Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population

DING Yi GUO Dao Xia JING Yang WANG Jie SHEN Chong ZHOU Hui DONG Chen

DING Yi, GUO Dao Xia, JING Yang, WANG Jie, SHEN Chong, ZHOU Hui, DONG Chen. Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population[J]. Biomedical and Environmental Sciences, 2018, 31(8): 619-622. doi: 10.3967/bes2018.084
Citation: DING Yi, GUO Dao Xia, JING Yang, WANG Jie, SHEN Chong, ZHOU Hui, DONG Chen. Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population[J]. Biomedical and Environmental Sciences, 2018, 31(8): 619-622. doi: 10.3967/bes2018.084

doi: 10.3967/bes2018.084
基金项目: 

grants from the National Nature Science Foundation of China 81502869

Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201605

Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201706

Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201506

Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population

Funds: 

grants from the National Nature Science Foundation of China 81502869

Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201605

Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201706

Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201506

More Information
    Author Bio:

    DING Yi, female, born in 1985, PHD candidate, majoring in epidemiology and health statistics

    Corresponding author: ZHOU Hui, E-mail:childhealth@suda.edu.cnDONG Chen, E-mail:cdong@suda.edu.cn
  • Supplementary Table S1.   Biological Information about Candidate SNPs of PPARG Gene and AGTR1 Gene

    Gene tagSNPs HGVS Nomenclature Chromosome Allelesa Region Biological Effect Transcription Factor Binding Site MAFb, c
    PPARG rs12631819 NC_000003.12: g.12301362G > T 3 G/T 12301362 intron variant cap 0.378/0.402
    rs2920502 NC_000003.12: g.12287696G > C 3 G/C 12287696 upstream variant 2KB cap 0.273/0.244
    rs3856806 NC_000003.12: g.12434058C > 3 C/T 12434058 intron variant, synonymous codon, utr variant 3 prime - 0.268/0.250
    rs13433696 NC_000003.12: g.12316993G > A 3 G/A 12316993 intron variant Skn-1, cap 0.378/0.390
    rs1175543 NC_000003.12: g.12424934A > G 3 A/G 12424934 intron variant CdxA 0.450/0.484
    rs9817428 NC_000003.12: g.12298768C > A 3 A/C 12298768 intron variant HSF, SRY 0.435/0.402
    rs2972164 NC_000003.12: g.12292917T > C 3 C/T 12292917 intron variant CdxA, Abd-B 0.090/0.073
    AGTR1 rs2638360 NC_000003.12: g.148710569G > A 3 T/C 148710569 intron variant CdxA, Dfd, Oct-1, Skn-1, cap, STATx, C/EBPa 0.077/0.156
    rs1492100 NC_000003.12: g.148719640T > A 3 A/T 148719640 intron variant Hb 0.122/0.098
    rs5182 NC_000003.12: g.148741608C > T 3 T/C 148741608 synonymous codon - 0.306/0.433
    rs2933249 NC_000003.12: g.148698733G > A 3 C/T 148698733 intron variant HSF 0.128/0.098
    rs275646 NC_000003.12: g.148745735T > C 3 C/T 148745735 - - 0.120/0.110
    Note. aMajor/minor allele. bMAF in the control. cMAF in CHB.
    下载: 导出CSV

    Supplementary Table S2.   The Informations about the Primers and Probes of the Candidate SNPs of PPARG Gene and AGTR1 Gene

