Gene–environment interactions increase the risk of pediatric-onset multiple sclerosis associated with ozone pollution

Background: We previously reported a relationship between air pollutants and increased risk of pediatric-onset multiple sclerosis (POMS). Ozone is an air pollutant that may play a role in multiple sclerosis (MS) pathoetiology. CD86 is the only non-HLA gene associated with POMS for which expression on antigen-presenting cells (APCs) is changed in response to ozone exposure. Objectives: To examine the association between county-level ozone and POMS, and the interactions between ozone pollution, CD86, and HLA- DRB1*15, the strongest genetic variant associated with POMS. Methods: Cases and controls were enrolled in the Environmental and Genetic Risk Factors for Pediatric MS study of the US Network of Pediatric MS Centers. County-level-modeled ozone data were acquired from the CDC’s Environmental Tracking Network. Participants were assigned ozone values based on county of residence. Values were categorized into tertiles based on healthy controls. The association between ozone tertiles and having MS was assessed by logistic regression. Interactions between tertiles of ozone level and the GG genotype of the rs928264 (G/A) single nucleotide polymorphism (SNP) within CD86, and the presence of DRB1*15:01 ( DRB1*15) on odds of POMS were evaluated. Models were adjusted for age, sex, genetic ancestry, and mother’s education. Additive interaction was estimated using relative excess risk due to interaction (RERI) and attributable proportions (APs) of disease were calculated. Results: A total of 334 POMS cases and 565 controls contributed to the analyses. County-level ozone was associated with increased odds of POMS (odds ratio 2.47, 95% confidence interval (CI): 1.69–3.59 and 1.95, 95% CI: 1.32–2.88 for the upper two tertiles, respectively, compared with the lowest tertile). There was a significant additive interaction between high ozone tertiles and presence of DRB1*15, with a RERI of 2.21 (95% CI: 0.83–3.59) and an AP of 0.56 (95% CI: 0.33–0.79). Additive interaction between high ozone tertiles and the CD86 GG genotype was present, with a RERI of 1.60 (95% CI: 0.14–3.06) and an AP of 0.37 (95% CI: 0.001–0.75) compared to the lowest ozone tertile. AP results indicated that approximately half of the POMS risk in subjects can be attributed to the possible interaction between higher county-level ozone carrying either DRB1*15 or the CD86 GG genotype. Conclusions: In addition to the association between high county-level ozone and POMS, we report evidence for additive interactions between higher county-level ozone and DRB1*15 and the CD86 GG genotype. Identifying gene–environment interactions may provide mechanistic insight of biological processes at play in MS susceptibility. Our work suggests a possible role of APCs for county-level ozone-induced POMS risk.

Quantile-specific heritability of plasma fibrinogen concentrations

Background Fibrinogen is a moderately heritable blood protein showing different genetic effects by sex, race, smoking status, pollution exposure, and disease status. These interactions may be explained in part by “quantile-dependent expressivity”, where the effect size of a genetic variant depends upon whether the phenotype (e.g. plasma fibrinogen concentration) is high or low relative to its distribution. Purpose Determine whether fibrinogen heritability (h2) is quantile-specific, and whether quantile-specific h2 could account for fibrinogen gene-environment interactions. Methods Plasma fibrinogen concentrations from 5689 offspring-parent pairs and 1932 sibships from the Framingham Heart Study were analyzed. Quantile-specific heritability from offspring-parent (βOP, h2 = 2βOP/(1+rspouse)) and full-sib regression slopes (βFS, h2 = {(1+8rspouseβFS)0.05–1}/(2rspouse)) were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. Results Quantile-specific h2 (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted fibrinogen distribution when estimated from βOP (Ptrend = 5.5x10-6): 0.30±0.05 at the 10th, 0.37±0.04 at the 25th, 0.48±0.05 at the 50th, 0.61±0.06 at the 75th, and 0.65±0.08 at the 90th percentile, and when estimated from βFS (Ptrend = 0.008): 0.28±0.04 at the 10th, 0.31±0.04 at the 25th, 0.36±0.03 at the 50th, 0.41±0.05 at the 75th, and 0.50±0.06 at the 90th percentile. The larger genetic effect at higher average fibrinogen concentrations may contribute to fibrinogen’s greater heritability in women than men and in Blacks than Whites, and greater increase from smoking and air pollution for the FGB -455G>A A-allele. It may also explain greater fibrinogen differences between: 1) FGB -455G>A genotypes during acute phase reactions than usual conditions, 2) GTSM1 and IL-6 -572C>G genotypes in smokers than nonsmokers, 3) FGB -148C>T genotypes in untreated than treated diabetics, and LPL PvuII genotypes in macroalbuminuric than normoalbuminuric patients. Conclusion Fibrinogen heritability is quantile specific, which may explain or contribute to its gene-environment interactions. The analyses do not disprove the traditional gene-environment interpretations of these examples, rather quantile-dependent expressivity provides an alternative explanation that warrants consideration.

