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RNA-seq of 36 individuals with autism spectrum disorder

ABSTRACT: RNA-seq of 36 individuals with autism spectrum disorder

PROVIDER: PRJEB29726 | ENA |

REPOSITORIES: ENA

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RNA-seq of 36 individuals with autism spectrum disorder

Project description:To assess the clinical impact of splice-altering noncoding mutations in autism spectrum disorder (ASD), we used a deep learning framework (SpliceAI) to predict the splice-altering potential of de novo mutations in 3,953 individuals with ASD from the Simons Simplex Collection. To validate these predictions, we selected 36 individuals that harbored predicted de-novo cryptic splice mutations; each individual represented the only case of autism within their immediate family. We obtained peripheral blood-derived lymphoblastoid cell lines (LCLs) and performed high-depth mRNA sequencing (approximately 350 million 150 bp single-end reads per sample). We used OLego to align the reads against a reference created from hg19 by substituting de novo variants of each individual with the corresponding alternate allele.

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Project description:Autism spectrum disorder (ASD) is a common neurodevelopmental condition affecting 2.3% of 8-year-old children and is attributable to polygenic risks in most cases. Gene discovery studies catalogued >1000 genes with de novo, rare and common genetic variants that are likely associated with ASD; however, the candidate genes are rarely translated to diagnostic and treatment biomarkers. As such no pharmacological treatment option is available for targeting core symptoms. Neural circuits involved in verbal/nonverbal communications and social interaction are likely changed, which may be caused by an excitatory-inhibitory (E-I) imbalance in individuals with ASD. To date, clinical trials targeting excitatory glutamatergic or inhibitory GABAergic receptors showed mixed results. These early clinical trials highlight the unmet need of biomarkers for target populations and outcome indicators. We investigated whether plasma biomarkers would be associated with genetic risk factors and core symptoms of ASD. Plasma samples were collected for metabolomics profiling from the Autism Genetics Resource Exchange (AGRE). Detailed phenotype information is available at NIMH Data Archive (Collection ID: 4214) and can be accessed using NDAR GUID for the individuals.

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Transcription profiling of human blood from children with autism spectrum disorder

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