    Gene SNP Primers and Probes
    PPARG rs12631819 forward sequence AAATGAGGCCAAAACTTGATAGTGT
    reverse sequence AAGGTTTACAATAATGCCCAGTACAA
    probes 1 FAM-AAGTTTAAGAAGAGAACCAG-MGB
    probes 2 HEX-AGTTTAAGAAGAGAACAAGT-MGB
    rs2920502 forward sequence GCACAGTAGGGCCCACG
    reverse sequence GGATCCCTCCTCGGAAATG
    probes 1 FAM-CCACTCTCTGCCC-MGB
    probes 2 HEX-CCACTGTCTGCCC-MGB
    rs3856806 forward sequence CGTCTTCTTGATCACCTGCAGTA
    reverse sequence AAAATGACAGACCTCAGACAGATTGT
    probes 1 FAM-CTGCACGTGTTCC-MGB
    probes 2 HEX-CTGCACATGTTCC-MGB
    rs13433696 forward sequence GAGGGAGAAAAGGGTTTAGATAAAAGA
    reverse sequence TGCTCCATCCAGTACATCTATAATTGA
    probes 1 FAM-AACTTGTTTGGTCTCAGTG-MGB
    probes 2 VIC-ACTTGTTTGGTCTCAATGA-MGB
    rs1175543 forward sequence ATGTGAAGCCTCTGGCACAAT
    reverse sequence ATATAGGGCAAAAGGGAAAATTAGC
    probes 1 FAM-TTCAGCACACAGTAAA-MGB
    probes 2 VIC-TTCAGCACACAATAA-MGB
    rs9817428 forward sequence AAAATAAAACGCATCAGTCTCAGTAGAT
    reverse sequence GCCAAGACAAACTTCAGCTAACAA
    probes 1 FAM-ATCATCACATCGAGTTT-MGB
    probes 2 VIC-TATCATCACATCGAGGTT-MGB
    rs2972164 forward sequence CTGGACTGGCAAGCCACTCT
    reverse sequence GCATCCTTTTAGTGAAGTCCCTACTT
    probes 1 FAM-AGTGTGGAGCTATAAA-MGB
    probes 2 VIC-AGTGTGGAGCTACAAA-MGB
    AGTR1 rs2638360 forward sequence GCCAATATTTTCTTCCTTACTCATTACC
    reverse sequence GTTTGGCTCTCCAACTGCTTAAA
    probes 1 FAM-TTTCTTTAGTTTTCCAGTAAT-MGB
    probes 2 HEX-TCTTTAGTTTTCCAATAAT-MGB
    rs1492100 forward sequence CCTGTGCTGTTCTCAGGTTCTG
    reverse sequence CACATGGAGTTTCCCTCTCATG
    probes 1 FAM-ATTGGATGGCTTTTT-MGB
    probes 2 VIC-ATTGGATGGCTATTTAG-MGB
    rs5182 forward sequence TGCTTTCCATTATGAGTCCCAAA
    reverse sequence GAAAAGGAAACAGGAAACCCAGTA
    probes 1 FAM-CAACCCTTCCGATAGG-MGB
    probes 2 VIC-TTCAACCCTCCCGATAG-MGB
    rs2933249 forward sequence GGCTAAGGCTGTAGGGATTGG
    reverse sequence TCCCAGATGTCCTTTGAATAATCA
    probes 1 FAM-TGCTTCTCCTTCTTCAGT-MGB
    probes 2 VIC-TGCTTCTCCTTCCTC-MGB
    rs275646 forward sequence GGAAATTCATCTTTTTGGACATCA
    reverse sequence CAACAAGAGTGAAACTCCATCTCAA
    probes 1 FAM-ATCATTTTTCAAGTATGGTGAG-MGB
    probes 2 VIC-CATCATTTTTCAAGTACGG-MGB
    下载: 导出CSV

    Table  1.   Baseline Characteristics of the Participants in This Study

    Variables Group HTG (n = 482) Normal-TG (n = 1, 109) t/χ2 P
    Gender Male 214 (44.4) 468 (42.2) 0.66 0.421
    Female 268 (55.6) 641 (57.8)
    Age (year) 56.10 ± 9.95 53.90 ± 9.59 0.38 0.886
    Blood pressure (mmHg) SBP 127.51 ± 16.32 123.81 ± 16.41 4.13 < 0.001
    DBP 80.72 ± 11.76 78.42 ± 11.20 3.71 < 0.001
    TC (mmol/L) 5.23 ± 1.03 4.70 ± 0.82 9.95 < 0.001
    TG (mmol/L) 2.97 ± 1.40 1.03 ± 0.34 29.92 < 0.001
    HDL-C (mmol/L) 1.31 ± 0.33 1.43 ± 0.28 6.53 < 0.001
    LDL-C (mmol/L) 2.74 ± 0.85 2.58 ± 0.61 3.70 < 0.001
    FBG (mmol/L) 5.81 ± 1.04 5.57 ± 0.79 4.47 < 0.001
    BMI (kg/m2) 24.63 ± 2.87 23.07 ± 2.95 9.78 < 0.001
    WC (cm) 83.16 ± 8.06 79.32 ± 8.11 6.76 < 0.001
    Smoking Yes 156 (32.4) 300 (27.1) 4.64 0.032
    No 326 (67.6) 809 (72.9)
    Drinking Yes 100 (20.7) 183 (16.5) 4.14 0.041
    No 382 (79.3) 926 (83.5)
    下载: 导出CSV

