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

项目摘要 近年来，全基因组功能分析的数据如洪水般泛滥。快速扩张 定位中间分子性状(如基因)的遗传相关的计算技术 表达，为探索疾病易感性的分子机制提供了机会。这里 我们将开发机械合理的统计方法，使特定等位基因的调控评估成为可能 个体基因组中的活性。我们假设结合调节剂的剂量调节作用 将等位基因纳入遗传关联分析将提高当前基因-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：为调节性变异推导生物学上可解释的效应大小，并使等位基因特异性 预测基因和异构体的剂量，我们将建立基因调控的单倍型感知模型 并将其应用于大规模参考转录组数据集。 AIM2：为了提高目前的基因-表型图谱的分辨率，我们将开发基因关联 结合基因和转录异构体的预期等位基因剂量并将其应用于 大量的生物库和全球水文学数据。 目的：为了提高遗传关联信号对未被研究的人群的泛化能力，我们将开发 结合群体特有的基因和基因调控体系的遗传关联方法 转录异构体。

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.