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Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia

  • Authors:
    • Yoshiji Yamada
    • Kimihiko Kato
    • Mitsutoshi Oguri
    • Hideki Horibe
    • Tetsuo Fujimaki
    • Yoshiki Yasukochi
    • Ichiro Takeuchi
    • Jun Sakuma

  • View Affiliations

  • 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.

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Abstract

The circulating concentrations of triglycerides, high density lipoprotein (HDL)‑cholesterol, and low density lipoprotein (LDL)‑cholesterol have a substantial genetic component, and the heritability of early‑onset dyslipidemia might be expected to be higher compared with late‑onset forms. In the present study, exome‑wide association studies (EWASs) were performed for early‑onset hypertriglyceridemia, hypo‑HDL‑cholesterolemia, and hyper‑LDL‑cholesterolemia, with the aim to identify genetic variants that confer susceptibility to these conditions in the Japanese population. A total of 8,073 individuals aged ≤65 years were enrolled in the study. The 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 Illumina Human Exome‑12 v1.2 DNA Analysis BeadChip or Infinium Exome‑24 v1.0 BeadChip arrays. The association of allele frequencies for 31,198, 31,133, or 31,175 single nucleotide polymorphisms (SNPs) to hypertriglyceridemia, hypo‑HDL‑cholesterolemia, or hyper‑LDL‑cholesterolemia, respectively, was examined with Fisher's exact test. To compensate for multiple comparisons of genotypes with each of the three conditions, Bonferroni's correction was applied for statistical significance of association. The results demonstrated that 25, 28 and 65 SNPs were significantly associated with hypertriglyceridemia, hypo‑HDL‑cholesterolemia and hyper‑LDL‑cholesterolemia, respectively. Multivariable logistic regression analysis with adjustment for age and sex revealed that all 25, 28 and 65 of these SNPs were significantly associated with hypertriglyceridemia, hypo‑HDL‑cholesterolemia and hyper‑LDL‑cholesterolemia, respectively. Following examination of the association of the identified SNPs to serum concentrations of triglycerides, HDL‑cholesterol, or LDL‑cholesterol, linkage disequilibrium of the SNPs, and results of previous genome‑wide association studies, we newly identified chromosomal region 19p12 as a susceptibility locus for hypertriglyceridemia, eight loci (MOB3C‑TMOD4, LPGAT1, EHD3, COL6A3, ZNF860‑CACNA1D, COL6A5, DCLRE1C, ZNF77) for hypo‑HDL‑cholesterolemia, and three loci (KIAA0319‑FAM65B, UBD, LOC105375015) for hyper‑LDL‑cholesterolemia. The present study thus identified 12 novel loci that may confer susceptibility to early‑onset dyslipidemia. Determination of genotypes for the SNPs at these loci may prove informative for assessment of genetic risk for hypertriglyceridemia, hypo‑HDL‑cholesterolemia, or hyper‑LDL‑cholesterolemia in the Japanese population.

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.

Table I

Characteristics of subjects with hypertriglyceridemia and control individuals.

Table I

Characteristics of subjects with hypertriglyceridemia and control individuals.

CharacteristicControl HypertriglyceridemiaP-value
Number of subjects5,2942,664
Age (years)51.3±10.253.3±8.8<0.0001
Sex (men/women, %)50.2/49.875.6/24.4<0.0001
Smoking (%)35.954.0<0.0001
Obesity (%)22.942.6<0.0001
Body mass index (kg/m2)22.7±3.424.8±3.6<0.0001
Hypertension (%)35.357.2<0.0001
Systolic BP (mmHg)125±23132±23<0.0001
Diastolic BP (mmHg)75±1480±13<0.0001
Diabetes mellitus (%)17.533.8<0.0001
Fasting plasma glucose (mmol/l)5.83±2.046.55±2.81<0.0001
Blood hemoglobin A1c (%)5.83±1.086.26±1.50<0.0001
Serum triglycerides (mmol/l)0.94±0.342.40±1.37<0.0001
Serum HDL-cholesterol (mmol/l)1.66±0.471.30±0.36<0.0001
Serum LDL-cholesterol (mmol/l)3.06±0.803.35±0.950.8593
Chronic kidney disease (%)12.319.9<0.0001
Serum creatinine (µmol/l)71.9±69.882.1±85.2<0.0001
eGFR (ml min−1 1.73 m−2)78.5±19.474.1±21.6<0.0001
Hyperuricemia (%)11.728.4<0.0001
Serum uric acid (µmol/l)308±90362±91<0.0001

[i] Quantitative data are presented as mean ± standard deviation and were compared between subjects with hypertriglyceridemia and controls with the unpaired Student’s t-test. Categorical data were compared between the two groups with Pearson’s Chi-square test. Based on Bonferroni’s correction, P<0.0026 (0.05/19) was considered statistically significant. BP, blood pressure; HDL, high density lipoprotein; LDL, low density lipoprotein; eGFR, estimated glomerular filtration rate.

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.

Table II

Characteristics of subjects with hypo-HDL-cholesterolemia and control individuals.

Table II

Characteristics of subjects with hypo-HDL-cholesterolemia and control individuals.

CharacteristicControl Hypo-HDL-cholesterolemiaP-value
Number of subjects7,085974
Age (years)51.7±9.954.3±8.7<0.0001
Sex (men/women, %)55.1/44.985.3/14.7<0.0001
Smoking (%)40.554.6<0.0001
Obesity (%)27.048.2<0.0001
Body mass index (kg/m2)23.2±3.525.1±3.9<0.0001
Hypertension (%)39.664.8<0.0001
Systolic BP (mmHg)126±23137±27<0.0001
Diastolic BP (mmHg)76±1480±16<0.0001
Diabetes mellitus (%)19.548.2<0.0001
Fasting plasma glucose (mmol/l)5.93±2.167.16±3.26<0.0001
Blood hemoglobin A1c (%)5.89±1.176.59±1.60<0.0001
Serum triglycerides (mmol/l)1.35±0.962.12±1.62<0.0001
Serum HDL-cholesterol (mmol/l)1.63±0.420.88±0.12<0.0001
Serum LDL-cholesterol (mmol/l)3.16±0.843.13±0.990.2860
Chronic kidney disease (%)13.426.2<0.0001
Serum creatinine (µmol/l)72.6±66.696.0±122.7<0.0001
eGFR (ml min−1 1.73 m−2)77.5±18.573.3±29.8<0.0001
Hyperuricemia (%)15.927.2<0.0001
Serum uric acid (µmol/l)323±91361±0.3<0.0001

[i] Quantitative data are presented as mean ± standard deviation and were compared between subjects with hypo-HDL-cholesterolemia and controls with the unpaired Student’s t-test. Categorical data were compared between the two groups with Pearson’s Chi-square test. Based on Bonferroni’s correction, P<0.0026 (0.05/19) was considered statistically significant. BP, blood pressure; HDL, high density lipoprotein; LDL, low density lipoprotein; eGFR, estimated glomerular filtration rate.

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.

Table III

Characteristics of subjects with hyper-LDL-cholesterolemia and control individuals.

Table III

Characteristics of subjects with hyper-LDL-cholesterolemia and control individuals.

