Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia
- Authors:
- Published online on: October 19, 2018 https://doi.org/10.3892/ijmm.2018.3943
- Pages: 57-82
-
Copyright: © Yamada et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Dyslipidemia, including hypertriglyceridemia, hypo-high density lipoprotein (HDL)-cholesterolemia and hyper-low density lipoprotein (LDL)-cholesterolemia, has a substantial genetic component (1-3). Familial hypercholesterolemia is an autosomal dominant disorder characterized by pronounced increases in the circulating concentrations of total cholesterol and LDL-cholesterol (1,2). One of the underlying causes of familial hypercholesterolemia is a defect in the LDL receptor that is responsible for the uptake of most circulating LDL-cholesterol by the liver (2,4). In addition to mutations of the LDL receptor gene (LDLR), familial hypercholesterolemia can be caused by mutations in the apolipoprotein B gene (APOB), proprotein convertase subtilisin/kexin type 9 gene (PCSK9), cytochrome P450 family 7 subfamily A member 1 gene (CYP7A1), and LDL receptor adaptor protein 1 gene (LDLRAP1) (2,4). Common forms of dyslipidemia are multifactorial and polygenic disorders, that result from an interaction between genetic background and both lifestyle and environmental factors, such as consumption of high-fat or high-calorie diets and physical inactivity (1,5). The heritability of plasma concentrations of triglycerides, HDL-cholesterol, or LDL-cholesterol was demonstrated to be 33-43, 40-74, and 41-59%, respectively (6-8). Given that dyslipidemia is a key risk factor for coronary artery disease and ischemic stroke (9,10), as well as for colorectal cancer (11,12), personalized prevention is an important public health goal.
Genome-wide association studies (GWASs) and gene-centric meta-analyses have implicated various genes and loci as determinants of blood lipid levels or of predisposition to dyslipidemia in European-ancestry populations (13-17). Genetic variants associated with lipid profiles have been extensively investigated, with one recent study having identified 157 such loci, including 62 variants not previously reported (18). Recent GWASs (19,20) or studies based on whole-exome (21) or whole-genome (22) sequencing in European-ancestry populations have also identified low-frequency or rare variants associated with circulating lipid levels. A more recent exome-wide association study (EWAS) identified 444 independent variants at 250 loci as being significantly associated with plasma levels of total cholesterol, LDL-cholesterol, HDL-cholesterol, or triglycerides (23). An electronic health record-based study reported that genetic risk scores for circulating LDL-cholesterol and triglyceride levels based on 477 single nucleotide polymorphisms (SNPs) were predictive of age at initiation of treatment with lipid-lowering medication (24). Although various SNPs have been demonstrated to be associated with blood lipid profiles in East Asian (25,26) or Japanese (27) populations, genetic variants that contribute to susceptibility to dyslipidemia in Japanese remain to be identified definitively.
Given the substantial genetic component of the circulating concentrations of triglycerides, HDL-cholesterol, and LDL-cholesterol (6-8), the genetic contribution to early-onset forms of hypertriglyceridemia, hypo-HDL-cholesterolemia, and hyper-LDL-cholesterolemia may be greater compared with late-onset forms (2,3). The statistical power of genetic association studies may thus be increased by focusing on subjects with early-onset forms of these disorders.
The present study performed EWASs for early-onset forms of hypertriglyceridemia, hypo-HDL-cholesterolemia, and hyper-LDL-cholesterolemia with the use of human exome array-based genotyping methods. The aim was to identify genetic variants that confer susceptibility to these conditions in the Japanese population.
Materials and methods
Study subjects
In our previous studies, the median age of subjects with hypertriglyceridemia, hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia was 64, 68 or 62 years, respectively (28). Therefore, in the present study, early-onset dyslipidemia was defined as that occurring at an age of ≤65 years. A total of 8,073 individuals aged ≤65 years were examined. Recruited subjects either visited outpatient clinics or were admitted to participating hospitals in Japan (Gifu Prefectural Tajimi Hospital, Tajimi; Gifu Prefectural General Medical Center, Gifu; Japanese Red Cross Nagoya First Hospital, Nagoya; Northern Mie Medical Center Inabe General Hospital, Inabe; Hirosaki University Hospital and Hirosaki Stroke and Rehabilitation Center, Hirosaki) because of various symptoms or for an annual health checkup between 2002 and 2014, or were community-dwelling individuals recruited to a population-based cohort study in Inabe between 2010 and 2014 (29).
