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

基本信息

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
项目类别：
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

项目摘要

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）：对患有精神障碍的家庭进行下一代基因组测序，正在识别数千种对疾病有不同影响的突变风险。遗传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）如何区分致病突变和中性突变？ (2) 每种致病性突变会破坏哪些生物学功能而导致疾病？这些都是需要紧急关注的基本问题。大量已识别的突变和受影响基因的功能异质性不允许开发通用的实验高通量方法来解决这些问题。尽管现代技术（例如 CRISPR）为这个方向的未来带来了希望，但距离我们能够将其应用于数千种突变还有很长的路要走。因此，需要有助于基因和突变优先排序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异。我们以自闭症谱系障碍（ASD）为中心的遗传变异影响模型将把异质遗传数据与大脑特定的功能数据源整合起来，例如专门针对大脑过程定制的基因表达和大脑剪接异构体相互作用网络。我们方法的独特之处在于，它首先预测由携带突变的基因编码的蛋白质的生物功能，然后进行基因和变异的优先级排序以及功能影响分配，最后以早期 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定候选基因的优先顺序； (2) 预测导致自闭症谱系障碍高风险的编码和非编码变体的功能影响； (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) 开发从外显子组或基因组序列估计 ASD 风险的算法。这是 ASD 研究的一种新颖方法，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。研究人员及其合作者广泛的专业知识确保了对问题采取有原则和全面的方法。