Rebecca A. Muhle

The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment

Recent studies implicate chromatin modifiers in autism spectrum disorder (ASD) through the identification of recurrent de novo loss of function mutations in affected individuals. ASD risk genes are co-expressed in human midfetal cortex, suggesting that ASD risk genes converge in specific regulatory networks during neurodevelopment. To elucidate such networks, we identify genes targeted by CHD8, a chromodomain helicase strongly associated with ASD, in human midfetal brain, human neural stem cells (hNSCs) and embryonic mouse cortex. CHD8 targets are strongly enriched for other ASD risk genes in both human and mouse neurodevelopment, and converge in ASD-associated co-expression networks in human midfetal cortex. CHD8 knockdown in hNSCs results in dysregulation of ASD risk genes directly targeted by CHD8. Integration of CHD8-binding data into ASD risk models improves detection of risk genes. These results suggest loss of CHD8 contributes to ASD by perturbing an ancient gene regulatory network during human brain development.

DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics

BackgroundDe novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes.MethodsTo accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk.ResultsUsing currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model.ConclusionsValidation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.

The Emerging Clinical Neuroscience of Autism Spectrum Disorder: A Review

Importance Autism spectrum disorder (ASD) is a highly prevalent disorder, and community psychiatrists are likely to treat many individuals with ASD during their clinical practice. This clinical case challenge describes a routine evaluation of irritability and self-injury in a preschool-aged child who meets the criteria for ASD. The case also illustrates the importance of known risk factors for ASD, such as chromosomal deletion and prematurity. This clinical neuroscience article seeks to educate the clinician of current avenues of research that can inform and may already affect clinical practice for this patient, while providing a preview of research that may yield biological treatments for ASD within the next decade. Observations A diagnosis of ASD is defined behaviorally; therefore, many genetic and environmental risk factors, working singly or in concert, are linked to ASD. The prenatal period of brain development is particularly sensitive to risk factors such as gene mutation or drug exposure that affect brain development and circuitry formation. Currently, neuroimaging researchers can detect changes in brain connectivity of children with ASD as young as 6 months, followed by an atypical trajectory of brain development through preschool age and ongoing connectivity inefficiencies across the lifespan. Animal and cellular model systems have provided a means for defining the molecular and cellular changes associated with risk factors for ASD. The ability to connect specific treatments to particular subgroups of people with ASD is the defining hope of precision medicine initiatives. Conclusions and Relevance The advent of next-generation sequencing technology, advanced imaging techniques, and cutting-edge molecular techniques for modeling ASD has allowed researchers to define ASD risk-related biological pathways and circuits that may, for the first time, unify the effects of disparate risk factors into common neurobiological mechanisms. The path from these mechanisms to biological treatments that improve the lives of individuals with autism remains unclear, but the cumulative output of multiple lines of research suggests that subtyping by genetic risk factors may be a particularly tractable way to capitalize on individual differences amenable to specific treatments.

A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies

Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, to learn from data which annotations are informative of pathogenic mutations, and to combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.

Contribution of de novo non-coding mutations to autism and identification of risk genes from whole-genome sequencing of affected families

Analyzing de novo mutations (DNMs) in protein-coding genes from whole-exome sequencing (WES) data has emerged as a powerful tool for mapping risk genes of autism spectrum disorder (ASD). The impact of non-coding mutations in ASD, however, has been largely unknown. This represents a large gap in our understanding of the genetics of ASD, as the majority of GWAS hits for a range of disorders fall into non-coding regions. To address this question, we performed a meta-analysis of DNMs using whole-genome sequencing (WGS) data from more than 300 individuals with ASD. We found that DNMs are enriched within brain transcriptional regulatory elements near genes involved in neuropsychiatric disorders. In these genes and in evolutionarily constrained genes, we also found an excess of DNMs that are predicted to affect pre-mRNA splicing. Collectively, we estimate that non-coding mutations explain at least one third of the ASD genetic risk attributable to DNMs. By combining information of non-coding DNMs with published WES data, we identified three new ASD risk genes at a false discovery rate (FDR)Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWAS) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is, however, challenging because the functional significance of non-coding mutations is difficult to predict. We propose a new statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, learn from data which annotations are informative of pathogenic mutations and combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism family trios across five studies, and discovered several new autism risk genes. The software is freely available for all research uses.

Autism Spectrum Disorder: Genes to Pathways to Circuits

Autism spectrum disorders (ASD) are a collection of etiologically diverse and overlapping syndromes that share the core diagnostic features of fundamental impairments in reciprocal social communication and highly restricted interests and/or repetitive behaviors. Dramatic progress in the genomics of ASD has emerged as a consequence of rapidly advancing genomic technologies; a highly productive focus on rare and de novo mutations; the consolidation of large patient and family cohorts of sufficient size to power systematic discovery efforts; highly effective partnerships among governmental agencies, advocacy groups, and philanthropy to support research; and an early and sustained commitment to data sharing across the ASD genomics community. These forces have resulted in a rapidly increasing number of risk genes and regions and an emerging picture of the genomic architecture of ASD. Despite challenges imparted by identifying extreme locus and allelic heterogeneity, initial efforts at characterizing the underlying mechanisms of ASD have shown a surprising degree of convergence with regard to molecular mechanisms, anatomical regions, and developmental epochs.