Olga Troyanskaya, Robert Darnell and colleagues applied deep-learning methods to whole-genome sequencing data from SSC families and identified a clear enrichment for de novo noncoding variation in ASD.
Research Highlights
Highlights of SFARI-funded papers, selected by the SFARI science team.
James Ellis and colleagues used a sparse co-culture system for iPSC-derived cortical neurons to assess neuronal connectivity, demonstrating increased connectivity in SHANK2-mediated ASD.
Development of a single-cell mouse cortical gene expression pattern resource that identifies putative brain disorder subtypes Mark Zylka and colleagues generated single-cell RNA-seq data from wild-type mouse cortex during early development and demonstrated how such a resource can be used to identify putative brain disorder subtypes based on expression profiles.
Cryptic splice mutations: A major new source of risk variants for neurodevelopmental disorders Kyle Kai-How Farh and Stephan Sanders developed a deep-learning method to predict risk mutations that affect mRNA splicing and contribute substantially to neurodevelopmental disorders.
Impaired reward circuitry during voice processing predicts social communication abilities in autism Vinod Menon and colleagues report that a difference in the activation of a voice-processing network comprising reward and salience detection systems is a distinguishing feature of autism.
Largest genome-wide association study yields common variants associated with risk of autism Mark Daly and colleagues report the results of a large genome-wide association meta-analysis of more than 18,000 individuals with ASD, including the newly genotyped Danish iPSYCH cohort.
Meta-analysis of de novo mutations yields longer list of risk genes for neurodevelopmental disorders Evan Eichler and colleagues performed a meta-analysis of de novo mutations from individuals with ASD, intellectual disability or developmental delay and found new risk genes for neurodevelopmental disorders.
Large-scale SSC whole-genome sequencing data key to identifying statistically significant de novo noncoding variation in autism Stephan Sanders and colleagues used a machine learning approach to analyze whole-genome sequencing data from more than 1,900 SSC families and found that de novo noncoding variants in distal regions of promoters confer risk of ASD.
Alterations in neuronal excitability are compensatory changes, not causal effects, in autism Dan Feldman and colleagues analyzed four mouse models of autism and found that alterations in the excitation-inhibition ratio may serve as a homeostatic mechanism to prevent network hyperexcitability.
Alternations in network communication, not synaptic dysfunction, linked to behavioral alterations in fragile X syndrome Assessing network and synaptic communication in the hippocampus of behaving Fmr-1 null mice, André Fenton and colleagues showed altered network communication linked to behavioral alterations in fragile X syndrome.