sizes were estimated in a generalized linear mixed model, with serostatus as response variable, and age and sex as treatment variables. This model includes both scaled linear and quadratic terms for the age variable. Scaling was achieved by centering age variable at the mean age. All results from this analysis are provided in Additional file 1: Table S5. Dots represent the mean of the beta. Lines represent the 95% confidence intervals. b Odds of being seropositive towards EBV EBNA (Profile 1; upper left), Toxoplasma gondii (Profile 2; upper right), Helicobacter Pylori (Profile 3; bottom left), and HBs antigen of HBV (Profile 4; bottom right), as a function of age in men (blue) and women (red) in the 1000 healthy donors. Indicated P values were obtained using a logistic regression with Wald test, with serostatus binary variables (seropositive versus seronegative) as the response, and age and sex as treatments. Similar plots from all examined serologies are provided in Additional file 2: Figure S5. c Effect sizes of significant associations (adjusted P values (adj. P < 0.05) between sex (men = reference vs. women) and serostatus. Effect sizes were estimated in a generalized linear mixed model, with serostatus as response variable, and age and sex as treatment variables. All results from this analysis are provided in Additional file 1: Table S5. Dots represent the mean of the beta. Lines represent the 95% confidence intervals

Fig. 2

Age and sex impact on total and antigen-specific antibody levels. a Relationships between Log10-transformed IgG (upper left), IgA (upper right), IgM (bottom left), and IgE (bottom right) levels and age. Regression lines were fitted using linear regression, with Log10-transformed total antibody levels as response variable, and age and sex as treatment variables. Indicated adj. P were obtained using the mixed model and corrected for multiple testing using the FDR method. b, c Effect sizes of significant associations (adjusted P values (adj. P < 0.05) between age (b) and sex (c) on Log10-transformed antigen-specific IgG levels in the 1000 healthy individuals from the Milieu Intérieur cohort. Because of low number of seropositive donors (n = 15), HBc serology was removed from this analysis. Effect sizes were estimated in a linear mixed model, with Log10-transformed antigen-specific IgG levels as response variables, and age and sex as treatment variables. All results from this analysis are provided in Additional file 1: Table S5. Dots represent the mean of the beta. Lines represent the 95% confidence intervals

Fig. 3

Association between host genetic variants and serological phenotypes. Manhattan plots of association results for a EBV anti-EBNA IgG and b rubella IgG levels. The dashed horizontal line denotes genome-wide significance (P = 2.6 × 10⁻⁹)