Decrypting the genetic architecture of heart failure imaging signatures through machine learning in large-scale genome-wide association studies

在大规模全基因组关联研究中通过机器学习解密心力衰竭成像特征的遗传结构

• 批准号: MR/X020924/1
• 负责人: Nay Aung
• 金额: $ 196.52万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX020924%2F1
• 关键词: Decrypting; genetic; architecture; heart; failure

Heart failure is a common medical condition in which the heart does not efficiently pump blood around the body. It frequently leads to disabling symptoms and early death. Detection of abnormal changes in the heart structure and function by imaging aids heart failure diagnosis and can predict poor outcomes. Genetics determine variation in the heart structure and function as supported by the heritability assessment in family studies. Prior research into the genetic influence of heart structure and function focused on conventional heart measurements such as the heart size or overall pump function in a relatively modest number of mostly European (White) individuals. These research studies found a limited number of genetic loci (regions in the genome) explaining a small proportion of observed heritability in a population. This study will assemble one of the largest datasets containing heart imaging, electrocardiogram (ECG, a simple test to check heart rhythm), detailed lifestyle information and health outcomes in up to 888,000 individuals with diverse racial backgrounds. The large dataset will permit the development of reliable artificial intelligence (AI) techniques to automatically extract or estimate heart measurements from imaging and ECG. Conventional imaging metrics such as the volume of the heart chambers and the pump function as well as more advanced measures such as the pressure changes in the heart, which could be a more sensitive marker of a failing heart, will be developed. Using these imaging measurements along with individual genetic data, computational experiments will be carried out to discover the genetic markers which could provide additional knowledge on the origin and causes of HF and assist in the development of new medications which are urgently needed for certain types of heart failure. The results will also allow evaluation of the cause-and-effect relationships between cardiovascular risk factors such as obesity and high blood pressure and abnormal changes in the heart and eventual development of heart failure using a technique called "Mendelian Randomisation". This information will help in crafting targeted public health and preventive strategies. Lastly, with the data on genetic associations, we will build the genetic risk scores for each individual and test if they can predict heart failure development and perform well across different ethnic groups. If successful, this approach will transform the future management of heart failure by allowing doctors to provide a personalised assessment of each person's lifetime susceptibility to heart failure which will in turn permit early diagnosis, tailored lifestyle advice and pre-emptive medical treatment.

心力衰竭是一种常见的医疗状况，心脏无法有效地在体内泵入血液。它经常导致症状和早期死亡。通过成像来检测心脏结构和功能异常变化，有助于心力衰竭诊断，并可以预测不良的预后。遗传学决定了家庭研究中遗传力评估支持的心脏结构和功能的变化。对心脏结构和功能的遗传影响的先前研究集中在传统的心脏测量上，例如相对较少的欧洲人（白人）个体，例如心脏大小或整体泵功能。这些研究发现，基因组中的遗传基因座（基因组中的区域）有限地解释了人群中观察到的遗传力的一小部分。这项研究将组装包含心脏成像，心电图（ECG，简单检查心律的简单测试）的最大数据集之一，详细的生活方式信息和健康状况，最多有888,000名具有不同种族背景的人。大型数据集将允许开发可靠的人工智能（AI）技术，以自动从成像和ECG中提取或估算心脏测量。传统的成像指标，例如心脏室的体积和泵功能以及更高级的度量，例如心脏的压力变化，这可能是衰竭心脏的更敏感的标记。使用这些成像测量以及单个遗传数据，将进行计算实验，以发现遗传标记，这些标记可以提供有关HF的起源和原因的额外知识，并有助于开发某些类型的心力衰竭需要的新药物。结果还将允许评估心血管危险因素（例如肥胖症，高血压）和心脏异常变化等心血管危险因素之间的因果关系，并使用称为“ Mendelian随机化”的技术对心力衰竭的最终发展。这些信息将有助于制定针对性的公共卫生和预防策略。最后，有了有关遗传关联的数据，我们将为每个人建立遗传风险评分，并测试他们是否可以预测心力衰竭发展并在不同种族中表现良好。如果成功，这种方法将通过允许医生对每个人的心力衰竭敏感性进行个性化评估来改变心力衰竭的未来管理，这又可以允许早期诊断，量身定制的生活方式建议和先发制人的医疗治疗。