From genes to therapy: A comprehensive exploration of congenital heart disease through the lens of genetics and emerging technologies.
Khalid NawazNur AlifahTalib HussainHamza HameedHaider AliShah HamayunAwal MirAbdul WahabMuhammad NaeemMohammad ZakriaErmina PakkiNurhasni HasanPublished in: Current problems in cardiology (2024)
Congenital heart disease (CHD) affects approximately 1 % of live births worldwide, making it the most common congenital anomaly in newborns. Recent advancements in genetics and genomics have significantly deepened our understanding of the genetics of CHDs. While the majority of CHD etiology remains unclear, evidence consistently indicates that genetics play a significant role in its development. CHD etiology holds promise for enhancing diagnosis and developing novel therapies to improve patient outcomes. In this review, we explore the contributions of both monogenic and polygenic factors of CHDs and highlight the transformative impact of emerging technologies on these fields. We also summarized the state-of-the-art techniques, including targeted next-generation sequencing (NGS), whole genome and whole exome sequencing (WGS, WES), single-cell RNA sequencing (scRNA-seq), human induced pluripotent stem cells (hiPSCs) and others, that have revolutionized our understanding of cardiovascular disease genetics both from diagnosis perspective and from disease mechanism perspective in children and young adults. These molecular diagnostic techniques have identified new genes and chromosomal regions involved in syndromic and non-syndromic CHD, enabling a more defined explanation of the underlying pathogenetic mechanisms. As our knowledge and technologies continue to evolve, they promise to enhance clinical outcomes and reduce the CHD burden worldwide.
Keyphrases
- congenital heart disease
- single cell
- induced pluripotent stem cells
- young adults
- rna seq
- cardiovascular disease
- genome wide
- intellectual disability
- healthcare
- pregnant women
- endothelial cells
- copy number
- high throughput
- type diabetes
- big data
- gene expression
- stem cells
- risk factors
- dna methylation
- gestational age
- single molecule
- preterm infants
- bone marrow
- deep learning
- low birth weight
- transcription factor
- preterm birth
- cord blood