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Congenital heart disease (CHD) is a multifaceted cardiovascular anomaly that occurs when there are structural abnormalities in the heart before birth. Although various risk factors are known to influence the development of this disease, a full comprehension of the etiology and treatment for different patient populations remains elusive. For instance, racial minorities are disproportionally affected by this disease and typically have worse prognosis, possibly due to environmental and genetic disparities. Although research into CHD has highlighted a wide range of causal factors, the reasons for these differences seen in different patient populations are not fully known. Cardiovascular disease modeling using induced pluripotent stem cells (iPSCs) is a novel approach for investigating possible genetic variants in CHD that may be race specific, making it a valuable tool to help solve the mystery of higher incidence and mortality rates among minorities. Herein, we first review the prevalence, risk factors, and genetics of CHD and then discuss the use of iPSCs, omics, and machine learning technologies to investigate the etiology of CHD and its connection to racial disparities. We also explore the translational potential of iPSC-based disease modeling combined with genome editing and high throughput drug screening platforms.

Race and Genetics in Congenital Heart Disease: Application of iPSCs, Omics, and Machine Learning Technologies