STATISTICAL METHODS FOR GENOMIC DISSECTION OF CARDIOVASCULAR DISEASES

摘要这项职业发展指导申请提出了一个培训计划，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成为一名独立的CVD遗传学研究统计学家，这样她就可以更有效地参与临床和转化CVD研究人员的多学科研究项目，并更好地开发和应用统计方法，为CVD遗传学领域做出更有意义的贡献。这将通过在心血管疾病的临床和研究方面建立坚实的基础，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多学科的研究人员组成，他们有着丰富的研究经验。复杂的心脏代谢特征包括高血压、血脂异常和糖尿病导致心血管疾病，这是工业化世界中死亡率和发病率的主要原因。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发现尚未转化为临床护理，因为遗传关联的功能机制仍然难以捉摸，环境在调节这些关联中所起的作用仍然不明确。该K25应用程序的研究目的是通过结合GxE相互作用和调控注释信息来破译心脏代谢性状的遗传和环境结构。我们假设，对环境、调控变异体和编码变异体的联合分析将有助于发现假定的遗传变异体和心脏代谢特征的功能机制。为了验证这一假设，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调控信息识别假定的功能变异。

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.