CharacteristicControl Hyper-LDL-cholesterolemiaP-value
Number of subjects5,1112,911
Age (years)51.3±10.353.2±8.9<0.0001
Sex (men/women, %)59.0/41.058.2/41.80.4936
Smoking (%)40.944.30.0034
Obesity (%)27.732.6<0.0001
Body mass index (kg/m2)23.2±3.623.9±3.5<0.0001
Hypertension (%)42.542.60.8966
Systolic BP (mmHg)128±25126±210.0025
Diastolic BP (mmHg)76±1477±130.0004
Diabetes mellitus (%)23.222.00.2593
Fasting plasma glucose (mmol/l)6.11±2.496.02±2.120.0831
Blood hemoglobin A1c (%)5.96±1.255.99±1.260.4573
Serum triglycerides (mmol/l)1.40±1.191.52±0.91<0.0001
Serum HDL-cholesterol (mmol/l)1.56±0.491.51±0.42<0.0001
Serum LDL-cholesterol (mmol/l)2.70±0.563.90±0.74<0.0001
Chronic kidney disease (%)15.314.30.2118
Serum creatinine (µmol/l)78.3±88.771.7±58.90.0001
eGFR (ml min−1 1.73 m−2)77.0±21.776.8±17.80.7007
Hyperuricemia (%)16.318.90.0028
Serum uric acid (µmol/l)323±97334±87<0.0001

[i] Quantitative data are presented as mean ± standard deviation and were compared between subjects with hyper-LDL-cholesterolemia and controls with the unpaired Student’s t-test. Categorical data were compared between the two groups with Pearson’s Chi-square test. Based on Bonferroni’s correction, P<0.0026 (0.05/19) was considered statistically significant. BP, blood pressure; HDL, high density lipoprotein; LDL, low density lipoprotein; eGFR, estimated glomerular filtration rate.

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 IV

The 25 SNPs significantly associated with hypertriglyceridemia in the exome-wide association study.

Table IV

The 25 SNPs significantly associated with hypertriglyceridemia in the exome-wide association study.

GeneSNPNucleotide substitutionaAmino acid substitutionChromosomePositionMAF (%)Allele ORAllele frequency (P-value)
APOA5rs2075291G/TG185C111167906767.31.89 2.83×10−24
BUD13rs10790162G/A1111663910426.31.47 3.58×10−24
ZPR1rs964184C/G1111677820126.31.46 8.82×10−24
APOA5rs2266788T/C1111678997026.21.47 1.27×10−23
rs7350481C/T1111671556727.71.44 7.94×10−23
rs9326246G/C1111674101726.51.45 1.36×10−22
ZPR1rs2075290T/C1111678258026.71.43 1.58×10−21
rs12678919A/G81998671112.60.71 6.07×10−11
rs10503669C/A81999017912.60.71 8.32×10−11
rs17482753G/T81997513512.60.72 2.04×10−10
rs10096633C/T81997341012.70.72 2.50×10−10
LPLrs328C/GS474*81996221312.90.72 2.72×10−10
APOA4rs5104A/GN147S1111682161835.71.25 2.78×10−10
rs7016880G/C82001923512.00.72 1.72×10−9
GCKRrs1260326T/CL446P22750807343.60.82 6.27×10−9
GCKRrs780093A/G22751973643.00.83 2.18×10−8
rs1260333T/C22752575742.90.83 4.38×10−8
rs11085421A/C192098594819.10.79 1.59×10−7
rs1441756T/G82001087519.00.80 5.61×10−7
rs2083637T/C82000766419.00.81 6.24×10−7
LPLrs301T/C81995942319.30.81 7.70×10−7
LPLrs13702A/G81996698119.20.81 9.15×10−7
LPLrs15285G/A81996715619.20.81 1.02×10−6
rs2954033G/A812548150433.31.19 1.02×10−6
LPLrs326A/G81996192819.40.81 1.24×10−6

{ label (or @symbol) needed for fn[@id='tfn4-ijmm-43-01-0057'] } Allele frequencies were analyzed with Fisher’s exact test. P<1.60×10−6 was considered statistically significant.

a Major allele/minor allele. SNP, single nucleotide polymorphism; MAF, minor allele frequency; OR, odds ratio.

Table V

The 28 SNPs significantly associated with hypo-HDL-cholesterolemia in the exome-wide association study.

Table V

The 28 SNPs significantly associated with hypo-HDL-cholesterolemia in the exome-wide association study.

GeneSNPNucleotide substitutionaAmino acid substitutionChromosomePositionMAF (%)Allele ORAllele frequency (P-value)
LPGAT1rs150552771T/CK200E12117833585.016.67 1.09×10−12
APOA5rs2075291G/TG185C111167906767.31.79 4.10×10−12
COL6A5rs200982668G/AE2501K31304708941.30.18 4.51×10−11
ZNF860rs140232911C/TS161L33198956110.44.39 5.21×10−11
rs9261800C/G6304088222.819.65 6.69×10−11
VPS33Brs199921354C/TR80Q15910138411.20.17 1.03×10−10
ADGRL3rs192210727G/TR580I4619096151.30.19 1.33×10−10
TMOD4rs115287176G/AR277W11511709611.20.18 1.46×10−10
COL6A3rs146092501C/TE1386K22373718611.20.18 2.10×10−10
MARCH1rs61734696G/TQ137K41641973031.20.19 2.74×10−10
PLCB2rs200787930C/TE1106K15402892981.20.19 2.79×10−10
MOB3Crs139537100C/TR24Q1466150061.20.19 2.87×10−10
CXCL8rs188378669G/TE31*4737415681.20.19 3.94×10−10
EHD3rs116417209G/AV151I2312494173.512.5 5.66×10−10
ZNF77rs146879198G/AR340*1929341091.20.20 1.17×10−9
OR4F6rs141569282G/AA117T151018060681.70.23 2.31×10−9
ACAD10rs11066015G/A1211173020527.51.37 2.61×10−9
CACNA1Drs35874056G/AG460S3537027982.0100.00 2.95×10−9
ALDH2rs671G/AE504K1211180396227.61.35 4.23×10−9
rs3764261G/T165695941219.80.69 7.90×10−9
rs247616C/T165695567819.70.69 9.31×10−9
BRAPrs3782886A/G1211167268529.31.34 1.19×10−8
CETPrs1532624G/T165697156729.60.74 2.63×10−8
HECTD4rs11066280T/A1211237997929.01.33 3.54×10−8
HECTD4rs2074356C/T1211220759725.41.32 1.43×10−7
LILRB2rs73055442C/TR103H19542798381.634.09 1.80×10−7
DCLRE1Crs150854849C/TR179Q10149347042.459.56 3.16×10−7
SPOPLrs114501427G/AD349N21385689468.83.23 4.01×10−7

{ label (or @symbol) needed for fn[@id='tfn6-ijmm-43-01-0057'] } Allele frequencies were analyzed with Fisher’s exact test. P<1.61×10−6 was considered statistically significant.

a Major allele/minor allele. SNP, single nucleotide polymorphism; MAF, minor allele frequency; OR, odds ratio.

Table VI

The 65 SNPs significantly associated with hyper-LDL-cholesterolemia in the exome-wide association study.

Table VI

The 65 SNPs significantly associated with hyper-LDL-cholesterolemia in the exome-wide association study.

