Association of BUD13-ZNF259-APOA5-APOA1-SIK3 cluster polymorphism in 11q23.3 and structure of APOA5 with increased plasma triglyceride levels in a Korean population

Interaction of energy and sulfur microbial diet and smoking status with polygenic variants associated with lipoprotein metabolism

Introduction

Hypo-high-density lipoprotein cholesterolemia (hypo-HDL-C) contributes to the development of cardiovascular diseases. The hypothesis that the polygenic variants associated with hypo-HDL-C interact with lifestyle factors was examined in 58,701 middle-aged Korean adults who participated in the Korean Genome and Epidemiology Study (KoGES).

Methods

Participants were categorized into the Low-HDL (case; n = 16,980) and Normal-HDL (n = 41,721) groups. The participants in the Low-HDL group were selected using the guideline-based cutoffs for hypo-HDL-C (<40 mg/dL for men and < 50 mg/dL for women) and included those taking medication for dyslipidemia. The genes associated with hypo-HDL-C were determined through a genome-wide association study (GWAS) in a city hospital-based cohort, and the results were validated in the Ansan/Anung study. The genetic variants for the single nucleotide polymorphism (SNP)-SNP interaction were selected using a generalized multifactor dimensionality reduction analysis, and the polygenic risk score (PRS) generated was evaluated for interaction with lifestyle parameters.

Results

The participants with hypo-HDL-C showed a 1.45 and 1.36-fold higher association with myocardial infarction and stroke, respectively. The High-PRS with four SNPs, namely ZPR1_rs3741297, CETP_rs708272, BUD13_rs180327, and ALDH1A2_rs588136, and that with the 11q23.3 haplotype were positively associated with hypo-HDL-C by about 3 times, which was a 2.4-fold higher association than the PRS of 24 SNP with p < 5×10-8. The risk alleles of CETP_rs708272 and ALDH1A2_rs588136 were linked to increased expression in the heart and decreased in the brain, respectively. The selected SNPs were linked to the reverse cholesterol transport pathway, triglyceride-rich lipoprotein particle remodeling pathway, cholesterol storage, and macrophage-derived foam cell differentiation regulation. The PRS of the 4-SNP model interacted with energy intake and smoking status, while that of the haplotype interacted with a glycemic index of the diet, sulfur microbial diet, and smoking status.

Discussion

Adults with a genetic risk for hypo-HDL-C need to modulate their diet and smoking status to reduce their risk.

Somatic gain-of-function mutations in BUD13 promote oncogenesis by disrupting Fbw7 function

Somatic mutations occurring on key enzymes are extensively studied and targeted therapies are developed with clinical promises. However, context-dependent enzyme function through distinct substrates complicated targeting a given enzyme. Here, we develop an algorithm to elucidate a new class of somatic mutations occurring on enzyme-recognizing motifs that cancer may hijack to facilitate tumorigenesis. We validate BUD13-R156C and -R230Q mutations evading RSK3-mediated phosphorylation with enhanced oncogenicity in promoting colon cancer growth. Further mechanistic studies reveal BUD13 as an endogenous Fbw7 inhibitor that stabilizes Fbw7 oncogenic substrates, while cancerous BUD13-R156C or -R230Q interferes with Fbw7Cul1 complex formation. We also find this BUD13 regulation plays a critical role in responding to mTOR inhibition, which can be used to guide therapy selections. We hope our studies reveal the landscape of enzyme-recognizing motif mutations with a publicly available resource and provide novel insights for somatic mutations cancer hijacks to promote tumorigenesis with the potential for patient stratification and cancer treatment.

Association of Apolipoprotein A5 Gene Variants with Hyperlipidemic Acute Pancreatitis in Southeastern China

Background: Apolipoprotein A5 (APOA5) is involved in serum triglyceride (TG) regulation. Several studies have reported that the rs651821 locus in the APOA5 gene is associated with serum TG levels in the Chinese population. However, no research has been performed regarding the association between the variants of rs651821 and the risk of hyperlipidemic acute pancreatitis (HLAP). Methods: A case-control study was conducted and is reported following the STROBE guidelines. We enrolled a total of 88 participants in this study (60 HLAP patients and 28 controls). APOA5 was genotyped using PCR and Sanger sequencing. Logistic regression models were conducted to calculate odds ratios and a 95% confidence interval. Results: The genotype distribution of the rs651821 alleles in both groups follow the Hardy-Weinberg distribution. The frequency of the "C" allele in rs651821 was increased in HLAP patients compared to controls. In the recessive model, subjects with the "CC" genotype had an 8.217-fold higher risk for HLAP (OR = 8.217, 95% CI: 1.023-66.01, p = 0.046) than subjects with the "TC+TT" genotypes. After adjusting for sex, the association remained significant (OR = 9.898, 95% CI: 1.176-83.344, p = 0.035). Additionally, the "CC" genotype was related to an increased TG/apolipoprotein B (APOB) ratio and fasting plasma glucose (FPG) levels. Conclusions: Our findings suggest that the C allele of rs651821 in APOA5 increases the risk of HLAP in persons from Southeastern China.

Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study

OBJECTIVE

Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors.

MATERIALS AND METHODS

A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the GCKR, BUD13 and APOA5 genes of eligible participants were assessed to classify TCGS participant status in MetS development. The models' performance was evaluated by 10-repeated 10-fold crossvalidation. Various assessment measures of sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC-ROC) and AUC-precision-recall (AUC-PR) curves were used to compare the models.

RESULTS

During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors.

CONCLUSION

Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS.

Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population

The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177-13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250-5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective.

