Joon Yong An

Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system

Many putative genetic factors that confer risk to neurodevelopmental disorders such as autism spectrum disorders (ASDs) and X-linked intellectual disability (XLID), and to neuropsychiatric disorders including attention deficit hyperactivity disorder (ADHD) and schizophrenia (SZ) have been identified in individuals from diverse human populations. Although there is significant aetiological heterogeneity within and between these conditions, recent data show that genetic factors contribute to their comorbidity. Many studies have identified candidate gene associations for these mental health disorders, albeit this is often done in a piecemeal fashion with little regard to the inherent molecular complexity. Here, we sought to abstract relationships from our knowledge of systems level biology to help understand the unique and common genetic drivers of these conditions. We undertook a global and systematic approach to build and integrate available data in gene networks associated with ASDs, XLID, ADHD and SZ. Complex network concepts and computational methods were used to investigate whether candidate genes associated with these conditions were related through mechanisms of gene regulation, functional protein–protein interactions, transcription factor (TF) and microRNA (miRNA) binding sites. Although our analyses show that genetic variations associated with the four disorders can occur in the same molecular pathways and functional domains, including synaptic transmission, there are patterns of variation that define significant differences between disorders. Of particular interest is DNA variations located in intergenic regions that comprise regulatory sites for TFs or miRNA. Our approach provides a hypothetical framework, which will help discovery and analysis of candidate genes associated with neurodevelopmental and neuropsychiatric disorders.

Towards a molecular characterization of autism spectrum disorders: an exome sequencing and systems approach

The hypothetical ‘AXAS’ gene network model that profiles functional patterns of heterogeneous DNA variants overrepresented in autism spectrum disorder (ASD), X-linked intellectual disability, attention deficit and hyperactivity disorder and schizophrenia was used in this current study to analyze whole exome sequencing data from an Australian ASD cohort. An optimized DNA variant filtering pipeline was used to identify loss-of-function DNA variations. Inherited variants from parents with a broader autism phenotype and de novo variants were found to be significantly associated with ASD. Gene ontology analysis revealed that putative rare causal variants cluster in key neurobiological processes and are overrepresented in functions involving neuronal development, signal transduction and synapse development including the neurexin trans-synaptic complex. We also show how a complex gene network model can be used to fine map combinations of inherited and de novo variations in families with ASD that converge in the L1CAM pathway. Our results provide an important step forward in the molecular characterization of ASD with potential for developing a tool to analyze the pathogenesis of individual affected families.

Genetic heterogeneity in autism: From single gene to a pathway perspective.

The extreme genetic heterogeneity of autism spectrum disorder (ASD) represents a major challenge. Recent advances in genetic screening and systems biology approaches have extended our knowledge of the genetic etiology of ASD. In this review, we discuss the paradigm shift from a single gene causation model to pathway perturbation model as a guide to better understand the pathophysiology of ASD. We discuss recent genetic findings obtained through next-generation sequencing (NGS) and examine various integrative analyses using systems biology and complex networks approaches that identify convergent patterns of genetic elements associated with ASD.

Whole genome sequencing in psychiatric disorders: the WGSPD consortium

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome through pilot WGS projects will be critical to determining which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The Whole Genome Sequencing for Psychiatric Disorders Consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.This study presents a framework to evaluate rare and de novo variation from whole-genome sequencing (WGS). The work suggests that robust results from WGS studies will require large cohorts and strategies that consider the substantial multiple-testing burden.

Limited contribution of rare, noncoding variation to autism spectrum disorder from sequencing of 2,076 genomes in quartet families

Genomic studies to date in autism spectrum disorder (ASD) have largely focused on newly arising mutations that disrupt protein coding sequence and strongly influence risk. We evaluate the contribution of noncoding regulatory variation across the size and frequency spectrum through whole genome sequencing of 519 ASD cases, their unaffected sibling controls, and parents. Cases carry a small excess of de novo (1.02-fold) noncoding variants, which is not significant after correcting for paternal age. Assessing 51,801 regulatory classes, no category is significantly associated with ASD after correction for multiple testing. The strongest signals are observed in coding regions, including structural variation not detected by previous technologies and missense variation. While rare noncoding variation likely contributes to risk in neurodevelopmental disorders, no category of variation has impact equivalent to loss-of-function mutations. Average effect sizes are likely to be smaller than that for coding variation, requiring substantially larger samples to quantify this risk.

National human genome projects: an update and an agenda

Population genetic and human genetic studies are being accelerated with genome technology and data sharing. Accordingly, in the past 10 years, several countries have initiated genetic research using genome technology and identified the genetic architecture of the ethnic groups living in the corresponding country or suggested the genetic foundation of a social phenomenon. Genetic research has been conducted from epidemiological studies that previously described the health or disease conditions in defined population. This perspective summarizes national genome projects conducted in the past 10 years and introduces case studies to utilize genomic data in genetic research.

