Understanding the genetic liability to schizophrenia through the neuroepigenome

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Abstract

The Psychiatric Genomics Consortium-Schizophrenia Workgroup (PGC-SCZ) recently identified 108 loci associated with increased risk for schizophrenia (SCZ). The vast majority of these variants reside within non-coding sequences of the genome and are predicted to exert their effects by affecting the mechanism of action of cis regulatory elements (CREs), such as promoters and enhancers. Although a number of large-scale collaborative efforts (e.g. ENCODE) have achieved a comprehensive mapping of CREs in human cell lines or tissue homogenates, it is becoming increasingly evident that many risk-associated variants are enriched for expression Quantitative Trait Loci (eQTLs) and CREs in specific tissues or cells. As such, data derived from previous research endeavors may not capture fully cell-type and/or region specific changes associated with brain diseases. Coupling recent technological advances in genomics with cell-type specific methodologies, we are presented with an unprecedented opportunity to better understand the genetics of normal brain development and function and, in turn, the molecular basis of neuropsychiatric disorders. In this review, we will outline ongoing efforts towards this goal and will discuss approaches with the potential to shed light on the mechanism(s) of action of cell-type specific cis regulatory elements and their putative roles in disease, with particular emphasis on understanding the manner in which the epigenome and CREs influence the etiology of SCZ.

Introduction

Recent years have witnessed renewed interest in studying genetic risk for SCZ, largely driven by advances in genomic technologies and a massive increase in sample sizes through the efforts of large consortia. The largest genome-wide association study (GWAS) analysis, conducted by the Psychiatric Genomics Consortium-Schizophrenia Workgroup (PGC-SCZ), comprises a sample set of 36,989 cases and 113,075 controls and identified 108 common variants that show statistical associations with SCZ (PGC-SCZ, 2014). Concurrently, the advent of next generation sequencing technologies has identified rare and de novo mutations conferring a high risk for the disease (Fromer et al., 2014, Purcell et al., 2014). In these exome sequencing studies, rare variants and de novo alleles were spread across a large number of SCZ genes, converging onto common, albeit broad, biological pathways, including genes involved in postsynaptic protein complexes and calcium signaling pathways.

Despite these efforts, a precise variant or target gene for SCZ has not been identified. There are several explanations for this, including unidentified rare variants with high penetrance or somatic mosaicism, and current methodological advances will be able to test these hypotheses in future studies. Here, we focus on findings that emerge from the largest and more recent GWAS in SCZ that set out to identify common risk loci. First, the variants associated with SCZ have small effect sizes that confer moderate risk but that, collectively, contribute to SCZ (i.e. SCZ is a polygenic disease with no single variant accounting for the entire risk). We will, therefore, need to adapt current methods to allow for multiple causal variants and genes to be studied simultaneously. Second, the variants most associated with SCZ often fall within large regions of high linkage disequilibrium (LD) containing multiple variants, any of which may be driving the association. As such, additional information is required to determine which variants are more likely to have functional effects. Third, and, from the perspective of this review, perhaps most importantly, the majority of identified variants are located outside of exons and, as such, do not change the protein coding sequence of genes, suggesting a substantial role for regulatory neuroepigenomic variation in the pathogenesis of SCZ.

In this review, we first describe the neuroepigenome and our current understanding of the ways in which it can be modified. We will then discuss its role in development, how it changes across the lifespan of an individual and its impact on disease. Finally, we provide a perspective for ongoing and future approaches to further our understanding of the neuroepigenome with an emphasis on applications utilizing frozen human postmortem brain tissue. While numerous epigenomic studies have focused on peripheral tissues and animal models, the aim of this review is to discuss studies that pertain to the human brain and, more specifically, to the neuroepigenome.

Section snippets

What is the neuroepigenome & why is it important?

Nuclei are between 2 and 10 μm in diameter yet contain approximately 2 m of DNA. In order to fit inside the nucleus, chromosomes are packaged in to a condensed mass consisting of genomic DNA and protein, termed chromatin. Chromatin falls into two broad categories: the more densely packed, transcriptionally repressed, heterochromatin and the less densely packed, transcriptionally active, euchromatin. The basic unit of chromatin is the nucleosome, which is composed of ~ 147 base pairs of genomic DNA

The genome in 3-dimensions

Importantly, DNA methylation and its variants (hydroxymethylation, etc.), multiple post-translational histone modifications and other types of epigenetic regulation, fail to fully characterize the epigenome and localized chromatin architecture at any given genomic locus. This is because the chromosomal arrangements in the interphase nucleus are not random and it is now generally accepted that genetic information is not only encoded in nucleotide sequence but also in the dynamic 3-dimensional

The neuroepigenome in development

Unlike the underlying genome, the composition of the epigenome can be dynamically modified during development and both histones and DNA can display the hallmarks of epigenetic modification (V.W. Zhou et al., 2011). The epigenome is, therefore, variable, and this variability is a major determinant of the distinct patterns of gene expression observed across a wide array of developmental stages, cell lineages, and environmental conditions (Bernstein et al., 2007). Lister and colleagues examined

