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

Abstract

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.

Keywords: congenital heart disease; disease modeling; disparity; genomics; iPSC; race.

Figure 1

Risk factors of congenital heart disease. Modifiable risk factors associated with congenital heart disease include diet, obesity, substance abuse, and air pollution, whereas non-modifiable risk factors include rheumatologic disorders, genetic variants, specific medications, and infection. Each factor influences the development of this disease throughout pregnancy, effectively comprising the etiology of CHD and irreversibly altering the morphology of the fetal heart.

Figure 2

Applications of genomics and patient-specific iPSCs to reveal race-related genetic contribution to congenital heart disease. In combination with next generation sequencing and genome editing technology, iPSC-based disease modeling could be utilized to identify genetic variants that exist disproportionally within racial minority communities, thus providing a valuable tool for developing novel therapeutic treatment options to help those who have offspring suffering from CHD.