Experimental and Computational Methods for Scaling-up Transcriptome Analyses and Improving Disease Risk Predictions

项目摘要 了解基因组的复杂调控图景将揭示 疾病风险和病因学。转录学研究通过揭示基因组的功能本质来解开 变异体对基因表达的影响，但RNA测序的成本和侵入性带来了限制 继续扩大这些研究的范围。临床上使用基因组学研究数据的需求是 正在上升，但我们还没有建立以一种改善临床护理的方式合成基因组数据的方法。 我们工作的长期目标是调查疾病的环境和遗传决定因素，并 根据这些生物因素，开发具有临床意义的方法来对个体进行分层。我们的中央 假设为1。)开发更便宜、更容易获得的RNA测序方法将使大规模 转录研究的规模，并促进随后从这些研究中发现，以及2.)使用 用于临床预测的转录数据将增强目前的基因预测手段，将提供关键 对疾病机制的生物学见解，并将增加遗传风险评分的可移植性 人口。在目标1中，我们建议对唾液、毛囊、口腔组织和尿液进行采样，以允许 由于样本收集的侵入性降低，转录学研究的注册人数增加，我们 还提出了一种低成本的RNA测序方法，以克服目前研究扩展的资金障碍。 目的2研究这些非侵入性组织的表达谱，并验证它们在理解中的用途 身体的基因调控架构。在目标3中，我们将从基因上生成新的风险分数 预测基因表达和根据测量的基因表达。这些分数将与当前的 遗传临床预测的标准，多基因风险评分，我们将评估这些预测的实用性 在多民族队列中的得分。我们将进一步分析基因预测和测量之间的差异 基因表达，阐明基因表达调控的遗传和环境机制。完成 这项研究提案的提出将产生提高我们对人类表型理解的核心方法 并将介绍询问转录数据的方法，这些数据将产生基本的生物学和临床见解。

Project Summary Understanding the complex regulatory landscape of the genome will uncover fundamental principles of disease risk and etiology. Transcriptomic studies disentangle the functional nature of the genome by revealing the effects of variants on gene expression, but the cost and invasiveness of RNA-sequencing imposes limitations on the continued expansion of these studies. The demand to use data from genomics studies in the clinic is rising, but we have yet to establish methods of synthesizing genomics data in a way that improves clinical care. The long term goal of our work is to investigate environmental and genetic determinants of disease and to develop clinically meaningful ways of stratifying individuals according to these biological factors. Our central hypotheses are 1.) developing cheaper, more accessible methods of RNA-sequencing will enable massive scaling of transcriptomic studies and facilitate subsequent discovery from these studies, and 2.) using transcriptomic data for clinical predictions will augment current measures of genetic prediction, will provide key biological insights into disease mechanisms, and will increase portability of genetic risk scores across populations. In aim 1, we propose that sampling saliva, hair follicles, buccal tissue, and urine will allow for increased enrollment in transcriptomic studies due to the decreased invasiveness of sample collection, and we also put forward a low-cost RNA-sequencing method to overcome current financial barriers to study expansion. Aim 2 investigates the expression profiles of these non-invasive tissues and validates their use in understanding the genetic regulatory architecture of the body. In aim 3, we will generate novel risk scores from genetically predicted gene expression and from measured gene expression. These scores will be compared to the current standard for genetic clinical prediction, polygenic risk scores, and we will assess the predictive utility of these scores in multiethnic cohorts. We will further analyze differences between genetically predicted and measured gene expression to elucidate genetic and environmental mechanisms of gene expression regulation. Completion of this research proposal will produce methods central to improving our understanding of human phenotypes and will introduce ways of interrogating transcriptomic data that will yield essential biological and clinical insights.