Haplotype-aware models of gene and isoform expression with application to genetic studies of disease in diverse populations

基因和亚型表达的单倍型感知模型及其应用于不同人群疾病遗传学研究

• 批准号: 10360462
• 负责人: Eric R Gamazon
• 金额: $ 62.71万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360462
• 关键词: Address; Affect; Alleles; Architecture; Atlases; Awareness; Biological; Biological Assay; Chromosome Mapping; Code; Communities; Computational Technique; Computing Methodologies; Data; Data Set; Development; Disease; Disease susceptibility; Ensure; European; Gene Dosage; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Determinism; Genetic Models; Genetic Transcription; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Haplotypes; Heterogeneity; Individual; Joints; Lead; Maps; Mediating; Methods; Modeling; Molecular; Penetrance; Phenotype; Population; Population Heterogeneity; Protein Isoforms; Proteins; Regulation; Regulator Genes; Research Personnel; Resolution; Resources; Sampling; Scientist; Signal Transduction; Statistical Methods; Testing; Tissues; Transcript; Variant; analysis pipeline; biobank; disorder risk; dosage; genetic association; genetic variant; genome wide association study; genome-wide; human disease; improved; multi-ethnic; novel; phenome; public health relevance; tool; trait; transcriptome; virtual

Project Summary In recent years, there has been a deluge of data from genome-wide functional assays. Rapid expansion of computational techniques for mapping genetic correlates of intermediate molecular traits, such as gene expression, has offered opportunities to explore molecular mechanisms that underlie disease susceptibility. Here we will develop mechanistically justified statistical methods that enable allele-specific assessment of regulatory activity in individual genomes. We hypothesize that incorporating the dosage modifying effect of regulatory alleles into genetic association analyses will enhance the resolution of the current genotype-phenotype maps, and allow for a more refined mapping of the underlying biological mechanisms. Our proposal is organized into the following aims: Aim1: To derive biologically interpretable effect sizes for regulatory variants and to enable allele-specific prediction of gene and isoform dosage, we will develop haplotype-aware models of genetic regulatory variation and apply them to large-scale reference transcriptome datasets. Aim2: To enhance the resolution of the current genotype-phenotype maps, we will develop genetic association methods that incorporate expected allele dosages for genes and transcript isoforms and apply them to large biobank and GWAS data. Aim3: To improve generalizability of genetic association signals to understudied populations, we will develop genetic association methods that incorporate population-specific regulatory architecture of genes and transcript isoforms.

项目摘要 近年来，全基因组功能分析的数据如洪水般涌来。快速扩张 用于定位中间分子性状的遗传相关的计算技术，例如基因 表达，提供了探索疾病易感性的分子机制的机会。这里 我们将开发机械合理的统计方法，使等位基因特异性评估的监管， 个体基因组中的活性。我们假设，将调节性药物的剂量改变效应 将等位基因纳入遗传关联分析将提高当前基因型-表型图谱的分辨率， 并允许对潜在的生物机制进行更精细的映射。 我们的建议分为以下几个目标： 目的1：推导调控变体的生物学可解释效应量，并使等位基因特异性 预测基因和异构体剂量，我们将开发单倍型感知模型的遗传调控 变异并将其应用于大规模参考转录组数据集。 目的2：为了提高现有基因型-表型图谱的分辨率，我们将开发遗传关联 结合基因和转录物同种型的预期等位基因剂量并将其应用于 大型生物库和GWAS数据。 目的3：为了提高遗传关联信号对未充分研究群体的概括性，我们将开发 遗传关联方法，其结合了基因的群体特异性调控结构， 转录异构体。