Polygenic Modulation of Monogenic Diseases: Familial Hypercholesterolemia as the Exemplar.

Abstract

Purpose of review: Heterozygous Familial Hypercholesterolemia (HeFH) is among the most common genetic conditions worldwide that affects ~ 1 in 300 individuals. HeFH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the HeFH population. This variability in expression is incompletely explained by known risk factors. The purpose of this review is to discuss recent studies that have examined how polygenic risk can modulate the phenotypic expression of HeFH.

Recent findings: Over the past several years, polygenic risk scores (PRS) that summarize information about many genetic variants that influence various traits have been developed. This includes polygenic risk scores for levels of LDL-C and other lipid fractions, CAD, and various other cardio-metabolic traits. In some individuals with a clinical phenotype compatible with HeFH but in whom a pathogenic variant is not present, an elevated PRS for LDL-C may explain the hypercholesterolemia. Among individuals with monogenic HeFH, an elevated PRS for LDL-C or CAD can further exacerbate the clinical phenotype and increase the risk of cardiovascular events. Conversely, a low PRS for these traits can mask the presentation of HeFH by decreasing the clinical severity and thus lead to incomplete phenotypic penetrance of a pathogenic HeFH-causing variant. Although HeFH is a prototypical monogenic condition, recent studies have revealed how the genomic background, as reflected by PRSs, can further modulate the clinical phenotype up or down in severity, thus adding a previously unrecognized level of complexity to monogenic disease. Having identified PRSs that can alter the clinical trajectory of HeFH, the next challenge for the field will be to implement PRS testing into clinical practice to allow clinicians to tailor risk prediction and treatment approaches based on each individual's unique complement of genetic factors.