Associations of PRKN–PACRG SNPs and G × G and G × E interactions with the risk of hyperlipidaemia

This research aimed to assess the associations of 7 parkin RBR E3 ubiquitin protein ligase (PRKN) and 4 parkin coregulated gene (PACRG) single-nucleotide polymorphisms (SNPs), their haplotypes, gene–gene (G × G) and gene-environment (G × E) interactions with hyperlipidaemia in the Chinese Maonan minority. The genotypes of the 11 SNPs in 912 normal and 736 hyperlipidaemic subjects were detected with next-generation sequencing technology. The genotypic and allelic frequencies of the rs1105056, rs10755582, rs2155510, rs9365344, rs11966842, rs6904305 and rs11966948 SNPs were different between the normal and hyperlipidaemic groups (P < 0.05–0.001). Correlations between the above 7 SNPs and blood lipid levels were also observed (P < 0.0045–0.001, P < 0.0045 was considered statistically significant after Bonferroni correction). Strong linkage disequilibrium was found among the 11 SNPs (r2 = 0.01–0.64). The most common haplotypes were PRKN C-G-T-G-T-T-C (> 15%) and PACRG A-T-A-T (> 40%). The PRKN C-G-C-A-T-T-C and PRKN–PACRG C-G-T-G-T-T-C-A-T-A-T haplotypes were associated with an increased risk of hyperlipidaemia, whereas the PRKN–PACRG C-G-T-G-C-T-C-A-T-C-T and C-G-T-G-T-T-C-A-T-C-T haplotypes provided a protective effect. Association analysis based on the haplotypes and G × G interaction could improve the power to detect the risk of hyperlipidaemia over the analysis of any one SNP alone. The differences in serum lipid parameters between the hyperlipidaemic and normal groups might partly be due to the effects of the PRKN–PACRG SNPs and their haplotypes.

Genotypic and allelic occurrence of SNPs and the association with serum lipid levels. The genotypic and allelic occurrences of the SNPs within PRKN (rs10755582, rs9458363, rs2022991, rs9365344, Table 1. Comparison of demographic, lifestyle characteristics and serum lipid levels between the normal and hyperlipidemic populations. HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, Apo apolipoprotein. a Mean ± SD determined by t test. b The value of triglyceride was presented as median (interquartile range), the difference between the two groups was determined by the Wilcoxon-Mann-Whitney test. www.nature.com/scientificreports/ rs1105056, rs4636000 and rs2155510) and PACRG (rs2206256, rs11966842, rs11966948 and rs6904305) are represented in Table 2. The genotype distribution of all 11 SNPs was consistent with Hardy-Weinberg equilibrium (HWE) in both hyperlipidemic and normal groups (P > 0.05 for all). The genotypic and allelic frequencies of 7 SNPs (PRKN rs10755582, rs9365344, rs1105056, rs2155510 and PACRG rs11966842, rs11966948 and rs6904305) were significantly different between the two groups (P < 0.05-0.001).
Haplotype-based association with hyperlipidaemia. As shown in Fig Gene-gene/environment interaction effect on hyperlipidaemia. Generalized multifactor dimensionality reduction (GMDR) was used to evaluate the association between gene-gene/environment factor   www.nature.com/scientificreports/ (including hypertension, diabetes, smoking, drinking and BMI) interactions and the risk of hyperlipidaemia after adjusting for covariates. A significant three-locus model (P < 0.001) involving the rs9458363-rs4636000-rs11966842 SNPs was noted (Table 5, indicating a potential SNP-SNP interaction among the three SNPs). In general, this model exhibited a testing accuracy of 65.51% and a cross-validation consistency (CVC) of 10 of 10. A significant three-locus model (P < 0.001) including rs4636000-rs11966842-BMI > 24 kg/m 2 was observed (the Table 4. Association between the G × G interaction haplotypes among 11 SNPs of the PRKN-PACRG cluster and hyperlipidaemia [n ( parkin RBR E3 ubiquitin protein ligase gene; PACRG , the parkin coregulated gene; Rare Hap (frequency < 1%) in both groups was dropped. P was obtained by unconditional logistic regression analysis.