Venous blood was collected in the early morning after subjects had fasted overnight. The blood samples were centrifuged at 1,600 x g for 15 min at 4°C, and serum was separated for subsequent analysis. Serum concentrations of triglycerides, HDL-cholesterol, and LDL-cholesterol were measured in the clinical laboratory of each hospital. The 2,664 subjects with hypertriglyceridemia and 5,294 controls had serum triglyceride concentrations of ≥1.69 mmol/l (150 mg/dl) and <1.69 mmol/l, respectively. The 974 subjects with hypo-HDL-cholesterolemia and 7,085 controls had serum HDL-cholesterol concentrations of <1.03 mmol/l (40 mg/dl) and ≥1.03 mmol/l, respectively. The 2,911 subjects with hyper-LDL-cholesterolemia and 5,111 controls had serum LDL-cholesterol concentrations of ≥3.62 mmol/l (140 mg/dl) and <3.62 mmol/l, respectively. The 562 subjects with both hypertriglyceridemia and hypo-HDL-cholesterolemia, and the 4,907 controls, overlapped between the corresponding studies, as did the 1,312 subjects with both hypertriglyceridemia and hyper-LDL-cholesterolemia and 3,723 controls, as well as the 317 subjects with both hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia and 4,484 controls. Individuals with single-gene disorders, such as familial hypercholesterolemia, or with endocrinologic or metabolic diseases that cause dyslipidemia were excluded from the study. Those taking medications that may cause secondary dyslipidemia were also excluded.
EWAS
Venous blood was collected into tubes containing 50 mmol/l EDTA (disodium salt), peripheral blood leukocytes were isolated, and genomic DNA was extracted from these cells with the use of a kit (Genomix, Talent Srl, Trieste, Italy; or SMITEST EX-R&D, Medical & Biological Laboratories, Co., Ltd., Nagoya, Japan). EWASs for hypertriglyceridemia (2,664 cases and 5,294 controls), hypo-HDL-cholesterolemia (974 cases and 7,085 controls), and hyper-LDL-cholesterolemia (2,911 cases and 5,111 controls) were performed with Human Exome-12 v1.2 DNA Analysis BeadChip or Infinium Exome-24 v1.0 BeadChip arrays (Illumina, San Diego, CA, USA). These exome arrays include putative functional exonic variants selected from ~12,000 individual exome and whole-genome sequences. The exonic content consists of ~244,000 SNPs from diverse populations, including European, African, Chinese, and Hispanic individuals (30). SNPs contained in only one of the arrays (~2.6% of all SNPs) were excluded from analysis. Quality control was performed, as previously described (31). Briefly, genotyping data with a call rate of <97% were discarded, with the mean call rate for the remaining data being 99.9%. Gender specification was checked for all samples, and those for which gender phenotype in the clinical records was inconsistent with genetic sex were discarded. Duplicated samples and cryptic relatedness were checked by calculation of identity by descent; all pairs with a value of >0.1875 were inspected and one sample from each pair was excluded. Heterozygosity of SNPs was calculated for all samples, and those with extremely low or high heterozygosity (>3 standard deviations from the mean) were discarded. SNPs in sex chromosomes or mitochondrial DNA were excluded from analysis, as were non-polymorphic SNPs or SNPs with a minor allele frequency of <1.0%. SNPs whose genotype distributions deviated significantly (P<0.01) from Hardy-Weinberg equilibrium in control individuals were discarded. Finally, genotype data for each EWAS were examined for population stratification by principal components analysis (32), and population outliers were excluded from further study. A total of 31,198, 31,133, and 31,175 SNPs passed quality control in the EWASs for hypertriglyceridemia, hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia, respectively, and were subjected to further analyses.
Statistical analysis
For analysis of characteristics of the study subjects, quantitative and categorical data were compared between cases and controls with the unpaired Student’s t-test and Pearson’s Chi-square test, respectively. Allele frequencies were estimated by the gene counting method, and departure from Hardy-Weinberg equilibrium was identified with Fisher’s exact test. The association of allele frequencies of SNPs to hypertriglyceridemia, hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia in the EWASs was examined with Fisher’s exact test. To compensate for multiple comparisons of allele frequencies with hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia, Bonferroni’s correction was applied for statistical significance of association. Given that 31,198, 31,133 or 31,175 SNPs were analyzed, a P-value of <1.60×10−6 [0.05/(31,198 or 31,175)] for hypertriglyceridemia and hyper-LDL-cholesterolemia and a P-value of <1.61×10−6 (0.05/31,133) for hypo-HDL-cholesterolemia was considered statistically significant for association. The inflation factor (λ) was 1.07 for hypertriglyceridemia, 1.05 for hypo-HDL-cholesterolemia, and 1.00 for hyper-LDL-cholesterolemia. Multivariable logistic regression analysis was performed with hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia as a dependent variable and independent variables including age, sex (0, woman; 1, man), and genotype of each SNP. Genotypes of SNPs were assessed according to dominant [0, AA; 1, AB + BB (A, major allele; B, minor allele)], recessive (0, AA + AB; 1, BB), and additive genetic models, and the P-value, odds ratio, and 95% confidence interval were calculated. Additive models comprised additive 1 (0, AA; 1, AB; 0, BB) and additive 2 (0, AA; 0, AB; 1, BB) scenarios, which were analyzed simultaneously with a single statistical model. Associations of genotypes of identified SNPs to serum concentrations of triglycerides, HDL-cholesterol, or LDL-cholesterol were examined by one-way analysis of variance. Bonferroni’s correction was also applied to other statistical analysis as indicated. Statistical tests were performed with JMP Genomics version 9.0 software (SAS Institute, Cary, NC, USA).
