Opioid medication use and blood DNA methylation:epigenome-wide association meta-analysis

Aim: To identify differential methylation related to prescribed opioid use. Methods: This study examined whether blood DNA methylation, measured using Illumina arrays, differs by recent opioid medication use in four population-based cohorts. We meta-analyzed results (282 users; 10,560 nonusers) using inverse-variance weighting. Results: Differential methylation (false discovery rate \u3c0.05) was observed at six CpGs annotated to the following genes: KIAA0226, CPLX2, TDRP, RNF38, TTC23 and GPR179. Integrative epigenomic analyses linked implicated loci to regulatory elements in blood and/or brain. Additionally, 74 CpGs were differentially methylated in males or females. Methylation at significant CpGs correlated with gene expression in blood and/or brain. Conclusion: This study identified DNA methylation related to opioid medication use in general populations. The results could inform the development of blood methylation biomarkers of opioid use

COVID-19 Vaccination of Individuals with Down Syndrome—Data from the Trisomy 21 Research Society Survey on Safety, Efficacy, and Factors Associated with the Decision to Be Vaccinated

Individuals with Down syndrome (DS) are among the groups with the highest risk for severe COVID-19. Better understanding of the efficacy and risks of COVID-19 vaccines for individuals with DS may help improve uptake of vaccination. The T21RS COVID-19 Initiative launched an international survey to obtain information on safety and efficacy of COVID-19 vaccines for individuals with DS. De-identified survey data collected between March and December 2021 were analyzed. Of 2172 individuals with DS, 1973 (91%) had received at least one vaccine dose (57% BNT162b2), 107 (5%) were unvaccinated by choice, and 92 (4%) were unvaccinated for other reasons. Most participants had either no side effects (54%) or mild ones such as pain at the injection site (29%), fatigue (12%), and fever (7%). Severe side effects occurred in <0.5% of participants. About 1% of the vaccinated individuals with DS contracted COVID-19 after vaccination, and all recovered. Individuals with DS who were unvaccinated by choice were more likely to be younger, previously recovered from COVID-19, and also unvaccinated against other recommended vaccines. COVID-19 vaccines have been shown to be safe for individuals with DS and effective in terms of resulting in minimal breakthrough infections and milder disease outcomes among fully vaccinated individuals with DS

COVID-19 Vaccination of Individuals with Down Syndrome—Data from the Trisomy 21 Research Society Survey on Safety, Efficacy, and Factors Associated with the Decision to Be Vaccinated

Individuals with Down syndrome (DS) are among the groups with the highest risk for severe COVID-19. Better understanding of the efficacy and risks of COVID-19 vaccines for individuals with DS may help improve uptake of vaccination. The T21RS COVID-19 Initiative launched an international survey to obtain information on safety and efficacy of COVID-19 vaccines for individuals with DS. De-identified survey data collected between March and December 2021 were analyzed. Of 2172 individuals with DS, 1973 (91%) had received at least one vaccine dose (57% BNT162b2), 107 (5%) were unvaccinated by choice, and 92 (4%) were unvaccinated for other reasons. Most participants had either no side effects (54%) or mild ones such as pain at the injection site (29%), fatigue (12%), and fever (7%). Severe side effects occurred in <0.5% of participants. About 1% of the vaccinated individuals with DS contracted COVID-19 after vaccination, and all recovered. Individuals with DS who were unvaccinated by choice were more likely to be younger, previously recovered from COVID-19, and also unvaccinated against other recommended vaccines. COVID-19 vaccines have been shown to be safe for individuals with DS and effective in terms of resulting in minimal breakthrough infections and milder disease outcomes among fully vaccinated individuals with DS

A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation

Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P &lt; 0.05) and 1107 regions. Forty CpGs were located within known quantitative trait loci for gene expression traits in blood, and trait enrichment analysis suggested a strong association with immune-related, transcriptional control, and blood pressure regulation phenotypes. Decreasing fertility rates worldwide with the concomitant increased proportion of first-born children highlights a potential reflection of birth order-related epigenomic states on changing disease incidence trends.</p

