Association between DNA methylation levels in brain tissue and late-life depression in community-based participants.

OBJECTIVE: Major depressive disorder (MDD) arises from a combination of genetic and environmental risk factors and DNA methylation is one of the molecular mechanisms through which these factors can manifest. However, little is known about the epigenetic signature of MDD in brain tissue. This study aimed to investigate associations between brain tissue-based DNA methylation and late-life MDD. METHODS: We performed a brain epigenome-wide association study (EWAS) of late-life MDD in 608 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex generated using the Illumina HumanMethylation450 Beadchip array. We also conducted an EWAS of MDD in each sex separately. RESULTS: We found epigenome-wide significant associations between brain tissue-based DNA methylation and late-life MDD. The most significant and robust association was found with altered methylation levels in the YOD1 locus (cg25594636, p value = 2.55 × 10-11; cg03899372, p value = 3.12 × 10-09; cg12796440, p value = 1.51 × 10-08, cg23982678, p value = 7.94 × 10-08). Analysis of differentially methylated regions (p value = 5.06 × 10-10) further confirmed this locus. Other significant loci include UGT8 (cg18921206, p value = 1.75 × 10-08), FNDC3B (cg20367479, p value = 4.97 × 10-08) and SLIT2 (cg10946669, p value = 8.01 × 10-08). Notably, brain tissue-based methylation levels were strongly associated with late-life MDD in men more than in women. CONCLUSIONS: We identified altered methylation in the YOD1, UGT8, FNDC3B, and SLIT2 loci as new epigenetic factors associated with late-life MDD. Furthermore, our study highlights the sex-specific molecular heterogeneity of MDD

Patient stratification by genetic risk in Alzheimer\u27s disease is only effective in the presence of phenotypic heterogeneity

Case-only designs in longitudinal cohorts are a valuable resource for identifying disease-relevant genes, pathways, and novel targets influencing disease progression. This is particularly relevant in Alzheimer\u27s disease (AD), where longitudinal cohorts measure disease progression, defined by rate of cognitive decline. Few of the identified drug targets for AD have been clinically tractable, and phenotypic heterogeneity is an obstacle to both clinical research and basic science. In four cohorts (n = 7241), we performed genome-wide association studies (GWAS) and Mendelian randomization (MR) to discover novel targets associated with progression and assess causal relationships. We tested opportunities for patient stratification by deriving polygenic risk scores (PRS) for AD risk and severity and tested the value of these scores in predicting progression. Genome-wide association studies identified no loci associated with progression at genome-wide significance (α = 5×10-8); MR analyses provided no significant evidence of an association between cognitive decline in AD patients and protein levels in brain, cerebrospinal fluid (CSF), and plasma. Polygenic risk scores for AD risk did not reliably stratify fast from slow progressors; however, a deeper investigation found that APOE ε4 status predicts amyloid-β and tau positive versus negative patients (odds ratio for an additional APOE ε4 allele = 5.78 [95% confidence interval: 3.76-8.89], P\u3c0.001) when restricting to a subset of patients with available CSF biomarker data. These results provided no evidence for large-effect, common-variant loci involved in the rate of memory decline, suggesting that patient stratification based on common genetic risk factors for progression may have limited utility. Where clinically relevant biomarkers suggest diagnostic heterogeneity, there is evidence that a priori identified genetic risk factors may have value in patient stratification. Mendelian randomization was less tractable due to the lack of large-effect loci, and future analyses with increased samples sizes are needed to replicate and validate our results

Brain DNA Methylation Patterns in CLDN5 Associated With Cognitive Decline.

BACKGROUND: Cognitive trajectory varies widely and can distinguish people who develop dementia from people who remain cognitively normal. Variation in cognitive trajectory is only partially explained by traditional neuropathologies. We sought to identify novel genes associated with cognitive trajectory using DNA methylation profiles from human postmortem brain. METHODS: We performed a brain epigenome-wide association study of cognitive trajectory in 636 participants from the ROS (Religious Orders Study) and MAP (Rush Memory and Aging Project) using DNA methylation profiles of the dorsolateral prefrontal cortex. To maximize our power to detect epigenetic associations, we used the recently developed Gene Association with Multiple Traits test to analyze the 5 measured cognitive domains simultaneously. RESULTS: We found an epigenome-wide association for differential methylation of sites in the CLDN5 locus and cognitive trajectory (p = 9.96 × 10-7) that was robust to adjustment for cell type proportions (p = 8.52 × 10-7). This association was primarily driven by association with declines in episodic (p = 4.65 × 10-6) and working (p = 2.54 × 10-7) memory. This association between methylation in CLDN5 and cognitive decline was significant even in participants with no or little signs of amyloid-β and neurofibrillary tangle pathology. CONCLUSIONS: Differential methylation of CLDN5, a gene that encodes an important protein of the blood-brain barrier, is associated with cognitive trajectory beyond traditional Alzheimers disease pathologies. The association between CLDN5 methylation and cognitive trajectory in people with low pathology suggests an early role for CLDN5 and blood-brain barrier dysfunction in cognitive decline and Alzheimers disease

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimers disease.