Results
Characteristics of subjects
The characteristics of the 7,958 subjects enrolled in the hypertriglyceridemia study are listed in Table I. Age, the frequency of men, and the prevalence of smoking, obesity, hypertension, diabetes mellitus, chronic kidney disease, and hyperuricemia as well as body mass index (BMI), systolic and diastolic blood pressure (BP), fasting plasma glucose (FPG) level, blood glycosylated hemoglobin (hemoglobin A1c) content, and serum concentrations of creatinine and uric acid were greater, whereas the serum concentration of HDL-cholesterol and estimated glomerular filtration rate (eGFR) were lower, in subjects with hypertriglyceridemia compared with controls.
The characteristics of the 8,059 subjects enrolled in the hypo-HDL-cholesterolemia study are listed in Table II. Age, the frequency of men, and the prevalence of smoking, obesity, hypertension, diabetes mellitus, chronic kidney disease, and hyperuricemia as well as BMI, systolic and diastolic BP, FPG level, blood hemoglobin A1c content, and serum concentrations of triglycerides, creatinine, and uric acid were greater, whereas eGFR was lower, in subjects with hypo-HDL-cholesterolemia compared with controls.
The characteristics of the 8,022 subjects enrolled in the hyper-LDL-cholesterolemia study are listed in Table III. Age, the prevalence of obesity, BMI, diastolic BP, and serum concentrations of triglycerides and uric acid were greater, whereas systolic BP and serum concentrations of HDL-cholesterol and creatinine were lower, in subjects with hyper-LDL-cholesterolemia compared with controls.
EWASs for hypertriglyceridemia, hypo-HDL-cholesterolemia, and hyper-LDL-cholesterolemia
The association of allele frequencies for 31,198 SNPs that passed quality control to hyper-triglyceridemia was examined with the use of Fisher’s exact test. Following Bonferroni’s correction, 25 SNPs were significantly associated with hypertriglyceridemia (P<1.60×10−6; Table IV). Similar analysis of the association of allele frequencies for 31,133 SNPs to hypo-HDL-cholesterolemia or of those for 31,175 SNPs to hyper-LDL-cholesterolemia revealed that 28 SNPs were significantly associated with hypo-HDL-cholesterolemia (P<1.61×10−6; Table V) and 65 SNPs with hyper-LDL-cholesterolemia (P<1.60×10−6; Table VI).
Table IVThe 25 SNPs significantly associated with hypertriglyceridemia in the exome-wide association study. |
Table VThe 28 SNPs significantly associated with hypo-HDL-cholesterolemia in the exome-wide association study. |
Table VIThe 65 SNPs significantly associated with hyper-LDL-cholesterolemia in the exome-wide association study. |
Multivariable logistic regression analysis of the association of SNPs to hypertriglyceridemia, hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia
The association of the 25 SNPs identified in the EWAS for hypertriglyceridemia to this condition was further examined by multivariable logistic regression analysis, following adjustment for age and sex. All 25 SNPs were significantly [P<0.0005 (0.05/100) in at least one genetic model] associated with hypertriglyc-eridemia (Table VII). Similar analysis revealed that all 28 SNPs identified in the EWAS for hypo-HDL-cholesterolemia [P<0.0004 (0.05/112); Table VIII] and all 65 SNPs identified in the EWAS for hyper-LDL-cholesterolemia [P<0.0002 (0.05/260); Table IX] were significantly associated with the respective conditions.
Table VIIAssociation of SNPs to hypertriglyceridemia as determined by multivariable logistic regression analysis. |
Table VIIIAssociation of SNPs to hypo-HDL-cholesterolemia as determined by multivariable logistic regression analysis. |
Table IXAssociation of SNPs to hyper-LDL-cholesterolemia as determined by multivariable logistic regression analysis. |
Association of identified SNPs to serum concentrations of triglycerides, HDL-cholesterol or LDL-cholesterol
The association of the genotypes of identified SNPs to serum concentrations of triglycerides, HDL-cholesterol, or LDL-cholesterol was examined by one-way analysis of variance. The 25 SNPs identified in the EWAS for hypertriglyceridemia were all significantly associated with serum triglyceride concentration [P<0.0020 (0.05/25); Table X]. Among the 28 SNPs identified in the EWAS for hypo-HDL-cholesterolemia, 27 polymorphisms were significantly associated with the serum HDL-cholesterol level [P<0.0018 (0.05/28)], whereas rs114501427 of SPOPL was not related to this parameter (Table XI). Among the 65 SNPs identified in the EWAS for hyper-LDL-cholesterolemia, 37 SNPs were significantly associated with serum LDL-cholesterol concentration [P<0.0008 (0.05/65); Table XII]. It is possible that the lack of significant correlation between the remaining 28 SNPs and the serum LDL-cholesterol levels may be due to effects of medical treatment.
