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: 8835598
• 负责人: Nilah Monnier Ioannidis
• 金额: $ 5.23万
• 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 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8835598
• 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; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype; Goals; Histocompatibility Testing; Human; Human body; Individual; Joints; Machine Learning; Mentorship; Methods; Modeling; Organized by Structure Protein; Patients; Performance; Phenotype; Population; Positioning Attribute; Probability; Prostate; Protein Isoforms; Public Health; Quantitative Trait Loci; RNA; RNA Splicing; Regulation; Relative (related person); Reporting; Research Personnel; Specificity; Spliced Genes; Staging; Technology; Testing; Tissues; Training; Transcript; Transcription Initiation Site; Universities; Variant; base; clinically significant; exome sequencing; follow-up; forest; genetic variant; genome sequencing; genome-wide; improved; insight; lymphoblastoid cell line; novel; predictive modeling; professor; protein structure; public health relevance; tool

 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（由申请方提供）：本项目的目的是通过开发预测个体变体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鉴定的遗传变体的解释。基因组规模的测序技术越来越多地使大量个体的遗传变异研究成为可能;然而，解释这些研究中确定的数十万遗传变异的临床意义仍然是一个关键挑战。基因表达调控是变体可导致疾病或其他临床显著表型的一种机制。这种机制可能对于致病变异特别重要，这些致病变异不会通过改变蛋白质结构来直接影响蛋白质结构。 氨基酸序列。这里开发的方法将使研究人员能够预测遗传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预测这种监管效果。这些模型将被训练来识别调控变异，使用一组变体，这些变体在最近对数百个个体的基因表达的研究中被证明参与表达调控。将开发单独的算法来预测两种不同类型的调控效应：从特定基因产生的RNA总量的变化（表达水平变化）和从特定基因产生的RNA的特定形式的变化（剪接或异构体比例变化）。该项目的第二阶段是评估这些模型在来自单独人群和人体内不同组织的基因表达数据集上的性能，探索其普遍性，并确定调节变体的特征在组织和人群中保守的程度。该项目的最后阶段是使用已知致病的公共数据库中的遗传变异来表征这些模型在预测临床意义方面的表现。这一阶段将检验以下假设：调节基因表达的变异比不调节表达的变异更可能具有临床意义。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鉴定的遗传变异的临床意义的预测，从而影响公共卫生。此外，该项目还将提供生物学方面的见解， 组织特异性和群体特异性的基因组特征，表征调控遗传变异。