Genome-Wide Association Study and Meta-Analysis of Phytosterols Identifies a Novel Locus for Serum Levels of Campesterol

Abstract

Background: Sitosterolemia is a rare inherited disorder caused by mutations in the ABCG5/ABCG8 genes. These genes encode proteins that are involved in the transport of plant sterols (phytosterols) out of the body. Mutations in these genes lead to decreased excretion of phytosterols, which can accumulate in the body and lead to a variety of health problems, including xanthoma formation, atherosclerosis, and premature coronary artery disease. Methods: We conducted the first genome-wide association study (GWAS) in the Middle East/North Africa (MENA) population to identify genetic determinants of plant sterol levels in Qatari people. GWAS was performed on serum levels of β-sitosterol and campesterol measured using the Metabolon platform from the Qatar Biobank Cohort and genome sequence data provided by Qatar Genome Program. Furthermore, a trans-ancestry meta-analysis of data from our Qatari cohort with summary statistics from a previously published large cohort (9,758 subjects) of European ancestry was conducted. Results: Using conditional analysis, we identified two independent single nucleotide polymorphisms (SNPs) associated with β-sitosterol (rs145164937 and rs4299376), and two independent SNPs associated with campesterol (rs7598542 and rs75901165) in the Qatari population in addition to two previously reported variants (rs199689137 and rs4245791). All of them map to the ABCG5/8 locus except rs75901165 which is located within the Intraflagellar Transport 43 (IFT43) gene. The meta-analysis replicated most of the reported variants, and our study provided significant support for the association of variants in SCARB1 and ABO with sitosterolemia. Conclusions: This is the first study to shed light on genetic determinants of phytosterols in the MENA region using a genome-wide association approach. We also established the first polygenic risk score for this trait using the European large cohort study. These findings may have future implications on the personalized treatment of hyperlipidemia in general while showing the importance of building population-specific multi-omics databases.