GeneSNPNucleotide substitutionaAmino acid substitutionChromosomePositionMAF (%)Allele ORAllele frequency (P-value)
APOErs7412C/TR176C19449088224.30.42 6.62×10−22
COL6A3rs146092501C/TE1386K22373718611.22.14 1.42×10−11
TMOD4rs115287176G/AR277W11511709611.22.13 1.83×10−11
CXCL8rs188378669G/TE31*4737415681.22.10 1.95×10−11
VPS33Brs199921354C/TR80Q15910138411.22.11 2.32×10−11
ZNF77rs146879198G/AR340*1929341091.22.08 4.91×10−11
MOB3Crs139537100C/TR24Q1466150061.22.06 5.58×10−11
PLCB2rs200787930C/TE1106K15402892981.22.03 1.70×10−10
MUC17rs78010183A/TT1305S71010353291.81.81 2.30×10−10
MARCH1rs61734696G/TQ137K41641973031.22.01 3.49×10−10
APOBrs13306206G/AP955S2210198593.21.75 7.32×10−10
COL6A5rs200982668G/AE2501K31304708941.31.92 9.85×10−10
PTCH2rs147284320C/TV503I1448285892.01.86 1.23×10−9
ADGRL3rs192210727G/TR580I4619096151.31.94 1.49×10−9
APOC1rs445925C/T19449123836.60.66 3.62×10−9
HSPA1Brs6457452C/T6318277739.71.33 2.97×10−8
C6orf48rs11968400C/T6318369529.71.33 3.96×10−8
rs12210887G/T6318479469.71.33 5.26×10−8
UBDrs64036C/A62955949022.51.23 7.55×10−8
CCHCR1rs147733073C/GH539Q63114546210.21.32 8.01×10−8
VARSrs11751198G/A6317857499.51.33 8.12×10−8
rs2596574G/A6313663979.71.32 8.83×10−8
LY6G6Frs9267547G/AA107T63170772410.01.32 9.72×10−8
NEU1rs13118T/A6318595099.71.32 1.01×10−7
SLC17A3rs34902660C/AG239V6258508748.41.35 1.04×10−7
FAM65Brs150142878C/TR371Q6248476575.61.42 1.18×10−7
TUBBrs9500864G/A63072545520.41.23 1.19×10−7
rs204999A/G6321422029.11.33 1.34×10−7
rs3130663A/G63069881710.71.28 1.38×10−7
VARSrs5030798G/AV1055I6317797339.51.32 1.43×10−7
LY6G6Frs9267546G/A6317056599.81.32 1.44×10−7
PRRC2Ars11538264G/AV1774M6316354129.51.32 1.55×10−7
DPCR1rs11970154G/CG1213R63095210113.31.28 1.60×10−7
MSH5rs11754464C/T6317559589.51.32 1.73×10−7
PCSK9rs151193009C/TR93C1550439121.10.42 1.85×10−7
GABBR1rs29243C/T62963132510.61.30 1.94×10−7
KIAA0319rs4576240G/TP133T6245962505.51.41 2.10×10−7
LY6G6Frs17200983C/AP34Q6317075069.51.32 2.38×10−7
TNXBrs140770834C/GL2271V6320648518.81.32 2.42×10−7
TNXBrs11751545A/C6320732668.81.32 2.42×10−7
DPCR1rs6933400C/T63093939913.21.27 2.43×10−7
LY6G6Crs117894946G/CG75A6317192509.51.32 2.66×10−7
SLC44A4rs117127493G/CE344Q6318692328.91.32 3.22×10−7
ZSCAN26rs76463649A/GN15S6282719639.61.30 4.26×10−7
DHX16rs7749235T/C63066781617.81.23 4.67×10−7
LOC105-375015rs9264942T/C63130660340.11.18 4.82×10−7
ZSCAN31rs6922302C/GP128A6283275339.61.30 5.06×10−7
MDC1rs2269702A/G63070735817.71.23 5.13×10−7
MDC1rs28986465C/TP386L63071278517.71.23 5.17×10−7
MDC1rs2075015G/AE371K63071283117.71.23 5.17×10−7
DHX16rs2285321T/C63067022117.81.23 5.18×10−7
SKIV2Lrs492899A/G6319657419.11.31 5.33×10−7
MDC1rs6924270A/G63071420317.81.23 5.81×10−7
rs2508015C/T63104242311.71.27 6.07×10−7
TUBBrs25527C/T63072316121.01.22 6.08×10−7
PPP1R18rs9468805G/A63067593217.71.23 7.07×10−7
DHX16rs6937357T/C63067254717.81.23 7.07×10−7
HECTD4rs11066280T/A1211237997929.01.19 7.09×10−7
TRIM40rs2523995G/A63013440710.61.28 7.88×10−7
PPP1R18rs6457254C/T63068135717.91.22 1.20×10−6
TUBBrs3132584C/A63072065021.21.20 1.22×10−6
rs3130685T/C63123842941.60.85 1.43×10−6
GIT2rs2292354C/T1210993039626.20.83 1.45×10−6
PPP1R18rs2394392T/G63068254118.01.22 1.50×10−6
rs2524272T/C62971462326.81.19 1.55×10−6

{ label (or @symbol) needed for fn[@id='tfn8-ijmm-43-01-0057'] } Allele frequencies were analyzed with Fisher’s exact test. P<1.60×10−6 was considered statistically significant.

a Major allele/minor allele. SNP, single nucleotide polymorphism; MAF, minor allele frequency; OR, odds ratio.

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 VII

Association of SNPs to hypertriglyceridemia as determined by multivariable logistic regression analysis.

Table VII

Association of SNPs to hypertriglyceridemia as determined by multivariable logistic regression analysis.

GeneSNPDominant
Recessive
Additive 1
Additive 2
P-valueOR95% CIP-valueOR95% CIP-valueOR95% CIP-valueOR95% CI
APOA5rs2075291G/T<0.00011.991.74-2.28<0.00013.191.79-5.66<0.00011.931.68-2.22<0.00013.501.97-6.22
BUD13rs10790162G/A<0.00011.661.51-1.83<0.00011.801.50-2.15<0.00011.581.42-1.75<0.00012.171.81-2.62
ZPR1rs964184C/G<0.00011.671.51-1.84<0.00011.741.46-2.09<0.00011.591.44-1.76<0.00012.121.76-2.56
APOA5rs2266788T/C<0.00011.671.51-1.84<0.00011.741.45-2.09<0.00011.591.44-1.76<0.00012.111.75-2.55
rs7350481C/T<0.00011.651.50-1.82<0.00011.681.41-2.00<0.00011.581.42-1.75<0.00012.061.72-2.47
rs9326246G/C<0.00011.641.49-1.81<0.00011.751.46-2.09<0.00011.561.41-1.73<0.00012.111.75-2054
ZPR1rs2075290T/C<0.00011.621.47-1.78<0.00011.701.42-2.04<0.00011.541.39-1.71<0.00012.051.70-2.46
rs12678919A/G<0.00010.700.62-0.790.03620.620.39-0.97<0.00010.710.63-0.800.01630.570.36-0.90
rs10503669C/A<0.00010.700.62-0.790.04340.630.40-0.99<0.00010.710.63-0.800.01990.580.37-0.92
rs17482753G/T<0.00010.710.63-0.800.03570.620.40-0.97<0.00010.720.64-0.820.01630.580.37-0.90
rs10096633C/T<0.00010.710.64-0.800.03060.610.39-0.96<0.00010.720.64-0.820.01380.570.37-0.89
LPLrs328C/G<0.00010.720.64-0.810.02530.610.39-0.94<0.00010.730.64-0.820.01100.570.37-0.88
APOA4rs5104A/G<0.00011.381.25-1.52<0.00011.371.19-1.58<0.00011.321.19-1.47<0.00011.601.37-1.86
rs7016880G/C<0.00010.720.64-0.820.0548<0.00010.730.65-0.830.03270.610.39-0.96
GCKRrs1260326T/C<0.00010.750.68-0.83<0.00010.770.68-0.88<0.00010.780.70-0.87<0.00010.670.58-0.77
GCKRrs780093A/G<0.00010.770.69-0.85<0.00010.770.68-0.88<0.00010.800.72-0.89<0.00010.680.59-0.78
rs1260333T/C<0.00010.770.70-0.86<0.00010.770.68-0.880.00010.810.73-0.90<0.00010.680.59-0.78
rs11085421A/C<0.00010.780.70-0.860.00320.660.49-0.87<0.00010.800.72-0.890.00070.610.46-0.81
rs1441756T/G<0.00010.790.71-0.880.01250.700.53-0.93<0.00010.810.72-0.900.00320.660.49-0.87
rs2083637T/C<0.00010.790.71-0.880.01660.710.54-0.94<0.00010.810.72-0.900.00440.660.50-0.88
LPLrs301T/C<0.00010.790.72-0.880.01070.700.53-0.920.00010.810.73-0.900.00270.660.50-0.86
LPLrs13702A/G<0.00010.800.72-0.880.01300.710.54-0.930.00010.810.73-0.900.00340.660.50-0.87
LPLrs15285G/A<0.00010.800.72-0.880.01490.710.54-0.940.00010.810.73-0.910.00400.670.51-0.88
rs2954033G/A<0.00011.261.14-1.390.00111.281.10-1.490.00011.221.10-1.35<0.00011.421.21-1.67
LPLrs326A/G<0.00010.800.72-0.880.02330.730.56-0.960.00010.810.73-0.900.00620.690.52-0.90

[i] Multivariable logistic regression analysis was performed with adjustment for age and sex. Based on Bonferroni’s correction, P<0.0005 (0.05/100) was considered statistically significant. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.

