Genomic variants associated with angina and health status outcome after MI

描述(由申请人提供)：从心肌梗死(MI)中恢复与一系列症状相关，包括心绞痛、抑郁和较差的生活质量(QOL)，但很少有人关注这些以患者为中心的健康状况结果。美国有1000多万人患有心绞痛，每年约有50万新病例发生，估计每年的成本为200亿美元。我们建议通过使用凯旋人群来识别导致心肌梗死后心绞痛和健康状况结果个体间差异的基因组变异，凯旋人群是由美国国立卫生研究院资助的队列，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定健康状况评估，以及判定1年主要不良心血管事件和5年死亡率。研究小组特别有资格进行这项研究，他们在基因组学、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拥有专业知识。我们还将利用华盛顿大学CTSA的优势，包括其核心和项目。总而言之，我们将解决以下目标：目标1.确定基因对观察到的心肌梗塞后心绞痛个体间差异的贡献。主要结果将是第一年的心肌梗塞后心绞痛，由经过充分验证的特定疾病的西雅图心绞痛问卷(SAQ)心绞痛频率评分来衡量。次要结果是SAQ生活质量评分和用PHQ-9测量的抑郁症状。使用两种新的统计基因组学方法(生长曲线估计和多效性)，无偏GWAS方法将识别与这些结果相关的常见遗传变异。然后，我们将使用一种新的、具有成本效益的(多链、‘条形码’)外显子测序方法，精细地定位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。目的2.确定可能调节AIM 1中确定的遗传变异的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，可以作为估计作为治疗功能的症状结果的基础，并且使用这些模型，我们可以生成个性化的治疗策略。目的3.测试将包括AIM 1确定的基因变异的预后建模工具(PrismTM)转化为现实世界实践的可行性，以预测心肌梗塞治疗后的健康状况反应。我们的团队已经开发了信息技术，用于在临床护理过程中利用患者特定的数据执行多变量预测模型。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梗后心绞痛和其他健康状况结果相关的变量，并为个性化心梗后护理和减轻症状负担奠定基础。 公共卫生相关性：基因序列差异可能会影响个体在心脏病发作后对胸痛(心绞痛)的反应。这项研究建议确定特定的基因序列差异，这些差异可以预测心脏病发作后或多或少的胸痛，并确定如何最好地将发现转化为医疗实践。这项提议的最终目标是推进对心脏病发作的冠心病成年人的个性化护理。

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. PUBLIC HEALTH RELEVANCE: Sequence differences in genes may influence an individual's response to chest pain (angina) after a heart attack. This study proposes identifying specific gene sequence differences that predict more or less chest pain after heart attack and to determine how to best translate the findings into medical practice. The ultimate goal of this proposal is to advance personalized care for adults with coronary artery disease who have had a heart attack.