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Background: Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. Results: We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. Conclusions: Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue. [ABSTRACT FROM AUTHOR]

Motivation Data normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes; thus, it poses a significant challenge to normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. Results Here, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalization methods. By this new strategy, the autosomal CpGs are first normalized independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome-linked CpGs are estimated as the weighted average of their nearest neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalization effect. Availability and implementation The proposed methods are available by calling the function ' adjustedDasen ' or ' adjustedFunnorm ' in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Gene Set Enrichment (GSE) is a computational technique which determines whether a priori defined set of genes show statistically significant differential expression between two phenotypes. Currently, the gene sets used for GSE are derived from annotation or pathway databases, which often contain computationally based and unrepresentative data. Here, we propose a novel approach for the generation of comprehensive and biologically derived gene sets, deriving sets through the application of machine learning techniques to gene expression data. These gene sets can be produced for specific tissues, developmental stages or environments. They provide a powerful and functionally meaningful way in which to mine genomewide association and next generation sequencing data in order to identify disease-associated variants and pathways. [ABSTRACT FROM AUTHOR]

Background: Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. Results: Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p<0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first principal components of the DNA methylation data of sex-associated probes mapped on sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. Conclusions: This proposed sex classifier not only can be used for sex predictions but also applied to identify samples with sex chromosome aneuploidy, and it is freely and easily accessible by calling the 'estimateSex' function from the newest wateRmelon Bioconductor package (https://github.com/schalkwyk/wateRmelon). [ABSTRACT FROM AUTHOR]

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. [ABSTRACT FROM AUTHOR]

Epidemiological research suggests that paternal obesity may increase the risk of fathering small for gestational age offspring. Studies in non-human mammals indicate that such associations could be mediated by DNA methylation changes in spermatozoa that influence offspring development in utero. Human obesity is associated with differential DNA methylation in peripheral blood. It is unclear, however, whether this differential DNA methylation is reflected in spermatozoa. We profiled genome-wide DNA methylation using the Illumina MethylationEPIC array in a cross-sectional study of matched human blood and sperm from lean (discovery n = 47; replication n = 21) and obese (n = 22) males to analyse tissue covariation of DNA methylation, and identify obesity-associated methylomic signatures. DNA methylation signatures of human blood and spermatozoa are highly discordant, and methylation levels are correlated at only a minority of CpG sites (~1%). At the majority of these sites, DNA methylation appears to be influenced by genetic variation. Obesity-associated DNA methylation in blood was not generally reflected in spermatozoa, and obesity was not associated with altered covariation patterns or accelerated epigenetic ageing in the two tissues. However, one cross-tissue obesity-specific hypermethylated site (cg19357369; chr4:2429884; P = 8.95 × 10−8; beta = 0.02) was identified, warranting replication and further investigation. When compared to a wide range of human somatic tissue samples (n = 5,917), spermatozoa displayed differential DNA methylation across pathways enriched in transcriptional regulation. Human sperm displays a unique DNA methylation profile that is highly discordant to, and practically uncorrelated with, that of matched peripheral blood. We observed that obesity was only nominally associated with differential DNA methylation in sperm, and therefore suggest that spermatozoal DNA methylation is an unlikely mediator of intergenerational effects of metabolic traits. Author summary: Research primarily conducted in mice suggests that obesity in fathers can have effects on the health of their offspring via changes in the fathers' sperm. It is not confirmed whether this is true for humans. In this study, we examined sperm and blood from lean and obese men to understand whether obesity affects DNA methylation in both tissues. DNA methylation can impact on gene function and therefore may affect offspring health. We found that there was almost no association between obesity and DNA methylation in sperm. We also showed that DNA methylation patterns found in the blood of obese individuals are not present in sperm from obese men. Generally, DNA methylation patterns across the whole genome were completely different and uncorrelated between the two tissues. Lastly, we compared DNA methylation in sperm to those in many other tissues, including for example blood and brain samples, and found that sperm has a unique signature of DNA methylation—one that points to genes involved in regulating overall levels of transcription. We conclude that obesity probably does not affect DNA methylation in sperm and that, although more research is needed, if obesity in fathers does influence the health of their children, this process is unlikely to be mediated by spermatozoal DNA methylation. [ABSTRACT FROM AUTHOR]

