Detection of suspicious interactions of spiking covariates in methylation data

BACKGROUND: In methylation analyses like epigenome-wide association studies, a high amount of biomarkers is tested for an association between the measured continuous outcome and different covariates. In the case of a continuous covariate like smoking pack years (SPY), a measure of lifetime exposure to tobacco toxins, a spike at zero can occur. Hence, all non-smokers are generating a peak at zero, while the smoking patients are distributed over the other SPY values. Additionally, the spike might also occur on the right side of the covariate distribution, if a category "heavy smoker" is designed. Here, we will focus on methylation data with a spike at the left or the right of the distribution of a continuous covariate. After the methylation data is generated, analysis is usually performed by preprocessing, quality control, and determination of differentially methylated sites, often performed in pipeline fashion. Hence, the data is processed in a string of methods, which are available in one software package. The pipelines can distinguish between categorical covariates, i.e. for group comparisons or continuous covariates, i.e. for linear regression. The differential methylation analysis is often done internally by a linear regression without checking its inherent assumptions. A spike in the continuous covariate is ignored and can cause biased results. RESULTS: We have reanalysed five data sets, four freely available from ArrayExpress, including methylation data and smoking habits reported by smoking pack years. Therefore, we generated an algorithm to check for the occurrences of suspicious interactions between the values associated with the spike position and the non-spike positions of the covariate. Our algorithm helps to decide if a suspicious interaction can be found and further investigations should be carried out. This is mostly important, because the information on the differentially methylated sites will be used for post-hoc analyses like pathway analyses. CONCLUSIONS: We help to check for the validation of the linear regression assumptions in a methylation analysis pipeline. These assumptions should also be considered for machine learning approaches. In addition, we are able to detect outliers in the continuous covariate. Therefore, more statistical robust results should be produced in methylation analysis using our algorithm as a preprocessing step

Estimands in epigenome-wide association studies

Background: In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both. Results: We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results. Conclusions: The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference

Stored Stallion Sperm Quality Depends on Sperm Preparation Method in INRA82 or INRA96

Removal of seminal plasma facilitates stallion sperm survival during storage, but washing may damage sperm chromatin. Therefore, sperm quality was compared in samples following single-layer centrifugation (SLC) or sperm washing and controls (extension only) in two extenders, INRA82 and INRA96. Ejaculates from six stallions were split among six treatments: SLC, sperm washing, and controls, in INRA82 and INRA96. Sperm motility and acrosome status were evaluated at 0, 24, 48, 72 and 96 hours; morphology at 0, 24, 48, 72 hours and chromatin integrity at 0 and 96 hours, with storage at 6 degrees C. Sperm samples in INRA96 had better motility, acrosome status, and normal morphology than samples in INRA82. The SLC samples had higher motility and fewer reacted acrosomes than controls, and lower fragmented chromatin than washed samples. Fewer spermatozoa with tail defects were observed after SLC than after sperm washing; spermatozoa washed in INRA82 had fewer tail defects than those washed in INRA96. In conclusion, sperm quality (except for morphology) was better in INRA96 than in INRA82 and was better in SLC samples than in washed samples or controls. The SLC method is a useful adjunct to stallion sperm preparation, especially for storage before artificial insemination. (C) 2020 The Author(s). Published by Elsevier Inc

CDKN2BAS is associated with periodontitis in different European populations and is activated by bacterial infection

Epidemiological studies have indicated a relationship between coronary heart disease (CHD) and periodontitis. Recently, CDKN2BAS was reported as a shared genetic risk factor of CHD and aggressive periodontitis (AgP), but the causative variant has remained unknown. To identify and validate risk variants in different European populations, we first explored 150 kb of the genetic region of CDKN2BAS including the adjacent genes CDKN2A and CDKN2B, covering 51 tagging single nucleotide polymorphisms (tagSNPs) in AgP and chronic periodontitis (CP) in individuals of Dutch origin (n=313). In a second step, we tested the significant SNP associations in an independent AgP and CP population of German origin (n=1264). For the tagSNPs rs1360590, rs3217992, and rs518394, we could validate the associations with AgP before and after adjustment for the covariates smoking, gender and diabetes, with SNP rs3217992 being the most significant (OR 1.48, 95% CI 1.19 to 1.85; p=0.0004). We further showed in vivo gene expression of CDKN2BAS, CDKN2A, CDKN2B, and CDK4 in healthy and inflamed gingival epithelium (GE) and connective tissue (CT), and detected a significantly higher expression of CDKN2BAS in healthy CT compared to GE (p=0.004). After 24 h of stimulation with Porphyromonas gingivalis in Streptococcus gordonii pre-treated gingival fibroblast (HGF) and cultured gingival epithelial cells (GECs), we observed a 25-fold and fourfold increase of CDKN2BAS gene expression in HGFs (p=0.003) and GECs (p=0.004), respectively. Considering the global importance of CDKN2BAS in the disease risk of CHD, this observation supports the theory of inflammatory components in the disease physiology of CHD

A genome-wide association study meta-analysis in a European sample of stage III/IV grade C periodontitis patients ≤35 years of age identifies new risk loci

