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）：精神障碍折磨的家庭的下一代基因组测序是确定数千个对疾病影响的突变风险。遗传界在将其发现转化为疾病机制之前所面临的两个主要问题是：（1）如何区分致病性疾病疾病突变与中性突变？（2）每个致病性突变会破坏引起疾病的哪种生物学功能？这些是需要紧急关注的基本问题。受影响基因的大量已鉴定的突变和功能异质性不允许开发广泛的实验高通量方法来解决这些问题。尽管现代技术（例如CRISPR）在这个方向上为未来带来了希望，但我们还可以将其应用于千种突变还有很长的路要走。因此，需要采用有助于基因的预测计算方法，并需要优先考虑突变和功能表征。在这里，我们建议开发此类方法，并将其应用于自闭症谱系障碍家庭（ASD）中鉴定出的编码和非编码变体（ASD）。我们以ASD为重点的遗传变异影响模型将将异质遗传数据与脑特异性功能数据源相结合，例如基因表达和脑剪接同工型相互作用网络，这些网络是针对大脑过程独特量身定制的。我们方法的独特特征是，它始于由基因携带突变编码的蛋白质生物学功能的预测，以基因和变异的优先次序和功能影响分配进行，并以早期ASD诊断的风险模型结束。我们将通过以下特定目的实现这些目标：（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次序；（2）预测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）开发算法以估算外显子组或基因组序列的ASD风险。这是ASD研究中一种新颖的方法，它结合了分子精神病学，分子生物学和计算生物学领域的专业知识。调查人员及其合作者的广泛专业知识确保了解决该问题的原则性和全面方法。