    Table  2.   Association of the Selected SNP Genotypes with HTG

    SNP Model Genotype n(%) Me Se dif 95% CI P AIC
    rs13433696 Codominant G/G 658 (41.6) 1.639 0.048 0.000 1 0.112 5, 099
    G/A 714 (45.1) 1.650 0.047 0.016 -0.112, 0.145
    A/A 210 (13.3) 1.461 0.067 -0.178 -0.366, 0.010
    Dominant G/G 658 (41.6) 1.639 0.048 0.000 1 0.652 5, 101
    G/A-A/A 924 (58.4) 1.607 0.040 -0.028 -0.149, 0.093
    Recessive G/G-G/A 1, 372 (86.7) 1.645 0.034 0.000 1 0.038 5, 097
    A/A 210 (13.3) 1.461 0.067 -0.186 -0.362, -0.011
    Overdominant G/G-A/A 868 (54.9) 1.596 0.040 0.000 1 0.332 5, 100
    G/A 714 (45.1) 1.650 0.047 0.059 -0.061, 0.179
    rs5182 Codominant T/T 754 (47.4) 1.638 0.045 0.000 1 0.069 5, 121
    T/C 692 (43.5) 1.563 0.044 -0.077 -0.202, 0.048
    C/C 144 (9.1) 1.811 0.108 0.171 -0.044, 0.387
    Dominant T/T 754 (47.4) 1.638 0.045 0.000 1 0.577 5, 124
    T/C-C/C 836 (52.6) 1.606 0.041 -0.034 -0.153, 0.085
    Recessive T/T-T/C 1, 446 (91.9) 1.602 0.032 0.000 1 0.049 5, 120
    C/C 144 (9.1) 1.811 0.108 0.208 0.001, 0.415
    Overdominant T/T-C/C 898 (76.7) 1.666 0.042 0.000 1 0.657 5, 124
    T/C 692 (43.5) 1.563 0.044 -0.105 -0.225, 0.016
    Note. Adjusted for age, sex, BMI, drinking and smoking propensities. Dif, difference. AIC, Akake information criterion.
    下载: 导出CSV

    Supplementary Table S3.   Associations of the Selected SNPs Genotypes in PPARγ Gene with HTG

