The Role of Genetics in Preterm Birth

Preterm birth (PTB), defined as the birth of a child before 37 completed weeks gestation, affects approximately 11% of live births and is the leading cause of death in children under 5 years. PTB is a complex disease with multiple risk factors including genetic variation. Much research has aimed to establish the biological mechanisms underlying PTB often through identification of genetic markers for PTB risk. The objective of this review is to present a comprehensive and updated summary of the published data relating to the field of PTB genetics. A literature search in PubMed was conducted and English studies related to PTB genetics were included. Genetic studies have identified genes within inflammatory, immunological, tissue remodeling, endocrine, metabolic, and vascular pathways that may be involved in PTB. However, a substantial proportion of published data have been largely inconclusive and multiple studies had limited power to detect associations. On the contrary, a few large hypothesis-free approaches have identified and replicated multiple novel variants associated with PTB in different cohorts. Overall, attempts to predict PTB using single -omics datasets including genomic, transcriptomic, and epigenomic biomarkers have been mostly unsuccessful and have failed to translate to the clinical setting. Integration of data from multiple -omics datasets has yielded the most promising results.

Automated summary

Preterm birth (PTB), defined as the birth of a child before 37 completed weeks gestation, is a major health issue affecting about 11% of live births and is the leading cause of death in children under 5 years old. Genetic studies have identified genes within various pathways that may be involved in PTB, but many of the published data are inconclusive and have limited power to detect associations. On the other hand, some large hypothesis-free approaches have identified multiple novel genetic variants associated with PTB. Attempts to predict PTB using single -omics datasets have mostly been unsuccessful, while integrating data from multiple -omics datasets has shown promising results.