Stephan J. Sanders, Ph.D/BMBS/MRCPCH - US grants

? DESCRIPTION (provided by applicant): Autism Spectrum Disorder (ASD) is characterized by impairments in social communication and restricted or repetitive behavior or interests. The application of genomic technologies has led to the identification of many of the genes underlying ASD, presenting the opportunity to assess the insight these risk genes can give into the etiology of ASD. In this proposal we aim to: 1) Generate a list of ASD-associated genes; 2) Identify points of convergence between these genes in biological data (e.g. gene regulation and expression); and 3) Validate these points of convergence in model systems. Since ASD is a human neurodevelopmental disorder we will prioritize biological data that is collected longitudinally across development from human brain tissue. In our prior work we have demonstrated that de novo mutations, specifically copy number variants (CNVs) and loss of function (LoF) point mutations, are strongly associated with ASD. Furthermore, these mutations cluster at ASD risk genes and loci in cases but not in controls. By comparing the distribution of these mutations between cases and controls we can identify the points of mutational clustering that represent ASD risk loci (e.g. CNVs at the 500kbp 16p11.2 locus and LoFs at the gene CHD8). We have developed a statistical framework to assess this clustering as well as incorporating evidence from inherited variants and case-control data. This framework is called the Transmitted and De novo Associated Test (TADA). In Aim 1 we will develop this test further to incorporate all the available CNV, exome, genome, and targeted sequencing data into a single ASD gene list, ranked by the degree of ASD association. Previously we used the top nine ASD risk genes as seeds for gene co-expression networks and assessed the validity of these networks by their ability to incorporate 120 independent ASD risk genes. By limiting the co- expression input data to narrow windows of development and specific brain regions we could identify the spatiotemporal networks with the greatest enrichment, for example pre-frontal cortex in mid-fetal development. In Aim 2, we propose a similar approach, but using the DAWN (Detecting Association With Networks) method developed by our group. DAWN uses the narrow windows of co-expression data as before, but is able to incorporate evidence from other datasets such as gene regulation, and protein-protein interaction (PPI). By seeding the DAWN networks with the highest confidence genes we will assess the spatiotemporal networks that best predict other ASD genes. ASD shows a significant sex bias implicating an interaction between ASD etiology and sexually dimorphic factors. Building on our work of identifying sexually dimorphic transcripts in the developing human brain we will test their enrichment within specific networks identified by DAWN. To validate the ASD-associated networks, in Aim 3 we will identify the gene that best represents each network and assess if disrupting it also disrupts the other genes within the network. We will disrupt each gene using CRISPR/Cas9 in both mice and human-derived iPSCs and assess the genes disrupted using RNA-Seq.

ABSTRACT Genetic and genomic investigations have yielded important findings as to the genetic contributions to major psychiatric illnesses, illustrating significant etiological heterogeneity, as well as cross-disorder overlap. It has also become clear that understanding how this genetic variation leads to alterations in brain development and function that underlies psychiatric disease pathophysiology will be greatly advanced by a roadmap of the transcriptomic and epigenetic landscape of the human cerebral cortex across key developmental windows. Here, we propose, via a highly collaborative group of investigators, each with distinct areas of expertise and research focus, to create a scaffold of genomic data for understanding ASD pathophysiology, and psychiatric disorders more broadly. The work proposed here represents an ambitious multi-PI project (Yale, UCLA, and UCSF) that brings together three principal investigators and collaborators with strong publication records and expertise in all approaches necessary to perform this work using state-of-the-art and novel methodologies. We will perform time-, region-, and cell type-specific molecular profiling of control and ASD brains (Aim 1), including RNA-seq based transcriptomics, identifying cis-regulatory elements via ChIP-seq, and use Hi-C to determine the 3D chromatin architecture and physical relationships that underlie transcriptional regulation in three major regions implicated in neuropsychiatric disease (frontal and temporal cortex and striatum) across five major epochs representing disease-relevant stages in human brain development. This will include complementary genomic analyses in controls and matched post mortem ASD brain to identify genetic mechanisms underlying processes altered in ASD brain. We will address cellular heterogeneity via fluorescence-activated nuclear sorting (FANS) so as to profile neurons and non-neural cells separately, which will complement the whole tissue analyses. We will analyze and integrate these datasets to identify regional, developmental, and ASD-related processes to gain insight into underlying mechanisms, harmonizing these multi-omic data with other psychENCODE studies, as well as other large scale data sets, such as BrainSpan, ENCODE, GTEx and Roadmap Epigenomics Project (Aim 2). We will perform integrated analysis of germ-line ASD variations identified in more than 1000 families from the Simons Simplex Collection to characterize causal enrichments in developmental periods, brain regions, and cell types to better characterize the mechanisms by which genetic variation in humans alters brain development and function in health and disease (Aim 3). Completion of these aims will lead to a well-integrated resource across major periods in human cortical and striatal development that will permit generation of concrete testable hypotheses of ASD mechanisms, and inform our pathophysiological understanding of other related neuropsychiatric disorders.

