Polygenic risk score and phenome-wide association study of the Epstein-Barr virus antibody response

Introduction The Epstein-Barr virus (EBV) infection is nearly ubiquitous and has established links to malignancy and autoimmune disease. Here we evaluate the genetic factors influencing the humoral immune response to EBV and establish a polygenic risk score (PRS) for anti-EBNA1 responses. Methods We conducted a multi-biobank genetic study for the serologic humoral IgG antibody response to EBV-EBNA1, including data from the UK Biobank (UKB, N = 9695 individuals) and the Milieu Intérieur (MI) cohort from Institute Pasteur (IP) (N = 1000), as well as GWAS summary statistics for individuals of African ancestry (N = 4365). We divided the cohort into discovery and validation, performed GWAS analyses, and developed a PRS using a Bayesian multi-ancestry approach application (PRS-CSx). After successfully validating PRS predictive performance, we then applied PheWAS analyses to all UKB datasets. Results Consistent with a previous report, our GWAS analyses identified an association at chromosome 6 within the MHC region as the most significant SNP (rs6927022; p = 8.21 × 10 −113 ). Meta-analyses of all cohorts identified several regions outside of the MHC region with the strongest effects at 10p12 within the armadillo repeat containing three gene ( ARMC3 ) (rs10763372; p = 9.93 × 10 −08 ) and at 10q22 upstream of Neuregulin-3 ( NRG3 ) (rs61851627; p = 8.62 × 10 −9 ). A multi-ancestry PRS model was then trained and developed, obtaining successful predictive performance in validation cohorts (AUC ranges from 65% to 72%). Finally, PheWAS analyses using the developed PRS confirmed a significant positive association with multiple sclerosis (p = 1.14 × 10 −10 ), and also identified novel negative associations with other spectra of autoimmune disease, including Celiac disease (2.03 × 10 −127 ). Discussion We developed and validated a multi-ancestry PRS for EBNA1 IgG response that enables genetic profiling of EBV antibody responsiveness in genotyped cohorts lacking serologic measurements. PheWAS results support shared and divergent genetic relationships between EBV antibody response and autoimmune disease, motivating mechanistic and longitudinal follow-up studies. Further research is needed to evaluate the future implementation of this PRS in the clinic to improve long-term health outcomes.

Authors

Institutions

Publication Details

Journal
Frontiers in Genetics
Published
2026-03-15
DOI
https://doi.org/10.3389/fgene.2026.1786510
Primary Topic
Viral-associated cancers and disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Polygenic risk score and phenome-wide association study of the Epstein-Barr virus antibody response

Bahram Namjou, Ken Kaufman, Michael Lape, Matthew T. Weirauch et al.
Frontiers in Genetics
Viral-associated cancers and disorders
article

Polygenic risk score and phenome-wide association study of the Epstein-Barr virus antibody response

Bahram Namjou, Ken Kaufman, Michael Lape, Matthew T. Weirauch, Leah C. Kottyan
article en

Abstract

Introduction The Epstein-Barr virus (EBV) infection is nearly ubiquitous and has established links to malignancy and autoimmune disease. Here we evaluate the genetic factors influencing the humoral immune response to EBV and establish a polygenic risk score (PRS) for anti-EBNA1 responses. Methods We conducted a multi-biobank genetic study for the serologic humoral IgG antibody response to EBV-EBNA1, including data from the UK Biobank (UKB, N = 9695 individuals) and the Milieu Intérieur (MI) cohort from Institute Pasteur (IP) (N = 1000), as well as GWAS summary statistics for individuals of African ancestry (N = 4365). We divided the cohort into discovery and validation, performed GWAS analyses, and developed a PRS using a Bayesian multi-ancestry approach application (PRS-CSx). After successfully validating PRS predictive performance, we then applied PheWAS analyses to all UKB datasets. Results Consistent with a previous report, our GWAS analyses identified an association at chromosome 6 within the MHC region as the most significant SNP (rs6927022; p = 8.21 × 10 −113 ). Meta-analyses of all cohorts identified several regions outside of the MHC region with the strongest effects at 10p12 within the armadillo repeat containing three gene ( ARMC3 ) (rs10763372; p = 9.93 × 10 −08 ) and at 10q22 upstream of Neuregulin-3 ( NRG3 ) (rs61851627; p = 8.62 × 10 −9 ). A multi-ancestry PRS model was then trained and developed, obtaining successful predictive performance in validation cohorts (AUC ranges from 65% to 72%). Finally, PheWAS analyses using the developed PRS confirmed a significant positive association with multiple sclerosis (p = 1.14 × 10 −10 ), and also identified novel negative associations with other spectra of autoimmune disease, including Celiac disease (2.03 × 10 −127 ). Discussion We developed and validated a multi-ancestry PRS for EBNA1 IgG response that enables genetic profiling of EBV antibody responsiveness in genotyped cohorts lacking serologic measurements. PheWAS results support shared and divergent genetic relationships between EBV antibody response and autoimmune disease, motivating mechanistic and longitudinal follow-up studies. Further research is needed to evaluate the future implementation of this PRS in the clinic to improve long-term health outcomes.

Frontiers in GeneticsVol. 17
Cincinnati Children's Hospital Medical Center (US), United States Department of Veterans Affairs (US), University of Cincinnati Medical Center (US)
Good health and well-being
Openalex Percentile: Top 21%
Viral-associated cancers and disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.