STATISTICAL METHODS FOR GENOMIC DISSECTION OF CARDIOVASCULAR DISEASES

心血管疾病基因组解剖的统计方法

基本信息

批准号：
8767880
负责人：
YunJu Sung
金额：
$ 14.38万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-12 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8767880
关键词：
Age related macular degeneration Alcohol consumption Alcohols Alleles American Applications Grants Architecture Binding Biochemical Blood Pressure Candidate Disease Gene Cardiovascular Diseases Cholesterol Chromatin Structure Clinical Clinical Research Clinical Trials Code Complement Complex Data Deoxyribonucleases Detection Diabetes Mellitus Diastolic blood pressure Dissection Dyslipidemias Elements Encyclopedia of DNA Elements Environment Environmental Risk Factor Ethnic group European Family Fasting Foundations Framingham Heart Study Functional RNA Genes Genetic Genetic Research Genetic Transcription Genome Genomics Goals Haplotypes Heart High Density Lipoprotein Cholesterol Hypertension Insulin Joints LDL Cholesterol Lipoproteins Letters Link Lipids Maps Medicine Mentors Morbidity - disease rate Nature Nucleic Acid Regulatory Sequences Participant Pathogenesis Pathway interactions Pharmaceutical Preparations Play Prevalence Province Regulation Research Research Personnel Risk Factors Role Smoking Statistical Methods Statistical Models Training Programs Translating Triglycerides Variant abstracting analytical method career career development cell type clinical care clinical practice exome fasting glucose genetic association genetic variant genome sequencing genome wide association study genome-wide analysis histone modification improved innovation lifestyle factors mortality novel programs public health relevance racial and ethnic disparities rare variant trait transcription factor

项目摘要

DESCRIPTION (provided by applicant): Statistical Methods for Genomic Dissection of Cardiovascular Diseases Abstract This mentored career development grant application proposes a training program to integrate Dr. Sung's previous research in statistical genetics into cardiovascular disease (CVD). Her long-term career goal is to establish herself as an independent statistician in CVD genetics research so that she can more effectively participate in multi-disciplinary research programs with clinical and translational CVD researchers and be better equipped to develop and apply statistical methods to contribute more meaningfully to the field of CVD genetics. This will be achieved through building a strong foundation in the clinical and research aspects of CVD and enhancing her understanding of genomics and whole-genome sequence data. Dr. Sung's mentoring team consists of multi-disciplinary researchers with a strong research track record. Complex cardiometabolic traits including hypertension, dyslipidemia, and diabetes contribute to CVD, the leading cause of mortality and morbidity in the industrialized world. Genome-wide association studies (GWAS) have led to many exciting discoveries. However, most GWAS discoveries have not been translated to clinical care because the functional mechanisms underlying the genetic associations remain elusive and the roles played by the environment in modulating these associations remain poorly defined. The research objective of this K25 application is to decipher the genetic and environmental architecture of cardiometabolic traits by incorporating GxE interactions and regulatory annotation information. We hypothesize that joint analysis of the environment, regulatory variants and coding variants will enhance the discovery of putative genetic variants and the functional mechanisms underlying cardiometabolic traits. To evaluate this hypothesis, we aim to identify genetic variants involving GxE interactions and identify putative functional variants by incorporating ENCODE regulation information.

描述（由申请人提供）：心血管疾病基因组解剖的统计方法摘要这项指导性职业发展补助金申请提出了一项培训计划，将宋博士之前在统计遗传学方面的研究整合到心血管疾病（CVD）中。她的长期职业目标是使自己成为CVD遗传学研究领域的独立统计学家，以便她能够更有效地与临床和转化CVD研究人员一起参与多学科研究项目，并更好地开发和应用统计方法，为人类做出更多贡献对CVD遗传学领域具有重要意义。这将通过在心血管疾病的临床和研究方面打下坚实的基础并增强她对基因组学和全基因组序列数据的理解来实现。宋博士的指导团队由具有良好研究记录的多学科研究人员组成。复杂的心脏代谢特征，包括高血压、血脂异常和糖尿病，会导致心血管疾病，而心血管疾病是工业化国家死亡和发病的主要原因。全基因组关联研究（GWAS）带来了许多令人兴奋的发现。然而，大多数 GWAS 的发现尚未转化为临床护理，因为遗传关联背后的功能机制仍然难以捉摸，而且环境在调节这些关联中所起的作用仍然不明确。该 K25 应用程序的研究目标是通过结合 GxE 相互作用和监管注释信息来破译心脏代谢特征的遗传和环境结构。我们假设对环境、调控变异和编码变异的联合分析将增强对假定遗传变异和心脏代谢特征背后的功能机制的发现。为了评估这一假设，我们的目标是识别涉及 GxE 相互作用的遗传变异，并通过结合 ENCODE 调控信息来识别假定的功能变异。