The role of the neuroepigenome in schizophrenia

Numerous studies link dysregulation of the epigenome to disease [for examples see (Robertson, 2005, Mirabella et al., 2015)] such as cancer (Deb et al., 2014), heart disease (Zhang and Liu, 2015) and metabolic disorders (Martinez-Jimenez and Sandoval, 2015). In addition, a number of groups have attempted to assess the role of the epigenome in the etiology of neurological disorders, including autism (Shulha et al., 2012a), addiction (Z. Zhou et al., 2011), Huntington's disease (HD) (Bai et al.,

Recent large-scale efforts to study the non-coding genome

Large-scale, coordinated, efforts are required to systematically explore the regulatory function of the non-coding genome. Projects such as ENCyclopedia Of DNA Elements (ENCODE) project (ENCODE Project Consortium, 2012, Bernstein et al., 2012, Maurano et al., 2012), the NIH Roadmap Epigenome Mapping Consortium (REMC) (Bernstein et al., 2010, Roadmap Epigenomics Consortium et al., 2015) and FANTOM5 (Andersson et al., 2014) have made great strides towards providing a detailed catalogue of CREs in

Future approaches to better characterize the non-coding regulatory regions of the genome

In the following section we outline ongoing and future approaches to analyze dynamic modifications of chromatin and further our understanding of the structure and function of non-coding regulatory regions of the genome. Ideally, such approaches will facilitate the study of the neuroepigenome at a cell-type specific resolution while utilizing low amounts of input material.

Cell-type specific analysis within the postmortem brain

In cortical gray matter, significant differences are observed when comparing the distribution of trimethylated histone H3K4 (H3K4me3) in neurons (identified using an antibody against the neuron-specific antigen, NeuN) to that of non-neurons (NeuN−) in cell populations residing in the same tissue, the prefontal cortex (PFC) (Cheung et al., 2010, Shulha et al., 2012b). Therefore, the study of homogenous cell populations may fail to distinguish signals unique to specific cell-types, potentially

Whole-genome bisulfite sequencing

DNA methylation is critically important to regulate gene expression and cellular functions (Bibikova and Fan, 2010). So far, various techniques have been developed to profile DNA methylation but most do not allow for measuring methylation status in large sample sets at high resolution and may be insensitive to subtle, disease associated, methylation changes. By treating DNA with bisulfite it is possible to introduce specific DNA sequence changes based on the methylation status of individual

Chromatin immunoprecipitation followed by sequencing

Chromatin immunoprecipitation (ChIP) is a method to identify fragments of genomic DNA bound by a particular protein. ChIP works by enriching specific crosslinked DNA–protein complexes using an antibody against the protein of interest, such as a transcription factor (Mahony and Pugh, 2015). Provided the appropriate antibody exists, ChIP can also be employed to isolate DNA bound by modified proteins (e.g. methylated or acetylated histones). ChIP-seq combines DNA fragment isolation by ChIP with

Open chromatin assays to identify potential cis regulatory elements

The nucleosome is known to play a central role in regulating gene transcription from promoters and exists in a dynamic equilibrium between open and closed states (Mellor, 2005). Nucleosome rearrangement (leading to open chromatin) at promoters and enhancers results from the binding of specific regulatory factors responsible for transcriptional activation (Henikoff, 2008). Open or accessible regions of the genome are regarded as primary positions for regulatory elements and are crucial in

Refining the search to identify active enhancer elements

All of the aforementioned approaches allow for the genome wide identification of potential regulatory elements, however, they fail to provide a direct functional readout of the activity of these elements. In order to fully understand the genetics underpinning brain development and function in health and disease, it will be necessary to distill down this broad inventory of putative regulatory elements to a list of the most salient actors. Several different methodologies render this goal

Linking the activity of enhancers to specific genes

Having identified active cis regulatory elements within a given cellular context, the next requirement would be to assign their activities to a specific gene, or set of genes. Chromosome Conformation Capture (3C) based methodologies are a useful tool towards this purpose, as they allow for the identification of physical interactions between distal genetic elements, e.g. between a gene and an enhancer (Naumova et al., 2012, Simonis et al., 2007), and can range from target-specific (3C) to

The future of genome wide analysis of precious materials — less is more

A major limitation of traditional epigenomic approaches (e.g. ChIPseq) is the large number of cells required to generate high-quality data sets. The ATACseq method has recently been modified to allow profiling of chromatin accessibility at the resolution of single cells (Buenrostro et al., 2015, Cusanovich et al., 2015). A number of new approaches now allow for the performance of ChIP-seq, genome wide transcription factor binding and methylome analysis using as little as 1000 cells (Adli and

Role of funding source

No funding source agreements.

Conflict of interest

The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed

Acknowledgments

Writing of this manuscript was supported by the Office of Academic Affiliations, Advanced Fellowship Program in Mental Illness Research and Treatment, Department of Veterans Affairs (Halene), the National Institutes of Health (R01AG050986 Roussos), Brain Behavior Research Foundation (20540 Roussos), Alzheimer's Association (NIRG-340998 Roussos) and the Veterans Affairs (Merit grant BX002395 Roussos).

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