Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a PLOS Computational Biology | https://doi. Data Availability Statement:DeepWAS is an open source collaborative initiative available in the GitHub repository https://github.com/cellmapslab/ DeepWAS. The informed consents given by KORA study participants do not cover data posting in public databases. However, data are available upon request from KORA-gen (https://epi.helmholtzmuenchen.de/). Data requests can be submitted online and are subject to approval by the KORA Board. KKNMS data are available upon approved multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/ cellmapslab/DeepWAS. Author summaryIn the era of steadily increasing amounts of available genetic data, we still lack novel and innovative ideas on how to improve fine-mapping of regulatory variants identified by genome-wide association studies (GWAS), especially in non-coding regions. Current approaches for the identification of functional variants conduct functional annotation after the GWAS analysis either using position-based overlaps of each variant with regulatory elements or deep-learning-based methods predicting regulatory effects per variant on cell-type-specific chromatin features. We here present DeepWAS, which integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Our results provide evidence that DeepWAS results directly identify disease/trait-associated SNPs with a common effect on a specific chromatin feature in a relevant tissue. We can show for multiple sclerosis, major depressive disorder, and body height, that the SNPs identified by DeepWAS are at least nominally significant in classical univariate GWAS analysis of the same cohorts or larger published GWAS. By integ...

Background Studies reporting accelerated ageing in children with affective disorders or maltreatment exposure have relied on algorithms for estimating epigenetic age derived from adult samples. These algorithms have limited validity for epigenetic age estimation during early development. We here use a pediatric buccal epigenetic (PedBE) clock to predict DNA methylation-based ageing deviation in children with and without internalizing disorder and assess the moderating effect of maltreatment exposure. We further conduct a gene set enrichment analysis to assess the contribution of glucocorticoid signaling to PedBE clock-based results. Method DNA was isolated from saliva of 158 children [73 girls, 85 boys; mean age (SD) = 4.25 (0.8) years] including children with internalizing disorder and maltreatment exposure. Epigenetic age was estimated based on DNA methylation across 94 CpGs of the PedBE clock. Residuals of epigenetic age regressed against chronological age were contrasted between children with and without internalizing disorder. Maltreatment was coded in 3 severity levels and entered in a moderation model. Genome-wide dexamethasone-responsive CpGs were derived from an independent sample and enrichment of these CpGs within the PedBE clock was identified. Results Children with internalizing disorder exhibited significant acceleration of epigenetic ageing as compared to children without internalizing disorder (F 1,147 = 6.67, p = .011). This association was significantly moderated by maltreatment severity (b = 0.49, 95% CI [0.073, 0.909], t = 2.322, p = .022). Children with internalizing disorder who had experienced maltreatment exhibited ageing acceleration relative to children with no internalizing disorder (1–2 categories: b = 0.50, 95% CI [0.170, 0.821], t = 3.008, p = .003; 3 or more categories: b = 0.99, 95% CI [0.380, 1.593], t = 3.215, p = .002). Children with internalizing disorder who were not exposed to maltreatment did not show epigenetic ageing acceleration. There was significant enrichment of dexamethasone-responsive CpGs within the PedBE clock (OR = 4.36, p = 1.65*10–6). Among the 94 CpGs of the PedBE clock, 18 (19%) were responsive to dexamethasone. Conclusion Using the novel PedBE clock, we show that internalizing disorder is associated with accelerated epigenetic ageing in early childhood. This association is moderated by maltreatment severity and may, in part, be driven by glucocorticoids. Identifying developmental drivers of accelerated epigenetic ageing after maltreatment will be critical to devise early targeted interventions.

Stressful life events are major environmental risk factors for anxiety disorders, although not all individuals exposed to stress develop clinical anxiety. The molecular mechanisms underlying the influence of environmental effects on anxiety are largely unknown. To identify biological pathways mediating stress-related anxiety and resilience to it, we used the chronic social defeat stress (CSDS) paradigm in male mice of two inbred strains, C57BL/6NCrl (B6) and DBA/2NCrl (D2), that differ in their susceptibility to stress. Using a multi-omics approach, we identified differential mRNA, miRNA and protein expression changes in the bed nucleus of the stria terminalis (BNST) and blood cells after chronic stress. Integrative gene set enrichment analysis revealed enrichment of mitochondrial-related genes in the BNST and blood of stressed mice. To translate these results to human anxiety, we investigated blood gene expression changes associated with exposure-induced panic attacks. Remarkably, we found reduced expression of mitochondrial-related genes in D2 stress-susceptible mice and in exposure-induced panic attacks in humans, but increased expression of these genes in B6 stress-susceptible mice. Moreover, stress-susceptible vs. stress-resilient B6 mice displayed more mitochondrial cross-sections in the post-synaptic compartment after CSDS. Our findings demonstrate mitochondrial-related alterations in gene expression as an evolutionarily conserved response in stress-related behaviors and validate the use of cross-species approaches in investigating the biological mechanisms underlying anxiety disorders.

Lasting effects of adversity, such as exposure to childhood adversity (CA) on disease risk, may be embedded via epigenetic mechanisms but findings from human studies investigating the main effects of such exposure on epigenetic measures, including DNA methylation (DNAm), are inconsistent. Studies in perinatal tissues indicate that variability of DNAm at birth is best explained by the joint effects of genotype and prenatal environment. Here, we extend these analyses to postnatal stressors. We investigated the contribution of CA, cis genotype (G), and their additive (G + CA) and interactive (G × CA) effects to DNAm variability in blood or saliva from five independent cohorts with a total sample size of 1074 ranging in age from childhood to late adulthood. Of these, 541 were exposed to CA, which was assessed retrospectively using self-reports or verified through social services and registries. For the majority of sites (over 50%) in the adult cohorts, variability in DNAm was best explained by G + CA or G × CA but almost never by CA alone. Across ages and tissues, 1672 DNAm sites showed consistency of the best model in all five cohorts, with G × CA interactions explaining most variance. The consistent G × CA sites mapped to genes enriched in brain-specific transcripts and Gene Ontology terms related to development and synaptic function. Interaction of CA with genotypes showed the strongest contribution to DNAm variability, with stable effects across cohorts in functionally relevant genes. This underscores the importance of including genotype in studies investigating the impact of environmental factors on epigenetic marks.

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.