Aim:Few genome-wide association studies (GWAS) have been conducted for severe forms of periodontitis (stage III/IV grade C), and the number of known risk genes is scarce. To identify further genetic risk variants to improve the understanding of the disease aetiology, a GWAS meta-analysis in cases with a diagnosis at <= 35 years of age was performed.Materials and Methods:Genotypes from German, Dutch and Spanish GWAS studies of III/IV-C periodontitis diagnosed at age <= 35 years were imputed using TopMed. After quality control, a meta-analysis was conducted on 8,666,460 variants in 1306 cases and 7817 controls with METAL. Variants were prioritized using FUMA for gene-based tests, functional annotation and a transcriptome-wide association study integrating eQTL data.Results:The study identified a novel genome-wide significant association in the FCER1G gene (p = 1.0 x 10(-9)), which was previously suggestively associated with III/IV-C periodontitis. Six additional genes showed suggestive association with p < 10(-5), including the known risk gene SIGLEC5. HMCN2 showed the second strongest association in this study (p = 6.1 x 10(-8)).Conclusions:This study expands the set of known genetic loci for severe periodontitis with an age of onset <= 35 years. The putative functions ascribed to the associated genes highlight the significance of oral barrier tissue stability, wound healing and tissue regeneration in the aetiology of these periodontitis forms and suggest the importance of tissue regeneration in maintaining oral health

CDKN2BAS is associated with periodontitis in different European populations and is activated by bacterial infection

Epidemiological studies have indicated a relationship between coronary heart disease (CHD) and periodontitis. Recently, CDKN2BAS was reported as a shared genetic risk factor of CHD and aggressive periodontitis (AgP), but the causative variant has remained unknown. To identify and validate risk variants in different European populations, we first explored 150 kb of the genetic region of CDKN2BAS including the adjacent genes CDKN2A and CDKN2B, covering 51 tagging single nucleotide polymorphisms (tagSNPs) in AgP and chronic periodontitis (CP) in individuals of Dutch origin (n=313). In a second step, we tested the significant SNP associations in an independent AgP and CP population of German origin (n=1264). For the tagSNPs rs1360590, rs3217992, and rs518394, we could validate the associations with AgP before and after adjustment for the covariates smoking, gender and diabetes, with SNP rs3217992 being the most significant (OR 1.48, 95% CI 1.19 to 1.85; p=0.0004). We further showed in vivo gene expression of CDKN2BAS, CDKN2A, CDKN2B, and CDK4 in healthy and inflamed gingival epithelium (GE) and connective tissue (CT), and detected a significantly higher expression of CDKN2BAS in healthy CT compared to GE (p=0.004). After 24 h of stimulation with Porphyromonas gingivalis in Streptococcus gordonii pre-treated gingival fibroblast (HGF) and cultured gingival epithelial cells (GECs), we observed a 25-fold and fourfold increase of CDKN2BAS gene expression in HGFs (p=0.003) and GECs (p=0.004), respectively. Considering the global importance of CDKN2BAS in the disease risk of CHD, this observation supports the theory of inflammatory components in the disease physiology of CHD

Epigenetic adaptations of the masticatory mucosa to periodontal inflammation

Background: In mucosal barrier interfaces, flexible responses of gene expression to long-term environmental changes allow adaptation and fine-tuning for the balance of host defense and uncontrolled not-resolving inflammation. Epigenetic modifications of the chromatin confer plasticity to the genetic information and give insight into how tissues use the genetic information to adapt to environmental factors. The oral mucosa is particularly exposed to environmental stressors such as a variable microbiota. Likewise, persistent oral inflammation is the most important intrinsic risk factor for the oral inflammatory disease periodontitis and has strong potential to alter DNA-methylation patterns. The aim of the current study was to identify epigenetic changes of the oral masticatory mucosa in response to long-term inflammation that resulted in periodontitis. Methods and results: Genome-wide CpG methylation of both inflamed and clinically uninflamed solid gingival tissue biopsies of 60 periodontitis cases was analyzed using the Infinium MethylationEPIC BeadChip. We validated and performed cell-type deconvolution for infiltrated immune cells using the EpiDish algorithm. Effect sizes of DMPs in gingival epithelial and fibroblast cells were estimated and adjusted for confounding factors using our recently developed “intercept-method”. In the current EWAS, we identified various genes that showed significantly different methylation between periodontitis-inflamed and uninflamed oral mucosa in periodontitis patients. The strongest differences were observed for genes with roles in wound healing (ROBO2, PTP4A3), cell adhesion (LPXN) and innate immune response (CCL26, DNAJC1, BPI). Enrichment analyses implied a role of epigenetic changes for vesicle trafficking gene sets. Conclusions: Our results imply specific adaptations of the oral mucosa to a persistent inflammatory environment that involve wound repair, barrier integrity, and innate immune defense

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Identification of a Shared Genetic Susceptibility Locus for Coronary Heart Disease and Periodontitis

Recent studies indicate a mutual epidemiological relationship between coronary heart disease (CHD) and periodontitis. Both diseases are associated with similar risk factors and are characterized by a chronic inflammatory process. In a candidate-gene association study, we identify an association of a genetic susceptibility locus shared by both diseases. We confirm the known association of two neighboring linkage disequilibrium regions on human chromosome 9p21.3 with CHD and show the additional strong association of these loci with the risk of aggressive periodontitis. For the lead SNP of the main associated linkage disequilibrium region, rs1333048, the odds ratio of the autosomal-recessive mode of inheritance is 1.99 (95% confidence interval 1.33–2.94; P = 6.9×10−4) for generalized aggressive periodontitis, and 1.72 (1.06–2.76; P = 2.6×10−2) for localized aggressive periodontitis. The two associated linkage disequilibrium regions map to the sequence of the large antisense noncoding RNA ANRIL, which partly overlaps regulatory and coding sequences of CDKN2A/CDKN2B. A closely located diabetes-associated variant was independent of the CHD and periodontitis risk haplotypes. Our study demonstrates that CHD and periodontitis are genetically related by at least one susceptibility locus, which is possibly involved in ANRIL activity and independent of diabetes associated risk variants within this region. Elucidation of the interplay of ANRIL transcript variants and their involvement in increased susceptibility to the interactive diseases CHD and periodontitis promises new insight into the underlying shared pathogenic mechanisms of these complex common diseases