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10−14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using s ummary-data-based M endelian r andomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community. [ABSTRACT FROM AUTHOR]

Objective: Advanced paternal age (APA) at conception has been linked with autism and schizophrenia in offspring, neurodevelopmental disorders that affect social functioning. The current study explored the effects of paternal age on social development in the general population.Method: We used multilevel growth modeling to investigate APA effects on socioemotional development from early childhood until adolescence, as measured by the Strengths and Difficulties Questionnaire (SDQ) in the Twins Early Development Study (TEDS) sample. We also investigated genetic and environmental underpinnings of the paternal age effects on development, using the Additive genetics, Common environment, unique Environment (ACE) and gene-environment (GxE) models.Results: In the general population, both very young and advanced paternal ages were associated with altered trajectory of social development (intercept: p = .01; slope: p = .03). No other behavioral domain was affected by either young or advanced age at fatherhood, suggesting specificity of paternal age effects. Increased importance of genetic factors in social development was recorded in the offspring of older but not very young fathers, suggesting distinct underpinnings of the paternal age effects at these two extremes.Conclusion: Our findings highlight that the APA-related deficits that lead to autism and schizophrenia are likely continuously distributed in the population. [ABSTRACT FROM AUTHOR]

Recent studies have revealed that antidepressants affect the expression of constitutively expressed 'housekeeping genes' commonly used as normalizing reference genes in quantitative polymerase chain reaction (qPCR) experiments. There has yet to be an investigation however on the effects of mood-stabilizers on housekeeping gene stability. The current study utilized lymphoblastoid cell lines (LCLs) derived from patients with mood disorders to investigate the effects of a range of doses of lithium (0, 1, 2 and 5 mM) and sodium valproate (0, 0.06, 0.03 and 0.6 mM) on the stability of 12 housekeeping genes. RNA was extracted from LCLs and qPCR was used to generate cycle threshold ( Ct) values which were input into RefFinder analyses. The study revealed drug-specific effects on housekeeping gene stability. The most stable housekeeping genes in LCLs treated: acutely with sodium valproate were ACTB and RPL13A; acutely with lithium were GAPDH and ATP5B; chronically with lithium were ATP5B and CYC1. The stability of GAPDH and B2M were particularly affected by duration of lithium treatment. The study adds to a growing literature that the selection of appropriate housekeeping genes is important for the accurate normalization of target gene expression in experiments investigating the molecular effects of mood disorder pharmacotherapies. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Childhood general cognitive ability ( g) is important for a wide range of outcomes in later life, from school achievement to occupational success and life expectancy. Large-scale association studies will be essential in the quest to identify variants that make up the substantial genetic component implicated by quantitative genetic studies. We conducted a three-stage genome-wide association study for general cognitive ability using over 350,000 single nucleotide polymorphisms (SNPs) in the quantitative extremes of a population sample of 7,900 7-year-old children from the UK Twins Early Development Study. Using two DNA pooling stages to enrich true positives, each of around 1,000 children selected from the extremes of the distribution, and a third individual genotyping stage of over 3,000 children to test for quantitative associations across the normal range, we aimed to home in on genes of small effect. Genome-wide results suggested that our approach was successful in enriching true associations and 28 SNPs were taken forward to individual genotyping in an unselected population sample. However, although we found an enrichment of low P values and identified nine SNPs nominally associated with g ( P < 0.05) that show interesting characteristics for follow-up, further replication will be necessary to meet rigorous standards of association. These replications may take advantage of SNP sets to overcome limitations of statistical power. Despite our large sample size and three-stage design, the genes associated with childhood g remain tantalizingly beyond our current reach, providing further evidence for the small effect sizes of individual loci. Larger samples, denser arrays and multiple replications will be necessary in the hunt for the genetic variants that influence human cognitive ability. [ABSTRACT FROM AUTHOR]

Two separate genome-wide association studies were conducted to identify single nucleotide polymorphisms (SNPs) associated with social and nonsocial autistic-like traits. We predicted that we would find SNPs associated with social and non-social autistic-like traits and that different SNPs would be associated with social and nonsocial. In Stage 1, each study screened for allele frequency differences in ~430,000 autosomal SNPs using pooled DNA on microarrays in high-scoring versus low-scoring boys from a general population sample ( N = ~400/group). In Stage 2, 22 and 20 SNPs in the social and non-social studies, respectively, were tested for QTL association by individually genotyping an independent community sample of 1,400 boys. One SNP (rs11894053) was nominally associated ( P < .05, uncorrected for multiple testing) with social autistic-like traits. When the sample was increased by adding females, 2 additional SNPs were nominally significant ( P < .05). These 3 SNPs, however, showed no significant association in transmission disequilibrium analyses of diagnosed ASD families. [ABSTRACT FROM AUTHOR]

Studying the causes and correlates of natural variation in gene expression in healthy populations assumes that individual differences in gene expression can be reliably and stably assessed across time. However, this is yet to be established. We examined 4-hour test–retest reliability and 10 month test–retest stability of individual differences in gene expression in ten 12-year-old children. Blood was collected on four occasions: 10 a.m. and 2 p.m. on Day 1 and 10 months later at 10 a.m. and 2 p.m. Total RNA was hybridized to Affymetrix-U133 plus 2.0 arrays. For each probeset, the correlation across individuals between 10 a.m. and 2 p.m. on Day 1 estimates test–retest reliability. We identified 3,414 variable and abundantly expressed probesets whose 4-hour test–retest reliability exceeded .70, a conventionally accepted level of reliability, which we had 80% power to detect. Of the 3,414 reliable probesets, 1,752 were also significantly reliable 10 months later. We assessed the long-term stability of individual differences in gene expression by correlating the average expression level for each probe-set across the two 4-hour assessments on Day 1 with the average level of each probe-set across the two 4-hour assessments 10 months later. 1,291 (73.7%) of the 1,752 probe-sets that reliably detected individual differences across 4 hours on two occasions, 10 months apart, also stably detected individual differences across 10 months. Heritability, as estimated from the MZ twin intraclass correlations, is twice as high for the 1,752 reliable probesets versus all present probesets on the array (0.68 vs 0.34), and is even higher (0.76) for the 1,291 reliable probesets that are also stable across 10 months. The 1,291 probesets that reliably detect individual differences from a single peripheral blood collection and stably detect individual differences over 10 months are promising targets for research on the causes (e.g., eQTLs) and correlates (e.g., psychopathology) of individual differences in gene expression. [ABSTRACT FROM AUTHOR]

Children's intellectual development is influenced by both genetic inheritance and environmental experiences. Breastfeeding is one of the earliest such postnatal experiences. Breastfed children attain higher IQ scores than children not fed breast milk, presumably because of the fatty acids uniquely available in breast milk. Here we show that the association between breastfeeding and lQ is moderated by a genetic variant in FADS2, a gene involved in the genetic control of fatty acid pathways. We confirmed this gene-environment interaction in two birth cohorts, and we ruled out alternative explanations of the finding involving gene-exposure correlation, intrauterine growth, social class, and maternal cognitive ability, as well as maternal genotype effects on breastfeeding and breast milk. The finding shows that environmental exposures can be used to uncover novel candidate genes in complex phenotypes. It also shows that genes may work via the environment to shape the IQ, helping to close the nature versus nurture debate. [ABSTRACT FROM AUTHOR]

This report includes the first sibling study of mouse behavior, and presents evidence for a heritable general cognitive ability ( g) factor influencing cognitive batteries. Data from a population of male and female outbred mice ( n = 84), and a replication study of male sibling pairs ( n = 167) are reported. Arenas employed were the T-maze, the Morris water maze, the puzzle box, the Hebb–Williams maze, object exploration, a water plus-maze, and a second food-puzzle arena. The results show a factor structure consistent with the presence of g in mice. Employing one score per arena, this factor accounts for 41% of the variance in the first study (or 36% after sex regression) and 23% in the second, where this factor also showed sibling correlations of 0.17–0.21, which translates into an upper-limit heritability estimate of around 40%. Reliabilities of many tasks are low and consequently set an even lower ceiling for inter-arena or sibling correlations. Nevertheless, the factor structure is seen to remain fairly robust across permutations of the battery composition and the current findings fit well with other recent studies. [ABSTRACT FROM AUTHOR]

