Unlocking the Regulatory Genome: Interpreting the Clinical Impact of Noncoding Variants in Genetic Cardiomyopathies

Clinical genetic testing is now the standard of care for cardiomyopathy, guiding risk stratification, clinical management, and earlier diagnosis in family members. Yet, a large proportion of the genetic basis of cardiomyopathy remains incompletely explained. Prior efforts to identify genetic causes of cardiomyopathy have largely focused on coding DNA sequence, which accounts for only 3% of the human genome, leaving the noncoding regulatory sequence space relatively unexplored. A confluence of emerging technologies is now transforming our capability to identify and interpret noncoding variants. This review summarizes the field's current knowledge of how noncoding variants influence the development of cardiomyopathy, both from the standpoint of rare Mendelian disease variants and population-level risk alleles. In addition, we describe how new technologies have enabled systematic identification and prioritization of regulatory regions that govern gene expression. Beyond identification of regulatory regions, we discuss how causal testing of variants is now possible at an unprecedented scale through massively parallel reporter assays, allowing both detailed mapping of these regions and efficient validation of variants discovered through genome-wide association studies. Finally, we review deep learning approaches that hold the potential for genome-wide noncoding variant interpretation. Together, this review highlights strategies for large-scale interpretation of noncoding variants while also demonstrating the clear need for extension of clinical variant adjudication workflows to the noncoding genome to fully take advantage of increasingly available whole-genome sequencing data.