The metabolome-wide signature of major depressive disorder.

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues.

Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.

BACKGROUND: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. AIMS: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. METHOD: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. RESULTS: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, ßpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, ßpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (ßpooled = 0.16) and the IMD index (ßpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. CONCLUSIONS: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins.

BACKGROUND: The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. METHODS: Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. RESULTS: We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. CONCLUSIONS: Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Dissection of depression heterogeneity using proteomic clusters.

BACKGROUND: The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms. METHODS: The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA). RESULTS: Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster. CONCLUSIONS: Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

Qualitative Analysis of a Roundtable Exercise "Reflecting on the COVID-19 Experience of Public Health Professionals".

CONTEXT: Public health professionals around the country faced significant challenges responding to the COVID-19 pandemic. Reflecting on their experience is an essential element in making sense of their experience and learning from it. OBJECTIVE: The objective of this qualitative study was to (1) describe the lived experiences of public health professionals working during the COVID-19 pandemic, (2) discuss the effectiveness of a guided reflection exercise to help public health professionals process these experiences, and (3) provide lessons learned and best practices to inform preparation for a future infectious disease pandemic. DESIGN: Qualitative focus group study design. SETTING: This activity was conducted at a Midwestern state public health professional meeting. PARTICIPANTS: Forty-eight public health professionals self-selected to participate in this study. RESULTS: Five themes were elicited in this analysis, including Communication, Leadership and Collaboration, Data Management, Community Relationships, and Resources and Planning. In addition, public health professionals reported numerous lessons learned, including the need for more leadership from the state government, the conflicted response of their communities, and the benefits of community solidarity where it was present. CONCLUSIONS: This article provides a detailed account of public health workers' experiences during the COVID-19 pandemic. It also provides lessons learned that will help public health workers lead more effectively in the future. Guided reflection on a traumatic professional experience can assist participating individuals in making sense of their experience and learning important lessons from it.

Metabolomic and inflammatory signatures of symptom dimensions in major depression.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)-three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

DNA methylation signatures of aggression and closely related constructs: A meta-analysis of epigenome-wide studies across the lifespan.

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 × 10-7; Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.

WeFaceNano: a user-friendly pipeline for complete ONT sequence assembly and detection of antibiotic resistance in multi-plasmid bacterial isolates.

BACKGROUND: Bacterial plasmids often carry antibiotic resistance genes and are a significant factor in the spread of antibiotic resistance. The ability to completely assemble plasmid sequences would facilitate the localization of antibiotic resistance genes, the identification of genes that promote plasmid transmission and the accurate tracking of plasmid mobility. However, the complete assembly of plasmid sequences using the currently most widely used sequencing platform (Illumina-based sequencing) is restricted due to the generation of short sequence lengths. The long-read Oxford Nanopore Technologies (ONT) sequencing platform overcomes this limitation. Still, the assembly of plasmid sequence data remains challenging due to software incompatibility with long-reads and the error rate generated using ONT sequencing. Bioinformatics pipelines have been developed for ONT-generated sequencing but require computational skills that frequently are beyond the abilities of scientific researchers. To overcome this challenge, the authors developed 'WeFaceNano', a user-friendly Web interFace for rapid assembly and analysis of plasmid DNA sequences generated using the ONT platform. WeFaceNano includes: a read statistics report; two assemblers (Miniasm and Flye); BLAST searching; the detection of antibiotic resistance- and replicon genes and several plasmid visualizations. A user-friendly interface displays the main features of WeFaceNano and gives access to the analysis tools. RESULTS: Publicly available ONT sequence data of 21 plasmids were used to validate WeFaceNano, with plasmid assemblages and anti-microbial resistance gene detection being concordant with the published results. Interestingly, the "Flye" assembler with "meta" settings generated the most complete plasmids. CONCLUSIONS: WeFaceNano is a user-friendly open-source software pipeline suitable for accurate plasmid assembly and the detection of anti-microbial resistance genes in (clinical) samples where multiple plasmids can be present.

Effects of dietary interventions on depressive symptom profiles: results from the MooDFOOD depression prevention study.