GxG interaction haplotypes
Normal Hyperlipidemia x 2 P OR (95% CI)    Table 6. The participants with the rs9458363 GT/TT and rs4636000 CT/CC genotypes exhibited a higher risk of hyperlipidaemia than those with the rs9458363 GG and rs4636000 TT genotypes (adjusted OR = 1.795, 95% CI = 1.352-2.383, P < 0.001). The subjects with the rs11966842 TT/CT genotype and a BMI > 24 kg/m 2 presented an increased risk of hyperlipidaemia (adjusted OR = 1.

Discussion
The main findings of the current research included the following. (1) The frequencies of 11 PRKN-PACRG SNPs, their haplotypes and corresponding gene-gene inter-locus interactions in the Chinese Maonan nationality were revealed, which may provide a more complete complement to the 1,000 Genomes database. (2) Some new evidence about the potential PRKN-PACRG SNP-SNP/environment, haplotype-haplotype/environment, and gene-gene/environment interaction effects on blood lipid parameters was provided. (3) Several different effects of the PRKN-PACRG SNP-SNP/environment, haplotype-haplotype/environment and gene-gene/environment interactions on the risk of hyperlipidaemia were observed in our study populations.
The 2019 ESC/EAS guidelines for the management of dyslipidaemia indicate that the combined effects of LDL-C and other cholesterol-rich lipoproteins that accumulate in the subintima play a key role in the initiation of atherosclerosis, and the comprehensive reduction of lipid levels is more effective in reducing the risk of cardiovascular events than decreasing LDL-C levels alone, which is especially applicable to ACS patients with a high risk of cardiovascular events 18 . Many previous studies have also proven that hyperlipidaemia, a severe risk factor for CAD, may be due to the combined effects of various elements, such as age, sex, unhealthy lifestyles, genetic background, environmental factors and the interactions of these factors 16,19 .
The current research demonstrated the correlation between PRKN and PACRG SNPs and serum lipid parameters including LDL-C (rs11966842 and rs2155510), TC (rs1105056, rs10755582, rs2155510, rs11966842 and rs6904305), and TG (rs9365344 and rs11966948) in subjects with hyperlipidaemia and TG (rs10755582 and rs11966948) in the normal group. At the same time, we noted that the genotypic and allelic frequencies of the www.nature.com/scientificreports/ PRKN (rs10755582, rs9365344, rs1105056 and rs2155510) and PACRG (rs11966842, rs11966948 and rs6904305) SNPs were obviously different between the hyperlipidaemic and normal groups. When the relationship between the above SNPs and the onset of hyperlipidaemia was analysed, we found that the dominant models of the rs1105056, rs2155510, rs9365344 and rs6904305 SNPs increased the risk of hyperlipidaemia, whereas the dominant models of the rs10755582, rs11966842 and rs11966948 SNPs exhibited a protective effect. These results suggested that genetic factors were important risk factors for the prevalence of hyperlipidaemia. The reasons for these differences in the current study are not entirely clear. Differences in genetic factors and LD patterns may account for some of these differences. The existence of multiple-locus LD indicates that nearby SNPs in adjacent genes are not statistically independent of each other genetically 10 . Strong LD was also found among the 11 SNPs (r 2 = 0.01-0.64) examined in this research. We found that the most common haplotypes were PRKN C-G-T-G-T-T-C (> 15%) and PACRG A-T-A-T (> 40%). At the same time, the PRKN C-G-C-A-T-T-C haplotype was associated with an increased risk of hyperlipidaemia, whereas the interactions of the PRKN-PACRG C-G-T-G-C-T-C-A-T-C-T and C-G-T-G-T-T-C-A-T-C-T were correlated with a decreased risk of hyperlipidaemia. However, the assessment of SNP-environment interactions showed that the rs11966842 SNP and a BMI > 24 kg/m 2 were associated with an increased risk of hyperlipidaemia in the participants. We also revealed that the occurrence of PACRG A-T-A-T Table 6. Different types of interaction detected by logistic regression analyses. P was obtained by adjusting for height and weight. The haplotypes of PRKN were composed in the order of rs10755582, rs9458363, rs2022991, rs9365344, rs1105056, rs4636000, and rs2155510 SNPs. The haplotypes of PACRG were composed in the order of rs2206256, rs11966842, rs11966948, and rs6904305 SNPs. Gender, cigarette smoking, blood pressure, alcohol consumption, blood glucose and age were adjusted for the statistical analysis.   