Project Summary/Abstract The past decade has seen outstanding advances in the genetics of autism spectrum disorder (ASD), however only a moderate number of the hundreds of genes and genomic regions thought to be involved in ASD have been identified. Advances have come largely from the study of rare genetic variants, especially de novo variation, including single nucleotide variation (SNV), insertion/deletions (indels), copy number variation (CNV), and larger chromosomal imbalances. A portion of the progress for ASD has come through the efforts of the Autism Sequencing Consortium (ASC), which represents a coordinated effort by more than 40 independent groups to rapidly identify ASD risk genes. Here we propose to continue the work of the ASC, largely by continued production and analysis of sequence data from ASD subjects and their families. The ASC benefits from substantial leveraging of resources, including the Exome Aggregation Consortium (ExAC) centered at the Broad Institute (BI) and whole-exome sequencing (WES) of ASC samples, supported by an NHGRI Center Grant to BI, to make this renewal as low cost as possible. We also plan new avenues of research, such as integrating whole genome sequence (WGS) data and building on ideas that have emerged from the study of common variants to understand the interplay of common and rare variants to impact risk. Through this new research we will accelerate our overall objective, which is the identification of ASD genes, thereby facilitating our long-term goal of building the foundation from which therapeutic targets for ASD emerge. Our rationale is that the identification of genes conferring significant risk to ASD and associated neurodevelopmental disorders can form the basis of studies to understand pathogenesis, as well as the basis for novel therapies. Moreover, such variants have direct implications for patients and their families in terms of etiological diagnosis, genetic counseling and patient care. Our central hypothesis ? formulated based on results over the past decade ? is that rare and common variation contributes additively to risk for ASD, but only certain rare variants confer substantial risk. The objectives will be accomplished with the following Specific Aims: 1) Produce and/or analyze WES of 30,000 new ASD subjects, parents and other controls, for a total of more than 50,000 samples; 2) Develop and apply approaches to find ?hidden? risk variants, and, 3) Use results from common and rare variant studies to describe the interplay of such variation in ASD risk. This contribution is significant because it represents the first step in research to understand pathogenesis of ASD and to the development of pharmacological strategies for treatment of core symptoms of ASD and etiologically related neurodevelopmental disorders. The research proposed is innovative, in our opinion, because it uses groundbreaking and novel statistical methods for identifying risk variants and for integrating rare and common variation. This is a new and substantively different approach to gene discovery in ASD that departs significantly from the status quo and provides the means to achieve these important goals.

ABSTRACT Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder of unknown etiology and with limited effective therapeutic options that affects millions of individuals. Our research team has a longstanding commitment to understanding the cause of ASD and the molecular processes underlying brain development, function, and pathology. We will use this experience to apply the latest molecular techniques to samples from a new repository of brain tissue from individuals with ASD to create the largest and most detailed analysis of the molecular consequences of ASD. Genetic analyses of gene disrupting de novo mutations have identified over one hundred genes associated with ASD with three main functional groups: regulation of gene expression, neuronal communication, and cytoskeleton. Prior analyses of brain tissue from individuals with ASD have identified a group of downregulated neuronal communication genes, that overlap with ASD-associated genes, and a group of upregulated glial genes that do not overlap with ASD-associated genes or variants. It is unclear if these changes reflect altered cell composition or cell function and how they relate to genetic factors. We propose to analyze post-mortem brain samples from 40 individuals with ASD and 40 unaffected controls, sourced from the Autism BrainNet BioBank, to assess the molecular changes that occur. We will use whole-genome sequencing to identify gene disruptive variants in genes previously associated with ASD and to identify rare and common variants that may alter gene expression or splicing. In tissue samples the prefrontal cortex and striatum in from 40 cases and 40 controls, we will use recently developed single-nuclei methods to perform RNA-seq and ATAC-seq at single-cell resolution to identify ASD-related changes in gene regulation and expression in specific cell types and brain regions. For tissue samples from the prefrontal cortex of 20 cases and 20 controls we will also use cutting-edge single nuclei long-read RNA-seq (Iso-seq), along with bulk tissue RNA-seq, for an in-depth analysis of how gene isoforms differ between ASD cases and controls. Finally, we will assess how single-nuclei gene expression varies in brain organoids grown from pluripotent stem cells edited to contain mutations in three ASD-associated genes. Integrating these data, we will profile the molecular changes associated with ASD and assess how these changes vary by cell type, brain region, age, sex, seizure status, and genotype. We will use RNAscope in situ hybridization to validate the molecular and cell composition changes we observe and a lentivirus-based massively parallel reporter assay to test the function of regulatory regions or variants in proximity to genes with ASD-related differences in expression to validate these effects and assess causality. We hope that these insights will provide a basis for understanding the heterogeneity of ASD and the neurobiological features of this disorder and provide molecular signatures that could be developed into future biomarkers for ASD model systems.