A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy.

Infectious agents contribute significantly to the global burden of diseases through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens, including those from the herpes, retro-, and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant and could play a key role in regulation of the immune response. Using 121 infection- and inflammation-related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates hematopoietic pathways and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation.

The combined impact of persistent infections and human genetic variation on C-reactive protein levels.

Multiple human pathogens establish chronic, sometimes life-long infections. Even if they are often latent, these infections can trigger some degree of local or systemic immune response, resulting in chronic low-grade inflammation. There remains an incomplete understanding of the potential contribution of both persistent infections and human genetic variation on chronic low-grade inflammation. We searched for potential associations between seropositivity for 13 persistent pathogens and the plasma levels of the inflammatory biomarker C-reactive protein (CRP), using data collected in the context of the UK Biobank and the CoLaus|PsyCoLaus Study, two large population-based cohorts. We performed backward stepwise regression starting with the following potential predictors: serostatus for each pathogen, polygenic risk score for CRP, and demographic and clinical factors known to be associated with CRP. We found evidence for an association between Chlamydia trachomatis (P-value = 5.04e - 3) and Helicobacter pylori (P-value = 8.63e - 4) seropositivity and higher plasma levels of CRP. We also found an association between pathogen burden and CRP levels (P-value = 4.12e - 4). These results improve our understanding of the relationship between persistent infections and chronic inflammation, an important determinant of long-term morbidity in humans.

Predicting the Occurrence of Variants in RAG1 and RAG2.

While widespread genome sequencing ushers in a new era of preventive medicine, the tools for predictive genomics are still lacking. Time and resource limitations mean that human diseases remain uncharacterized because of an inability to predict clinically relevant genetic variants. A strategy of targeting highly conserved protein regions is used commonly in functional studies. However, this benefit is lost for rare diseases where the attributable genes are mostly conserved. An immunological disorder exemplifying this challenge occurs through damaging mutations in RAG1 and RAG2 which presents at an early age with a distinct phenotype of life-threatening immunodeficiency or autoimmunity. Many tools exist for variant pathogenicity prediction, but these cannot account for the probability of variant occurrence. Here, we present a method that predicts the likelihood of mutation for every amino acid residue in the RAG1 and RAG2 proteins. Population genetics data from approximately 146,000 individuals was used for rare variant analysis. Forty-four known pathogenic variants reported in patients and recombination activity measurements from 110 RAG1/2 mutants were used to validate calculated scores. Probabilities were compared with 98 currently known human cases of disease. A genome sequence dataset of 558 patients who have primary immunodeficiency but that are negative for RAG deficiency were also used as validation controls. We compared the difference between mutation likelihood and pathogenicity prediction. Our method builds a map of most probable mutations allowing pre-emptive functional analysis. This method may be applied to other diseases with hopes of improving preparedness for clinical diagnosis.

Associations of genetic and infectious risk factors with coronary heart disease.

Coronary heart disease (CHD) is one of the most pressing health problems of our time and a major cause of preventable death. CHD results from complex interactions between genetic and environmental factors. Using multiplex serological testing for persistent or frequently recurring infections and genome-wide analysis in a prospective population study, we delineate the respective and combined influences of genetic variation, infections, and low-grade inflammation on the risk of incident CHD. Study participants are enrolled in the CoLaus|PsyCoLaus study, a longitudinal, population-based cohort with baseline assessments from 2003 through 2008 and follow-up visits every 5 years. We analyzed a subgroup of 3459 individuals with available genome-wide genotyping data and immunoglobulin G levels for 22 persistent or frequently recurring pathogens. All reported CHD events were evaluated by a panel of specialists. We identified independent associations with incident CHD using univariable and multivariable stepwise Cox proportional hazards regression analyses. Of the 3459 study participants, 210 (6.07%) had at least one CHD event during the 12 years of follow-up. Multivariable stepwise Cox regression analysis, adjusted for known cardiovascular risk factors, socioeconomic status, and statin intake, revealed that high polygenic risk (hazard ratio [HR] 1.31, 95% CI 1.10-1.56, p=2.64 × 10-3) and infection with Fusobacterium nucleatum (HR 1.63, 95% CI 1.08-2.45, p=1.99 × 10-2) were independently associated with incident CHD. In a prospective, population-based cohort, high polygenic risk and infection with F. nucleatum have a small, yet independent impact on CHD risk.