    SNP Model Genotype n(%) me se dif Lower, upper P AIC
    rs12631819 Codominant G/G 623 (39.3) 1.674 0.052 0.000 1 0.186 5104
    G/T 759 (47.9) 1.609 0.042 -0.006 -0.193, 0.064
    T/T 202 (12.8) 1.505 0.070 -0.176 -0.368, 0.016
    Dominant G/G 623 (39.3) 1.674 0.000 0.000 1 0.156 5103
    G/T-T/T 961 (60.7) 1.587 0.037 -0.088 -0.211, 0.034
    Recessive G/G-G/T 1, 382 (87.2) 1.638 0.033 0.000 1 0.123 5103
    T/T 202 (12.8) 1.505 0.070 -0.141 -0.320, -0.038
    Overdominant G/G-T/T 825 (52.1) 1.632 0.043 0.000 1 0.719 5105
    G/T 759 (47.9) 1.609 0.043 -0.022 -0.142, 0.098
    rs12920502 Codominant G/G 817 (51.6) 1.603 0.040 0.000 1 0.713 5106
    G/C 659 (41.6) 1.632 0.048 0.032 -0.093, 0.156
    C/C 107 (6.8) 1.701 0.149 0.094 -0.150, 0.338
    Dominant G/G 817 (51.6) 1.603 0.040 0.000 1 0.509 5104
    G/C-C/C 766 (48.4) 1.642 0.046 0.040 -0.079, 0.160
    Recessive G/G-G/C 1, 476 (83.2) 1.616 0.031 0.000 1 0.511 5104
    C/C 107 (6.8) 1.701 0.149 0.080 -0.158, 0.317
    Overdominant G/G-T/T 924 (58.4) 1.614 0.039 0.000 1 0.738 5105
    G/T 659 (41.6) 1.609 0.048 0.021 -0.100, 0.142
    rs3656806 Codominant C/C 882 (55.9) 1.654 0.042 0.000 1 0.391 5066
    C/T 588 (37.3) 1.583 0.048 -0.066 -0.192, 0.059
    T/T 107 (6.8) 1.515 0.093 -0.135 -0.377, 0.106
    Dominant C/C 882 (55.9) 1.615 0.034 0.000 1 0.689 5126
    C/T-T/T 695 (44.1) 1.573 0.043 -0.077 -0.197, 0.043
    Recessive C/C-C/T 1, 470 (93.2) 1.625 0.032 0.000 1 0.367 5065
    T/T 107 (6.8) 1.515 0.093 -0.109 -0.345, 0.127
    Overdominant C/C-T/T 989 (62.7) 1.639 0.039 0.000 1 0.411 5065
    C/T 588 (37.3) 1.583 0.048 -0.052 -0.174, 0.071
    rs1175543 Codominant A/A 479 (30.2) 1.606 0.057 0.000 1 0.551 5114
    A/G 800 (50.4) 1.601 0.038 0.003 -0.134, 0.140
    G/G 307 (19.4) 1.696 0.083 0.086 -0.088, 0.259
    Dominant A/A 479 (30.2) 1.606 0.057 0.000 1 0.692 5113
    A/G-G/G 1, 107 (69.8) 1.627 0.036 0.026 -0.104, 0.156
    Recessive A/A-A/G 1, 279 (80.6) 1.603 0.032 0.000 1 0.275 5112
    G/G 307 (19.4) 1.696 0.083 0.084 -0.067, 0.235
    Overdominant A/A-G/G 786 (49.6) 1.641 0.047 0.000 1 0.618 5113
    A/G 800 (50.4) 1.601 0.038 -0.030 -0.150, 0.089
    rs9817428 Codominant A/A 477 (30.1) 1.617 0.059 0.000 1 0.259 5116
    A/C 786 (49.5) 1.585 0.038 0.003 -0.162, 0.114
    C/C 324 (20.4) 1.720 0.078 0.086 -0.064, 0.277
    Dominant A/A 477 (30.1) 1.617 0.059 0.000 1 0.829 5117
    A/C-C/C 1, 110 (69.9) 1.625 0.035 0.014 -0.116, 0.144
    Recessive A/A-A/C 1, 263 (79.6) 1.597 0.033 0.000 1 0.108 5114
    C/C 324 (20.4) 1.720 0.077 0.121 -0.026, 0.269
    Overdominant A/A-C/C 801 (49.6) 1.659 0.047 0.000 1 0.271 5116
    A/C 786 (50.4) 1.585 0.038 -0.067 -0.186, 0.052
    rs2972164 Codominant C/C 1, 344 (84.5) 1.625 0.033 0.000 1 0.778 5126
    C/T 237 (14.9) 1.610 0.081 -0.017 -0.185, 0.150
    T/T 9 (0.6) 1.324 0.156 -0.277 -1.071, 0.516
    Dominant C/C 1, 344 (84.5) 1.625 0.033 0.000 1 0.749 5124
    C/T-T/T 246 (15.5) 1.600 0.078 -0.027 -0.191, 0.138
    Recessive C/C-C/T 1, 581 (99.4) 1.623 0.031 0.000 1 0.497 5124
    T/T 9 (0.6) 1.324 0.156 -0.275 -1.068, 0.518
    Overdominant C/C-T/T 1, 353 (85.1) 1.623 0.033 0.000 1 0.856 5124
    C/T 237 (14.9) 1.610 0.081 -0.016 -0.183, 0.152
    Note. Adjusted for age, sex, BMI, drinking and smoking.
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    Supplementary Table S4.   Associations of the Selected SNPs Genotypes in AGTR1 Gene with HTG

    SNP Model Genotype n(%) me se dif Lower, upper P AIC
    rs2638360 Codominant T/T 1, 294 (81.3) 1.615 0.034 0.000 1 0.544 5127
    T/C 286 (18.0) 1.633 0.068 0.017 -0.138, 0.172
    C/C 11 (0.7) 2.011 0.326 0.399 -0.319, 1.117
    Dominant T/T 1, 294 (81.9) 1.615 0.034 0.000 1 0.689 5126
    T/C-C/C 297 (18.7) 1.647 0.067 -0.031 -0.121, 0.184
    Recessive T/T-T/C 1, 580 (99.3) 1.618 0.030 0.000 1 0.279 5125
    C/C 11 (0.7) 2.011 0.326 0.396 -0.321, 1.113
    Overdominant T/T-C/C 1, 305 (82.0) 1.618 0.034 0.000 1 0.863 5127
    T/C 286 (18.0) 1.633 0.068 0.014 -0.141, 0.168
    rs1492100 Codominant A/A 1, 200 (76.1) 1.651 0.035 0.000 1 0.148 5088
    A/T 347 (22.0) 1.515 0.063 -0.133 -0.278, 0.012
    T/T 30 (1.9) 1.781 0.234 0.140 -0.299, 0.579
    Dominant A/A 1, 200 (76.1) 1.651 0.035 0.000 1 0.120 5088
    A/T-T/T 377 (23.9) 1.536 0.061 -0.111 -0.252, 0.029
    Recessive A/A-A/T 1, 547 (90.1) 1.620 0.031 0.000 1 0.448 5090
    T/T 30 (1.9) 1.781 0.233 0.170 -0.269, 0.608
    Overdominant A/A-T/T 1, 230 (88.0) 1.654 0.035 0.000 1 0.064 5087
    A/T 347 (22.0) 1.515 0.063 -0.137 -0.281, 0.008
    rs293249 Codominant C/C 1, 198 (75.6) 1.608 0.035 0.000 1 0.458 5108
    C/T 356 (22.5) 1.651 0.065 0.044 -0.100, 0.187
    T/T 30 (1.9) 1.851 0.212 0.255 -0.184, 0.694
    Dominant C/C 1, 198 (75.6) 1.608 0.035 0.000 1 0.397 5106
    C/T-T/T 386 (24.4) 1.666 0.624 -0.060 -0.079, 0.199
    Recessive C/C-C/T 1, 554 (98.1) 1.618 0.031 0.000 1 0.272 5106
    T/T 30 (1.9) 1.851 0.212 0.245 -0.192, 0.683
    Overdominant C/C-T/T 1, 228 (77.5) 1.614 0.034 0.000 1 0.608 5107
    C/T 356 (22.5) 1.651 0.065 0.037 -0.105, 0.180
    rs275646 Codominant C/C 1, 212 (76.8) 1.619 0.034 0.000 1 0.290 5071
    C/T 344 (21.8) 1.647 0.069 0.032 -0.112, 0.176
    T/T 23 (1.4) 1.267 0.150 -0.376 -0.872, 0.121
    Dominant C/C 1, 212 (76.8) 1.619 0.034 0.000 1 0.930 5071
    C/T-T/T 367 (23.2) 1.623 0.066 0.006 -0.134, 0.147
    Recessive C/C-C/T 1, 556 (98.6) 1.625 0.031 0.000 1 0.131 5069
    T/T 23 (1.4) 1.267 0.150 -0.382 -0.878, 0.113
    Overdominant C/C-T/T 1, 235 (78.2) 1.613 0.034 0.000 1 0.597 5071
    C/T 344 (21.8) 1.647 0.069 0.039 -0.105, 0.183
    Note. Adjusted for age, sex, BMI, drinking and smoking.
    下载: 导出CSV

    Supplementary Table S5.   HWE Test for Candidate SNPs of PPARG Gene and AGTR1 Gene for Both HTG and Normal-TG Group