Publisher Correction: Whole genome sequencing in psychiatric disorders: the WGSPD consortium

In the version of this article initially published, the consortium authorship and corresponding authors were not presented correctly. In the PDF and print versions, the Whole Genome Sequencing for Psychiatric Disorders (WGSPD) consortium was missing from the author list at the beginning of the paper, where it should have appeared as the seventh author; it was present in the author list at the end of the paper, but the footnote directing readers to the Supplementary Note for a list of members was missing. In the HTML version, the consortium was listed as the last author instead of as the seventh, and the line directing readers to the Supplementary Note for a list of members appeared at the end of the paper under Author Information but not in association with the consortium name itself. Also, this line stated that both member names and affiliations could be found in the Supplementary Note; in fact, only names are given. In all versions of the paper, the corresponding author symbols were attached to A. Jeremy Willsey, Steven E. Hyman, Anjene M. Addington and Thomas Lehner; they should have been attached, respectively, to Steven E. Hyman, Anjene M. Addington, Thomas Lehner and Nelson B. Freimer. As a result of this shift, the respective contact links in the HTML version did not lead to the indicated individuals. The errors have been corrected in the HTML and PDF versions of the article.

An integrative analysis of non-coding regulatory DNA variations associated with autism spectrum disorder

A number of genetic studies have identified rare protein-coding DNA variations associated with autism spectrum disorder (ASD), a neurodevelopmental disorder with significant genetic etiology and heterogeneity. In contrast, the contributions of functional, regulatory genetic variations that occur in the extensive non-protein-coding regions of the genome remain poorly understood. Here we developed a genome-wide analysis to identify the rare single nucleotide variants (SNVs) that occur in non-coding regions and determined the regulatory function and evolutionary conservation of these variants. Using publicly available datasets and computational predictions, we identified SNVs within putative regulatory regions in promoters, transcription factor binding sites, and microRNA genes and their target sites. Overall, we found that the regulatory variants in ASD cases were enriched in ASD-risk genes and genes involved in fetal neurodevelopment. As with previously reported coding mutations, we found an enrichment of the regulatory variants associated with dysregulation of neurodevelopmental and synaptic signaling pathways. Among these were several rare inherited SNVs found in the mature sequence of microRNAs predicted to affect the regulation of ASD-risk genes. We show a paternally inherited miR-873-5p variant with altered binding affinity for several risk-genes including NRXN2 and CNTNAP2 putatively overlay maternally inherited loss-of-function coding variations in NRXN1 and CNTNAP2 to likely increase the genetic liability in an idiopathic ASD case. Our analysis pipeline provides a new resource for identifying loss-of-function regulatory DNA variations that may contribute to the genetic etiology of complex disorders.

Appreciating the Population-wide Impact of Copy Number Variants on Cognition

Over the past decade, large-scale deletions and duplications of DNA, called copy number variants (CNVs), have emerged as important risk factors for multiple neuropsychiatric disorders. While some CNVs appear to be 100% penetrant (e.g., 1p36 deletions), the majority of CNVs are observed in both affected and unaffected individuals. In this issue of Biological Psychiatry, Kendall et al. (1) investigate the impact of CNVs, discovered with genotyping arrays, in over 150,000 mostly unaffected individuals by assessing scores from multiple cognitive tests. These samples were collected as part of the UK Biobank project, which recruited volunteers 40 to 69 years of age with no exclusion criteria. This volunteer-based ascertainment strategy is likely to limit the inclusion of individuals with severe neurodevelopmental disorders, resulting in a cohort that lies between a true cross section of the population (e.g., a birth cohort) and control subjects selected for the absence of neurodevelopmental disorders. Carrier status for CNVs was assessed at 53 loci selected on the basis of significant association with developmental delay in 29,085 cases (2) and excluding 15p11.2 BP1-BP2 duplications due to their high population frequency (0.5%; Figure 1A). Integrating these developmental delay cases with the new data from the Biobank “controls” allows estimates of both CNV frequency in the population and relative risk of developmental delay per CNV (Figure 1A), and these metrics help interpret the three groups analyzed in the Kendall et al. article: 1) 1087 carriers (0.71% of cohort) with CNVs at one of 12 loci associated with both schizophrenia (3,4) and developmental delay (2); 2) 484 carriers (0.32% of cohort) with CNVs at one of 41 loci associated with developmental delay only (2); and 3) 480 individuals (0.32% of cohort) who report a diagnosis of schizophrenia. Of note, there are several reasons why a particular CNV locus might fall into the second group due to the absence of significant association with schizophrenia, including a more severe clinical phenotype (e.g., 1p36 deletions), the absence of schizophrenia risk (e.g., 22q11.2 duplications), and, almost certainly, insufficient power in existing schizophrenia studies. Therefore, the 12 CNVs in the first group can be considered as mutational hotspots with comparatively high population frequencies for a given relative risk, resulting in sufficient power to detect schizophrenia association (Figure 1A). Seven different tests of cognition were analyzed, though the number of individuals who performed each test varies, ranging from 90% of the Biobank cohort for the Reaction Time test to 9.4% of the cohort for the Digit Span test. Scores consistent with reduced cognitive abilities were reported across all seven tests for individuals with schizophrenia; this reduction in cognitive score was equivalent to 0.35 to 0.90