PWD/Ph is an inbred mouse strain derived from wild mice trapped in central Czech Republic. These mice are of the Mus musculus musculus subspecies, whose ancestors separated from those of Mus musculus domesticus about one million years ago. There is a high degree of variation in the genomic sequence and a wide range of phenotypes between PWD/Ph and standard laboratory inbred mouse strains, the genomes of which are principally Mus musculus domesticus in origin, making PWD/Ph mice an useful resource for complex trait research. As a first step in taking advantage of this resource, a preliminary characterization of the behavior of PWD/Ph mice was performed. Groups of 10 PWD/Ph and C57BL/6J male mice were tested in the open field, novel object exploration task and Morris water maze. PWD/Ph were marginally more anxious than C57BL/6J mice in the open field but subsequently displayed much higher levels of exploration and lower anxiety than C57BL/6J mice following introduction of a novel object. As C57BL/6J itself is rated as highly explorative among classical inbred strains, PWD/Ph probably represents an extreme among mouse strains for this specific behavior. PWD/Ph and C57BL/6J mice differed in their water escape training profiles in the Morris water maze, perhaps reflecting different motivational factors. However, there were no differences in overall cognitive ability (spatial learning) as both groups learned to find the hidden platform and performed equally well when the location of the platform was changed. This is the first quantification of the behavior of PWD/Ph mice and the results are promising for the potential of the consomic panel currently being generated with PWD/Ph and C57BL/6J as a tool for the molecular dissection of behavior. [ABSTRACT FROM AUTHOR]

The floating head (flh) gene in zebrafish encodes a homeodomain protein, which is essential for notochord formation along the entire body axis. flh orthologs, termed Not genes, have been isolated from chick and Xenopus, but no mammalian ortholog has yet been identified. Truncate (tc) is an autosomal recessive mutation in mouse that specifically disrupts the development of the caudal notochord. Here, we demonstrate that truncate arose by a mutation in the mouse Not gene. The truncate allele (Nottc) contains a point mutation in the homeobox of Not that changes a conserved Phenylalanine residue in helix 1 to a Cysteine (F20C), and significantly destabilizes the homeodomain. Reversion of F20C in one allele of homozygous tc embryonic stem (ES) cells is sufficient to restore normal notochord formation in completely ES cell-derived embryos. We have generated a targeted mutation of Not by replacing most of the Not coding sequence, including the homeobox with the eGFP gene. The phenotype of NoteGFP/eGFP NoteGFP/tc, and Nottc/tc embryos is very similar but slightly more severe in NoteGFP/eGFP than in Nottc/tc embryos. This confirms allelism of truncate and Not, and indicates that tc is not a complete null allele. Not expression is abolished in Foxa2 and T mutant embryos, suggesting that Not acts downstream of both genes during notochord development. This is in contrast to zebrafish embryos, in which flh interacts with ntl (zebrafish T) in a regulatory loop and is essential for development of the entire notochord, and suggests that different genetic control circuits act in different vertebrate species during notochord formation. [ABSTRACT FROM AUTHOR]

Background: A gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC. Methods: Genome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis. Results: In the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation. Limitations: Identical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis. Conclusions: Using a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals. [ABSTRACT FROM AUTHOR]

Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionize the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the etiology of complex disease., (© The Author(s) 2022. Published by Oxford University Press.)