www.nature.com/scientificreports/ and a BMI > 24 kg/m 2 or PRKN-PACRG C-G-T-G-T-T-C-A-T-C-T and a BMI > 24 kg/m 2 was related to an increased risk of hyperlipidaemia in the subjects. The above results indicated that correlation analysis based on haplotypes and gene-gene interactions could illuminate more changes in serum lipid levels compared to the analysis of a single SNP alone. Additionally, when the SNP/haplotype/gene-environment interactions were analyzed, we noted that a BMI > 24 kg/m 2 could reverse the effects of the SNPs or haplotypes. A reasonable explanation for this finding may be that a genetic factor, combined with environmental and lifestyle factors, contributes to the development of hyperlipidaemia 16 . The Maonan ethnic group is known in China for the unique marriage culture and eating habits of its members. The Maonan marriage culture is relatively conservative. Parents mainly arrange the marriages of their children. Maonans maintain the custom of intraethnic marriages, and intermarriage with other ethnic groups is rare. Therefore, the inherited features and genotypes of some lipid metabolism-associated genes in Maonans might differ from those in other populations. Rice is the staple food of Maonan people. They also eat corn, potatoes, wheat, sorghum, etc. The Maonan people especially prefer foods that are spicy, acidic and rich in salt as well as oil. The consumption of this type of diet, rich in long-chain highly saturated fat, might lead to high blood glucose levels, obesity, hyperlipidaemia, hypertension and atherosclerosis 20 . The main long-chain saturated fatty acids in the diet could produce harmful effects on blood lipid metabolism, impacting the levels of serum TG and TC in particular 21 .

No-carriers
Unhealthy lifestyle factors such as excessive drinking and cigarette smoking have been linked to hyperlipidaemia 22,23 . In the present study, we found that the percentage of the participants who smoked was greater in the hyperlipidaemic group than in the normal group. In recent years, the influence of smoking on hyperlipidaemia has attracted increasing attention. Several recent studies have indicated the existence of lower HDL-C and higher TC, LDL-C and TG levels in smokers compared to non-smokers 23 . Moderate drinking reduced the incidence of cardiovascular events, the potential mechanism may be related to increased HDL-C and ApoA1 levels 24 . However, the beneficial effect of drinking on HDL-C levels is negated by smoking. This may explain the difference in the serum lipid profiles between the two groups. Thus, the combined effects of various eating habits, lifestyle factors and environmental aspects may further alter the relationship between hereditary variations and serum lipid levels observed in the current study.
Several limitations of the current study were inevitable. First, the sample size was much smaller than those in previous large GWASes; therefore, further studies with a larger sample size are needed to confirm the findings. Second, although we examined the effects of 11 SNPs in the PRKN-PACRG cluster on lipid levels, numerous potential lipid-related SNPs were overlooked in the current study. Third, we did not determine the potential functional roles of the significant SNPs identified in the development of hyperlipidaemia; thus, the correlation of the findings needs to be validated by further in-depth studies with the incorporation of the genetic information of single-nucleotide mutations in the PRKN-PACRG cluster, their haplotypes, and G × G and G × E interactions via in vitro and in vivo functional studies to verify the effects of this variation at the molecular level, including the effects on transcription and translation.
In summary, there are potential correlations between PRKN-PACRG SNPs, environmental exposure and serum lipid parameters in the Maonan population. Furthermore, correlation analysis based on haplotypes and gene-gene interactions could improve the power of detecting the risk of dyslipidaemia compared with the analysis of any single SNP alone. The use of GMDR to analyse the interactions indicated that the different patterns of interaction identified between genetic and environmental factors result in different redundant or synergistic effects on the morbidity associated with hyperlipidaemia.

Subjects.
