Genomic imprinting is an epigenetic mechanism leading to parent-of-origin silencing of alleles. So far, the precise number of imprinted regions in humans is uncertain. In this study, we leveraged genome-wide DNA methylation in whole blood measured longitudinally at three time points (birth, childhood and adolescence) and genome-wide association studies (GWAS) data in 740 mother-child duos from the Avon Longitudinal Study of parents and children to identify candidate imprinted loci. We reasoned that cis-meQTLs at genomic regions that were imprinted would show strong evidence of parent-of-origin associations with DNA methylation, enabling the detection of imprinted regions. Using this approach, we identified genome-wide significant cis-meQTLs that exhibited parent-of-origin effects (POEs) at 82 loci, 34 novel and 48 regions previously implicated in imprinting (3.7-10

Alzheimer's disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. We show that self-report of parental history of Alzheimer's dementia for case ascertainment in a genome-wide association study of 314,278 participants from UK Biobank (27,696 maternal cases, 14,338 paternal cases) is a valid proxy for an AD genetic study. After meta-analysing with published consortium data (n = 74,046 with 25,580 cases across the discovery and replication analyses), three new AD-associated loci (P < 5 × 10-8) are identified. These contain genes relevant for AD and neurodegeneration: ADAM10, BCKDK/KAT8 and ACE. Novel gene-based loci include drug targets such as VKORC1 (warfarin dose). We report evidence that the association of SNPs in the TOMM40 gene with AD is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. However, it is likely that multiple variants are affecting the trait and gene methylation/expression. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, as they contain genes that are drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications.

Schizophrenia and bipolar disorder are leading causes of morbidity across all populations, with heritability estimates of approximately 80% indicating a substantial genetic component. Population genetics and genome-wide association studies suggest an overlap of genetic risk factors between these illnesses but it is unclear how this genetic component is divided between common gene polymorphisms, rare genomic copy number variants, and rare gene sequence mutations. We report evidence that the lipid transporter gene ABCA13 is a susceptibility factor for both schizophrenia and bipolar disorder. After the initial discovery of its disruption by a chromosome abnormality in a person with schizophrenia, we resequenced ABCA13 exons in 100 cases with schizophrenia and 100 controls. Multiple rare coding variants were identified including one nonsense and nine missense mutations and compound heterozygosity/homozygosity in six cases. Variants were genotyped in additional schizophrenia, bipolar, depression (n > 1600), and control (n > 950) cohorts and the frequency of all rare variants combined was greater than controls in schizophrenia (OR = 1.93, p = 0.0057) and bipolar disorder (OR = 2.71, p = 0.00007). The population attributable risk of these mutations was 2.2% for schizophrenia and 4.0% for bipolar disorder. In a study of 21 families of mutation carriers, we genotyped affected and unaffected relatives and found significant linkage (LOD = 4.3) of rare variants with a phenotype including schizophrenia, bipolar disorder, and major depression. These data identify a candidate gene, highlight the genetic overlap between schizophrenia, bipolar disorder, and depression, and suggest that rare coding variants may contribute significantly to risk of these disorders.

Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70-79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3-51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.

People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.

Advanced age is associated with cognitive and physical decline and is a major risk factor for a multitude of disorders. There is also a gap in life expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in blood-based DNA methylation in an unrelated cohort of 2586 individuals between the ages of 18 and 87 years, with replication in a further 4450 individuals between the ages of 18 and 93 years.

DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age.

Congenital sensorineural deafness is an inherited condition found in many dog breeds, including Australian Stumpy-tail Cattle Dogs (ASCD). This deafness is evident in young pups and may affect one ear (unilateral) or both ears (bilateral). The genetic locus/loci involved is unknown for all dog breeds. The aims of this study were to determine incidence, inheritance mechanism, and possible association of congenital sensorineural deafness with coat colour in ASCD and to identify the genetic locus underpinning this disease.

Epigenetic mechanisms such as DNA methylation (DNAm) are essential for regulation of gene expression. DNAm is dynamic, influenced by both environmental and genetic factors. Epigenetic drift is the divergence of the epigenome as a function of age due to stochastic changes in methylation. Here we show that epigenetic drift may be constrained at many CpGs across the human genome by DNA sequence variation and by lifetime environmental exposures. We estimate repeatability of DNAm at 234,811 autosomal CpGs in whole blood using longitudinal data (2-3 repeated measurements) on 478 older people from two Scottish birth cohorts--the Lothian Birth Cohorts of 1921 and 1936. Median age was 79 yr and 70 yr, and the follow-up period was ∼10 yr and ∼6 yr, respectively. We compare this to methylation heritability estimated in the Brisbane Systems Genomics Study, a cross-sectional study of 117 families (offspring median age 13 yr; parent median age 46 yr). CpG repeatability in older people was highly correlated (0.68) with heritability estimated in younger people. Highly heritable sites had strong underlying cis-genetic effects. Thirty-seven and 1687 autosomal CpGs were associated with smoking and sex, respectively. Both sets were strongly enriched for high repeatability. Sex-associated CpGs were also strongly enriched for high heritability. Our results show that a large number of CpGs across the genome, as a result of environmental and/or genetic constraints, have stable DNAm variation over the human lifetime. Moreover, at a number of CpGs, most variation in the population is due to genetic factors, despite some sites being highly modifiable by the environment.

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

Genome-wide association studies have identified loci influencing circulating lipid concentrations in humans; further information on novel contributing genes, pathways, and biology may be gained through studies of epigenetic modifications.

DNA methylation is affected by the activities of the key enzymes and intermediate metabolites of the one-carbon pathway, one of which involves homocysteine. We investigated the effect of the well-known genetic variant associated with mildly elevated homocysteine: MTHFR 677C>T independently and in combination with other homocysteine-associated variants, on genome-wide leukocyte DNA-methylation.

DNA methylation has fundamental roles in gene programming and aging that may help predict mortality. However, no large-scale study has investigated whether site-specific DNA methylation predicts all-cause mortality. We used the Illumina-HumanMethylation450-BeadChip to identify blood DNA methylation sites associated with all-cause mortality for 12, 300 participants in 12 Cohorts of the Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium. Over an average 10-year follow-up, there were 2,561 deaths across the cohorts. Nine sites mapping to three intergenic and six gene-specific regions were associated with mortality (P < 9.3x10-7) independently of age and other mortality predictors. Six sites (cg14866069, cg23666362, cg20045320, cg07839457, cg07677157, cg09615688)-mapping respectively to BMPR1B, MIR1973, IFITM3, NLRC5, and two intergenic regions-were associated with reduced mortality risk. The remaining three sites (cg17086398, cg12619262, cg18424841)-mapping respectively to SERINC2, CHST12, and an intergenic region-were associated with increased mortality risk. DNA methylation at each site predicted 5%-15% of all deaths. We also assessed the causal association of those sites to age-related chronic diseases by using Mendelian randomization, identifying weak causal relationship between cg18424841 and cg09615688 with coronary heart disease. Of the nine sites, three (cg20045320, cg07839457, cg07677157) were associated with lower incidence of heart disease risk and two (cg20045320, cg07839457) with smoking and inflammation in prior CHARGE analyses. Methylation of cg20045320, cg07839457, and cg17086398 was associated with decreased expression of nearby genes (IFITM3, IRF, NLRC5, MT1, MT2, MARCKSL1) linked to immune responses and cardiometabolic diseases. These sites may serve as useful clinical tools for mortality risk assessment and preventative care.

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood ([Formula: see text]), we predict 34,797 PAIs which show strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites are enriched in enhancers or near expression QTLs. Genes whose promoters are involved in PAIs are more actively expressed, and gene pairs with promoter-promoter interactions are enriched for co-expression. Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Herein, we characterize variation in DNA methylation (DNAm) and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genotype data and methylation in endometrial samples from 984 deeply-phenotyped participants. We estimate that 15.4% of the variation in endometriosis is captured by DNAm and identify significant differences in DNAm profiles associated with stage III/IV endometriosis, endometriosis sub-phenotypes and menstrual cycle phase, including opening of the window for embryo implantation. Menstrual cycle phase was a major source of DNAm variation suggesting cellular and hormonally-driven changes across the cycle can regulate genes and pathways responsible for endometrial physiology and function. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis, highlighting candidate genes contributing to disease risk. Our work provides functional evidence for epigenetic targets contributing to endometriosis risk and pathogenesis. Data generated serve as a valuable resource for understanding tissue-specific effects of methylation on endometrial biology in health and disease.

Several studies have investigated the relationship between genetic variation and DNA methylation with respect to type 2 diabetes, but it is unknown if DNA methylation is a mediator in the disease pathway or if it is altered in response to disease state. This study uses genotypic information as a causal anchor to help decipher the likely role of DNA methylation measured in peripheral blood in the etiology of type 2 diabetes. Illumina HumanMethylation450 BeadChip data were generated on 1,018 young individuals from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. In stage 1, 118 unique associations between published type 2 diabetes single nucleotide polymorphisms (SNPs) and genome-wide methylation (methylation quantitative trait loci [mQTLs]) were identified. In stage 2, a further 226 mQTLs were identified between 202 additional independent non-type 2 diabetes SNPs and CpGs identified in stage 1. Where possible, associations were replicated in independent cohorts of similar age. We discovered that around half of known type 2 diabetes SNPs are associated with variation in DNA methylation and postulated that methylation could either be on a causal pathway to future disease or could be a noncausal biomarker. For one locus (KCNQ1), we were able to provide further evidence that methylation is likely to be on the causal pathway to disease in later life.

The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain.

Many health conditions, ranging from psychiatric disorders to cardiovascular disease, display notable seasonal variation in severity and onset. In order to understand the molecular processes underlying this phenomenon, we have examined seasonal variation in the transcriptome of 606 healthy individuals. We show that 74 transcripts associated with a 12-month seasonal cycle were enriched for processes involved in DNA repair and binding. An additional 94 transcripts demonstrated significant seasonal variability that was largely influenced by blood cell count levels. These transcripts were enriched for immune function, protein production, and specific cellular markers for lymphocytes. Accordingly, cell counts for erythrocytes, platelets, neutrophils, monocytes, and CD19 cells demonstrated significant association with a 12-month seasonal cycle. These results demonstrate that seasonal variation is an important environmental regulator of gene expression and blood cell composition. Notable changes in leukocyte counts and genes involved in immune function indicate that immune cell physiology varies throughout the year in healthy individuals.

Despite the important role DNA methylation plays in transcriptional regulation, the transgenerational inheritance of DNA methylation is not well understood. The genetic heritability of DNA methylation has been estimated using twin pairs, although concern has been expressed whether the underlying assumption of equal common environmental effects are applicable due to intrauterine differences between monozygotic and dizygotic twins. We estimate the heritability of DNA methylation on peripheral blood leukocytes using Illumina HumanMethylation450 array using a family based sample of 614 people from 117 families, allowing comparison both within and across generations.