Table XIIAssociation of SNPs identified in the present study to serum LDL-cholesterol concentration. |
Linkage disequilibrium analysis
The linkage disequilibrium (LD) was assessed among SNPs associated with hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia. For the hypertriglyceridemia study, strong LD was apparent among rs1260326 and rs780093 of GCKR and rs1260333 at chromosome 2p23 [square of the correlation coefficient (r2), 0.876-0.983]. An LD plot for the 12 SNPs located at chromosomal region 8p21.3 is illustrated in Fig. 1. Strong LD was observed among rs301, rs326, rs13702, and rs15285 of LPL, rs2083637 and rs1441756 (r2, 0.942-0.988), as well as among rs328 of LPL, rs10096633, rs17482753, rs12678919, rs10503669 and rs7016880 (r2, 0.910-0.999). An LD plot for the eight SNPs located at chromosomal region 11q23.3 is illustrated in Fig. 2. Significant LD was apparent among rs10790162 of BUD13, rs7350481, rs9326246, both rs964184 and rs2075290 of ZPR1, and rs2266788 of APOA5 (r2, 0.687-0.990).
For the hypo-HDL-cholesterolemia study, significant LD was apparent between rs139537100 of MOB3C and rs115287176 of TMOD4 (r2, 0.984); between rs116417209 of EHD3 and rs114501427 of SPOPL (r2, 0.703); between rs140232911 of ZNF860 and rs35874056 of CACNA1D (r2, 1.00); among rs192210727 of ADGRL3, rs188378669 of CXCL8, and rs61734696 of MARCH1 (r2, 0.901 to 0.972); between rs199921354 of VPS33B and rs141569282 of OR4F6 (r2, 0.994); and between rs247616 and rs3764261 at chromosome 16q13 (r2, 0.992). An LD plot for the five SNPs located at chromosomal region 12q24.12 to 12q24.13 is illustrated in Fig. 3. Significant LD was detected among rs3782886 of BRAP, rs11066015 of ACAD10, rs671 of ALDH2, and rs2074356 and rs11066280 of HECTD4 (r2, 0.813-0.995).
For the hyper-LDL-cholesterolemia study, significant LD was apparent among rs147284320 of PTCH2, rs139537100 of MOB3C and rs115287176 of TMOD4 (r2, 0.828-0.984); among rs192210727 of ADGRL3, rs188378669 of CXCL8, and rs61734696 of MARCH1 (r2, 0.901-0.972); and between rs200787930 of PLCB2 and rs199921354 of VPS33B (r2, 0.994). An LD plot for the 47 SNPs located at chromosomal region 6p22.3 to 6p21.3 is illustrated in Fig. 4. There were two major LD blocks. The first LD block (r2, 0.571-1.000) comprised rs7749235, rs2285321, and rs6937357 of DHX16; rs9468805, rs6457254, and rs2394392 of PPP1R18; rs3130663 at 6p21.3; rs2269702, rs28986465, rs2075015, and rs6924270 of MDC1; and rs3132584, rs25527, and rs9500864 of TUBB. The second LD block (r2, 0.762-1.000) comprised rs147733073 of CCHCR1; rs2596574 at 6p21.3; rs11538264 of PRRC2A; rs9267546, rs17200983, and rs9267547 of LY6G6F; rs117894946 of LY6G6C; rs11754464 of MSH5; rs5030798 and rs11751198 of VARS; rs6457452 of HSPA1B; rs11968400 of C6orf48; rs12210887 at 6p21.3; rs13118 of NEU1; rs117127493 of SLC44A4; rs492899 of SKIV2L; rs140770834 and rs11751545 of TNXB; and rs204999 at 6p21.3. Significant LD was also observed between rs4576240 of KIAA0319 and rs150142878 of FAM65B (r2, 0.893); among rs34902660 of SLC17A3, rs76463649 of ZSCAN26, rs6922302 of ZSCAN31, rs29243 of GABBR1, and rs2523995 of TRIM40 (r2, 0.601-0.995); between rs64036 of UBD and rs2524272 at 6p22.1 (r2, 0.753); and among rs6933400 and rs11970154 of DPCR1, rs2508015 at 6p21.3, and rs147733073 of CCHCR1 (r2, 0.596-0.987).
Relation of SNPs identified in the present study to dyslipidemia-related phenotypes examined in previous GWASs
The relation of genes, chromosomal loci and SNPs identified in the present study was further analyzed against phenotypes previously examined by GWASs available in the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) Search v.2.0.0.0 (https://grasp.nhlbi.nih.gov/Search.aspx) developed by the Information Technology and Applications Center, National Center for Biotechnology Information, National Heart, Lung, and Blood Institute, National Institutes of Health (Bethesda, MD, USA) (33,34).
In the hypertriglyceridemia study, GCKR, chromosomal region 2p23, LPL, region 8p21.3, region 8q24.2, BUD13, region 11q23.3, ZPR1, APOA5, and APOA4 have been previously reported to be associated with circulating triglyceride concentrations, whereas chromosome 19p12 had not been related to triglyceride levels or other dyslipidemia-related traits (Table XIII).
