The application of genetic risk score in genetic studies of complex human diseases

Abstract

Complex diseases such as cardiovascular disease, type 2 diabetes, essential hypertension, asthma, obesity and cancer have spread across the globe and become the predominant cause of death. There are growing concerns over the role of genetic susceptibility in pathogenesis of complex diseases. However, the related susceptibility genes and sequence variations are still unknown. To elucidate the genetic basis of complex diseases, researchers have identified a large number of genetic variants associated with complex diseases through genome-wide association studies (GWAS) and candidate gene studies recently. The identification of these causal and/or associated variants promotes the development of approaches for complex diseases prediction and prevention. Genetic risk score (GRS), an emerging method for exploring correlation between single nucleotide polymorphisms (SNPs) and clinical phenotypes of complex diseases, integrates weak effects of multiple SNPs and dramatically enhances predictability of complex diseases by gene polymorphisms. This method has been applied successfully in genetic studies of many complex diseases. Here we focus on the introduction of the computational methods and evaluation criteria of GRS, enumerate a series of achievements through GRS application, discuss some limitations during application, and finally prospect the future of GRS.