Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study

Abstract

Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role in the pathogenesis of MetS. The present study aimed to assess the association between 18 single nucleotide polymorphisms (SNPs) located at the BUD13, ZPR1, and APOA5 genes with MetS in the Tehran Cardio-metabolic Genetics Study (TCGS). In 5421 MetS affected and non-affected participants, we analyzed the data using two models. The first model (MetS model) examined SNPs' association with MetS. The second model (HTg-MetS Model) examined the association of SNPs with MetS affection participants who had a high plasma triglyceride (TG). The four-gamete rules were used to make SNP sets from correlated nearby SNPs. The kernel machine regression models and single SNP regression evaluated the association between SNP sets and MetS. The kernel machine results showed two sets over three sets of correlated SNPs have a significant joint effect on both models (p < 0.0001). Also, single SNP regression results showed that the odds ratios (ORs) for both models are almost similar; however, the p-values had slightly higher significance levels in the HTg-MetS model. The strongest ORs in the HTg-MetS model belonged to the G allele in rs2266788 (MetS: OR = 1.3, p = 3.6 × 10–7; HTg-MetS: OR = 1.4, p = 2.3 × 10–11) and the T allele in rs651821 (MetS: OR = 1.3, p = 2.8 × 10–7; HTg-MetS: OR = 1.4, p = 3.6 × 10–11). In the present study, the kernel machine regression models could help assess the association between the BUD13, ZPR1, and APOA5 gene variants (11p23.3 region) with lipid-related traits in MetS and MetS affected with high TG.

Title: Cohort profile update: Tehran Cardiometabolic Genetic Study, a path toward precision medicine

“…Moreover, a single SNP regression analysis indicated that although the ORs of both models were the same, the p-values in the HTG MetS model had a marginally higher signi cance level. In addition, we observed that the highest OR in the HTG MetS model was for the G allele in rs2266788 (MetS: OR = 1.3, HTG MetS: OR = 1.4) and the T allele in rs651821 (MetS: OR=1.3, HTG MetS: OR = 1.4) [43].…”

“…Besides, to make SNP sets from correlated SNPs, four-gamete rules were used. To estimate the association between SNP sets and MetS, the kernel machine regression models and single SNP regression were used [43].…”

Genetic variations in IKZF3, LET7‐a2, and CDKN2B‐AS1: Exploring associations with metabolic syndrome susceptibility and clinical manifestations

“…MetS is a multifactorial disorder which may be influenced by genetic polymorphisms involved in the regulation of the obesity process, blood pressure, blood sugar levels, and lipid metabolism, 30,31 as evidenced by studies on protein‐coding genes nitric oxide synthase ( NOS4 ) cluster of differentiation 36 ( CD36 ), BUD13 Homolog ( BUD13 ), ZPR1 Zinc Finger ( ZPR1 ), and apolipoprotein A5 ( APOA5 ) SNPs in the Iranian population. However, in the present study, our focus shifted towards non‐coding RNA SNPs 13–15 . Specifically, we investigated the association of IKZF3 rs907091 C>T, microRNA‐ LET7‐a‐2 rs1143770C>T, and lncRNA CDKN2B‐AS1 rs1333045T>C with susceptibility to MetS.…”

“…Adherence to a diet rich in fiber, fish, and dairy products demonstrated a more pronounced effect on cardiometabolic risk factors in A‐allele carriers compared to the GG genotype of rs1761667 polymorphism 14 . Additionally, kernel machine regression models in the Tehran Cardio‐metabolic Genetics Study assessed the association between BUD13 homolog ( BUD13 ), ZPR1 zinc finger ( ZPR1 ), and apolipoprotein A‐V ( APOA5 ) SNPs in the 11p23.3 region with lipid‐related traits in MetS, especially those affected by high triglyceride levels 15 …”

Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture

“…In practice, the choice of the SNP-set formation strategy can influence the power of the approach [ 17 ]. Existing analyses grouped SNPs together into SNP sets based on a variety of genomic features such as physical location or biological functions [ 13 , 30 , 31 ]. However, it is reasonable to expect that we can extract the joint information at both the gene-level and pathway-level to improve the power for detecting true effects [ 24 ].…”