Alzheimers disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs

Genetic control of the human brain proteome

We generated an online brain pQTL resource for 7,376 proteins through the analysis of genetic and proteomic data derived from post-mortem samples of the dorsolateral prefrontal cortex of 330 older adults. The identified pQTLs tend to be non-synonymous variation, are over-represented among variants associated with brain diseases, and replicate well (77%) in an independent brain dataset. Comparison to a large study of brain eQTLs revealed that about 75% of pQTLs are also eQTLs. In contrast, about 40% of eQTLs were identified as pQTLs. These results are consistent with lower pQTL mapping power and greater evolutionary constraint on protein abundance. The latter is additionally supported by observations of pQTLs with large effects' tending to be rare, deleterious, and associated with proteins that have evidence for fewer protein-protein interactions. Mediation analyses using matched transcriptomic and proteomic data provided additional evidence that pQTL effects are often, but not always, mediated by mRNA. Specifically, we identified roughly 1.6 times more mRNA-mediated pQTLs than mRNA-independent pQTLs (550 versus 341). Our pQTL resource provides insight into the functional consequences of genetic variation in the human brain and a basis for novel investigations of genetics and disease

Proteomic signatures improve risk prediction for common and rare diseases

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81–6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02–0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases

Whole-genome sequencing of 490,640 UK Biobank participants

Whole-genome sequencing provides an unbiased and complete view of the human genome and enables the discovery of genetic variation without the technical limitations of other genotyping technologies. Here we report on whole-genome sequencing of 490,640 UK Biobank participants, building on previous genotyping effort1. This advance deepens our understanding of how genetics associates with disease biology and further enhances the value of this open resource for the study of human biology and health. Coupling this dataset with rich phenotypic data, we surveyed within- and cross-ancestry genomic associations and identified novel genetic and clinical insights. Although most associations with disease traits were primarily observed in individuals of European ancestries, strong or novel signals were also identified in individuals of African and Asian ancestries. With the improved ability to accurately genotype structural variants and exonic variation in both coding and UTR sequences, we strengthened and revealed novel insights relative to whole-exome sequencing2,3 analyses. This dataset, representing a large collection of whole-genome sequencing data that is available to the UK Biobank research community, will enable advances of our understanding of the human genome, facilitate the discovery of diagnostics and therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies with the potential to improve global health

Restricting access to reproductive healthcare due to body size: a scoping review

Recent weight stigma and reproductive healthcare research has revealed that access to some reproductive healthcare services, such as pap-smears and intrauterine device (IUD) removal, is limited based on body mass index (BMI) or body weight. The use of such BMI limits is not uncommon in fertility clinics for people seeking to access in-vitro fertilisation (IVF). In Australia, BMI limits may also be applied in maternity care settings when women over a certain BMI are referred to a larger, more specialist hospital to give birth, and may also be used to determine eligibility for use of birth options such as water immersion. Reasons for the use of BMI limits in reproductive care vary, but include the reduced success rates of procedures such as IVF in larger bodied people, the increased risks associated with obesity and labour/delivery, and manual handling limits of hospital staff. The use of BMI limits have raised questions regarding the ethics of their use, and in denying treatment to people based on their body size, irrespective of their overall health. Such limits are likely contributors to weight stigma in healthcare, which has been documented as being a significant and harmful issue for larger bodied people. Weight stigma is linked with reduced engagement with health services, stress and poorer mental health. BMI limits also present as an issue of health equity, as weight stigma is a recognised source of health inequality, and BMI limits directly inhibit access to healthcare

An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells

Abstract Background Gene expression can be influenced by DNA methylation 1) distally, at regulatory elements such as enhancers, as well as 2) proximally, at promoters. Our current understanding of the influence of distal DNA methylation changes on gene expression patterns is incomplete. Here, we characterize genome-wide methylation and expression patterns for ~ 13 k genes to explore how DNA methylation interacts with gene expression, throughout the genome. Results We used a linear mixed model framework to assess the correlation of DNA methylation at ~ 400 k CpGs with gene expression changes at ~ 13 k transcripts in two independent datasets from human blood cells. Among CpGs at which methylation significantly associates with transcription (eCpGs), > 50% are distal (> 50 kb) or trans (different chromosome) to the correlated gene. Many eCpG-transcript pairs are consistent between studies and ~ 90% of neighboring eCpGs associate with the same gene, within studies. We find that enhancers (P < 5e-18) and microRNA genes (P = 9e-3) are overrepresented among trans eCpGs, and insulators and long intergenic non-coding RNAs are enriched among cis and distal eCpGs. Intragenic-eCpG-transcript correlations are negative in 60–70% of occurrences and are enriched for annotated gene promoters and enhancers (P < 0.002), highlighting the importance of intragenic regulation. Gene Ontology analysis indicates that trans eCpGs are enriched for transcription factor genes and chromatin modifiers, suggesting that some trans eCpGs represent the influence of gene networks and higher-order transcriptional control. Conclusions This work sheds new light on the interplay between epigenetic changes and gene expression, and provides useful data for mining biologically-relevant results from epigenome-wide association studies