Table VIII

Association of SNPs to hypo-HDL-cholesterolemia as determined by multivariable logistic regression analysis.

Table VIII

Association of SNPs to hypo-HDL-cholesterolemia as determined by multivariable logistic regression analysis.

GeneSNPDominant
Recessive
Additive 1
Additive 2
P-valueOR95% CIP-valueOR95% CIP-valueOR95% CIP-valueOR95% CI
LPGAT1rs150552771T/C<0.000119.168.16-45.01<0.000119.168.16-45.01
APOA5rs2075291G/T<0.00011.851.55-2.200.00312.661.39-5.07<0.00011.801.50-2.160.00122.911.52-5.57
COL6A5rs200982668G/A<0.00010.180.09-0.35<0.00010.180.09-0.35
ZNF860rs140232911C/T<0.00014.883.12-7.63<0.00014.883.12-7.63
rs9261800C/G<0.000128.1110.15-77.87<0.000128.1110.15-77.87
VPS33Brs199921354C/T<0.00010.170.08-0.34<0.00010.170.08-0.34
ADGRL3rs192210727G/T<0.00010.190.10-0.370.2341<0.00010.190.10-0.380.9964
TMOD4rs115287176G/A<0.00010.170.08-0.35<0.00010.170.08-0.35
COL6A3rs146092501C/T<0.00010.170.08-0.35<0.00010.170.08-0.35
MARCH1rs61734696G/T<0.00010.190.10-0.37<0.00010.190.10-0.37
PLCB2rs200787930C/T<0.00010.190.10-0.36<0.00010.190.10-0.36
MOB3Crs139537100C/T<0.00010.190.10-0.37<0.00010.190.10-0.37
CXCL8rs188378669G/T<0.00010.190.10-0.36<0.00010.190.10-0.36
EHD3rs116417209G/A<0.000114.346.16-33.35<0.000114.346.16-33.35
ZNF77rs146879198G/A<0.00010.190.10-0.38<0.00010.190.10-0.38
OR4F6rs141569282G/A<0.00010.250.13-0.46<0.00010.250.13-0.46
ACAD10rs11066015G/A<0.00011.401.22-1.60<0.00011.561.26-1.930.00031.321.14-1.52<0.00011.771.42-2.22
CACNA1Drs35874056G/A<0.000192.5911.37-753.91<0.000192.5911.37-753.91
ALDH2rs671G/A<0.00011.391.21-1.60<0.00011.551.25-1.920.00031.311.13-1.52<0.00011.761.40-2.20
rs3764261G/T<0.00010.680.58-0.790.00040.390.23-0.66<0.00010.720.61-0.840.00010.350.21-0.60
rs247616C/T<0.00010.680.58-0.790.00090.430.26-0.71<0.00010.710.61-0.830.00020.390.24-0.64
BRAPrs3782886A/G<0.00011.391.21-1.60<0.00011.521.24-1.870.00031.311.13-1.52<0.00011.741.40-2.17
CETPrs1532624G/T<0.00010.730.64-0.84<0.00010.500.37-0.670.00210.800.69-0.92<0.00010.450.33-0.61
HECTD4rs11066280T/A<0.00011.421.23-1.630.00101.431.15-1.76<0.00011.361.17-1.58<0.00011.661.32-2.08
HECTD4rs2074356C/T<0.00011.371.19-1.570.00161.451.15-1.830.00021.321.14-1.52<0.00011.641.29-2.09
LILRB2rs73055442C/T<0.000151.9110.66-252.93<0.000151.9110.66-252.93
DCLRE1Crs150854849C/T<0.000178.099.17-665.03<0.000178.099.17-665.03
SPOPLrs114501427G/A<0.00013.542.24-5.61<0.00013.542.24-5.61

[i] Multivariable logistic regression analysis was performed with adjustment for age and sex. Based on Bonferroni’s correction, P<0.0004 (0.05/112) was considered statistically significant. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.

Table IX

Association of SNPs to hyper-LDL-cholesterolemia as determined by multivariable logistic regression analysis.

Table IX

Association of SNPs to hyper-LDL-cholesterolemia as determined by multivariable logistic regression analysis.

GeneSNPDominant
Recessive
Additive 1
Additive 2
P-valueOR95% CIP-valueOR95% CIP-valueOR95% CIP-valueOR95% CI
APOErs7412C/T<0.00010.400.33-0.480.0738<0.00010.400.33-0.490.0984
COL6A3rs146092501C/T<0.00012.341.87-2.93<0.00012.341.87-2.93
TMOD4rs115287176G/A<0.00012.331.86-2.91<0.00012.331.86-2.91
CXCL8rs188378669G/T<0.00012.301.85-2.87<0.00012.301.85-2.87
VPS33Brs199921354C/T<0.00012.311.85-2.89<0.00012.311.85-2.89
ZNF77rs146879198G/A<0.00012.301.84-2.87<0.00012.301.84-2.87
MOB3Crs139537100C/T<0.00012.271.82-2.83<0.00012.271.82-2.83
PLCB2rs200787930C/T<0.00012.221.78-2.77<0.00012.221.78-2.77
MUC17rs78010183A/T<0.00011.991.65-2.40<0.00011.991.65-2.40
MARCH1rs61734696G/T<0.00012.201.77-2.74<0.00012.201.77-2.74
APOBrs13306206G/A<0.00011.691.41-2.030.01455.131.38-19.03<0.00011.651.37-1.980.01275.301.43-19.67
COL6A5rs200982668G/A<0.00012.121.71-2.63<0.00012.121.71-2.63
PTCH2rs147284320C/T<0.00012.021.65-2.49<0.00012.021.65-2.49
ADGRL3rs192210727G/T<0.00012.141.72-2.660.8431<0.00012.171.74-2.710.8732
APOC1rs445925C/T<0.00010.630.55-0.730.8258<0.00010.630.54-0.730.7212
HSPA1Brs6457452C/T<0.00011.371.22-1.530.02841.511.04-2.20<0.00011.351.20-1.520.01251.611.11-2.33
C6orf48rs11968400C/T<0.00011.371.22-1.530.04251.481.01-2.15<0.00011.361.21-1.520.01951.571.07-2.29
rs12210887G/T<0.00011.371.22-1.530.0590<0.00011.361.21-1.520.02721.531.05-2.24
UBDrs64036C/A<0.00011.281.17-1.400.00431.301.09-1.56<0.00011.261.14-1.380.00021.421.18-1.71
CCHCR1rs147733073C/G<0.00011.371.22-1.530.1210<0.00011.361.21-1.530.0592
VARSrs11751198G/A<0.00011.361.22-1.520.0505<0.00011.351.20-1.510.02381.561.06-2.29
rs2596574G/A<0.00011.361.22-1.530.0807<0.00011.351.21-1.520.03771.481.02-2.14
LY6G6Frs9267547G/A<0.00011.351.21-1.510.0797<0.00011.341.20-1.510.03701.471.02-2.11
NEU1rs13118T/A<0.00011.361.21-1.520.04501.471.01-2.16<0.00011.341.20-1.510.02171.561.07-2.29
SLC17A3rs34902660C/A<0.00011.401.24-1.570.2131<0.00011.401.24-1.570.1319
FAM65Brs150142878C/T<0.00011.481.29-1.700.5456<0.00011.491.30-1.710.4556
TUBBrs9500864G/A<0.00011.281.16-1.400.00251.361.11-1.66<0.00011.251.13-1.380.00021.471.20-1.81
rs204999A/G<0.00011.371.22-1.530.1767<0.00011.371.22-1.540.1002
rs3130663A/G<0.00011.351.21-1.490.1104<0.00011.341.21-1.500.03881.391.02-1.90
VARSrs5030798G/A<0.00011.351.21-1.520.0609<0.00011.341.19-1.510.02961.531.04-2.24
LY6G6Frs9267546G/A<0.00011.351.21-1.510.1053<0.00011.351.20-1.510.0525
PRRC2Ars11538264G/A<0.00011.361.21-1.520.0873<0.00011.351.20-1.510.04371.481.01-2.16
DPCR1rs11970154G/C<0.00011.331.20-1.470.1235<0.00011.331.19-1.470.04451.371.01-1.86
MSH5rs11754464C/T<0.00011.351.21-1.510.0735<0.00011.341.19-1.510.03651.501.03-2.20
PCSK9rs151193009C/T<0.00010.410.29-0.590.4126<0.00010.420.29-0.590.9943
GABBR1rs29243C/T<0.00011.341.20-1.500.0562<0.00011.331.19-1.490.2441.501.05-2.13
KIAA0319rs4576240G/T<0.00011.471.29-1.690.7773<0.00011.491.29-1.710.6763
LY6G6Frs17200983C/A<0.00011.351.20-1.510.0873<0.00011.341.19-1.500.04441.481.01-2.16
TNXBrs140770834C/G<0.00011.361.21-1.530.1723<0.00011.361.21-1.530.1013
TNXBrs11751545A/C<0.00011.361.21-1.530.1723<0.00011.361.21-1.530.1013
DPCR1rs6933400C/T<0.00011.321.20-1.470.1222<0.00011.321.19-1.470.04561.371.01-1.87
LY6G6Crs117894946G/C<0.00011.351.20-1.510.0871<0.00011.341.19-1.500.04451.481.01-2.16
SLC44A4rs117127493G/C<0.00011.361.21-1.520.2033<0.00011.361.20-1.530.1218
ZSCAN26rs76463649A/G<0.00011.361.21-1.520.3394<0.00011.361.21-1.530.2111
DHX16rs7749235T/C<0.00011.281.16-1.400.00641.371.09-1.72<0.00011.251.13-1.380.00101.471.17-1.86
LOC105-rs9264942T/C<0.00011.261.14-1.390.00041.241.10-1.390.00021.211.09-1.35<0.00011.381.21-1.58
375015
ZSCAN31rs6922302C/G<0.00011.361.21-1.520.3391<0.00011.361.21-1.530.2111
MDC1rs2269702A/G<0.00011.271.16-1.400.00481.391.11-1.75<0.00011.251.13-1.380.00071.491.18-1.88
MDC1rs28986465C/T<0.00011.271.16-1.400.00481.391.11-1.75<0.00011.241.13-1.380.00071.491.18-1.88
MDC1rs2075015G/A<0.00011.271.16-1.400.00481.391.11-1.75<0.00011.241.13-1.380.00071.491.18-1.88
DHX16rs2285321T/C<0.00011.271.16-1.400.00561.381.10-1.73<0.00011.251.13-1.380.00081.481.18-1.87
SKIV2Lrs492899A/G<0.00011.341.20-1.510.1465<0.00011.341.19-1.510.0843
MDC1rs6924270A/G<0.00011.271.16-1.400.00551.381.10-1.74<0.00011.251.13-1.380.00081.481.18-1.87
rs2508015C/T<0.00011.301.17-1.450.04091.391.01-1.91<0.00011.291.16-1.440.01661.471.07-2.02
TUBBrs25527C/T<0.00011.261.14-1.380.00281.351.11-1.64<0.00011.231.11-1.350.00021.451.19-1.78
PPP1R18rs9468805G/A<0.00011.271.15-1.390.00481.391.11-1.75<0.00011.241.12-1.370.00071.491.18-1.87
DHX16rs6937357T/C<0.00011.271.15-1.390.00481.391.11-1.75<0.00011.241.12-1.370.00071.491.18-1.87
HECTD4rs11066280T/A<0.00011.261.15-1.380.00831.231.05-1.43<0.00011.241.13-1.370.00021.361.16-1.59
TRIM40rs2523995G/A<0.00011.341.20-1.490.2147<0.00011.341.20-1.500.1152
PPP1R18rs6457254C/T<0.00011.261.15-1.390.00631.371.09-1.72<0.00011.241.12-1.370.00101.471.17-1.85
TUBBrs3132584C/A<0.00011.251.14-1.370.00351.341.10-1.63<0.00011.221.11-1.350.00031.441.18-1.76
rs3130685T/C0.00050.800.71-0.91<0.00010.820.74-0.900.02640.860.75-0.98<0.00010.730.64-0.84
GIT2rs2292354C/T0.00010.830.76-0.91<0.00010.680.56-0.820.00500.870.79-0.96<0.00010.640.52-0.78
PPP1R18rs2394392T/G<0.00011.261.15-1.390.00741.361.09-1.71<0.00011.241.12-1.370.00121.461.16-1.84
rs2524272T/C<0.00011.221.11-1.340.00101.301.11-1.520.00071.181.07-1.30<0.00011.401.19-1.65

[i] Multivariable logistic regression analysis was performed with adjustment for age and sex. Based on Bonferroni’s correction, P<0.0002 (0.05/260) was considered statistically significant. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.

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 X

Association of SNPs identified in the present study to serum triglyceride concentration.

Table X

Association of SNPs identified in the present study to serum triglyceride concentration.

GeneSNPSerum triglycerides (mmol/l)P-value
APOA5rs2075291G/TGGGTTT<0.0001
1.39±0.981.83±1.552.56±2.27
BUD13rs10790162G/AGGGAAA<0.0001
1.35±1.001.54±1.181.77±1.31
ZPR1rs964184C/GCCCGGG<0.0001
1.35±1.001.55±1.181.75±1.30
APOA5rs2266788T/CTTTCCC<0.0001
1.35±1.001.54±1.171.76±1.30
rs7350481C/TCCCTTT<0.0001
1.34±0.951.56±1.241.68±1.19
rs9326246G/CGGGCCC<0.0001
1.35±1.001.54±1.181.74±1.28
ZPR1rs2075290T/CTTTCCC<0.0001
1.36±1.011.54±1.171.74±1.29
rs12678919A/GAAAGGG<0.0001
1.49±1.111.33±1.031.32±1.37
rs10503669C/ACCCAAA<0.0001
1.49±1.111.33±1.031.32±1.38
rs17482753G/TGGGTTT<0.0001
1.49±1.111.33±1.021.31±1.35
rs10096633C/TCCCTTT<0.0001
1.49±1.111.33±1.031.31±1.34
LPLrs328C/GCCCGGG<0.0001
1.49±1.111.33±1.021.33±1.33
APOA4rs5104A/GAAAGGG<0.0001
1.37±1.001.49±1.121.57±1.28
rs7016880G/CGGGCCC<0.0001
1.49±1.111.33±1.041.30±1.35
GCKRrs1260326T/CTTTCCC<0.0001
1.54±1.141.44±1.141.33±0.89
GCKRrs780093A/GAAAGGG<0.0001
1.53±1.121.45±1.161.33±0.87
rs1260333T/CTTTCCC<0.0001
1.53±1.121.45±1.161.32±0.87
rs11085421A/CAAACCC<0.0001
1.49±1.141.39±1.051.27±0.70
rs1441756T/GTTTGGG<0.0001
1.49±1.121.38±1.041.34±1.16
rs2083637T/CTTTCCC<0.0001
1.49±1.121.38±1.051.34±1.16
LPLrs301T/CTTTCCC<0.0001
1.49±1.111.38±1.081.34±1.14
LPLrs13702A/GAAAGGG<0.0001
1.50±1.121.38±1.041.34±1.15
LPLrs15285G/AGGGAAA<0.0001
1.50±1.121.38±1.041.34±1.15
rs2954033G/AGGGAAA0.0007
1.41±1.021.47±1.161.55±1.18
LPLrs326A/GAAAGGG<0.0001
1.49±1.111.38±1.071.35±1.14