BACKGROUND: Dietary interventions did not prevent depression onset nor reduced depressive symptoms in a large multi-center randomized controlled depression prevention study (MooDFOOD) involving overweight adults with subsyndromal depressive symptoms. We conducted follow-up analyses to investigate whether dietary interventions differ in their effects on depressive symptom profiles (mood/cognition; somatic; atypical, energy-related). METHODS: Baseline, 3-, 6-, and 12-month follow-up data from MooDFOOD were used (n = 933). Participants received (1) placebo supplements, (2) food-related behavioral activation (F-BA) therapy with placebo supplements, (3) multi-nutrient supplements (omega-3 fatty acids and a multi-vitamin), or (4) F-BA therapy with multi-nutrient supplements. Depressive symptom profiles were based on the Inventory of Depressive Symptomatology. RESULTS: F-BA therapy was significantly associated with decreased severity of the somatic (B = -0.03, p = 0.014, d = -0.10) and energy-related (B = -0.08, p = 0.001, d = -0.13), but not with the mood/cognition symptom profile, whereas multi-nutrient supplementation was significantly associated with increased severity of the mood/cognition (B = 0.05, p = 0.022, d = 0.09) and the energy-related (B = 0.07, p = 0.002, d = 0.12) but not with the somatic symptom profile. CONCLUSIONS: Differentiating depressive symptom profiles indicated that food-related behavioral interventions are most beneficial to alleviate somatic symptoms and symptoms of the atypical, energy-related profile linked to an immuno-metabolic form of depression, although effect sizes were small. Multi-nutrient supplements are not indicated to reduce depressive symptom profiles. These findings show that attention to clinical heterogeneity in depression is of importance when studying dietary interventions.

A methylation study of long-term depression risk.

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Metabolomic profiles discriminating anxiety from depression.

OBJECTIVE: Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations. METHODS: Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites. RESULTS: As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration. CONCLUSION: While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders.

Associations between oil development and sexually transmitted infections: Public health nurse perspectives.

BACKGROUND: Oil development (OD) has been associated with increased sexually transmitted infection (STI) rates, with limited focus on the North Dakota (ND) oil boom. Public health (PH) nurse experiences can provide context related to health challenges during OD-related population booms. OBJECTIVE: To compare reported STI rates in ND oil-producing (OP) and non-oil-producing (NOP) counties before, during, and after the oil boom and describe PH nurse experiences during this time. DESIGN: We conducted secondary data analysis of oil production data and reported rates for chlamydia and gonorrhea, and conducted interviews with ND PH nurses. SAMPLE: PH nurses within ND counties geographically located in or near OD in the state. MEASUREMENTS: ND county-level OD data trends were compared to similarly timed reported rates of chlamydia and gonorrhea in OP and NOP counties. PH nurse interviews were conducted addressing their STI-related experiences working in PH during the oil boom. RESULTS: Significant findings include a correlation between OD and gonorrhea rates. PH nurses described a limited PH infrastructure to meet the health needs of a transient, increasing population. CONCLUSIONS: Expanding the role of PH nurses in ND to implement STI screening and treatment would improve access to STI testing allowing for comprehensive reporting of STIs.

RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting.

BACKGROUND: Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression. RESULTS: We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting. CONCLUSIONS: Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome.

Ripple Effects of the Communities Preventing Childhood Obesity Project.

This research examines the practice of community coaching within coalitions in the Communities Preventing Childhood Obesity project. A quasi-experimental design was used in seven Midwestern states. Each state selected two rural, low-income communities with functioning health coalitions. Coalitions were randomly assigned to be intervention or comparison communities. After 4 years of the coaching intervention, ripple effect mapping served as one method for examining the coalitions' work that may affect children's weight status. A research team from each state conducted ripple effect mapping with their two coalitions, resulting in 14 ripple maps. Community capitals framework and the social-ecological model were used for coding the items identified within the ripple maps. A quantitative scoring analysis determined if differences existed between the intervention and comparison coalitions in terms of the activities, programs, funding, and partnerships for social-ecological model score (e.g., individual, community, policy levels), community capitals score, and ripples score (e.g., number of branches formed within the maps). All scores were higher in intervention communities; however, the differences were not statistically significant (p > .05). Assessing community assets, such as availability of a community coach, is necessary in order to decide whether to deploy certain resources when designing health promotion strategies.

The effect of age on DNA methylation in whole blood among Bangladeshi men and women.

