The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.

Next-generation sequencing continues to grow in importance for researchers. Exome sequencing became a widespread tool to further study the genomic basis of Mendelian diseases. In an effort to identify pathogenic variants, reject benign variants and better predict variant effects in downstream analysis, the American College of Medical Genetics (ACMG) published a set of criteria in 2015. While there are multiple publicly available software’s available to assign the ACMG criteria, most of them do not take into account multi-sample variant calling formats. Here we present a tool for assessment and prioritisation in exome studies (TAPES, https://github.com/a-xavier/tapes), an open-source tool designed for small-scale exome studies. TAPES can quickly assign ACMG criteria using ANNOVAR or VEP annotated files and implements a model to transform the categorical ACMG criteria into a continuous probability, allowing for a more accurate classification of pathogenicity or benignity of variants. In addition, TAPES can work with cohorts sharing a common phenotype by utilising a simple enrichment analysis, requiring no controls as an input as well as providing powerful filtering and reporting options. Finally, benchmarks showed that TAPES outperforms available tools to detect both pathogenic and benign variants, while also integrating the identification of enriched variants in study cohorts compared to the general population, making it an ideal tool to evaluate a smaller cohort before using bigger scale studies. New sequencing techniques allow researchers to study the genetic basis of diseases. Predicting the effect of genetic variants is critical to understand the mechanisms underlying disease. Available software can predict how pathogenic a variant is, but do not take into account the abundance of a variants in a cohort. TAPES is a simple open-source tool that can both more accurately predict pathogenicity (using probability over categories) and provide insight on variants enrichment in a cohort sharing the same disease.

Background: Many families with a high burden of colorectal cancer fulfil the clinical criteria for Lynch Syndrome. However, in about half of these families, no germline mutation in the mismatch repair genes known to be associated with this disease can be identified. The aim of this study was to find the genetic cause for the increased colorectal cancer risk in these unsolved cases.Materials and methods: To reach the aim, we designed a gene panel targeting 112 previously known or candidate colorectal cancer susceptibility genes to screen 274 patient samples for mutations. Mutations were validated by Sanger sequencing and, where possible, segregation analysis was performed.Results: We identified 73 interesting variants, of whom 17 were pathogenic and 19 were variants of unknown clinical significance in well‐established cancer susceptibility genes. In addition, 37 potentially pathogenic variants in candidate colorectal cancer susceptibility genes were detected.Conclusion: In conclusion, we found a promising DNA variant in more than 25 % of the patients, which shows that gene panel testing is a more effective method to identify germline variants in CRC patients compared to a single gene approach.

Background and Objectives In multiple sclerosis (MS), accelerated aging of the immune system (immunosenescence) may be associated with disease onset or drive progression. DNA methylation (DNAm) is an epigenetic factor that varies among lymphocyte subtypes, and cell-specific DNAm is associated with MS. DNAm varies across the life span and can be used to accurately estimate biological age acceleration, which has been linked to a range of morbidities. The objective of this study was to test for cell-specific epigenetic age acceleration (EAA) in people with MS. Methods This was a case-control study of EAA using existing DNAm data from several independent previously published studies. Data were included if .idat files from Illumina 450K or EPIC arrays were available for both a case with MS and an age-matched and sex-matched control, from the same study. Multifactor statistical modeling was performed to assess the primary outcome of EAA. We explored the relationship of EAA and MS, including interaction terms to identify immune cell-specific effects. Cell-sorted DNA methylation data from 3 independent datasets were used to validate findings. Results We used whole blood DNA methylation data from 583 cases with MS and 643 non-MS controls to calculate EAA using the GrimAge algorithm. The MS group exhibited an increased EAA compared with controls (approximately 9 mths, 95% CI 3.6–14.4), p = 0.001). Statistical deconvolution showed that EAA is associated with MS in a B cell–dependent manner (β_int = 1.7, 95% CI 0.3–2.8), p = 0.002), irrespective of B-cell proportions. Validation analysis using 3 independent datasets enriched for B cells showed an EAA increase of 5.1 years in cases with MS compared with that in controls (95% CI 2.8–7.4, p = 5.5 × 10⁻⁵). By comparison, there was no EAA difference in MS in a T cell–enriched dataset. We found that EAA was attributed to the DNAm surrogates for Beta-2-microglobulin (difference = 47,546, 95% CI 10,067–85,026; p = 7.2 × 10⁻⁵), and smoking pack-years (difference = 8.1, 95% CI 1.9–14.2, p = 0.002). Discussion This study provides compelling evidence that B cells exhibit marked EAA in MS and supports the hypothesis that premature B-cell immune senescence plays a role in MS. Future MS studies should focus on age-related molecular mechanisms in B cells.

Background: Lynch‐like syndrome (LLS) represents around 50% of the patients fulfilling the Amsterdam Criteria II/revised Bethesda Guidelines, characterized by a strong family history of Lynch Syndrome (LS) associated cancer, where a causative variant was not identified during genetic testing for LS.Methods: Using data extracted from a larger gene panel, we have analyzed next‐generation sequencing data from 22 mismatch repair (MMR) genes (MSH3, PMS1, MLH3, EXO1, POLD1, POLD3 RFC1, RFC2, RFC3, RFC4, RFC5, PCNA, LIG1, RPA1, RPA2, RPA3, POLD2, POLD4, MLH1, MSH2, MSH6, and PMS2) in 274 LLS patients. Detected variants were annotated and filtered using ANNOVAR and FILTUS software.Results: Thirteen variants were revealed in MLH1, MSH2, and MSH6, all genes previously linked to LS. Five additional genes (EXO1, POLD1, RFC1, RPA1, and MLH3) were found to harbor 11 variants of unknown significance in our sample cohort, two of them being frameshift variants.Conclusion: We have shown that other genes associated with the process of DNA MMR have a high probability of being associated with LLS families. These findings indicate that the spectrum of genes that should be tested when considering an entity like Lynch‐like syndrome should be expanded so that a more inclusive definition of this entity can be developed.

Background: The variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene–environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker.Methods: We conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis usingToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance.Results: Females with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity.Conclusion: RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.