Table XIIIRelation of genes, chromosomal loci, and SNPs associated with hypertriglyceridemia in the present study to previously examined dyslipidemia-related phenotypes. |
In the study of hypo-HDL-cholesterolemia, APOA5, BRAP, ALDH2, HECTD4, chromosome 16q13, CETP, and LILRB2 were previously reported to be associated with circulating HDL-cholesterol levels, whereas CXCL8, ACAD10, and PLCB2 were associated with circulating levels of total cholesterol, LDL-cholesterol, and triglycerides, respectively (Table XIV). MARCH1, chromosome 6p22.1, and VPS33B were also associated with adiponectin concentrations, type 1 diabetes mellitus, and type 2 diabetes mellitus, respectively. The remaining 13 genes (MOB3C, TMOD4, LPGAT1, EHD3, SPOPL, COL6A3, ZNF860, CACNA1D, COL6A5, ADGRL3, DCLRE1C, OR4F6, ZNF77) had not been related to circulating HDL-cholesterol or other dyslipidemia-related phenotypes.
Table XIVRelation of genes, chromosomal loci and SNPs associated with hypo-HDL-cholesterolemia in the present study to previously examined dyslipidemia-related phenotypes. |
In the study of hyper-LDL-cholesterolemia, PCSK9, APOB, chromosome 6p21.3, SKIV2L, HECTD4, APOE, and APOC1 were previously reported to be associated with circulating LDL-cholesterol levels, whereas CXCL8, 6p21.3, CCHCR1, PRRC2A, LY6G6F, MSH5, C6orf48, SLC44A4, TNXB, and PLCB2 were associated with circulating concentrations of total cholesterol or triglycerides (Table XV). MARCH1, SLC17A3, ZSCAN31, GABBR1, 6p22.1, TRIM40, PPP1R18, 6p21.3, MDC1, TUBB, DPCR1, LY6G6C, VARS, HSPA1B, and VPS33B were associated with diabetes mellitus-associated phenotypes. The remaining 16 genes (PTCH2, MOB3C, TMOD4, COL6A3, COL6A5, ADGRL3, KIAA0319, FAM65B, ZSCAN26, UBD, DHX16, LOC105375015, NEU1, MUC17, GIT2, ZNF77) had not been related to circulating LDL-cholesterol or other dyslipidemia-related phenotypes.
Table XVRelation of genes, chromosomal loci and SNPs associated with hyper-LDL-cholesterolemia in the present study to previously examined dyslipidemia-related phenotypes. |
Network analysis of genes identified in the present study
Network analysis of the 10 genes (MOB3C, TMOD4, LPGAT1, EHD3, COL6A3, ZNF860, CACNA1D, COL6A5, DCLRE1C, ZNF77) or three genes (KIAA0319, FAM65B, UBD) associated in the present study with hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia, respectively, was performed to predict functional gene-gene interactions, with the use of the GeneMANIA Cytoscape plugin (http://apps.cytoscape.org/apps/genemania; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada) (35-37) and Cytoscape v3.4.0 software (http://www.cytoscape.org; The Cytoscape Consortium, San Diego, CA, USA) (38). Given that LOC105375015 protein has not been characterized, it was not examined. A set of 50 dyslipidemia-related genes were selected from the DisGeNET database (http://www.disgenet.org/web/DisGeNET; Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Barcelona Biomedical Research Park, Barcelona, Spain) (39,40) according to the rank order (high to low) of scores for association with dyslipidemia. The network analysis revealed that the 10 or three genes associated in the present study with hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia, respectively, have potential direct or indirect interactions with the 50 genes previously demonstrated to be associated with dyslipidemia (Fig. 5).
Discussion
Dyslipidemia is an important public health problem because of its high prevalence and role as a risk factor for more serious conditions, including coronary artery disease, ischemic stroke, and colorectal cancer (9-12). The identification of genetic variants that confer susceptibility to dyslipidemia is thus clinically important for prevention of these conditions. The present study has performed novel EWASs for hypertriglyceridemia, hypo-HDL-cholesterolemia, and hyper-LDL-cholesterolemia in subjects with early-onset forms of these disorders, which may have a greater genetic component compared with late-onset forms.
Among 25 SNPs in six genes and five chromosomal loci significantly associated with early-onset hypertriglyceridemia, the present study newly identified rs11085421 at 19p12 as a susceptibility locus for this condition. This SNP was significantly associated with serum triglyceride concentration, with the minor C allele being protective against hypertriglyceridemia.
Among 28 SNPs in 24 genes and two chromosomal loci significantly associated with hypo-HDL-cholesterolemia, the current study newly identified 13 genes (MOB3C, TMOD4, LPGAT1, EHD3, SPOPL, COL6A3, ZNF860, CACNA1D, COL6A5, ADGRL3, DCLRE1C, OR4F6, ZNF77) as potential susceptibility loci for this condition. SNPs of 12 of these genes were significantly associated with serum HDL-cholesterol levels, with minor alleles of LPGAT1, EHD3, ZNF860, CACNA1D, and DCLRE1C representing risk factors for hypo-HDL-cholesterolemia and minor alleles of MOB3C, TMOD4, COL6A3, COL6A5, ADGRL3, OR4F6, and ZNF77 being protective against this condition. Given that rs114501427 of SPOPL was not significantly related to serum HDL-cholesterol concentration, this gene was excluded as a novel susceptibility locus. Furthermore, given that significant LD was apparent among SNPs of ADGRL3, CXCL8, and MARCH1, as well as between SNPs of OR4F6 and VPS33B, ADGRL3 and OR4F6 were also removed from the final list of novel significant loci. In addition, significant LD was detected between SNPs of MOB3C and TMOD4 as well as between those of ZNF860 and CACNA1D. We thus identified eight novel loci (MOB3C-TMOD4, LPGAT1, EHD3, COL6A3, ZNF860-CACNA1D, COL6A5, DCLRE1C, ZNF77) that confer susceptibility to early-onset hypo-HDL-cholesterolemia.
