A computational framework for predicting the impact of mutations in autism

用于预测自闭症突变影响的计算框架

• 批准号: 8800216
• 负责人: LILIA M IAKOUCHEVA
• 金额: $ 53.34万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-25 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8800216
• 关键词: Address; Affect; Algorithms; Area; Attention; Autistic Disorder; Biological Assay; Biological Process; Brain; Candidate Disease Gene; Clinical; Clinical Data; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Communities; Complex; Computational Biology; Computing Methodologies; Data; Data Set; Data Sources; Development; Diagnosis; Disease; Early Diagnosis; Enhancers; Ensure; Event; Family; Functional RNA; Future; Gene Expression; Gene Mutation; Gene Proteins; Genes; Genetic; Genetic Transcription; Genomics; Goals; Heterogeneity; Individual; Inherited; Investigation; Likelihood Functions; Mental disorders; Methods; Modeling; Molecular; Molecular Biology; Mutation; Open Reading Frames; Population; Predisposition; Process; Protein Isoforms; Proteins; Proteomics; Psychiatry; RNA Splicing; Research; Research Personnel; Risk; Site; Structure; Technology; Testing; Therapeutic Intervention; Tissues; Training; Translating; Translations; Untranslated Regions; Validation; Variant; autism spectrum disorder; computer framework; disorder risk; exome; exome sequencing; gene function; genetic variant; genome sequencing; high risk; improved; innovation; insight; next generation; novel; novel strategies; promoter; protein function; public health relevance; research study; risk variant; tool

DESCRIPTION (provided by applicant): Next-generation genome sequencing of families afflicted by mental disorders is identifying thousands of mutations with variable effect on disease risk. Two main problems that genetic community is facing before it could move forward with translating their findings into disease mechanisms are: (1) how to distinguish the pathogenic disease-causative mutations from the neutral ones? (2) what biological function(s) does each pathogenic mutation disrupt to cause a disease? These are fundamental questions that need urgent attention. The large number of identified mutations and functional heterogeneity of the affected genes do not permit developing a generalized experimental high-throughput method for addressing these problems. Although modern technologies (such as CRISPR) provide hope for the future in this direction, there is still a long way before we are able to apply it to thousandsof mutations. Thus, predictive computational approaches that aid in genes and mutations prioritization and functional characterization are needed. Here, we propose to develop such methods and apply them to coding and non-coding variants identified in families with Autism Spectrum Disorders (ASD). Our ASD-focused model of genetic variant impact will integrate heterogeneous genetic data with brain-specific functional data sources, such as gene expression and brain splice isoform interaction networks that are uniquely tailored towards brain processes. The unique feature of our approach is that it starts with prediction of biological function of a protein encoded by a gene carrying mutation(s), proceeds with gene and variant prioritization and functional impact assignment, and ends with a risk model for early ASD diagnosis. We will accomplish these goals through the following specific aims: (1) Predicting Biological Function of Genes and Autism-Specific Candidate Gene Prioritization; (2) Predicting Functional Impact of Coding and Non-coding Variants Conferring High Risk for ASD; (3) Experimental Validation of Predictions by Characterizing the Effect of Mutations on Protein Interactions, Transcription and Translation; (4) Developing Algorithms for Estimating ASD Risk from the Exome or Genome Sequence. This is as a novel approach in ASD research that combines expertise from diverse areas of molecular psychiatry, molecular biology and computational biology. The broad range of expertise by the investigators and their collaborators ensures a principled and comprehensive approach to the problem.

描述（由申请人提供）：精神障碍家庭的下一代基因组测序正在识别数千种对疾病具有可变影响的突变 风险遗传学界在将其发现转化为疾病机制之前面临的两个主要问题是：（1）如何区分致病性致病突变和中性突变？(2)每一种致病性突变破坏了什么生物学功能而导致疾病？这些都是迫切需要关注的根本问题。受影响的基因的大量确定的突变和功能的异质性不允许开发一个通用的实验高通量的方法来解决这些问题。尽管现代技术（如CRISPR）为这一方向的未来提供了希望，但在我们能够将其应用于数千种突变之前，还有很长的路要走。因此，需要有助于基因和突变优先化和功能表征的预测计算方法。在这里，我们建议开发这样的方法，并将其应用于自闭症谱系障碍（ASD）家庭中识别的编码和非编码变体。我们以ASD为重点的遗传变异影响模型将整合异质性遗传数据与脑特异性功能数据源，如基因表达和脑剪接异构体相互作用网络，这些网络是专门针对大脑过程定制的。我们的方法的独特之处在于，它从预测携带突变的基因编码的蛋白质的生物学功能开始，进行基因和变体优先级排序和功能影响分配，并以早期ASD诊断的风险模型结束。我们将通过以下具体目标来实现这些目标：（1）预测基因的生物学功能和自闭症特异性候选基因的优先级;（2）预测编码和非编码变体对ASD的高风险的功能影响;（3）通过表征突变对蛋白质相互作用、转录和翻译的影响来实验验证预测;（4）预测基因的生物学功能和自闭症特异性候选基因的优先级。（4）开发用于从外显子组或基因组序列估计ASD风险的算法。这是ASD研究中的一种新方法，结合了分子精神病学，分子生物学和计算生物学不同领域的专业知识。调查人员及其合作者的广泛专业知识确保了对问题采取有原则和全面的方法。