Autism spectrum disorder (ASD) encompasses a collection of complex neuropsychiatric disorders characterized by deficits in social functioning, communication and repetitive behaviour. Building on recent studies supporting a role for developmentally moderated regulatory genomic variation in the molecular aetiology of ASD, we quantified genome-wide patterns of DNA methylation in 223 post-mortem tissues samples isolated from three brain regions [prefrontal cortex, temporal cortex and cerebellum (CB)] dissected from 43 ASD patients and 38 non-psychiatric control donors. We identified widespread differences in DNA methylation associated with idiopathic ASD (iASD), with consistent signals in both cortical regions that were distinct to those observed in the CB. Individuals carrying a duplication on chromosome 15q (dup15q), representing a genetically defined subtype of ASD, were characterized by striking differences in DNA methylationacross a discrete domain spanning an imprinted gene cluster within the duplicated region. In addition to the dramatic cis-effects on DNA methylation observed in dup15q carriers, we identified convergent methylomic signatures associated with both iASD and dup15q, reflecting the findings from previous studies of gene expression and H3K27ac. Cortical co-methylation network analysis identified a number of co-methylated modules significantly associated with ASD that are enriched for genomic regions annotated to genes involved in the immune system, synaptic signalling and neuronal regulation. Our study represents the first systematic analysis of DNA methylation associated with ASD across multiple brain regions, providing novel evidence for convergent molecular signatures associated with both idiopathic and syndromic autism., (© The Author(s) 2019. Published by Oxford University Press.)

We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer's disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-β and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease.

Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominately different molecular targets and mechanisms of action, the two drugs modulate the same Creb1 pathway which plays a key role in neurotrophic responses and in inflammatory processes. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc., (© 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.)

Genetic association studies provide evidence for a substantial polygenic component to schizophrenia, although the neurobiological mechanisms underlying the disorder remain largely undefined. Building on recent studies supporting a role for developmentally regulated epigenetic variation in the molecular aetiology of schizophrenia, this study aimed to identify epigenetic variation associated with both a diagnosis of schizophrenia and elevated polygenic risk burden for the disease across multiple brain regions. Genome-wide DNA methylation was quantified in 262 post-mortem brain samples, representing tissue from four brain regions (prefrontal cortex, striatum, hippocampus and cerebellum) from 41 schizophrenia patients and 47 controls. We identified multiple disease-associated and polygenic risk score-associated differentially methylated positions and regions, which are not enriched in genomic regions identified in genetic studies of schizophrenia and do not reflect direct genetic effects on DNA methylation. Our study represents the first analysis of epigenetic variation associated with schizophrenia across multiple brain regions and highlights the utility of polygenic risk scores for identifying molecular pathways associated with aetiological variation in complex disease., (© The Author 2016. Published by Oxford University Press.)

Mouse models of aggression have traditionally compared strains, most notably BALB/cJ and C57BL/6. However, these strains were not designed to study aggression despite differences in aggression-related traits and distinct reactivity to stress. This study evaluated expression of genes differentially regulated in a stress (behavioral) mouse model of aggression with those from a recent genetic mouse model aggression. The study used a discovery-replication design using two independent mRNA studies from mouse brain tissue. The discovery study identified strain (BALB/cJ and C57BL/6J) × stress (chronic mild stress or control) interactions. Probe sets differentially regulated in the discovery set were intersected with those uncovered in the replication study, which evaluated differences between high and low aggressive animals from three strains specifically bred to study aggression. Network analysis was conducted on overlapping genes uncovered across both studies. A significant overlap was found with the genetic mouse study sharing 1,916 probe sets with the stress model. Fifty-one probe sets were found to be strongly dysregulated across both studies mapping to 50 known genes. Network analysis revealed two plausible pathways including one centered on the UBC gene hub which encodes ubiquitin, a protein well-known for protein degradation, and another on P38 MAPK. Findings from this study support the stress model of aggression, which showed remarkable molecular overlap with a genetic model. The study uncovered a set of candidate genes including the Erg2 gene, which has previously been implicated in different psychopathologies. The gene networks uncovered points at a Redox pathway as potentially being implicated in aggressive related behaviors., (© 2016 Wiley Periodicals, Inc.)

Several studies have reported an association between traumatic stress and telomere length suggesting that traumatic stress has an impact on ageing at the cellular level. A newly derived tool provides an additional means to investigate cellular ageing by estimating epigenetic age based on DNA methylation profiles. We therefore hypothesise that in a longitudinal study of traumatic stress both indicators of cellular ageing will show increased ageing. We expect that particularly in individuals that developed symptoms of post-traumatic stress disorder (PTSD) increases in these ageing parameters would stand out. From an existing longitudinal cohort study, ninety-six male soldiers were selected based on trauma exposure and the presence of symptoms of PTSD. All military personnel were deployed in a combat zone in Afghanistan and assessed before and 6 months after deployment. The Self-Rating Inventory for PTSD was used to measure the presence of PTSD symptoms, while exposure to combat trauma during deployment was measured with a 19-item deployment experiences checklist. These groups did not differ for age, gender, alcohol consumption, cigarette smoking, military rank, length, weight, or medication use. In DNA from whole blood telomere length was measured and DNA methylation levels were assessed using the Illumina 450K DNA methylation arrays. Epigenetic ageing was estimated using the DNAm age estimator procedure. The association of trauma with telomere length was in the expected direction but not significant (B=-10.2, p=0.52). However, contrary to our expectations, development of PTSD symptoms was associated with the reverse process, telomere lengthening (B=1.91, p=0.018). In concordance, trauma significantly accelerated epigenetic ageing (B=1.97, p=0.032) and similar to the findings in telomeres, development of PTSD symptoms was inversely associated with epigenetic ageing (B=-0.10, p=0.044). Blood cell count, medication and premorbid early life trauma exposure did not confound the results. Overall, in this longitudinal study of military personnel deployed to Afghanistan we show an acceleration of ageing by trauma. However, development of PTSD symptoms was associated with telomere lengthening and reversed epigenetic ageing. These findings warrant further study of a perhaps dysfunctional compensatory cellular ageing reversal in PTSD., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour.

Aim: This study aims to identify novel genes associated with major depressive disorder and pharmacological treatment response using animal and human mRNA studies., Materials & Methods: Weighted gene coexpression network analysis was used to uncover genes associated with stress factors in mice and to inform mRNA probe set selection in a post-mortem study of depression., Results: A total of 171 genes were found to be differentially regulated in response to both early and late stress protocols in a mouse study. Ten human genes, orthologous to mouse genes differentially expressed by stress, were also found to be dysregulated in depressed cases in a human post-mortem brain study from the Stanley Foundation Brain Collection., Conclusion: Several novel genes associated with depression were uncovered, including NOVA1 and USP9X. Moreover, we found further evidence in support of hippocampal neurogenesis and peripheral inflammation in major depressive disorder.

Converging evidence suggests that the activation of the inflammatory cytokine pathway is important in the pathophysiology of unipolar depression. Antidepressants have anti-inflammatory properties and evidence suggests that inter-individual variability in response to antidepressants may reflect genetic differences in the inflammatory cytokine pathway. In particular, protein levels of Tumor Necrosis Factor (TNF) and the SNPs rs1126757 in interleukin-11 (IL11), and rs7801617 in interleukin-6 (IL6), have previously been implicated in the clinical response to the selective serotonin reuptake inhibitor (SSRI) antidepressant escitalopram. This study investigated the transcription of TNF, IL11 and IL6 as well as genes in the wider inflammatory cytokine pathway both at baseline and after escitalopram treatment in depressed patients who were either clinical "responders" (n=25) or "non-responders" (n=21). Samples were obtained as a subset of the Genome-Based Therapeutic Drugs for Depression (GENDEP) project and response status is based on changes in the Montgomery-Asberg Depression Rating Scores over a 12 wk treatment period. Binary logistic regressions revealed significant expression differences at baseline between responders and non-responders in TNF, and after escitalopram treatment in TNF and IL11. Differences in IL11 after treatment were found to be driven by drug-induced allele-specific expression differences relating to rs1126757. Top hits in the wider inflammatory cytokine pathway at both baseline and after escitalopram treatment were found to be targets of TNF. The current study adds substantial support for the role of the inflammatory cytokine pathway in mediating response to the SSRI escitalopram, and is the first to identify TNF and its targets as putative transcriptomic predictors of clinical response., (Copyright © 2012 Elsevier B.V. and ECNP. All rights reserved.)

Common SNPs in the transcription factor 4 (TCF4; ITF2, E2-2, SEF-2) gene, which encodes a basic Helix-Loop-Helix (bHLH) transcription factor, are associated with schizophrenia, conferring a small increase in risk. Other common SNPs in the gene are associated with the common eye disorder Fuch's corneal dystrophy, while rare, mostly de novo inactivating mutations cause Pitt-Hopkins syndrome. In this review, we present a systematic bioinformatics and literature review of the genomics, biological function and interactome of TCF4 in the context of schizophrenia. The TCF4 gene is present in all vertebrates, and although protein length varies, there is high conservation of primary sequence, including the DNA binding domain. Humans have a unique leucine-rich nuclear export signal. There are two main isoforms (A and B), as well as complex splicing generating many possible N-terminal amino acid sequences. TCF4 is highly expressed in the brain, where plays a role in neurodevelopment, interacting with class II bHLH transcription factors Math1, HASH1, and neuroD2. The Ca(2+) sensor protein calmodulin interacts with the DNA binding domain of TCF4, inhibiting transcriptional activation. It is also the target of microRNAs, including mir137, which is implicated in schizophrenia. The schizophrenia-associated SNPs are in linkage disequilibrium with common variants within putative DNA regulatory elements, suggesting that regulation of expression may underlie association with schizophrenia. Combined gene co-expression analyses and curated protein-protein interaction data provide a network involving TCF4 and other putative schizophrenia susceptibility genes. These findings suggest new opportunities for understanding the molecular basis of schizophrenia and other mental disorders., (Copyright © 2012 Wiley Periodicals, Inc.)

Studies of the major psychoses, schizophrenia (SZ) and bipolar disorder (BD), have traditionally focused on genetic and environmental risk factors, although more recent work has highlighted an additional role for epigenetic processes in mediating susceptibility. Since monozygotic (MZ) twins share a common DNA sequence, their study represents an ideal design for investigating the contribution of epigenetic factors to disease etiology. We performed a genome-wide analysis of DNA methylation on peripheral blood DNA samples obtained from a unique sample of MZ twin pairs discordant for major psychosis. Numerous loci demonstrated disease-associated DNA methylation differences between twins discordant for SZ and BD individually, and together as a combined major psychosis group. Pathway analysis of our top loci highlighted a significant enrichment of epigenetic changes in biological networks and pathways directly relevant to psychiatric disorder and neurodevelopment. The top psychosis-associated, differentially methylated region, significantly hypomethylated in affected twins, was located in the promoter of ST6GALNAC1 overlapping a previously reported rare genomic duplication observed in SZ. The mean DNA methylation difference at this locus was 6%, but there was considerable heterogeneity between families, with some twin pairs showing a 20% difference in methylation. We subsequently assessed this region in an independent sample of postmortem brain tissue from affected individuals and controls, finding marked hypomethylation (>25%) in a subset of psychosis patients. Overall, our data provide further evidence to support a role for DNA methylation differences in mediating phenotypic differences between MZ twins and in the etiology of both SZ and BD.

Methcathinone (ephedrone) is relatively easily accessible for abuse. Its users develop an extrapyramidal syndrome and it is not known if this is caused by methcathinone itself, by side-ingredients (manganese), or both. In the present study we aimed to clarify molecular mechanisms underlying this condition. We used microarrays to analyze whole-genome gene expression patterns of peripheral blood from 20 methcathinone users and 20 matched controls. Gene expression profile data were analyzed by Bayesian modeling and functional annotation. Of 28,869 genes on the microarrays, 326 showed statistically significant differential expression with FDR adjusted p-values below 0.05. Quantitative real-time PCR confirmed differential expression for the most of the genes selected for validation. Functional annotation and network analysis indicated activation of a gene network that included immunological disease, cellular movement, and cardiovascular disease functions (enrichment score 42). As HIV and HCV infections were confounding factors, we performed additional stratification of subjects. A similar functional activation of the "immunological disease" category was evident when we compared subjects according to injection status (past versus current users, balanced for HIV and HCV infection). However, this difference was not large therefore the major effect was related to the HIV status of the subjects. Mn-methcathinone abusers have blood RNA expression patterns that mostly reflect their HIV and HCV infections.