A total of 912 (405 males, 44.41%; 507 females, 55.59%) unrelated participants of normal lipid levels and 736 unrelated subjects (330 males, 44.84%; 406 females, 55.16%) of hyperlipidaemia were arbitrarily chosen based on our previously stratified randomized samples. All of the subjects were farmworkers who resided in Huanjiang Maonan Autonomous County, Guangxi Zhuang Autonomous Region of China. The age ranged from 22 to 88 years. There was not any difference in age distribution (55.83 ± 15.48 vs. 55.55 ± 14.33) and gender ratio between normal and hyperlipidemic groups. All participants were basically healthy and had no history of myocardial infarction, CAD, type 2 diabetes mellitus (T2DM) and ischemic stroke. They were not taking any medicines that could alter serum lipid levels. The selection criteria for Maonan individuals have been described in detail in our previous epidemiological studies 25,26 . In addition, all of the subjects were also confirmed by Y chromosome and mitochondrial diversity studies. All subjects had signed written informed consent. The research protocol was approved by the Ethics Committee of the First Affiliated Hospital, Guangxi Medical University (No. Lunshen-2014 KY-Guoji-001, Mar. 7, 2014). epidemiological analysis. Universally standardized methods and protocols were used to conduct the epidemiological survey 27 . Detailed lifestyle and demographic characteristics were collected with a standard set of questionnaires. Alcohol consumption (0 (non-drinker), < 25 g/day and ≥ 25 g/day) and smoking status (0 (nonsmoker), < 20 cigarettes/day and ≥ 20 cigarettes/day) were divided into three different subgroups. Waist circumference, BMI, height, blood pressure and weight were measured as previously described 28 . Biochemical assays. Fasting venous blood samples of 5 ml were collected from each subject. A portion of the sample (2 ml) was placed in a tube and used to measure serum lipid levels. The remaining sample of 3 ml was collected in a glass tube containing anticoagulants (14.70 g/L glucose, 13.20 g/L trisodium citrate, 4.80 g/L citric acid) and utilized to extract deoxyribonucleic acid (DNA). The methods for performing serum ApoA1, HDL-C, ApoB, TG, LDL-C and TC measurements were described in a previous study 29  (1.20-1.60 g/L), TC (3.10-5.17 mmol/L), TG (0.56-1.70 mmol/L), the ApoA1/ApoB ratio (1.00-2.50) and LDL-C (2.70-3.10 mmol/L) were defined as normal at our Clinical Science Experiment Center. The subjects with TG > 1.70 mmol/L and/or TC > 5.17 mmol/L were defined as hyperlipidaemic 33 . The participants with the fasting plasma (blood) glucose value ≥ 7.0 mmol/L were defined as diabetes 34 . The diagnostic criteria of hypertension 35 , obesity, normal weight and overweight were referred to our previous study 36 . Statistical analyses. All data were evaluated using SPSS (version 22.0). The results are presented as the mean ± SD except for TG levels, which are presented as medians and interquartile ranges. Direct counting was used to determine allele frequency. The independent-samples t test was used to analyse the differences in general characteristics between the normal and hyperlipidaemic groups. The chi-square test was used to determine the genotype distribution between the two groups. HWE, pairwise LD (measured by D′), haplotype frequencies and gene-gene interactions were analysed using Haploview (Broad Institute of MIT and Harvard; version 4.2). The analysis of covariance (ANCOVA) was used to test the correlation between serum lipid parameters and genotypes, and P < 0.0045 (corresponding to P < 0.05 after adjusting for 11 independent tests by Bonferroni correction) was considered statistically significant. Unconditional logistic regression analysis was used to detect the associations between the haplotypes, genotypes and gene-gene interactions and the risk of hyperlipidaemia. Cigarette smoking, gender, blood pressure, BMI, alcohol consumption, blood glucose and age were adjusted for the statistical analysis. The best interaction combination among the SNP-SNP/environment, haplotypehaplotype/environment, and gene-gene/environment exposure interactions was screened with GMDR software (beta version 0.9, University of Virginia, Charlottesville, VA) 37 . GMDR reduces high-dimensional genetic data to a single dimension by exploring interaction models through cross-validation and using maximum likelihood estimates to calculate the score-based statistics of each participant. A CVC value close to 10 indicates that the module is good. The degree of CVC is an effective method for identifying the best model among all considered possibilities. A score between 0.50 (indicating that the model prediction results are no better than chance) and 1.00 (indicating perfect prediction) in the testing of balanced accuracy is an indicator that accurately predicts the extent of case-control status. Additionally, 1,000 permutations were performed to obtain permutated P values for these models. All SNPs, haplotypes and several environmental factors, such as hypertension, diabetes, smoking, drinking, and BMI, were included in the GMDR analysis. All of the above analyses were performed under the additive model adjusted for sex, age, and the study populations. A P value < 0.05 was considered to be statistically significant.