Developing integrative methods to improve GWAS inference using epigenomic data

开发综合方法以利用表观基因组数据改进 GWAS 推断

• 批准号: 8996477
• 负责人: Yu-Han Huang Hsu
• 金额: $ 3.57万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8996477
• 关键词: Adult; Biology; Body mass index; Cells; Code; Complement; Complex; Computing Methodologies; Data; Data Set; Disease; Enhancers; Epigenetic Process; Gene Expression; Generations; Genes; Genetic; Genetic Variation; Genetic study; Genome; Health; Height; Human; Human Biology; Human Genetics; Human Genome; Indium-111; Investigation; Knockout Mice; Maps; Measures; Medicine; Mendelian disorder; Methods; Obesity; Pathway interactions; Phenotype; Proteins; Regulatory Element; Route; Signal Transduction; System; Testing; Time; Tissues; Variant; Waist-Hip Ratio; base; cell type; data integration; epigenomics; genetic approach; genetic variant; genome wide association study; human disease; improved; innovation; promoter; protein protein interaction; skeletal; tool; trait; transcription factor; transcriptome sequencing

 DESCRIPTION (provided by applicant): In order to understand the genetic basis of human diseases, over 1,800 genome-wide association studies (GWAS) have been conducted to identify genetic variants associated with common diseases and disease- related traits. However, since ~93% of GWAS variants are found in noncoding regions of the human genome, pinpointing causal variants or even genes can be a difficult task. Often, even the important pathways, tissues, and cell types are unknown, making it even more challenging to predict the functional consequences of identified variants and to set up appropriate experimental systems for validating these predictions. In an effort to deal with these challenges, our lab has recently developed an approach, DEPICT (Data-driven Expression- Prioritized Integration for Complex Traits), that combines data from gene expression, protein-protein interactions, mouse knockout phenotypes, and pathways/gene sets to prioritize important genes, pathways, and tissues/cell types from GWAS results for any disease or trait. While DEPICT performs better than several existing methods that only consider single data types, it does not yet include any epigenetic information. The recent generation of epigenomic maps in many human cell types facilitates the annotation of the noncoding human genome and can be very valuable for GWAS analysis. Therefore, in this project, we will utilize epigenetic information to complement and improve existing GWAS prioritization approaches such as DEPICT. Specifically, using regulatory element annotations and RNA sequencing datasets from the Roadmap Epigenomics project, we will both develop a new integrative method and also modify the original DEPICT implementation to include epigenetic data. We will validate and test both methods on GWAS data from the Genetic Investigation of Anthropometric Traits (GIANT) consortium, which include association results for height, body mass index (BMI), and waist-to-hip-ratio adjusted for BMI. Successful completion of this project will provide an innovative and powerful tool that incorporates epigenomic with other data types to prioritize tissues/cell types, genes, TF motifs, and pathways from GWAS results. The tool will be useful for studying a wide variety of complex traits and diseases and can help to fulfill the potential of GWAS to infer biology and advance biomedicine.

 描述(申请人提供)：为了了解人类疾病的遗传基础，已进行了1800多项全基因组关联研究(GWAS)，以确定与常见疾病和疾病相关性状相关的遗传变异。然而，由于约93%的GWA变异体是在人类基因组的非编码区发现的，准确定位因果变异体甚至基因可能是一项困难的任务。通常，即使是重要的途径、组织和细胞类型也是未知的，这使得预测已识别的变异的功能后果并建立适当的实验系统来验证这些预测变得更加困难。为了应对这些挑战，我们的实验室最近开发了一种方法DESCRIPE(数据驱动的表达优先整合复杂特征)，它结合了来自基因表达、蛋白质相互作用、小鼠敲除表型和途径/基因集的数据，以确定任何疾病或特征的GWA结果中的重要基因、途径和组织/细胞类型的优先顺序。虽然DESCRICE比几种只考虑单一数据类型的现有方法执行得更好，但它还不包括任何表观遗传信息。最近在许多人类细胞类型中生成的表观基因组图有助于对非编码人类基因组的注释，并且对于Gwas分析非常有价值。因此，在这个项目中，我们将利用表观遗传学信息来补充和改进现有的GWA优先方法，如DESCRATE。具体地说，使用来自路线图表观基因组学项目的调控元件注释和RNA测序数据集，我们将开发一种新的整合方法，并修改原始描述实现，以包括表观遗传学数据。我们将在来自人类测量特征遗传研究(巨人)联盟的Gwas数据上验证和测试这两种方法，其中包括身高、身体质量指数(BMI)和根据BMI调整后的腰臀比的关联结果。该项目的成功完成将提供一种创新和强大的工具，将表观基因组学与其他数据类型相结合，以根据GWAS结果确定组织/细胞类型、基因、转移因子基序和途径的优先顺序。该工具将有助于研究各种复杂的特征和疾病，并有助于发挥GWAs在推断生物学和推进生物医学方面的潜力。