A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation

Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P &lt; 0.05) and 1107 regions. Forty CpGs were located within known quantitative trait loci for gene expression traits in blood, and trait enrichment analysis suggested a strong association with immune-related, transcriptional control, and blood pressure regulation phenotypes. Decreasing fertility rates worldwide with the concomitant increased proportion of first-born children highlights a potential reflection of birth order-related epigenomic states on changing disease incidence trends.</p

Antibiotic resistances in livestock: a comparative approach to identify an appropriate regression model for count data

Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model

COVID-19 vaccination of individuals with Down syndrome-data from the trisomy 21 Research Society Survey on Safety, Efficacy, and Factors Associated with the Decision to be Vaccinated

Individuals with Down syndrome (DS) are among the groups with the highest risk for severe COVID-19. Better understanding of the efficacy and risks of COVID-19 vaccines for individuals with DS may help improve uptake of vaccination. The T21RS COVID-19 Initiative launched an international survey to obtain information on safety and efficacy of COVID-19 vaccines for individuals with DS. De-identified survey data collected between March and December 2021 were analyzed. Of 2172 individuals with DS, 1973 (91%) had received at least one vaccine dose (57% BNT162b2), 107 (5%) were unvaccinated by choice, and 92 (4%) were unvaccinated for other reasons. Most participants had either no side effects (54%) or mild ones such as pain at the injection site (29%), fatigue (12%), and fever (7%). Severe side effects occurred in &lt;0.5% of participants. About 1% of the vaccinated individuals with DS contracted COVID-19 after vaccination, and all recovered. Individuals with DS who were unvaccinated by choice were more likely to be younger, previously recovered from COVID-19, and also unvaccinated against other recommended vaccines. COVID-19 vaccines have been shown to be safe for individuals with DS and effective in terms of resulting in minimal breakthrough infections and milder disease outcomes among fully vaccinated individuals with DS.This work is supported by grants from: Down Syndrome Affiliates in Action, Down Syndrome Medical Interest Group-USA, GiGi’s Playhouse, Jerome Lejeune Foundation, LuMind IDSC Foundation, The Matthew Foundation, National Down Syndrome Society, National Task Group on Intellectual Disabilities and Dementia Practices. AH is supported by the HERCULES Center (NIEHS P30ES019776). AH and PTF are supported by the LuMind IDSC Foundation. The REDCap survey and database management system at Emory University was supported by Library Information Technology Services grant support (UL1 TR000424). MD is supported by the Centre for Genomic Regulation Severo Ochoa excellence grant, the CIBER of Rare Diseases, DURSI 2017SGR595, and acknowledges support of the Agencia Estatal de Investigación (PID2019-110755RB-I00/AEI/10.13039/501100011033), the Spanish Ministry of Science, Innovation and Universities (MSIU) to the EMBL partnership, the Centro de Excelencia Severo Ochoa and CERCA (GenCat). AS is supported by the MRC (MR/S011277/1; MR/S005145/1; MR/R024901/1), Lumind IDSC, The Lejeune Foundation and the European Commission (H2020 SC1 Gene overdosage and comorbidities during the early lifetime in Down Syndrome GO-DS21-848077). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and EDER (PT17/0009/0014). The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018-000782-M). The GRIB is also supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya (2017 SGR 00519). DRA was supported by the Fondo de Investigaciones Sanitarias (grant PI19/00634, from the Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) and co-funded by The European Regional Development Fund (ERDF) “A way to make Europe”) and the Fondation Jérôme Lejeune (grant no. 2021a-2069

Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression

Abstract Background For the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses. Methods In this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840). Results Applying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400). Conclusion In conclusion, weighted GRS with weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression were shown to be a powerful tool to detect gene-environment interactions in scenarios of high Linkage disequilibrium and noise