Variable DNA methylation in neonates mediates the association between prenatal smoking and birth weight

There is great interest in the role epigenetic variation induced by non-genetic exposures may play in the context of health and disease. In particular, DNA methylation has previously been shown to be highly dynamic during the earliest stages of development and is influenced by in utero exposures such as maternal smoking and medication. In this study we sought to identify the specific DNA methylation differences in blood associated with prenatal and birth factors, including birth weight, gestational age and maternal smoking. We quantified neonatal methylomic variation in 1263 infants using DNA isolated from a unique collection of archived blood spots taken shortly after birth (mean = 6.08 days; s.d. = 3.24 days). An epigenome-wide association study (EWAS) of gestational age and birth weight identified 4299 and 18 differentially methylated positions (DMPs) respectively, at an experiment-wide significance threshold of p < 1 × 10-7. Our EWAS of maternal smoking during pregnancy identified 110 DMPs in neonatal blood, replicating previously reported genomic loci, including AHRR. Finally, we tested the hypothesis that DNA methylation mediates the relationship between maternal smoking and lower birth weight, finding evidence that methylomic variation at three DMPs may link exposure to outcome. These findings complement an expanding literature on the epigenomic consequences of prenatal exposures and obstetric factors, confirming a link between the maternal environment and gene regulation in neonates. This article is part of the theme issue 'Developing differences: early-life effects and evolutionary medicine'.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.This study was supported by grant no. HD073978 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, and National Institute of Neurological Disorders and Stroke; and by the Beatrice and Samuel A. Seaver Foundation. The iPSYCH (The Lundbeck Foundation Initiative for Integrative Psychiatric Research) team acknowledges funding from The Lundbeck Foundation (grant no. R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, the European Research Council (project no: 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. This research has been conducted using the Danish National Biobank resource, supported by the Novo Nordisk Foundation. J.M. and E.H. are supported by funding from the UK Medical Research Council (K013807).published version, accepted version, submitted versio

Elevated polygenic burden for autism is associated with differential DNA methylation at birth

This is the final version of the article. Available from BioMed Central via the DOI in this record.Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder characterized by deficits in social communication and restricted, repetitive behaviors, interests, or activities. The etiology of ASD involves both inherited and environmental risk factors, with epigenetic processes hypothesized as one mechanism by which both genetic and non-genetic variation influence gene regulation and pathogenesis. The aim of this study was to identify DNA methylation biomarkers of ASD detectable at birth.This study was supported by grant HD073978 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, and National Institute of Neurological Disorders and Stroke; and by the Beatrice and Samuel A. Seaver Foundation. We acknowledge iPSYCH and The Lundbeck Foundation for providing samples and funding. The iPSYCH (The Lundbeck Foundation Initiative for Integrative Psychiatric Research) team acknowledges funding from The Lundbeck Foundation (grant numbers R102-A9118 and R155–2014-1724), the Stanley Medical Research Institute, the European Research Council (project number 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. This research has been conducted using the Danish National Biobank resource, supported by the Novo Nordisk Foundation. JM is supported by funding from the UK Medical Research Council (MR/K013807/1) and a Distinguished Investigator Award from the Brain & Behavior Research Foundation. The SEED study was supported by Centers for Disease Control and Prevention (CDC) Cooperative Agreements announced under the RFAs 01086, 02199, DD11–002, DD06–003, DD04–001, and DD09–002 and the SEED DNA methylation measurements were supported by Autism Speaks Award #7659 to MDF. SA was supported by the Burroughs-Wellcome Trust training grant: Maryland, Genetics, Epidemiology and Medicine (MD-GEM). The SSC was supported by Simons Foundation (SFARI) award and NIH grant MH089606, both awarded to STW

A Genetic Investigation of Sex Bias in the Prevalence of Attention-Deficit/Hyperactivity Disorder

Background Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is two to seven times more common in male individuals than in female individuals. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. Methods We analyzed genome-wide autosomal common variants from the Psychiatric Genomics Consortium and iPSYCH Project (n = 20,183 cases, n = 35,191 controls) and Swedish population register data (n = 77,905 cases, n = 1,874,637 population controls). Results Genetic correlation analyses using two methods suggested near complete sharing of common variant effects across sexes, with rg estimates close to 1. Analyses of population data, however, indicated that female individuals with ADHD may be at especially high risk for certain comorbid developmental conditions (i.e., autism spectrum disorder and congenital malformations), potentially indicating some clinical and etiological heterogeneity. Polygenic risk score analysis did not support a higher burden of ADHD common risk variants in female cases (odds ratio [confidence interval] = 1.02 [0.98–1.06], p = .28). In contrast, epidemiological sibling analyses revealed that the siblings of female individuals with ADHD are at higher familial risk for ADHD than the siblings of affected male individuals (odds ratio [confidence interval] = 1.14 [1.11–1.18], p = 1.5E-15). Conclusions Overall, this study supports a greater familial burden of risk in female individuals with ADHD and some clinical and etiological heterogeneity, based on epidemiological analyses. However, molecular genetic analyses suggest that autosomal common variants largely do not explain the sex bias in ADHD prevalence

Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study

<p>Abstract</p> <p>Background</p> <p>We investigate methods used to analyse the results of clinical trials with survival outcomes in which some patients switch from their allocated treatment to another trial treatment. These included simple methods which are commonly used in medical literature and may be subject to selection bias if patients switching are not typical of the population as a whole. Methods which attempt to adjust the estimated treatment effect, either through adjustment to the hazard ratio or via accelerated failure time models, were also considered. A simulation study was conducted to assess the performance of each method in a number of different scenarios.</p> <p>Results</p> <p>16 different scenarios were identified which differed by the proportion of patients switching, underlying prognosis of switchers and the size of true treatment effect. 1000 datasets were simulated for each of these and all methods applied. Selection bias was observed in simple methods when the difference in survival between switchers and non-switchers were large. A number of methods, particularly the AFT method of Branson and Whitehead were found to give less biased estimates of the true treatment effect in these situations.</p> <p>Conclusions</p> <p>Simple methods are often not appropriate to deal with treatment switching. Alternative approaches such as the Branson & Whitehead method to adjust for switching should be considered.</p

Matrix models and sensitivity analysis of populations classified by age and stage : a vec-permutation matrix approach

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Theoretical Ecology 5 (2012): 403-417, doi:10.1007/s12080-011-0132-2.Matrix population models in which individuals are classified by both age and stage can be constructed using the vec-permutation matrix. The resulting age-stage models can be used to derive the age-specific consequences of a stage-specific life history or to describe populations in which the vital rates respond to both age and stage. I derive a general formula for the sensitivity of any output (scalar, vector, or matrix-valued) of the model, to any vector of parameters, using matrix calculus. The matrices describing age-stage dynamics are almost always reducible; I present results giving conditions under which population growth is ergodic from any initial condition. As an example, I analyze a published stage-specific model of Scotch broom (Cytisus scoparius), an invasive perennial shrub. Sensitivity analysis of the population growth rate finds that the selection gradients on adult survival do not always decrease with age but may increase over a range of ages. This may have implications for the evolution of senescence in stage-classified populations. I also derive and analyze the joint distribution of age and stage at death and present a sensitivity analysis of this distribution and of the marginal distribution of age at death.This research was supported by National Science Foundation Grant DEB-0816514 and by a Research Award from the Alexander von Humboldt Foundation

Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesOver the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).We conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P = 9 × 10(-6)). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a 'neurodevelopmental hub' on chromosome 8p11.23.This study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4.National Institutes of Mental Health (NIMH, USA) ACE Network Autism Genetic Resource Exchange (AGRE) is a program of Autism Speaks (USA) The Autism Genome Project (AGP) from Autism Speaks (USA) Canadian Institutes of Health Research (CIHR), Genome Canada Health Research Board (Ireland) Hilibrand Foundation (USA) Medical Research Council (UK) National Institutes of Health (USA) Ontario Genomics Institute University of Toronto McLaughlin Centre Simons Foundation Johns Hopkins Autism Consortium of Boston NLM Family foundation National Institute of Health grants National Health Medical Research Council Scottish Rite Spunk Fund, Inc. Rebecca and Solomon Baker Fund APEX Foundation National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD) endowment fund of the Nancy Pritzker Laboratory (Stanford) Autism Society of America Janet M. Grace Pervasive Developmental Disorders Fund The Lundbeck Foundation universities and university hospitals of Aarhus and Copenhagen Stanley Foundation Centers for Disease Control and Prevention (CDC) Netherlands Scientific Organization Dutch Brain Foundation VU University Amsterdam Trinity Centre for High Performance Computing through Science Foundation Ireland Autism Genome Project (AGP) from Autism Speak

Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways

Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

BACKGROUND Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC. METHODS Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals). RESULTS Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation. CONCLUSION This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts