Genetic Determinants of Antibody-Mediated Immune Responses to Infectious Diseases Agents: A Genome-Wide and HLA Association Study

Abstract

Background: Infectious diseases are causally related to a large array of noncommunicable diseases (NCDs). Identifying genetic determinants of infections and antibody-mediated immune responses may shed light on this relationship and provide therapeutic targets for drug and vaccine development.

Methods: We used the UK biobank cohort of up to 10 000 serological measurements of infectious diseases and genome-wide genotyping. We used data on 13 pathogens to define 46 phenotypes: 15 seropositivity case-control phenotypes and 31 quantitative antibody measurement phenotypes. For each of these, we performed genome-wide association studies (GWAS) using the fastGWA linear mixed model package and human leukocyte antigen (HLA) classical allele and amino acid residue associations analyses using Lasso regression for variable selection.

Results: We included a total of 8735 individuals for case-control phenotypes, and an average (range) of 4286 (276-8555) samples per quantitative analysis. Fourteen of the GWAS yielded a genome-wide significant (P < 5 ×10^-8) locus at the major histocompatibility complex (MHC) on chromosome 6. Outside the MHC, we found a total of 60 loci, multiple associated with Epstein-Barr virus (EBV)-related NCDs (eg, RASA3, MED12L, and IRF4). FUT2 was also identified as an important gene for polyomaviridae. HLA analysis highlighted the importance of DRB1*09:01, DQB1*02:01, DQA1*01:02, and DQA1*03:01 in EBV serologies and of DRB1*15:01 in polyomaviridae.

Conclusions: We have identified multiple genetic variants associated with antibody immune response to 13 infections, many of which are biologically plausible therapeutic or vaccine targets. This may help prioritize future research and drug development.

Keywords: LASSO; genome-wide association study; human leukocyte antigen; infections; serology.

Figure 1.

QQ plot and Manhattan plots from selected GWAS from the seropositivity case–control and antibody log-transformed MFI analyses. Each dot on a Manhattan plot (right) represents the P value (y-axis, on a logarithmic scale) associated with the association test at a genetic variant. Values above the dashed line are considered genome-wide significant. QQ plots (left) show the observed P values (y-axis) against the expected P values (x-axis). Any deviance from the red line suggests that the effect seen is not explained only by chance alone. As can be seen, the MHC is a commonly identified locus. A, EBV EA-D MFI (log). B, EBV EBNA-1 MFI (log). C, EBV ZEBRA MFI (log). D, JCV seropositivity case–control. Abbreviations: EBV, Epstein-Barr virus; JCV, JC polyomavirus; GWAS, genome-wide association studies; MHC, major histocompatibility complex; MFI, mean fluorescence intensity.

Figure 1.

QQ plot and Manhattan plots from selected GWAS from the seropositivity case–control and antibody log-transformed MFI analyses. Each dot on a Manhattan plot (right) represents the P value (y-axis, on a logarithmic scale) associated with the association test at a genetic variant. Values above the dashed line are considered genome-wide significant. QQ plots (left) show the observed P values (y-axis) against the expected P values (x-axis). Any deviance from the red line suggests that the effect seen is not explained only by chance alone. As can be seen, the MHC is a commonly identified locus. A, EBV EA-D MFI (log). B, EBV EBNA-1 MFI (log). C, EBV ZEBRA MFI (log). D, JCV seropositivity case–control. Abbreviations: EBV, Epstein-Barr virus; JCV, JC polyomavirus; GWAS, genome-wide association studies; MHC, major histocompatibility complex; MFI, mean fluorescence intensity.