Improving polygenic prediction with genetically inferred ancestry.

Genome-wide association studies (GWASs) have demonstrated that most common diseases have a strong genetic component from many genetic variants each with a small effect size. GWAS summary statistics have allowed the construction of polygenic scores (PGSs) estimating part of the individual risk for common diseases. Here, we propose to improve PGS-based risk estimation by incorporating genetic ancestry derived from genome-wide genotyping data. Our method involves three cohorts: a base (or discovery) for association studies, a target for phenotype/risk prediction, and a map for ancestry mapping; successively, (1) it generates for each individual in the base and target cohorts a set of principal components based on the map cohort-called mapped PCs, (2) it associates in the base cohort the phenotype with the mapped-PCs, and (3) it uses the mapped PCs in the target cohort to generate a phenotypic predictor called the ancestry score. We evaluated the ancestry score by comparing a predictive model using a PGS with one combining a PGS and an ancestry score. First, we performed simulations and found that the ancestry score has a greater impact on traits that correlate with ancestry-specific variants. Second, we showed, using UK Biobank data, that the ancestry score improves genetic prediction for our nine phenotypes to very different degrees. Third, we performed simulations and found that the more heterogeneous the base and target cohorts, the more beneficial the ancestry score is. Finally, we validated our approach under realistic conditions with UK Biobank as the base cohort and Swiss individuals from the CoLaus|PsyCoLaus study as the target cohort.

Visual masking deficits in schizophrenia: a view into the genetics of the disease through an endophenotype.

Schizophrenia is a severe psychiatric disorder determined by a complex mixture of genetic and environmental factors. To better understand the contributions of human genetic variations to schizophrenia, we performed a genome-wide association study (GWAS) of a highly sensitive endophenotype. In this visual masking endophenotype, two vertical bars, slightly shifted in the horizontal direction, are briefly presented (vernier offset). Participants are asked to indicate the offset direction of the bars (either left or right). The bars are followed by a grating mask, which makes the task both spatially and temporally challenging. The inter-stimulus interval (ISI) between the vernier and the mask was determined in 206 patients with schizophrenia, 109 first-order relatives, and 143 controls. Usually, in GWAS studies, patients are compared to controls (i.e., a binary task) without considering the large differences in performance between patients and controls, as it occurs in many paradigms. The masking task allows for a particularly powerful analysis because the differences in ISI within the patient population are large. We genotyped all participants and searched for associations between human polymorphisms and the masking endophenotype using a linear mixed model. We did not identify any genome-wide significant associations (p < 5 × 10-8), indicating that common variants with strong effects are unlikely to contribute to the large inter-group differences in visual masking. However, we found significant differences in polygenetic risk scores (PRS) between patients and controls, and relatives and controls.

A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals.

BACKGROUND: The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the Milieu Intérieur Consortium, a total of 1000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20-69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition. RESULTS: Among 110 demographic, clinical, and environmental factors, 11 were identified as significantly correlated with α-diversity, ß-diversity, or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between > 5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics. CONCLUSION: In a well-characterized cohort of healthy individuals, we identified several non-genetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01699893.

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

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-DRß1 and HLA-DPß1, 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.

HIV-1 latent reservoir: size matters.

More than 35 million people remain infected with HIV-1. Upon antiretroviral therapy cessation, HIV-1-positive individuals systematically fail to achieve sustained virological remission, revealing the presence of a reservoir. This reservoir takes into account anatomical sanctuaries where HIV-1 continues to replicate, and latently infected cells also known as the latent reservoir (LR). A better understanding of the nature and features of the LR and its quantification are crucial to evaluate the efficiency of therapeutic strategies aiming at purging HIV-1. Culture- and PCR-based assays have already been implemented to measure the LR, and new assays are continuously being developed. In this review, we will discuss these methods highlighting the difficulties to accurately measure the LR, one main obstacle in curing HIV-1.