Forward genetic genome-wide association studies (GWAS) have successfully mapped thousands of loci regulating disorders of the heart, lung, blood and sleep (HLBS), implicating widespread sequence variation within the non-coding genome. However, their functions, mechanisms of action and how they impact disease is still unclear. To solve this new and important GWAS bottleneck, we use a functional genomics-inspired reverse genetics strategy to identify the `transcriptional machinery' (transcription factors (TF), cis-regulatory elements (CRE), target genes) controlling HLBS-relevant tissue functions and how DNA variants in them affect HLBS diseases. Taking advantage of our long-standing expertise and successes in complex, cardiovascular disorders, and novel computational methods we have recently developed, we propose novel genomics analyses of the Trans-Omics for Precision Medicine (TOPMed) Program phenotypes and their whole genome sequences, together with publicly available epigenomics data, to identify the molecular bases of HLBS disease. We will first focus on the transcriptional machinery controlling heart physiology and its disorders before exploring other HLBS-relevant tissues and disorders in collaboration with other TOPMed investigators.
Our specific aims are: (1) Identifying the transcriptional machinery in the heart and other HLBS relevant tissues; and, (2) Connecting genomic variation in the transcriptional machinery to HLBS traits. Our approach will enable identification of the core molecular components that control HLBS tissues and how they are compromised in HLBS disorders.
The major hypothesis explaining the results of heart, lung, blood and sleep (HLBS) genome-wide association studies (GWAS) is that sequence variants at specific cis-regulatory elements (CRE or enhancer) affect the binding of their cognate transcription factors (TF) to alter expression of specific HLBS genes and, thereby, modulate variation in the phenotype and disorders. In this proposal, we advance new computational approaches to identify the `transcriptional machinery' (TF, CRE, target genes) controlling HLBS-relevant tissue functions so that the effects of causal genetic variation can be identified within identified trait loci genome- wide. This tissue-based view provides an alternative, complementary approach for understanding HLBS trait and disease variation, a major public health challenge.
