Identification of genetic biomarkers of blood cholesterol levels using whole gene pathogenicity modelling

Abstract

Elevated cholesterol increases risk of diseases such as heart disease, chronic kidney disease and diabetes and early detection and diagnosis is desirable to enable preventative intervention. This study seeks to elucidate genetic factors affecting low-density lipoprotein cholesterol (LDL-C) levels in blood, enabling development of personalised strategies for lipid management and cardiovascular disease prevention. GenePy, a gene pathogenicity scoring tool, condenses genetic variant data into a single burden score for both individuals and genes. GenePy scores were evaluated across all genes to assess their association with blood cholesterol levels, excluding participants on cholesterol-lowering medications. Nonparametric tests analysed the relationship between GenePy scores and cholesterol levels in those aged < 60 years and ≥ 60 years. GenePy was effective in identifying PCSK9, APOE, and LDLR as the genes most critically influencing plasma cholesterol at a population level. Of note, the strongest genetic effect observed was a protective loss of function effect in the PCSK9 gene. Novel significant signals driving blood LDL-C levels that are common to both age groups include: BPIFB6 that has a role in lipid binding and transport; FAIM that has a role in regulation of lipogenesis, SLAMF9 previously implicated in macrophage cholesterol loading; CLU-a component of HDL; SAA1 with a known role in cholesterol homeostasis. A gene-based analysis integrating common, rare, and private variations identifies genes influencing blood LDL-C levels. Developing effective polygenic risk scores requires a comprehensive understanding of genetic factors affecting cholesterol to improve prediction and personalise treatment plans.

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ORCID for Sarah Ennis: orcid.org/0000-0003-2648-0869

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