Among 65 SNPs in 44 genes and two chromosomal loci significantly associated with hyper-LDL-cholesterolemia, the present study newly identified 16 genes (PTCH2, MOB3C, TMOD4, COL6A3, COL6A5, ADGRL3, KIAA0319, FAM65B, ZSCAN26, UBD, DHX16, LOC105375015, NEU1, MUC17, GIT2, ZNF77) as potential susceptibility loci for this condition. SNPs of six of these genes (KIAA0319, FAM65B, ZSCAN26, UBD, LOC105375015, NEU1) were significantly associated with serum LDL-cholesterol levels, with minor alleles of these genes representing risk factors for hyper-LDL-cholesterolemia. Given that SNPs of 10 genes (PTCH2, MOB3C, TMOD4, COL6A3, COL6A5, ADGRL3, DHX16, MUC17, GIT2, ZNF77) were not significantly related to the serum LDL-cholesterol concentration, these genes were removed from the list of novel susceptibility loci, even though this discrepancy may be attributable to the effects of lipid-lowering medication. Given that rs13118 of NEU1 was in significant LD with rs492899 of SKIV2L in a large LD block and that rs76463649 of ZSCAN26 was in LD with SNPs of SLC17A3, ZSCAN31, GABBR1, and TRIM40, both NEU1 and ZSCAN26 were also excluded from the final results. In addition, significant LD was detected between SNPs of KIAA0319 and FAM65B. We thus identified three novel loci (KIAA0319-FAM65B, UBD, LOC105375015) that confer susceptibility to early-onset hyper-LDL-cholesterolemia.
A recent study has reported that network analysis of functional gene-gene interactions may be informative with regard both to clarification of biological processes underlying coronary artery disease and to the identification of therapeutic targets for this condition (41). A network analysis was therefore performed in the present study in order to predict biological processes related to the identified genes and the interactions of these genes with those previously known to be associated with dyslipidemia. The results from the network analysis revealed that the 10 or three genes associated in the present study with hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia respectively, had direct or indirect interactions with the 50 dyslipidemia-related genes selected from the DisGeNET database (39,40). The underlying molecular mechanisms of these interactions, however, remain to be elucidated.
Our group has previously reported that two, three and nine SNPs were associated with hypertriglyceridemia (P<1.71×10−4), hypo-HDL-cholesterolemia (P<1.44×10−4), and hyper-LDL-cholesterolemia (P<1.10×10−4), respectively, as determined by multivariable logistic regression analysis with adjustment for age and sex, following an initial EWAS screening of allele frequencies among subjects with early- or late-onset forms of these conditions (28). The associations of the two SNPs [rs10790162 (P=3.58×10−24) and rs7350481 (P=7.94×10−23)] with hypertriglyceridemia were replicated (P<0.05) in the present study. The associations of two of the three SNPs [rs147317864 (P=0.0139) and rs12229654 (P= 0.0001)] with hypo-HDL-cholesterolemia were replicated in the present study. The associations of eight of the nine SNPs [rs7771335 (P=1.00×10−7), rs2071653 (P=1.61×10−5), rs2853969 (P= 6.16×10−8), rs2269704 (P=2.92×10−7), rs2269703 (P=3.67×10 −7), rs495089 (P= 0.0 0 01), rs2269702 (P=5.13×10−7) and rs1233399 (P=0.0009)] with hyper-LDL-cholesterolemia were replicated in the present study. Although the association of most SNPs with dyslipidemia identified in our previous study (28) was replicated, genetic variants associated with hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia appear to differ, at least in part, between early- and late-onset forms of the diseases.
There are several limitations to the present study. Firstly, given that the results were not replicated, their validation will be necessary in independent study populations or in other ethnic groups. The identified SNPs were not tested in a clinical setting to validate the assessment of genetic risk for dyslipidemia in patients. In addition, it is possible that SNPs identified in the present study may be in LD with other genetic variants in the same gene or in nearby genes that are actually responsible for the development of hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia. Finally, the functional relevance of identified SNPs to the pathogenesis of hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia remains to be elucidated.
In conclusion, the present study has identified chromosome 19p12, eight loci (MOB3C-TMOD4, LPGAT1, EHD3, COL6A3, ZNF860-CACNA1D, COL6A5, DCLRE1C, ZNF77) and three loci (KIAA0319-FAM65B, UBD, LOC105375015) that confer susceptibility to early-onset hypertriglyceridemia, hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia, respectively. Determination of genotypes for the SNPs at these loci may prove informative for assessment of genetic risk for hypertriglyceridemia, hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia in the Japanese population.
Acknowledgments
Not applicable.
