Novel DNA methylation profiles associated with key gene regulation and transcription pathways in blood and placenta of growth-restricted neonates

BB/H012494/1/ Biotechnology and Biological Sciences Research Counci

The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach

Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. The introduction of a new CTA alone will not contain COVID-19. The best-case scenario for uptake requires between 90 and 95% of the entire population for containment. This does not factor in any loss due to people dropping out or device incompatibility or that only 79% of the population own a smartphone, with less than 40% in the over-65 age group. Hence, the best-case scenario is beyond that which could conceivably be achieved. We propose to build on some of the digital solutions already under development, with the addition of a Bayesian network model that predicts likelihood for infection supplemented by traditional symptom and contact tracing. When combined with freely available COVID-19 testing with results in 24 hours or less, an effective communication strategy and social distancing, this solution can have a very beneficial effect on containing the spread of this pandemic

Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing?

Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. However, very few groups have taken a holistic approach and presented a combined solution. None has presented their CTA in such a way as to ensure that even the most suggestible member of our community does not become complacent and assume that CTA operates as an invisible shield, making us and our families impenetrable or immune to the disease. We propose to build on some of the digital solutions already under development that, with addition of a Bayesian model that predicts likelihood for infection supplemented by traditional symptom and contact tracing, that can enable us to reach 90% of a population. When combined with an effective communication strategy and social distancing, we believe solutions like the one proposed here can have a very beneficial effect on containing the spread of this pandemic

Sequencing PDX1 (insulin promoter factor 1) in 1788 UK individuals found 5% had a low frequency coding variant, but these variants are not associated with Type 2 diabetes

OnlineOpen Article. This is a copy of an article published in Diabetic Medicine. This journal is available online at: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1464-5491Genome-wide association studies have identified >30 common variants associated with Type 2 diabetes (>5% minor allele frequency). These variants have small effects on individual risk and do not account for a large proportion of the heritable component of the disease. Monogenic forms of diabetes are caused by mutations that occur in <1:2000 individuals and follow strict patterns of inheritance. In contrast, the role of low frequency genetic variants (minor allele frequency 0.1-5%) in Type 2 diabetes is not known. The aim of this study was to assess the role of low frequency PDX1 (also called IPF1) variants in Type 2 diabetes

Diet and physical activity in pregnancy to prevent gestational diabetes:A protocol for an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation

Introduction: Mothers with gestational diabetes mellitus (GDM) are at increased risk of pregnancy-related complications and developing type 2 diabetes after delivery. Diet and physical activity-based interventions may prevent GDM, but variations in populations, interventions and outcomes in primary trials have limited the translation of available evidence into practice. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to assess the differential effects and cost-effectiveness of diet and physical activity-based interventions in preventing GDM and its complications. Methods: The International Weight Management in Pregnancy Collaborative Network database is a living repository of IPD from randomised trials on diet and physical activity in pregnancy identified through a systematic literature search. We shall update our existing search on MEDLINE, Embase, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects and Health Technology Assessment Database without language restriction to identify relevant trials until March 2021. Primary researchers will be invited to join the Network and share their IPD. Trials including women with GDM at baseline will be excluded. We shall perform a one and two stage random-effect meta-analysis for each intervention type (all interventions, diet-based, physical activity-based and mixed approach) to obtain summary intervention effects on GDM with 95% CIs and summary treatment–covariate interactions. Heterogeneity will be summarised using I2 and tau2 statistics with 95% prediction intervals. Publication and availability bias will be assessed by examining small study effects. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool, and the Grading of Recommendations, Assessment, Development and Evaluations approach will be used to grade the evidence in the results. A model-based economic analysis will be carried out to assess the cost-effectiveness of interventions to prevent GDM and its complications compared with usual care. Ethics and dissemination: Ethics approval is not required. The study is registered on the International Prospective Register of Systematic Reviews (CRD42020212884). Results will be submitted for publication in peer-reviewed journals

Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus

Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10−4, permutation p = 1.0×10−3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10−7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within disease-associated loci, thus providing a novel route to aid unravelling common complex diseases

Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

In addition to lowering low density lipoprotein-cholesterol (LDL-C), statin therapy also raises high density lipoprotein-cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation