Methylomic trajectories across human fetal brain development

Open access articleEpigenetic processes play a key role in orchestrating transcriptional regulation during development. The importance of DNA methylation in fetal brain development is highlighted by the dynamic expression of de novo DNA methyltransferases during the perinatal period and neurodevelopmental deficits associated with mutations in the methyl-CpG binding protein 2 (MECP2) gene. However, our knowledge about the temporal changes to the epigenome during fetal brain development has, to date, been limited. We quantified genome-wide patterns of DNA methylation at ∼ 400,000 sites in 179 human fetal brain samples (100 male, 79 female) spanning 23 to 184 d post-conception. We identified highly significant changes in DNA methylation across fetal brain development at >7% of sites, with an enrichment of loci becoming hypomethylated with fetal age. Sites associated with developmental changes in DNA methylation during fetal brain development were significantly underrepresented in promoter regulatory regions but significantly overrepresented in regions flanking CpG islands (shores and shelves) and gene bodies. Highly significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small number of regions showing sex-specific DNA methylation trajectories across brain development. Weighted gene comethylation network analysis (WGCNA) revealed discrete modules of comethylated loci associated with fetal age that are significantly enriched for genes involved in neurodevelopmental processes. This is, to our knowledge, the most extensive study of DNA methylation across human fetal brain development to date, confirming the prenatal period as a time of considerable epigenomic plasticity.MRCUniversity of Exeter Medical SchoolWellcome Trus

PRIMUS: Galaxy Clustering as a Function of Luminosity and Color at 0.2<z<1

We present measurements of the luminosity and color-dependence of galaxy clustering at 0.2<z<1.0 in the PRIsm MUlti-object Survey (PRIMUS). We quantify the clustering with the redshift-space and projected two-point correlation functions, xi(rp,pi) and wp(rp), using volume-limited samples constructed from a parent sample of over 130,000 galaxies with robust redshifts in seven independent fields covering 9 sq. deg. of sky. We quantify how the scale-dependent clustering amplitude increases with increasing luminosity and redder color, with relatively small errors over large volumes. We find that red galaxies have stronger small-scale (0.1<rp<1 Mpc/h) clustering and steeper correlation functions compared to blue galaxies, as well as a strong color dependent clustering within the red sequence alone. We interpret our measured clustering trends in terms of galaxy bias and obtain values between b_gal=0.9-2.5, quantifying how galaxies are biased tracers of dark matter depending on their luminosity and color. We also interpret the color dependence with mock catalogs, and find that the clustering of blue galaxies is nearly constant with color, while redder galaxies have stronger clustering in the one-halo term due to a higher satellite galaxy fraction. In addition, we measure the evolution of the clustering strength and bias, and we do not detect statistically significant departures from passive evolution. We argue that the luminosity- and color-environment (or halo mass) relations of galaxies have not significantly evolved since z=1. Finally, using jackknife subsampling methods, we find that sampling fluctuations are important and that the COSMOS field is generally an outlier, due to having more overdense structures than other fields; we find that 'cosmic variance' can be a significant source of uncertainty for high-redshift clustering measurements.Comment: 22 pages, 21 figures, matches version published in Ap

Cis-effects on gene expression in the human prenatal brain associated with genetic risk for neuropsychiatric disorders

The majority of common risk alleles identified for neuropsychiatric disorders reside in non-coding regions of the genome and are therefore likely to impact gene regulation. However, the genes that are primarily affected and the nature and developmental timing of these effects remain unclear. Given the hypothesised role for early neurodevelopmental processes in these conditions, we here define genetic predictors of gene expression in the human fetal brain with which we perform transcriptome-wide association studies (TWASs) of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder and schizophrenia. We identify prenatal cis-regulatory effects on 63 genes and 166 individual transcripts associated with genetic risk for these conditions. We observe pleiotropic effects of expression predictors for a number of genes and transcripts, including those of decreased DDHD2 expression in association with risk for schizophrenia and bipolar disorder, increased expression of a ST3GAL3 transcript with risk for schizophrenia and ADHD, and increased expression of an XPNPEP3 transcript with risk for schizophrenia, bipolar disorder and major depression. For the protocadherin alpha cluster genes PCDHA7 and PCDHA8, we find that predictors of low expression are associated with risk for major depressive disorder while those of higher expression are associated with risk for schizophrenia. Our findings support a role for altered gene regulation in the prenatal brain in susceptibility to various neuropsychiatric disorders and prioritize potential risk genes for further neurobiological investigation

Common Genetic Variants Found in HLA and KIR Immune Genes in Autism Spectrum Disorder

The “common variant—common disease” hypothesis was proposed to explain diseases with strong inheritance. This model suggests that a genetic disease is the result of the combination of several common genetic variants. Common genetic variants are described as a 5% frequency differential between diseased vs. matched control populations. This theory was recently supported by an epidemiology paper stating that about 50% of genetic risk for autism resides in common variants. However, rare variants, rather than common variants, have been found in numerous genome wide genetic studies and many have concluded that the “common variant—common disease” hypothesis is incorrect. One interpretation is that rare variants are major contributors to genetic diseases and autism involves the interaction of many rare variants, especially in the brain. It is obvious there is much yet to be learned about autism genetics. Evidence has been mounting over the years indicating immune involvement in autism, particularly the HLA genes on chromosome 6 and KIR genes on chromosome 19. These two large multigene complexes have important immune functions and have been shown to interact to eliminate unwanted virally infected and malignant cells. HLA proteins have important functions in antigen presentation in adaptive immunity and specific epitopes on HLA class I proteins act as cognate ligands for KIR receptors in innate immunity. Data suggests that HLA alleles and KIR activating genes/haplotypes are common variants in different autism populations. For example, class I allele (HLA-A2 and HLA-G 14 bp-indel) frequencies are significantly increased by more than 5% over control populations (Table 2). The HLA-DR4 Class II and shared epitope frequencies are significantly above the control populations (Table 2). Three activating KIR genes: 3DS1, 2DS1, and 2DS2 have increased frequencies of 15, 22, and 14% in autism populations, respectively. There is a 6% increase in total activating KIR genes in autism over control subjects. And, more importantly there is a 12% increase in activating KIR genes and their cognate HLA alleles over control populations (Torres et al., 2012a). These data suggest the interaction of HLA ligand/KIR receptor pairs encoded on two different chromosomes is more significant as a ligand/receptor complex than separately in autism

Survival-Time Distribution for Inelastic Collapse

In a recent publication [PRL {\bf 81}, 1142 (1998)] it was argued that a randomly forced particle which collides inelastically with a boundary can undergo inelastic collapse and come to rest in a finite time. Here we discuss the survival probability for the inelastic collapse transition. It is found that the collapse-time distribution behaves asymptotically as a power-law in time, and that the exponent governing this decay is non-universal. An approximate calculation of the collapse-time exponent confirms this behaviour and shows how inelastic collapse can be viewed as a generalised persistence phenomenon.Comment: 4 pages, RevTe

Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics

Background A recent genome-wide association study (GWAS) of autism spectrum disorders (ASD) (Ncases=18,381, Ncontrols=27,969) has provided novel opportunities for investigating the aetiology of ASD. Here, we integrate the ASD GWAS summary statistics with summary-level gene expression data to infer differential gene expression in ASD, an approach called transcriptome-wide association study (TWAS). Methods Using FUSION software, ASD GWAS summary statistics were integrated with predictors of gene expression from 16 human datasets, including adult and fetal brain. A novel adaptation of established statistical methods was then used to test for enrichment within candidate pathways, specific tissues, and at different stages of brain development. The proportion of ASD heritability explained by predicted expression of genes in the TWAS was estimated using stratified linkage disequilibrium-score regression. Results This study identified 14 genes as significantly differentially expressed in ASD, 13 of which were outside of known genome-wide significant loci (±500kb). XRN2, a gene proximal to an ASD GWAS locus, was inferred to be significantly upregulated in ASD, providing insight into functional consequence of this associated locus. One novel transcriptome-wide significant association from this study is the downregulation of PDIA6, which showed minimal evidence of association in the GWAS, and in gene-based analysis using MAGMA. Predicted gene expression in this study accounted for 13.0% of the total ASD SNP-heritability. Conclusion This study has implicated several genes as significantly up-/down-regulated in ASD providing novel and useful information for subsequent functional studies. This study also explores the utility of TWAS-based enrichment analysis and compares TWAS results with a functionally agnostic approach