[i] Data are presented as mean ± standard deviation and were compared among genotypes by one-way analysis of variance. Based on Bonferroni’s correction, P<0.0020 (0.05/25) was considered statistically significant. SNP, single nucleotide polymorphism.

Table XI

Association of SNPs identified in the present study to serum HDL-cholesterol concentration.

Table XI

Association of SNPs identified in the present study to serum HDL-cholesterol concentration.

GeneSNPSerum HDL-cholesterol (mmol/l)P-value
LPGAT1rs150552771T/CTTTC<0.0001
1.54±0.471.11±0.63
APOA5rs2075291G/TGGGTTT<0.0001
1.56±0.471.42±0.451.29±0.40
COL6A5rs200982668G/AGGGA<0.0001
1.53±0.471.69±0.44
ZNF860rs140232911C/TCCCT0.0003
1.54±0.471.37±0.59
rs9261800C/GCCCG<0.0001
1.54±0.470.98±0.56
VPS33Brs199921354C/TCCCT<0.0001
1.53±0.471.69±0.44
ADGRL3rs192210727G/TGGGTTT<0.0001
1.53±0.471.67±0.441.91±0.49
TMOD4rs115287176G/AGGGA<0.0001
1.53±0.471.68±0.44
COL6A3rs146092501C/TCCCT<0.0001
1.53±0.471.68±0.44
MARCH1rs61734696G/TGGGT<0.0001
1.53±0.471.69±0.44
PLCB2rs200787930C/TCCCT<0.0001
1.53±0.471.69±0.44
MOB3Crs139537100C/TCCCT<0.0001
1.53±0.471.68±0.44
CXCL8rs188378669G/TGGGT<0.0001
1.53±0.471.69±0.44
EHD3rs116417209G/AGGGA<0.0001
1.54±0.471.09±0.48
ZNF77rs146879198G/AGGGA<0.0001
1.53±0.471.69±0.44
OR4F6rs141569282G/AGGGA<0.0001
1.48±0.461.72±0.43
ACAD10rs11066015G/AGGGAAA<0.0001
1.57±0.481.51±0.471.43±0.43
CACNA1Drs35874056G/AGGGA<0.0001
1.51±0.470.94±0.64
ALDH2rs671G/AGGGAAA<0.0001
1.57±0.481.51±0.471.43±0.43
rs3764261G/TGGGTTT<0.0001
1.50±0.461.59±0.481.72±0.49
rs247616C/TCCCTTT<0.0001
1.50±0.461.59±0.481.71±0.50
BRAPrs3782886A/GAAAGGG<0.0001
1.57±0.481.52±0.471.44±0.43
CETPrs1532624G/TGGGTTT<0.0001
1.50±0.461.56±0.471.64±0.48
HECTD4rs11066280T/ATTTAAA<0.0001
1.57±0.471.52±0.471.44±0.42
HECTD4rs2074356C/TCCCTTT<0.0001
1.57±0.481.51±0.471.44±0.43
LILRB2rs73055442C/TCCCT<0.0001
1.54±0.470.88±0.31
DCLRE1Crs150854849C/TCCCT<0.0001
1.54±0.470.92±0.68
SPOPLrs114501427G/AGGGA0.0041
1.54±0.471.41±0.58

[i] Data are presented as mean ± standard deviation and were compared among genotypes by one-way analysis of variance. Based on Bonferroni’s correction, P<0.0018 (0.05/28) was considered statistically significant. SNP, single nucleotide polymorphism; HDL, high density lipoprotein.

Table XII

Association of SNPs identified in the present study to serum LDL-cholesterol concentration.

Table XII

Association of SNPs identified in the present study to serum LDL-cholesterol concentration.

