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Highly parallel analysis of 5' and 3' UTR variants in Autism Spectrum Disorders

Highly parallel analysis of 5' and 3' UTR variants in Autism Spectrum Disorders
自闭症谱系障碍中 5 和 3 UTR 变异的高度并行分析
批准号:
9891101
负责人:
JOSEPH D DOUGHERTY
金额:
$57.92万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-07-01 至 2023-03-31

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中文摘要
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英文摘要
Substantial investments are being made to sequence the genomes of families with Autism Spectrum Disorder (ASD). However, identifying disease mutations outside the ~1% of protein coding sequences is challenging because 1) the ‘search space’ is so much larger, and thus many more mutations occur by chance, and 2) there is no simple code to identify deleterious mutations in non-coding sequence, and thus loss of function mutations must be defined experimentally. In addition, the consequences of mutations in non-coding (i.e. regulatory) sequences are often highly dependent on the specific cell type. Thus functional assays must be conducted in vivo, in the appropriate CNS cell types. To address the search space challenge, we propose to focus specifically on the untranslated regions (UTRs) of mRNAs. UTRs are important, conserved regulatory sequences that profoundly impact protein levels by altering translation rates or transcript stability for specific genes. Importantly, in ASD cases there is a 2-fold greater rate of UTR mutations in known ASD genes than expected by chance, indicating that roughly half of these UTR mutations may contribute to disease. To address the lack of a code for interpreting UTR mutations, we have assembled a team with a unique combination of expertise to conduct massively parallel functional analysis of UTR variants from ASD patients, in ASD-relevant cell types in vitro and in vivo. Combining two innovative but established components: post-transcriptional massively parallel reporter assays, and cell type specific translational profiling, we aim to establish a pipeline to 1) Identify UTR mutations that result in altered protein levels, 2) conduct genetic burden and association testing on these variants, and 3) define the molecular mechanisms altering protein levels for specific mutations. This pipeline will leverage the existing large investment in ASD genome sequencing by defining individual non-coding disease causing mutations in a class of sequences that has, so far, not been the focus of disease studies.
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