Funding
This work was supported by CREST, Japan Science and Technology Agency (grant no. JPMJCR1302; to YYam, JS and IT).
Availability of data and materials
All data underlying the findings described in the article are available on request from the corresponding author.
Authors’ contributions
YYam contributed to conception and design of the study; to acquisition, analysis, and interpretation of the data; and to drafting of the manuscript. KK, MO, HH, and TF each contributed to acquisition of the data and to revision of the manuscript. YYas, IT, and JS contributed to analysis and interpretation of the data as well as to revision of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study protocol complied with the Declaration of Helsinki and was approved by the Committees on the Ethics of Human Research of Mie University Graduate School of Medicine, Hirosaki University Graduate School of Medicine, and participating hospitals (Gifu Prefectural Tajimi Hospital, Gifu Prefectural General Medical Center, Japanese Red Cross Nagoya First Hospital, Northern Mie Medical Center Inabe General Hospital, and Hirosaki Stroke and Rehabilitation Center). Written informed consent for participation in the study was obtained from all subjects.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
Durrington P: Dyslipidaemia. Lancet. 362:717–731. 2003. View Article : Google Scholar : PubMed/NCBI | |
Paththinige CS, Sirisena ND and Dissanayake V: Genetic determinants of inherited susceptibility to hypercholesterolemia-a comprehensive literature review. Lipids Health Dis. 16:1032017. View Article : Google Scholar | |
Dron JS and Hegele RA: Genetics of triglycerides and the risk of atherosclerosis. Curr Atheroscler Rep. 19:312017. View Article : Google Scholar : PubMed/NCBI | |
Soutar AK and Naoumova RP: Mechanism of disease: Genetic causes of familial hypercholesterolemia. Nat Clin Pract Cardiovasc Med. 4:214–225. 2007. View Article : Google Scholar : PubMed/NCBI | |
Heller DA, DeFaire U, Pedersen N, Dahlén G and McClearn GE: Genetic and environmental influences on serum lipid levels in twins. N Engl J Med. 328:1150–1156. 1993. View Article : Google Scholar : PubMed/NCBI | |
Abney M, McPeek MS and Ober C: Broad and narrow herita-bilities of quantitative traits in a founder population. Am J Hum Genet. 68:1302–1307. 2001. View Article : Google Scholar : PubMed/NCBI | |
van Dongen J, Willemsen G, Chen WM, de Geus EJ and Boomsma DI: Heritability of metabolic syndrome traits in a large population-based sample. J Lipid Res. 54:2914–2923. 2013. View Article : Google Scholar : PubMed/NCBI | |
Woo JG, Morrison JA, Stroop DM, Aronson Friedman L and Martin LJ: Genetic architecture of lipid traits changes over time and differs by race: Princeton Lipid Follow-up Study. J Lipid Res. 55:1515–1524. 2014. View Article : Google Scholar : PubMed/NCBI | |
Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R, et al: Heart disease and stroke statistics-2018 update: A report from the American Heart Association. Circulation. 137:e67–e492. 2018. View Article : Google Scholar : PubMed/NCBI | |
Law MR, Wald NJ and Rudnicka AR: Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: Systematic review and meta-analysis. Br Med J. 326:14232003. View Article : Google Scholar | |
Agnoli C, Grioni S, Sieri S, Sacerdote C, Vineis P, Tumino R, Giurdanella MC, Pala V, Mattiello A, Chiodini P, et al: Colorectal cancer risk and dyslipidemia: A case-cohort study nested in an Italian multicentre cohort. Cancer Epidemiol. 38:144–151. 2014. View Article : Google Scholar : PubMed/NCBI | |
Yao X and Tian Z: Dyslipidemia and colorectal cancer risk: A meta-analysis of prospective studies. Cancer Causes Control. 26:257–268. 2015. View Article : Google Scholar | |
Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, et al: Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 40:189–197. 2008. View Article : Google Scholar : PubMed/NCBI | |
Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, Kaplan L, Bennett D, Li Y, Tanaka T, et al: Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 41:56–65. 2009. View Article : Google Scholar | |
Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, Penninx BW, Janssens AC, Wilson JF, Spector T, et al: Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 41:47–55. 2009. View Article : Google Scholar : | |
Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, et al: Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 466:707–713. 2010. View Article : Google Scholar : PubMed/NCBI | |
Asselbergs FW, Guo Y, van Iperen EP, Sivapalaratnam S, Tragante V, Lanktree MB, Lange LA, Almoguera B, Appelman YE, Barnard J, et al: Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet. 91:823–838. 2012. View Article : Google Scholar : PubMed/NCBI | |
Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al: Discovery and refinement of loci associated with lipid levels. Nat Genet. 45:1274–1283. 2013. View Article : Google Scholar : PubMed/NCBI | |
Peloso GM, Auer PL, Bis JC, Voorman A, Morrison AC, Stitziel NO, Brody JA, Khetarpal SA, Crosby JR, Fornage M, et al: Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am J Hum Genet. 94:223–232. 2014. View Article : Google Scholar : PubMed/NCBI | |