GeneSNPSerum LD L-cholesterol (mmol/l)P-value
APOErs7412C/TCCCTTT <0.0001
3.20±0.852.74±0.802.39±1.48
COL6A3rs146092501C/TCCCT0.1168
3.16±0.863.24±0.82
TMOD4rs115287176G/AGGGA0.1624
3.16±0.863.23±0.82
CXCL8rs188378669G/TGGGT0.1158
3.16±0.863.24±0.82
VPS33Brs199921354C/TCCCT0.1121
3.16±0.863.24±0.81
ZNF77rs146879198G/AGGGA0.2277
3.16±0.863.22±0.81
MOB3Crs139537100C/TCCCT0.2422
3.16±0.863.21±0.82
PLCB2rs200787930C/TCCCT0.1860
3.16±0.863.22±0.81
MUC17rs78010183A/TAAAT0.0096
3.15±0.873.26±0.78
MARCH1rs61734696G/TGGGT0.3029
3.16±0.863.21±0.80
APOBrs13306206G/AGGGAAA <0.0001
3.14±0.853.42±0.984.20±0.86
COL6A5rs200982668G/AGGGA0.3549
3.16±0.863.20±0.80
PTCH2rs147284320C/TCCCT0.5075
3.17±0.823.20±0.82
ADGRL3rs192210727G/TGGGTTT0.6309
3.16±0.863.20±0.812.99±0.82
APOC1rs445925C/TCCCTTT <0.0001
3.19±0.862.96±0.862.99±1.21
HSPA1Brs6457452C/TCCCTTT <0.0001
3.14±0.853.24±0.893.38±0.88
C6orf48rs11968400C/TCCCTTT <0.0001
3.14±0.853.24±0.893.37±0.88
rs12210887G/TGGGTTT <0.0001
3.14±0.853.24±0.893.37±0.89
UBDrs64036C/ACCCAAA 0.0004
3.13±0.853.19±0.873.27±0.85
CCHCR1rs147733073C/GCCCGGG <0.0001
3.14±0.853.23±0.893.37±0.88
VARSrs11751198G/AGGGAAA <0.0001
3.14±0.853.24±0.893.37±0.89
rs2596574G/AGGGAAA <0.0001
3.14±0.853.24±0.893.35±0.86
LY6G6Frs9267547G/AGGGAAA <0.0001
3.14±0.853.23±0.893.37±0.89
NEU1rs13118T/ATTTAAA <0.0001
3.14±0.853.24±0.893.37±0.89
SLC17A3rs34902660C/ACCCAAA <0.0001
3.14±0.853.26±0.903.35±0.91
FAM65Brs150142878C/TCCCTTT <0.0001
3.14±0.853.30±0.943.18±0.88
TUBBrs9500864G/AGGGAAA 0.0002
3.13±0.853.19±0.873.28±0.90
rs204999A/GAAAGGG <0.0001
3.14±0.853.24±0.893.36±0.91
rs3130663A/GAAAGGG0.0010
3.14±0.853.22±0.883.22±0.90
VARSrs5030798G/AGGGAAA <0.0001
3.14±0.853.24±0.893.37±0.89
LY6G6Frs9267546G/AGGGAAA <0.0001
3.14±0.853.24±0.893.36±0.87
PRRC2Ars11538264G/AGGGAAA <0.0001
3.14±0.853.24±0.903.36±0.88
DPCR1rs11970154G/CGGGCCC <0.0001
3.13±0.853.23±0.893.32±0.86
MSH5rs11754464C/TCCCTTT <0.0001
3.14±0.853.24±0.893.37±0.89
PCSK9rs151193009C/TCCCTTT <0.0001
3.17±0.862.77±0.732.05
GABBR1rs29243C/TCCCTTT 0.0001
3.14±0.853.23±0.893.34±0.86
KIAA0319rs4576240G/TGGGTTT <0.0001
3.14±0.853.30±0.933.17±0.89
LY6G6Frs17200983C/ACCCAAA <0.0001
3.14±0.853.24±0.893.36±0.88
TNXBrs140770834C/GCCCGGG <0.0001
3.14±0.853.24±0.893.37±0.92
TNXBrs11751545A/CAAACCC <0.0001
3.14±0.853.24±0.893.37±0.92
DPCR1rs6933400C/TCCCTTT <0.0001
3.13±0.853.23±0.893.32±0.87
LY6G6Crs117894946G/CGGGCCC <0.0001
3.14±0.853.24±0.893.36±0.88
SLC44A4rs117127493G/CGGGCCC <0.0001
3.14±0.853.24±0.893.36±0.91
ZSCAN26rs76463649A/GAAAGGG <0.0001
3.14±0.853.24±0.893.37±0.85
DHX16rs7749235T/CTTTCCC0.0010
3.14±0.853.20±0.873.28±0.90
LOC105375015rs9264942T/CTTTCCC 0.0001
3.13±0.843.15±0.873.25±0.88
ZSCAN31rs6922302C/GCCCGGG <0.0001
3.14±0.853.24±0.903.37±0.85
MDC1rs2269702A/GAAAGGG0.0013
3.14±0.853.20±0.873.28±0.91
MDC1rs28986465C/TCCCTTT0.0013
3.14±0.853.20±0.873.28±0.91
MDC1rs2075015G/AGGGAAA0.0013
3.14±0.853.20±0.873.28±0.91
DHX16rs2285321T/CTTTCCC0.0011
3.14±0.853.20±0.873.28±0.91
SKIV2Lrs492899A/GAAAGGG 0.0001
3.14±0.853.23±0.893.36±0.91
MDC1rs6924270A/GAAAGGG0.0012
3.14±0.853.20±0.873.28±0.91
rs2508015C/TCCCTTT 0.0005
3.14±0.853.22±0.883.31±0.86
TUBBrs25527C/TCCCTTT0.0008
3.14±0.863.19±0.863.27±0.89
PPP1R18rs9468805G/AGGGAAA0.0016
3.14±0.853.20±0.873.28±0.91
DHX16rs6937357T/CTTTCCC0.0016
3.14±0.853.20±0.873.28±0.91
HECTD4rs11066280T/ATTTAAA 0.0005
3.12±0.883.19±0.853.24±0.81
TRIM40rs2523995G/AGGGAAA0.0008
3.14±0.853.22±0.893.32±0.87
PPP1R18rs6457254C/TCCCTTT0.0010
3.14±0.853.20±0.873.28±0.91
TUBBrs3132584C/ACCCAAA0.0008
3.13±0.863.19±0.863.27±0.90
rs3130685T/CTTTCCC0.0234
3.20±0.893.14±0.843.14±0.85
GIT2rs2292354C/TCCCTTT0.0077
3.18±0.863.15±0.873.05±0.82
PPP1R18rs2394392T/GTTTGGG0.0013
3.14±0.853.20±0.873.28±0.91
rs2524272T/CTTTCCC 0.0005
3.13±0.863.18±0.873.27±0.87

[i] Data are presented as mean ± standard deviation and were compared among genotypes by one-way analysis of variance. Based on Bonferroni’s correction, P<0.0008 (0.05/65) was considered statistically significant (indicated in bold). SNP, single nucleotide polymorphism; LDL, low density lipoprotein.

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 XIII

Relation of genes, chromosomal loci, and SNPs associated with hypertriglyceridemia in the present study to previously examined dyslipidemia-related phenotypes.

Table XIII

Relation of genes, chromosomal loci, and SNPs associated with hypertriglyceridemia in the present study to previously examined dyslipidemia-related phenotypes.

Gene/chr locusSNPChrPositionPreviously examined phenotypes
GCKRrs1260326227508073Triglycerides (20686565, 21423719, 23063622, 22629316, 19060906, 18193043)
rs780093227519736
2p23rs1260333227525757Triglycerides (20686565, 19060906, 20864672, 19802338)
LPLrs301819959423Triglycerides (20686565)
rs32619961928
rs32819962213
rs1370219966981
rs1528519967156
8p21.3rs10096633819973410Triglycerides (20686565, 19060906, 19060911, 21943158, 20139978, 23063622, 23505323, 19060910, 19802338)
8p21.3rs17482753819975135Triglycerides (20686565, 23063622, 19060906, 18193043, 21943158, 18179892, 19913121, 17463246)
8p21.3rs12678919819986711Triglycerides (20686565, 19060906, 21909109, 21943158, 20139978, 23063622, 23505323)
8p21.3rs10503669819990179Triglycerides (20686565, 23063622, 21909109, 19060906, 18193043, 21943158, 19913121)
8p21.3rs2083637820007664Triglycerides (20686565, 19060906, 19060911, 21943158, 19802338, 23063622)
8p21.3rs1441756820010875Triglycerides (20686565, 19060906)
8p21.3rs7016880820019235Triglycerides (20686565, 19060906)
8q24.2rs29540338125481504Triglycerides (22479202, 20686565, 23063622, 19060906, 21386085)
BUD13rs1079016211116639104Triglycerides (20686565, 23202125, 23063622, 20139978, 19060906, 20864672, 18193046)
11q23.3rs735048111116715567Triglycerides (20686565, 20139978, 19060906)
11q23.3rs932624611116741017Triglycerides (23202125, 20686565, 19060906)
ZPR1rs96418411116778201Triglycerides (20686565, 23063622, 21909109, 19060906, 20864672, 22629316, 20139978, 21943158, 23505323, 23726366, 19913121)
rs207529011116782580
APOA5rs226678811116789970Triglycerides (20686565, 23063622, 22629316, 19060906, 21943158, 19913121, 18193043, 19802338, 23505323, 23236364)
rs207529111116790676
APOA4rs510411116821618Triglycerides (20686565, 23063622, 19060906, 23505323, 19197348, 19913121)
19p12rs110854211920985948None

[i] Data were obtained from the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) Search database (https://grasp.nhlbi.nih.gov/Search.aspx) with a P-value of <1.0×10-6. Numbers in parentheses are PubMed IDs. SNP, single nucleotide polymorphism; Chr, chromosome.

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 XIV

Relation of genes, chromosomal loci and SNPs associated with hypo-HDL-cholesterolemia in the present study to previously examined dyslipidemia-related phenotypes.

Table XIV

Relation of genes, chromosomal loci and SNPs associated with hypo-HDL-cholesterolemia in the present study to previously examined dyslipidemia-related phenotypes.