Sex-related differences in single nucleotide polymorphisms associated with dyslipidemia in a Korean population

BACKGROUND

The prevalence of dyslipidemia has increased steadily in Korea, and the incidence of dyslipidemia differs by sex. In this study, we identified single nucleotide polymorphisms (SNPs) related to dyslipidemia in Korean cohorts through genome-wide association study (GWAS) analysis.

METHODS

Genotyping was conducted to determine the genotypes of 72,298 participants and investigate genotypes for 7,079,946 SNPs. Sex, age, and BMI were set as covariates for GWAS, and significant SNPs were identified in the discovery and replication stages using logistic regression.

RESULTS

GWAS of the entire cohort revealed a total of five significant SNPs: rs117026536 (LPL), rs651821 (APOA5), rs9804646 (APOA5), rs9926440 (CETP), and rs429358 (APOE). GWAS of the male subjects revealed a total of four significant SNPs. While rs9804646 (APOA5) and rs429358 (APOE) were significant for all the subjects, rs662799 (APOA5) and rs56156922 (CETP) were significant only for the male subjects. GWAS of the female subjects revealed two significant SNPs, rs651821 (APOA5) and rs9804646 (APOA5), both of which were significant in all the subjects.

CONCLUSION

This is the first study to identify sex-related differences in genetic polymorphisms in Korean populations with dyslipidemia. Further studies considering environmental variables will be needed to elucidate these sex-related genetic differences in dyslipidemia.

Exome Sequencing Data Analysis and a Case-Control Study in Mexican Population Reveals Lipid Trait Associations of New and Known Genetic Variants in Dyslipidemia-Associated Loci

Background: Plasma lipid levels are a major risk factor for cardiovascular diseases. Although international efforts have identified a group of loci associated with the risk of dyslipidemia, Latin American populations have been underrepresented in these studies.

Objective: To know the genetic variation occurring in lipid-related loci in the Mexican population and its association with dyslipidemia.

Methods: We searched for single-nucleotide variants in 177 lipid candidate genes using previously published exome sequencing data from 2838 Mexican individuals belonging to three different cohorts. With the extracted variants, we performed a case-control study. Logistic regression and quantitative trait analyses were implemented in PLINK software. We used an LD pruning using a 50-kb sliding window size, a 5-kb window step size and a r2 threshold of 0.1.

Results: Among the 34251 biallelic variants identified in our sample population, 33% showed low frequency. For case-control study, we selected 2521 variants based on a minor allele frequency ≥1% in all datasets. We found 19 variants in 9 genes significantly associated with at least one lipid trait, with the most significant associations found in the APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster on chromosome 11. Notably, all 11 variants associated with hypertriglyceridemia were within this cluster; whereas variants associated with hypercholesterolemia were located at chromosome 2 and 19, and for low high density lipoprotein cholesterol were in chromosomes 9, 11, and 19. No significant associated variants were found for low density lipoprotein. We found several novel variants associated with different lipemic traits: rs3825041 in BUD13 with hypertriglyceridemia, rs7252453 in CILP2 with decreased risk to hypercholesterolemia and rs11076176 in CETP with increased risk to low high density lipoprotein cholesterol.

Conclusions: We identified novel variants in lipid-regulation candidate genes in the Mexican population, an underrepresented population in genomic studies, demonstrating the necessity of more genomic studies on multi-ethnic populations to gain a deeper understanding of the genetic structure of the lipemic traits.

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

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.

The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders

The genetic architecture of the small and isolated Greenlandic population is advantageous for identification of novel genetic variants associated with cardio-metabolic traits. We aimed to identify genetic loci associated with body mass index (BMI), to expand the knowledge of the genetic and biological mechanisms underlying obesity. Stage 1 BMI-association analyses were performed in 4,626 Greenlanders. Stage 2 replication and meta-analysis were performed in additional cohorts comprising 1,058 Yup'ik Alaska Native people, and 1,529 Greenlanders. Obesity-related traits were assessed in the stage 1 study population. We identified a common variant on chromosome 11, rs4936356, where the derived G-allele had a frequency of 24% in the stage 1 study population. The derived allele was genome-wide significantly associated with lower BMI (beta (SE), -0.14 SD (0.03), p = 3.2x10-8), corresponding to 0.64 kg/m2 lower BMI per G allele in the stage 1 study population. We observed a similar effect in the Yup'ik cohort (-0.09 SD, p = 0.038), and a non-significant effect in the same direction in the independent Greenlandic stage 2 cohort (-0.03 SD, p = 0.514). The association remained genome-wide significant in meta-analysis of the Arctic cohorts (-0.10 SD (0.02), p = 4.7x10-8). Moreover, the variant was associated with a leaner body type (weight, -1.68 (0.37) kg; waist circumference, -1.52 (0.33) cm; hip circumference, -0.85 (0.24) cm; lean mass, -0.84 (0.19) kg; fat mass and percent, -1.66 (0.33) kg and -1.39 (0.27) %; visceral adipose tissue, -0.30 (0.07) cm; subcutaneous adipose tissue, -0.16 (0.05) cm, all p<0.0002), lower insulin resistance (HOMA-IR, -0.12 (0.04), p = 0.00021), and favorable lipid levels (triglyceride, -0.05 (0.02) mmol/l, p = 0.025; HDL-cholesterol, 0.04 (0.01) mmol/l, p = 0.0015). In conclusion, we identified a novel variant, where the derived G-allele possibly associated with lower BMI in Arctic populations, and as a consequence also leaner body type, lower insulin resistance, and a favorable lipid profile.