Moore Anni, Venkatesh Rasika, Levin Michael G, Damrauer Scott M, Reza Nosheen, Cappola Thomas P, Ritchie Marylyn D
Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk Philadelphia, PA, 19104, USA.
Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd Philadelphia, PA, 19104, USA.
Pac Symp Biocomput. 2025;30:504-521. doi: 10.1142/9789819807024_0036.
Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.
心力衰竭（HF）是世界上最常见、最复杂、异质性最强的疾病之一，全球有1%至3%的人口患有此病。HF的进展可以通过心脏结构和功能变化的MRI测量来追踪，即左心室（LV），包括射血分数、质量、舒张末期容积和左心室收缩末期容积。此外，虽然全基因组关联研究（GWAS）是识别与HF风险相关的候选变异的有用工具，但它们缺乏关键的组织特异性和机制信息，而这些信息可以通过纳入其他数据模式来获得。本研究通过纳入全转录组和全蛋白质组关联研究（TWAS和PWAS）来解决这一差距，以深入了解使用MRI衍生的心脏测量以及全阶段全因HF测量的HF前驱体中基因表达和蛋白质丰度的基因调控变化。我们在左心室射血分数和收缩末期容积测量之间确定了几个基因和蛋白质重叠。通过TWAS和PWAS在MRI衍生测量中确定的许多重叠似乎与全因HF共享。我们通过基因集富集和蛋白质-蛋白质相互作用网络方法，暗示了与这些基因和蛋白质相关的许多HF相关的假定途径。本研究结果（1）突出了使用多组学更好地理解遗传学的益处，（2）提供了关于心脏结构和功能变化如何与HF相关的新见解。
