The landscape of host genetic factors involved in immune response to common viral infections
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Abstract
Background
Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities.
Methods
We conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort.
Results
Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr virus (EBV), Varicella zoster virus (VZV), human herpesvirus 7, (HHV7), and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response ( P < 5.0 × 10 −8 ), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for MCV, and CXCR5 (11q23.3) and TBKBP1 (17q21.32) for HHV7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR : P = 5.0 × 10 −15 (MCV), NTN5 : P = 1.1 × 10 −9 (BKV), and P2RY13 : P = 1.1 × 10 −8 EBV nuclear antigen. We also demonstrated pleiotropy between viral response genes and complex diseases, from autoimmune disorders to cancer to neurodegenerative and psychiatric conditions.
Conclusions
Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants beyond the HLA that contribute to host-virus interaction.
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SciScore for 10.1101/2020.05.01.20088054: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis These analyses were conducted among seropositive individuals only for antigens with seroprevalence of ≥20% (n=1500) based on 80% power to detect only common variants with large effect sizes at this sample size (Supplementary Figure 2). Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources only15. Genome-Wide Association Analysis: We evaluated the relationship between genetic variants across the genome and serological phenotypes using PLINK 2.0 (October 2017 version). PLINKsuggested: (PLINK, RRID:SCR_001757)The functional relevance of the lead … SciScore for 10.1101/2020.05.01.20088054: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis These analyses were conducted among seropositive individuals only for antigens with seroprevalence of ≥20% (n=1500) based on 80% power to detect only common variants with large effect sizes at this sample size (Supplementary Figure 2). Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources only15. Genome-Wide Association Analysis: We evaluated the relationship between genetic variants across the genome and serological phenotypes using PLINK 2.0 (October 2017 version). PLINKsuggested: (PLINK, RRID:SCR_001757)The functional relevance of the lead GWAS loci for antibody response was assessed using in-silico functional annotation analyses based on Combined Annotation Dependent Depletion (CADD)21 scores and RegulomeDB 2.022, and by leveraging external datasets, such as GTEx v8, DICE (Database of Immune Cell Expression)23, and the Human Plasma Proteome Atlas24,25. RegulomeDBsuggested: (RegulomeDB, RRID:SCR_017905)Transcriptome-Wide Association Analysis: Gene transcription levels were imputed and analyzed using the MetaXcan approach32, applied to GWAS summary statistics for quantitative antigen phenotypes. MetaXcansuggested: NonePathways represented by genes associated with antibody response to viral antigens were summarized by conducting enrichment analysis using curated Reactome gene sets and by examining protein interaction networks using the STRING database40. STRINGsuggested: (STRING, RRID:SCR_005223)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Several limitations of this work should be noted. First, the UK Biobank is unrepresentative of the general UK population due to low participation resulting in healthy volunteer bias103. However, since the observed pattern of seroprevalence is consistent with previously published estimates15 we believe the impact of this bias is likely to be minimal on genetic associations with serological phenotypes. Second, our analyses were restricted to participants of European ancestry due to limited serology data for other ancestries, which limits the generalizability of our findings to diverse populations. Third, we were unable to conduct formal statistical replication of novel GWAS and TWAS signals in an independent sample due to the lack of such a population. Nevertheless, our successful replication of multiple previously reported variants and, combined with the observation that newly discovered genes and variants are part of essential adaptive and innate immunity pathways, support the credibility of our findings. Lastly, we also stress caution in the interpretation of GWAS results for non-ubiquitous pathogens, such as HBV, HCV, and HPV, due to a lack of information on exposure, as well as low numbers of seropositive individuals. Our study also has distinct advantages. The large sample size of the UK Biobank facilitated more powerful genetic association analyses than previous studies, particularly in a population-based cohort unselected for disease status. Our detailed HLA analysis sh...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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