Inferring the effects of genetic variants on gene expression and splicing

推断遗传变异对基因表达和剪接的影响

• 批准号: 9174089
• 负责人: Nilah Monnier Ioannidis
• 金额: $ 4.66万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9174089
• 关键词: Address; Affect; Algorithms; Amino Acid Sequence; Biological; Biometry; Categories; Cell physiology; Characteristics; Clinical; Communities; Computer Simulation; Computing Methodologies; Consensus; Data Set; Databases; Development; Disease; Elements; Epidemiology; European; Exons; Follow-Up Studies; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Variation; Genetic study; Genomics; Genotype-Tissue Expression Project; Goals; Human; Human body; Individual; Joints; Machine Learning; Mentorship; Methods; Modeling; Pathogenicity; Patients; Performance; Phenotype; Population; Positioning Attribute; Probability; Prostate; Protein Isoforms; Public Health; Quantitative Trait Loci; RNA; RNA Splicing; Regulation; Reporting; Research Personnel; Specificity; Spliced Genes; Technology; Testing; Tissues; Training; Transcript; Transcription Initiation Site; Universities; Variant; base; clinical predictors; clinically significant; exome sequencing; forest; genetic variant; genome sequencing; genome-wide; improved; insight; lymphoblastoid cell line; novel; predictive modeling; professor; protein structure; public health relevance; tool; whole genome

 DESCRIPTION (provided by applicant): The objective of this project is to facilitate the interpretation of genetic variants identified in clinical whole- genome and whole-exome sequencing studies through the development of computational methods to predict functional effects of individual variants. Genome-scale sequencing technologies are increasingly enabling studies of genetic variation in large numbers of individuals; however, interpreting the clinical significance of the hundreds of thousands of genetic variants identified in these studies remains a critical challenge. Gene expression regulation is one mechanism by which variants can result in disease or other clinically-significant phenotypes. This mechanism is likely to be particularly important for disease-causing variants that do not directly affect protein structure by altering an amino acid sequence. The methods developed here will enable researchers to predict whether genetic variants are likely to have a regulatory effect on gene expression. The first stage of the project is to build computational models to predict such regulatory effects using a random forest machine learning approach. These models will be trained to recognize regulatory variation using a set of variants that have been shown to be involved in expression regulation in a recent study of gene expression across hundreds of individuals. Separate algorithms will be developed to predict two different types of regulatory effects: changes in the total amount of RNA produced from a particular gene (expression level variation) and changes in the specific form of RNA produced from a particular gene (splicing or isoform ratio variation). The second stage of the project is to evaluate the performance of these models on gene expression datasets from a separate human population and from different tissues within the human body, to explore their generalizability and to determine to what extent the characteristics of regulatory variants are conserved across tissues and populations. The final stage of the project is to use genetic variants in publicly-available databases that are known to be pathogenic to characterize how well these models perform at predicting clinical significance. This stage will test the hypothesis that variants that regulate gene expression are more likely to be clinically significant than variants that do not regulate expression. This project will impact public health by providing useful tools to improve prediction of the clinical significance of genetic variants identified in genome- scale sequencing studies. In addition, the project will provide biological insight into the tissue-specificity and population-specificity of genomic features that characterize regulatory genetic variants.

 描述(由申请人提供)：本项目的目标是通过开发预测单个变异的功能效应的计算方法，促进对临床全基因组和全外显子组测序研究中识别的遗传变异的解释。基因组规模的测序技术越来越多地使研究大量个体的遗传变异成为可能；然而，解释这些研究中发现的数十万种遗传变异的临床意义仍然是一个关键的挑战。基因表达调控是变异导致疾病或其他临床显著表型的机制之一。这一机制可能对那些不会通过改变蛋白质结构直接影响蛋白质结构的致病变异体特别重要 氨基酸序列。这里开发的方法将使研究人员能够预测基因变异是否可能对基因表达产生调节作用。该项目的第一阶段是建立计算模型，使用随机森林机器学习方法来预测这种监管效果。这些模型将接受训练，使用一组变异来识别调控变异，这些变异在最近一项针对数百个人的基因表达的研究中被证明参与了表达调控。将开发单独的算法来预测两种不同类型的调控效应：由特定基因产生的RNA总量的变化(表达水平变化)和由特定基因产生的特定形式的RNA的变化(剪接或异构体比率变化)。该项目的第二阶段是评估这些模型在来自单独的人类群体和人体内不同组织的基因表达数据集上的性能，探索它们的普适性，并确定调控变体的特征在组织和群体中的保守程度。该项目的最后阶段是使用公开可用的已知致病数据库中的基因变异来表征这些模型在预测临床意义方面的表现。这一阶段将检验这样一种假设，即调控基因表达的变体比不调控表达的变体更有可能具有临床意义。该项目将通过提供有用的工具来改善对基因组规模测序研究中确定的遗传变异的临床意义的预测，从而影响公众健康。此外，该项目将提供对 表征调节性遗传变异的基因组特征的组织特异性和群体特异性。