Surakka I, Horikoshi M, Mägi R, Sarin AP, Mahajan A, Lagou V, Marullo L, Ferreira T, Miraglio B, Timonen S, et al: The impact of low-frequency and rare variants on lipid levels. Nat Genet. 47:589–597. 2015. View Article : Google Scholar : PubMed/NCBI | |
Lange LA, Hu Y, Zhang H, Xue C, Schmidt EM, Tang ZZ, Bizon C, Lange EM, Smith JD, Turner EH, et al: Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet. 94:233–245. 2014. View Article : Google Scholar : PubMed/NCBI | |
Helgadottir A, Gretarsdottir S, Thorleifsson G, Hjartarson E, Sigurdsson A, Magnusdottir A, Jonasdottir A, Kristjansson H, Sulem P, Oddsson A, et al: Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease. Nat Genet. 48:634–639. 2016. View Article : Google Scholar : PubMed/NCBI | |
Liu DJ, Peloso GM, Yu H, Butterworth AS, Wang X, Mahajan A, Saleheen D, Emdin C, Alam D, Alves AC, et al: Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet. 49:1758–1766. 2017. View Article : Google Scholar : PubMed/NCBI | |
Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, Kvale MN, Kwok PY, Schaefer C, Krauss RM, et al: A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet. 50:401–413. 2018. View Article : Google Scholar : PubMed/NCBI | |
Peloso Lu X, Liu GM, Wu DJ, Zhang Y, Zhou H, Li W, Tang J, Dorajoo CS, Li RH, et al: Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat Genet. 49:1722–1730. 2017. View Article : Google Scholar : PubMed/NCBI | |
Spracklen CN, Chen P, Kim YJ, Wang X, Cai H, Li S, Long J, Wu Y, Wang YX, Takeuchi F, et al: Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum Mol Genet. 26:1770–1784. 2017. View Article : Google Scholar : PubMed/NCBI | |
Kurano M, Tsukamoto K, Kamitsuji S, Kamatani N, Hara M, Ishikawa T, Kim BJ, Moon S, Jin Kim Y and Teramoto T: Genome-wide association study of serum lipids confirms previously reported associations as well as new associations of common SNPs within PCSK7 gene with triglyceride. J Hum Genet. 61:427–433. 2016. View Article : Google Scholar : PubMed/NCBI | |
Yamada Y, Sakuma J, Takeuchi I, Yasukochi Y, Kato K, Oguri M, Fujimaki T, Horibe H, Muramatsu M, Sawabe M, et al: Identification of eight genetic variants as novel determinants of dyslipidemia in Japanese by exome-wide association studies. Oncotarget. 8:38950–38961. 2017.PubMed/NCBI | |
Yamada Y, Matsui K, Takeuchi I, Oguri M and Fujimaki T: Association of genetic variants with hypertension in a longitudinal population-based genetic epidemiological study. Int J Mol Med. 35:1189–1198. 2015. View Article : Google Scholar : PubMed/NCBI | |
Grove ML, Yu B, Cochran BJ, Haritunians T, Bis JC, Taylor KD, Hansen M, Borecki IB, Cupples LA, Fornage M, et al: Best practices and joint calling of the HumanExome BeadChip: The CHARGE Consortium. PLoS One. 8:e680952013. View Article : Google Scholar : PubMed/NCBI | |
Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP and Zondervan KT: Data quality control in genetic case-control association studies. Nat Protoc. 5:1564–1573. 2010. View Article : Google Scholar : PubMed/NCBI | |
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA and Reich D: Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 38:904–909. 2006. View Article : Google Scholar : PubMed/NCBI | |
Leslie R, O’Donnell CJ and Johnson AD: GRASP: Analysis of genotype-phenotype results from 1,390 genome-wide association studies and corresponding open access database. Bioinformatics. 30:i185-i1942014. View Article : Google Scholar | |
Eicher JD, Landowski C, Stackhouse B, Sloan A, Chen W, Jensen N, Lien JP, Leslie R and Johnson AD: GRASP v2.0: An update on the genome-wide repository of associations between SNPs and phenotypes. Nucleic Acids Res. 43(Database Issue): D799–D804. 2015. View Article : Google Scholar : | |
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al: The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38:W214–W220. 2010. View Article : Google Scholar : PubMed/NCBI | |
Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q and Bader GD: GeneMANIA cytoscape plugin: Fast gene function predictions on the desktop. Bioinformatics. 26:2927–2928. 2010. View Article : Google Scholar : PubMed/NCBI | |
Montojo J, Zuberi K, Rodriguez H, Bader GD and Morris Q: GeneMANIA: Fast gene network construction and function prediction for Cytoscape. F1000Res. 3:1532014.PubMed/NCBI | |
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI | |
Piñero J, Queralt-Rosinach N, Bravo À, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F and Furlong LI: DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015. bav0282015. View Article : Google Scholar | |
Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, García-García J, Sanz F and Furlong LI: DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45:D833–D839. 2017. View Article : Google Scholar : | |
Lempiäinen H, Brænne I, Michoel T, Tragante V, Vilne B, Webb TR, Kyriakou T, Eichner J, Zeng L, Willenborg C, et al: Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets. Sci Rep. 8:34342018. View Article : Google Scholar : PubMed/NCBI |