Unique Associations of DNA Methylation Regions With 24-Hour Blood Pressure Phenotypes in Blacks

Background: Epigenetic marks (eg, DNA methylation) may capture the effect of gene-environment interactions. DNA methylation is involved in blood pressure (BP) regulation and hypertension development; however, no studies have evaluated its relationship with 24-hour BP phenotypes (daytime, nighttime, and 24-hour average BPs). Methods: We examined the association of whole blood DNA methylation with 24-hour BP phenotypes and clinic BPs in a discovery cohort of 281 Blacks using reduced representation bisulfite sequencing. We developed a deep and region-specific methylation sequencing method, Bisulfite ULtrapLEx Targeted Sequencing and utilized it to validate our findings in a separate validation cohort (n=117). Results: Analysis of 38 215 DNA methylation regions (MRs), derived from 1 549 368 CpG sites across the genome, identified up to 72 regions that were significantly associated with 24-hour BP phenotypes. No MR was significantly associated with clinic BP. Two to 3 MRs were significantly associated with various 24-hour BP phenotypes after adjustment for age, sex, and body mass index. Together, these MRs explained up to 16.5% of the variance of 24-hour average BP, while age, sex, and BMI explained up to 11.0% of the variance. Analysis of one of the MRs in an independent cohort using Bisulfite ULtrapLEx Targeted Sequencing confirmed its association with 24-hour average BP phenotype. Conclusions: We identified several MRs that explain a substantial portion of variances in 24-hour BP phenotypes, which might be excellent markers of cumulative effect of factors influencing 24-hour BP levels. The Bisulfite ULtrapLEx Targeted Sequencing workflow has potential to be suitable for clinical testing and population screenings on a large scale.

Cell-type-specific DNA methylation analysis of the frontal cortices of mutant Polg1 transgenic mice with neuronal accumulation of deleted mitochondrial DNA

Bipolar disorder (BD) is a severe psychiatric disorder characterized by repeated conflicting manic and depressive states. In addition to genetic factors, complex gene–environment interactions, which alter the epigenetic status in the brain, contribute to the etiology and pathophysiology of BD. Here, we performed a promoter-wide DNA methylation analysis of neurons and nonneurons derived from the frontal cortices of mutant Polg1 transgenic (n = 6) and wild-type mice (n = 6). The mutant mice expressed a proofreading-deficient mitochondrial DNA (mtDNA) polymerase under the neuron-specific CamK2a promoter and showed BD-like behavioral abnormalities, such as activity changes and altered circadian rhythms. We identified a total of 469 differentially methylated regions (DMRs), consisting of 267 neuronal and 202 nonneuronal DMRs. Gene ontology analysis of DMR-associated genes showed that cell cycle-, cell division-, and inhibition of peptide activity-related genes were enriched in neurons, whereas synapse- and GABA-related genes were enriched in nonneurons. Among the DMR-associated genes, Trim2 and Lrpprc showed an inverse relationship between DNA methylation and gene expression status. In addition, we observed that mutant Polg1 transgenic mice shared several features of DNA methylation changes in postmortem brains of patients with BD, such as dominant hypomethylation changes in neurons, which include hypomethylation of the molecular motor gene and altered DNA methylation of synapse-related genes in nonneurons. Taken together, the DMRs identified in this study will contribute to understanding the pathophysiology of BD from an epigenetic perspective.

Coronary Heart Disease in Type 2 Diabetes Mellitus: Genetic Factors and Their Mechanisms, Gene-Gene, and Gene-Environment Interactions in the Asian Populations

Asians are more susceptible to type 2 diabetes mellitus (T2D) and its coronary heart disease (CHD) complications than the Western populations, possibly due to genetic factors, higher degrees of obesity, insulin resistance, and endothelial dysfunction that could occur even in healthy individuals. The genetic factors and their mechanisms, along with gene-gene and gene-environment interactions associated with CHD in T2D Asians, are yet to be explored. Therefore, the objectives of this paper were to review the current evidence of genetic factors for CHD, summarize the proposed mechanisms of these genes and how they may associate with CHD risk, and review the gene-gene and gene-environment interactions in T2D Asians with CHD. The genetic factors can be grouped according to their involvement in the energy and lipoprotein metabolism, vascular and endothelial pathology, antioxidation, cell cycle regulation, DNA damage repair, hormonal regulation of glucose metabolism, as well as cytoskeletal function and intracellular transport. Meanwhile, interactions between single nucleotide polymorphisms (SNPs) from different genes, SNPs within a single gene, and genetic interaction with environmental factors including obesity, smoking habit, and hyperlipidemia could modify the gene’s effect on the disease risk. Collectively, these factors illustrate the complexities of CHD in T2D, specifically among Asians.

Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation

Blood biomarkers include disease intervention targets that may interact with genetic and environmental factors resulting in subgroups of individuals who respond differently to treatment. Such interactions may be observed in genetic effects on trait variance. Variance prioritisation is an approach to identify genetic loci with interaction effects by estimating their association with trait variance, even where the modifier is unknown or unmeasured. Here, we develop and evaluate a regression-based Brown-Forsythe test and variance effect estimate to detect such interactions. We provide scalable open-source software (varGWAS) for genome-wide association analysis of SNP-variance effects (https://github.com/MRCIEU/varGWAS) and apply our software to 30 blood biomarkers in UK Biobank. We find 468 variance quantitative trait loci across 24 biomarkers and follow up findings to detect 82 gene-environment and six gene-gene interactions independent of strong scale or phantom effects. Our results replicate existing findings and identify novel epistatic effects of TREH rs12225548 x FUT2 rs281379 and TREH rs12225548 x ABO rs635634 on alkaline phosphatase and ZNF827 rs4835265 x NEDD4L rs4503880 on gamma glutamyltransferase. These data could be used to discover possible subgroup effects for a given biomarker during preclinical drug development.