BACKGROUND: It is well-known that methylation changes occur as humans age, however, understanding how age-related changes in DNA methylation vary by sex is lacking. In this study, we characterize the effect of age on DNA methylation in a sex-specific manner and determine if these effects vary by genomic context. We used the Illumina HumanMethylation 450 K array and DNA derived from whole blood for 400 adult participants (189 males and 211 females) from Bangladesh to identify age-associated CpG sites and regions and characterize the location of these age-associated sites with respect to CpG islands (vs. shore, shelf, or open sea) and gene regions (vs. intergenic). We conducted a genome-wide search for age-associated CpG sites (among 423,604 sites) using a reference-free approach to adjust for cell type composition (the R package RefFreeEWAS) and performed an independent replication analysis of age-associated CpGs. RESULTS: The number of age-associated CpGs (p < 5 x 10- 8) were 986 among men and 3479 among women of which 2027(63.8%) and 572 (64.1%) replicated (using Bonferroni adjusted p < 1.2 × 10- 5). For both sexes, age-associated CpG sites were more likely to be hyper-methylated with increasing age (compared to hypo-methylated) and were enriched in CpG islands and promoter regions compared with other locations and all CpGs on the array. Although we observed strong correlation between chronological age and previously-developed epigenetic age models (r ≈ 0.8), among our top (based on lowest p-value) age-associated CpG sites only 12 for males and 44 for females are included in these prediction models, and the median chronological age compared to predicted age was 44 vs. 51.7 in males and 45 vs. 52.1 in females. CONCLUSIONS: Our results describe genome-wide features of age-related changes in DNA methylation. The observed associations between age and methylation were generally consistent for both sexes, although the associations tended to be stronger among women. Our population may have unique age-related methylation changes that are not captured in the established methylation-based age prediction model we used, which was developed to be non-tissue-specific.

Conditional eQTL analysis reveals allelic heterogeneity of gene expression.

In recent years, multiple eQTL (expression quantitative trait loci) catalogs have become available that can help understand the functionality of complex trait-related single nucleotide polymorphisms (SNPs). In eQTL catalogs, gene expression is often strongly associated with multiple SNPs, which may reflect either one or multiple independent associations. Conditional eQTL analysis allows a distinction between dependent and independent eQTLs. We performed conditional eQTL analysis in 4,896 peripheral blood microarray gene expression samples. Our analysis showed that 35% of genes with a cis eQTL have at least two independent cis eQTLs; for several genes up to 13 independent cis eQTLs were identified. Also, 12% (671) of the independent cis eQTLs identified in conditional analyses were not significant in unconditional analyses. The number of GWAS catalog SNPs identified as eQTL in the conditional analyses increases with 24% as compared to unconditional analyses. We provide an online conditional cis eQTL mapping catalog for whole blood (https://eqtl.onderzoek.io/), which can be used to lookup eQTLs more accurately than in standard unconditional whole blood eQTL databases.

Identification of Common Genetic Variants Influencing Spontaneous Dizygotic Twinning and Female Fertility.

Spontaneous dizygotic (DZ) twinning occurs in 1%-4% of women, with familial clustering and unknown physiological pathways and genetic origin. DZ twinning might index increased fertility and has distinct health implications for mother and child. We performed a GWAS in 1,980 mothers of spontaneous DZ twins and 12,953 control subjects. Findings were replicated in a large Icelandic cohort and tested for association across a broad range of fertility traits in women. Two SNPs were identified (rs11031006 near FSHB, p = 1.54 × 10(-9), and rs17293443 in SMAD3, p = 1.57 × 10(-8)) and replicated (p = 3 × 10(-3) and p = 1.44 × 10(-4), respectively). Based on ∼90,000 births in Iceland, the risk of a mother delivering twins increased by 18% for each copy of allele rs11031006-G and 9% for rs17293443-C. A higher polygenic risk score (PRS) for DZ twinning, calculated based on the results of the DZ twinning GWAS, was significantly associated with DZ twinning in Iceland (p = 0.001). A higher PRS was also associated with having children (p = 0.01), greater lifetime parity (p = 0.03), and earlier age at first child (p = 0.02). Allele rs11031006-G was associated with higher serum FSH levels, earlier age at menarche, earlier age at first child, higher lifetime parity, lower PCOS risk, and earlier age at menopause. Conversely, rs17293443-C was associated with later age at last child. We identified robust genetic risk variants for DZ twinning: one near FSHB and a second within SMAD3, the product of which plays an important role in gonadal responsiveness to FSH. These loci contribute to crucial aspects of reproductive capacity and health.

Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study.

[This corrects the article DOI: 10.1371/journal.pgen.1005378.].