STATISTICAL METHODS FOR GENOMIC DISSECTION OF CARDIOVASCULAR DISEASES

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此指导职业发展资助申请提出了一个培训计划，将宋博士以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建立自己作为一个独立的统计学家在CVD遗传学研究，使她可以更有效地参与多学科的研究计划与临床和翻译CVD研究人员，并更好地开发和应用统计方法，以更有意义地贡献CVD遗传学领域。这将通过在CVD的临床和研究方面建立坚实的基础，并提高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研究人员组成，具有强大的研究记录。包括高血压、血脂异常和糖尿病在内的复杂的心脏代谢特征导致CVD，CVD是工业化国家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的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。这项K25应用的研究目标是通过整合GxE相互作用和监管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，联合分析的环境，调节变异和编码变异，将提高推定的遗传变异和功能机制的基础心脏代谢性状的发现。为了评估这一假设，我们的目标是确定涉及GxE相互作用的遗传变异，并通过纳入ENCODE调控信息来确定推定的功能变异。

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.