Gene/chr locusSNPChrPositionPreviously examined phenotypes
MOB3Crs139537100146615006None
TMOD4rs1152871761151170961None
LPGAT1rs1505527711211783358None
EHD3rs116417209231249417None
SPOPLrs1145014272138568946None
COL6A3rs1460925012237371861None
ZNF860rs140232911331989561None
CACNA1Drs35874056353702798None
COL6A5rs2009826683130470894None
ADGRL3rs192210727461909615None
CXCL8rs188378669473741568Total cholesterol (23063622)
MARCH1rs617346964164197303Adiponectin concentrations (20887962)
6p22.1rs9261800630408822Type 1 diabetes (17554300)
DCLRE1Crs1508548491014934704None
APOA5rs207529111116790676HDL-cholesterol (23063622, 20686565, 22629316, 21386085)
BRAPrs378288612111672685HDL-cholesterol (21572416)
ACAD10rs1106601512111730205LDL-cholesterol (20686565)
ALDH2rs67112111803962HDL-cholesterol (21572416)
HECTD4rs207435612112207597HDL-cholesterol (21572416, 21909109, 22751097)
rs1106628012112379979
PLCB2rs2007879301540289298Triglycerides (23063622)
VPS33Brs1999213541591013841Type 2 diabetes (22885922)
OR4F6rs14156928215101806068None
16q13rs2476161656955678HDL-cholesterol (20686565, 19060906, 20339536, 20838585, 19060911, 20031538)
16q13rs37642611656959412HDL-cholesterol (20686565, 23063622, 19060911, 21943158, 21347282, 18193043, 20694148, 21589926, 19802338, 19913121)
CETPrs15326241656971567HDL-cholesterol (20686565, 23063622, 22629316, 21347282, 21589926, 19060911, 20031564, 19060906, 18193044, 21943158)
ZNF77rs146879198192934109None
LILRB2rs730554421954279838HDL-cholesterol (20686565)

[i] Data were obtained from the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) Search database (https://grasp.nhlbi.nih.gov/Search.aspx) with a P-value of <1.0×10−6. Numbers in parentheses are PubMed IDs. SNP, single nucleotide polymorphism; Chr, chromosome.

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 XV

Relation of genes, chromosomal loci and SNPs associated with hyper-LDL-cholesterolemia in the present study to previously examined dyslipidemia-related phenotypes.

Table XV

Relation of genes, chromosomal loci and SNPs associated with hyper-LDL-cholesterolemia in the present study to previously examined dyslipidemia-related phenotypes.

Gene/chr locusSNPChrPositionPreviously examined phenotypes
PTCH2rs147284320144828589None
MOB3Crs139537100146615006None
PCSK9rs151193009155043912LDL-cholesterol (23063622, 21347282, 18193044, 20686565, 22629316)
TMOD4rs1152871761151170961None
APOBrs13306206221019859LDL-cholesterol (20686565, 23202125, 23063622)
COL6A3rs1460925012237371861None
COL6A5rs2009826683130470894None
ADGRL3rs192210727461909615None
CXCL8rs188378669473741568Total cholesterol (23063622)
MARCH1rs617346964164197303Adiponectin concentrations (20887962)
KIAA0319rs4576240624596250None
FAM65Brs150142878624847657None
SLC17A3rs34902660625850874Serum urate (23263486), type 1 diabetes (17554300)
ZSCAN26rs76463649628271963None
ZSCAN31rs6922302628327533Type 1 diabetes (17554300)
UBDrs64036629559490None
GABBR1rs29243629631325Type 1 diabetes (17554300)
6p22.1rs2524272629714623Type 1 diabetes (17554300)
TRIM40rs2523995630134407Type 1 diabetes (17554300, 17632545)
DHX16rs7749235630667816None
rs2285321630670221
rs6937357630672547
PPP1R18rs9468805630675932Type 1 diabetes (17632545)
rs6457254630681357
rs2394392630682541
6p21.3rs3130663630698817Type 1 diabetes (17632545, 17554300)
MDC1rs2269702630707358Fat mass (19584900), type 1 diabetes (17554300), body mass index (19584900)
rs28986465630712785
rs2075015630712831
rs6924270630714203
TUBBrs3132584630720650Type 1 diabetes (17554300)
rs25527630723161
rs9500864630725455
DPCR1rs6933400630939399Type 1 diabetes (17554300, 17632545)
rs11970154630952101
6p21.3rs2508015631042423Type 1 diabetes (17632545), triglycerides (20686565)
CCHCR1rs147733073631145462Triglycerides (20686565)
6p21.3rs3130685631238429Triglycerides (20686565), type 1 diabetes (17554300)
LOC105375015rs9264942631306603None
6p21.3rs2596574631366397LDL-cholesterol (23063622), total cholesterol (23063622), type 1 diabetes (17554300, 17632545)
PRRC2Ars11538264631635412Type 1 diabetes (17554300, 17632545), triglycerides (20686565), total cholesterol (20686565)
LY6G6Frs9267546631705659Triglycerides (20686565)
rs17200983631707506
rs9267547631707724
LY6G6Crs117894946631719250Type 1 diabetes (17554300, 17632545)
MSH5rs11754464631755958Type 1 diabetes (17554300, 17632545), triglycerides (20686565), total cholesterol (20686565)
VARSrs5030798631779733Type 1 diabetes (17632545)
rs11751198631785749
HSPA1Brs6457452631827773Type 1 diabetes (17554300, 17632545)
C6orf48rs11968400631836952Triglycerides (20686565), total cholesterol (20686565),
type 1 diabetes (17632545)
6p21.3rs12210887631847946Type 1 diabetes (17554300, 17632545,), triglycerides (20686565), total cholesterol (20686565)
NEU1rs13118631859509None
SLC44A4rs117127493631869232Type 1 diabetes (17632545), triglycerides (20686565), total cholesterol (20686565)
SKIV2Lrs492899631965741LDL-cholesterol (20686565), type 1 diabetes (17554300, 17632545), triglycerides (20686565), total cholesterol (20686565)
TNXBrs140770834632064851Type 1 diabetes (17554300, 17632545), triglycerides (20686565), total cholesterol (20686565)
rs11751545632073266
6p21.3rs204999632142202Type 1 diabetes (17632545)
MUC17rs780101837101035329None
GIT2rs229235412109930396None
HECTD4rs1106628012112379979LDL-cholesterol (21572416, 20686565), HDL-cholesterol (21572416, 21909109, 22751097)
PLCB2rs2007879301540289298Triglycerides (23063622)
VPS33Brs1999213541591013841Type 2 diabetes (22885922)
ZNF77rs146879198192934109None
APOErs74121944908822LDL-cholesterol (23100282, 23063622, 20686565, 22629316, 19060911)
APOC1rs4459251944912383LDL-cholesterol (20686565, 21347282, 18193044, 23063622, 18193043, 20864672, 23100282)

[i] Data were obtained from the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) Search database (https://grasp.nhlbi.nih.gov/Search.aspx) with a P-value of <1.0×10−6. Numbers in parentheses are PubMed IDs. SNP, single nucleotide polymorphism; Chr, chromosome.

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.

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January-2019
Volume 43 Issue 1

Print ISSN: 1107-3756
Online ISSN:1791-244X

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Copy and paste a formatted citation
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Spandidos Publications style
Yamada Y, Kato K, Oguri M, Horibe H, Fujimaki T, Yasukochi Y, Takeuchi I and Sakuma J: Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia. Int J Mol Med 43: 57-82, 2019
APA
Yamada, Y., Kato, K., Oguri, M., Horibe, H., Fujimaki, T., Yasukochi, Y. ... Sakuma, J. (2019). Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia. International Journal of Molecular Medicine, 43, 57-82. https://doi.org/10.3892/ijmm.2018.3943
MLA
Yamada, Y., Kato, K., Oguri, M., Horibe, H., Fujimaki, T., Yasukochi, Y., Takeuchi, I., Sakuma, J."Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia". International Journal of Molecular Medicine 43.1 (2019): 57-82.
Chicago
Yamada, Y., Kato, K., Oguri, M., Horibe, H., Fujimaki, T., Yasukochi, Y., Takeuchi, I., Sakuma, J."Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia". International Journal of Molecular Medicine 43, no. 1 (2019): 57-82. https://doi.org/10.3892/ijmm.2018.3943