DNA methylation is assumed to be complementary on both alleles across the genome, although there are exceptions, notably in regions subject to genomic imprinting. We present a genome-wide survey of the degree of allelic skewing of DNA methylation with the aim of identifying previously unreported differentially methylated regions (DMRs) associated primarily with genomic imprinting or DNA sequence variation acting in cis. We used SNP microarrays to quantitatively assess allele-specific DNA methylation (ASM) in amplicons covering 7.6% of the human genome following cleavage with a cocktail of methylation-sensitive restriction enzymes (MSREs). Selected findings were verified using bisulfite-mapping and gene-expression analyses, subsequently tested in a second tissue from the same individuals, and replicated in DNA obtained from 30 parent-child trios. Our approach detected clear examples of ASM in the vicinity of known imprinted loci, highlighting the validity of the method. In total, 2,704 (1.5%) of our 183,605 informative and stringently filtered SNPs demonstrate an average relative allele score (RAS) change > or =0.10 following MSRE digestion. In agreement with previous reports, the majority of ASM ( approximately 90%) appears to be cis in nature, and several examples of tissue-specific ASM were identified. Our data show that ASM is a widespread phenomenon, with >35,000 such sites potentially occurring across the genome, and that a spectrum of ASM is likely, with heterogeneity between individuals and across tissues. These findings impact our understanding about the origin of individual phenotypic differences and have implications for genetic studies of complex disease., (Copyright (c) 2010 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Differences in gene expression in the CNS influence behavior and disease susceptibility. To systematically explore the role of normal variation in expression on hippocampal structure and function, we generated an online microarray database for a diverse panel of strains of mice, including most common inbred strains and numerous recombinant inbred lines (www.genenetwork.org). Using this resource, coexpression networks for families of genes can be generated rapidly to test causal models related to function. The data set is optimized for quantitative trait locus (QTL) mapping and was used to identify over 5500 QTLs that modulate mRNA levels. We describe a wide variety of analyses and novel synthetic approaches that take advantage of this resource, and demonstrate how both the data and associated tools can be applied to the study of gene regulation in the hippocampus and relations to structure and function.

Microarrays are designed to measure genome-wide differences in gene expression. In cases where a tissue is not accessible for analysis (e.g. human brain), it is of interest to determine whether a second, accessible tissue could be used as a surrogate for transcription profiling. Surrogacy has applications in the study of behavioural and neurodegenerative disorders. Comparison between hippocampus and spleen mRNA obtained from a mouse recombinant inbred panel indicates a high degree of correlation between the tissues for genes that display a high heritability of expression level. This correlation is not limited to apparent expression differences caused by sequence polymorphisms in the target sequences and includes both cis and trans genetic effects. A tissue such as blood could therefore give surrogate information on expression in brain for a subset of genes, in particular those co-expressed between the two tissues, which have heritably varying expression.

Immobility in the tail suspension test (TST) is considered a model of despair in a stressful situation, and acute treatment with antidepressants reduces immobility. Inbred strains of mouse exhibit widely differing baseline levels of immobility in the TST and several quantitative trait loci (QTLs) have been nominated. The labor of manual scoring and various scoring criteria make obtaining robust data and comparisons across different laboratories problematic. Several studies have validated strain gauge and video analysis methods by comparison with manual scoring. We set out to find objective criteria for automated scoring parameters that maximize the biological information obtained, using a video tracking system on tapes of tail suspension tests of 24 lines of the BXD recombinant inbred panel and the progenitor strains C57BL/6J and DBA/2J. The maximum genetic effect size is captured using the highest time resolution and a low mobility threshold. Dissecting the trait further by comparing genetic association of multiple measures reveals good evidence for loci involved in immobility on chromosomes 4 and 15. These are best seen when using a high threshold for immobility, despite the overall better heritability at the lower threshold. A second trial of the test has greater duration of immobility and a completely different genetic profile. Frequency of mobility is also an independent phenotype, with a distal chromosome 1 locus.

The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?