Genomic variants associated with angina and health status outcome after MI

描述（由申请人提供）：心肌梗死（MI）的恢复与一系列症状相关，包括心绞痛、抑郁和生活质量（QoL）下降，但很少有人关注这些以患者为中心的健康状况结果。在美国，有超过1000万人患有心绞痛，每年大约有50万新病例发生，每年的成本估计为200亿美元。我们建议通过nih资助的TRIUMPH人群，在入院时、心肌梗死后1个月、6个月和1年进行精确的疾病特异性健康状况评估，以及确定的1年主要不良心血管事件和5年死亡率，来确定导致心肌梗死后心绞痛和健康状况结局的个体间差异的基因组变异。该研究小组在基因组学、药物基因组学、患者筛查和风险分析、结果研究和统计基因组学方面具有专业知识，特别有资格进行这项研究。我们还将利用华盛顿大学CTSA的优势，包括其核心和项目。我们将共同致力于以下目标：目标1。定义心肌梗死后心绞痛个体间变异的遗传贡献。主要结果将是心肌梗死后第一年的心绞痛，通过经过充分验证的疾病特异性西雅图心绞痛问卷（SAQ）心绞痛频率评分来衡量。次要结局是SAQ生活质量评分和抑郁症状，由PHQ-9测量。无偏GWAS方法将使用两种新的统计基因组方法（生长曲线估计和多效性）识别与这些结果相关的常见遗传变异。然后，我们将使用一种新颖的，具有成本效益的（多路复用的，“条形码”）外显子测序方法来精细地绘制关联峰下基因中的所有外显子，并识别与这些结果相关的罕见变异。目标2。确定可能潜在地缓和AIM 1中确定的遗传变异影响的非基因组因素。我们将构建包括遗传、临床和治疗特征在内的多变量模型，并特别关注相互作用。这些模型，特别是如果发现了与治疗的重要相互作用，可以作为估计症状结果作为治疗函数的基础，使用这些模型，我们可以生成个性化的治疗策略。目标3。为了测试将一种预后建模工具（PRISMTM）转化为“现实世界实践”的可行性，该工具包括由AIM 1识别的遗传变异，以预测对心肌梗死后治疗的健康状况反应。我们的团队已经开发了信息技术，可以在临床护理过程中使用患者特定数据实现多变量预测模型。我们将使用这些模型为心肌梗死后的治疗创建个性化的风险概况和治疗策略。总之，我们将使用尖端的实验、统计和诊断方法来识别与心肌梗死后心绞痛和其他健康状况结果相关的变异，为个性化心肌梗死后护理和减轻症状负担奠定基础。

DESCRIPTION (provided by applicant): Recovery from myocardial infarction (MI) is associated with a host of symptoms, including angina, depression, and worse quality of life (QoL) but little attention has been paid to these patient-centered health status outcomes. More than 10 million people in the US suffer from angina and approximately 500,000 new cases occur each year at an estimated cost of $20 billion dollars annually. We propose to identify genomic variants that contribute to inter-individual variation in post-MI angina and health status outcomes by using the TRIUMPH population, an NIH-funded cohort with exquisite disease-specific health status assessments at admission, and 1-month, 6-months and 1-year post-MI, along with adjudicated 1-year major adverse cardiovascular events and 5-year mortality. The study group is particularly well-qualified to perform this research, having expertise in genomics, pharmacogenomics, patient screening and risk profiling, outcomes research, and statistical genomics. We will also take advantage of the strengths of Washington University's CTSA, including its Cores and programs. Collectively, we will address the following Aims: AIM 1. To define the genetic contribution to the observed inter-individual variation in post-MI angina. The primary outcome will be post-MI angina over the first year, as measured by the well-validated, disease-specific Seattle Angina Questionnaire (SAQ) Angina Frequency score. Secondary outcomes are SAQ QoL score and depressive symptoms, as measured by PHQ-9. An unbiased GWAS approach will identify common genetic variants associated with these outcomes using two novel statistical genomic methods (Growth Curve Estimation and Pleiotropy). We will then use a novel, cost-efficient (multi-plexed, 'bar-coded') exomic sequencing method to finely map all exons in the genes under the association peaks and identify rare variants that are associated with these outcomes. AIM 2. To identify non-genomic factors that may potentially moderate the effects of the genetic variants identified in AIM 1. We will construct multivariable models that include genetic, clinical and treatment characteristics, with a specific focus upon interactions. These models, especially if important interactions with treatment are discovered, can serve as the foundation for estimating symptom outcomes as a function of treatment and, using these models, we can generate personalized treatment strategies. AIM 3. To test the feasibility of translating - into 'real world practice' - a prognostic modeling tool (PRISMTM) that includes genetic variants identified by AIM 1, to predict health status response to post-MI treatment. Our team has developed information technology with which to implement multivariable prediction models, executed with patient-specific data, in the process of clinical care. We will use these models to create a personalized risk profile and therapeutic strategy for post-MI treatment. In summary, we will use cutting edge experimental, statistical, and diagnostic methods to identify variants associated with post-MI angina and other health status outcomes and lay the foundation to personalize post-MI care and reduce symptom burden.