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

扩大转录组分析和改善疾病风险预测的实验和计算方法

• 批准号: 10266794
• 负责人: Molly Martorella
• 金额: $ 4.65万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266794
• 关键词: Affect; Architecture; Benchmarking; Biological; Biological Factors; Blood specimen; Cells; Child; Clinic; Clinical; Clinical Data; Complex; Computing Methodologies; Data; Diagnostic; Disease; Enrollment; Ensure; Environmental Impact; Environmental Risk Factor; Etiology; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Determinism; Genetic Predisposition to Disease; Genetic Risk; Genome; Genotype; Genotype-Tissue Expression Project; Goals; Hair follicle structure; Health; Human; Individual; Laboratories; Libraries; Life Style; Light; Lung; Measures; Medical Genetics; Methods; Nature; Pathogenesis; Phenotype; Population; Population Heterogeneity; Pregnancy; Preparation; Procedures; Quantitative Trait Loci; RNA; Reagent; Research Proposals; Risk; Saliva; Sample Size; Sampling; Series; Standardization; Structure; Swab; Testing; Time; Tissues; Trans-Omics for Precision Medicine; Transcript; Untranslated RNA; Urine; Variant; Whole Blood; Work; cell type; clinical care; cohort; comparative; cost; disorder prevention; disorder risk; functional genomics; genetic variant; genome wide association study; genomic data; healthy volunteer; improved; infancy; innovation; insight; interest; lifestyle factors; multi-ethnic; multiple omics; novel; polygenic risk score; portability; precision medicine; rare variant; recruit; risk prediction; saliva sample; sample collection; scale up; single cell sequencing; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; treatment response

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.

项目摘要 了解基因组复杂的调控格局将揭示基因组调控的基本原理。 疾病风险和病因。转录组学研究揭示了基因组的功能性质， 变异对基因表达的影响，但RNA测序的成本和侵入性带来了限制 继续扩大这些研究。在临床中使用基因组学研究数据的需求是 上升，但我们还没有建立一种方法来合成基因组数据的方式，改善临床护理。 我们工作的长期目标是调查疾病的环境和遗传决定因素， 根据这些生物学因素发展临床上有意义的个体分层方法。我们的中央 假设是1）。开发更便宜，更容易获得的RNA测序方法将使大规模的 转录组学研究的规模化，并促进这些研究的后续发现，以及2.）使用 用于临床预测的转录组学数据将增强目前的遗传预测措施， 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， 由于样本采集的侵入性降低，转录组学研究的入组人数增加， 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研究这些非侵袭性组织的表达谱，并验证它们在理解 身体的遗传调节结构。在aim 3中，我们将从遗传学角度产生新的风险评分， 预测的基因表达和测量的基因表达。这些分数将与当前的 标准的遗传临床预测，多基因风险评分，我们将评估这些预测效用 在多种族群体中的得分。我们将进一步分析基因预测和测量之间的差异， 基因表达，以阐明基因表达调控的遗传和环境机制。完成 这项研究计划的一部分将产生一些方法，这些方法对提高我们对人类表型的理解至关重要。 并将介绍询问转录组数据的方法，这些数据将产生重要的生物学和临床见解。