    Gene SNP WT/HT/MT HTG P Normal-TG P
    PPARG rs12631819 GG/GT/TT 191/233/56 0.235 432/526/146 0.476
    rs2920502 GG/GC/CC 246/200/33 0.371 541/459/74 0.154
    rs3856806 CC/CT/TT 271/174/33 0.572 611/414/75 0.669
    rs13433696 GG/GA/AA 205/220/53 0.599 493/494/157 0.235
    rs1175543 AA/AG/GG 141/246/92 0.406 338/554/215 0.655
    rs9817428 AA/AC/CC 210/225/103 0.100 267/531/221 0.157
    rs2972164 CC/CT/TT 416/64/2 0.782 928/173/7 0.729
    AGTR1 rs2638360 TT/TC/CC 384/92/6 0.854 910/194/5 0.115
    rs1492100 AA/AT/TT 330/136/12 0.648 870/211/18 0.213
    rs5182 TT/TC/CC 221/205/56 0.423 533/487/88 0.109
    rs2933249 CC/CT/TT 353/111/15 0.092 845/245/15 0.560
    rs275646 CC/CT/TT 364/109/6 0.497 848/235/17 0.876
    Note. WT wild type, HT heterozygote, MT mutant type.
    下载: 导出CSV

    Table  3.   Best Gene-gene Interaction Models Identified Using Model-based Multifactor Dimensionality Reduction Method

    Locus No. Best Model NHa βHb WHc NLd WLe βLf Wmaxg Riskh Permi
    2 rs9817428, rs1175543 1 3.76 19.54 0 NA NA 19.54 H 0.003
    3 rs9817428, rs13433696, rs2638360 2 2.81 27.38 1 3.57 -0.19 27.38 H 0.015
    4 rs2972164, rs13433696, rs6817428, rs2638360 3 4.39 54.37 2 5.41 -0.23 54.37 H 0.004
    5 rs5182, rs1175543, rs13433696, rs3856806, rs2920502 14 1.18 85.75 3 8.93 -0.51 85.75 H 0.014
    6 rs2972164, rs5182, rs9817428, rs1175543, rs3856806, rs2920502 17 1.87 144.20 3 12.22 -0.57 144.20 H 0.012
    7 rs2972164, rs5182, rs9817428, rs1175543, rs3856806, rs2920502, rs2638360 25 2.27 216.70 2 6.43 6.43 216.70 H 0.004
    8 rs275646, rs5182, rs9817428, rs1175543, rs13433696, rs3856806, rs2920502, rs2638360 32 2.31 262.00 1 3.41 -0.67 262.00 H 0.037
    9 rs5182, rs1492100, rs2972164, rs9817428, rs1175543, rs3856806, rs2920502, rs2638360, rs12631819 33 2.58 291.60 1 4.11 -0.74 291.60 H 0.030
    10 rs275646, rs5182, rs1492100, rs9817428, rs1175543, rs13433696, rs3856806, rs2920502, rs2638360, rs12631819 47 2.13 342.40 2 6.59 -0.77 342.40 H 0.139
    Note. aThe merged number of cells of high-risk categories. bThe regression coefficient of high-risk categories. cThe Wald test value of high-risk categories. dThe merged number of cells of low-risk categories. eThe regression coefficient of low-risk categories. fThe Wald test value of low-risk categories. gWmax = max(WH, WL). hThe categories of combinatorial model tested using Perm. P (H: high-risk; L: low-risk). iAdjusted for age, sex, BMI, TC, TG, HDL-C, LDL-C, FBG, smoking, and drinking with 1, 000 times replacement.
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    Supplementary Table S6.   Best Gene-gene Interaction Models Identified by the Model-based Multifactor Dimensionality Reduction Method

    Locus No. Best model NHa betaHb WHc NLd WLe betaLf Wmaxg Riskh Permi
    2 rs9817428, rs1175543 1 3.76 19.54 0 NA NA 19.54 H 0.004
    3 rs9817428, rs1175543, rs2638360 2 3.50 25.44 0 NA NA 25.44 H 0.013
    4 rs9817428, rs1175543, rs2920502, rs2638360 3 3.80 51.31 2 10.86 -0.33 51.31 H 0.005
    5 rs2933249, rs9817428, rs1175543 rs3856806, rs2920502 10 1.36 71.60 3 8.26 -0.34 71.60 H 0.018
    6 rs275646, rs9817428, rs1175543, rs2638360, rs3856806, rs2920502 16 1.65 116.10 2 6.90 -0.43 116.10 H 0.010
    7 rs275646, rs9817428, rs1175543, rs2638360, rs3856806, rs2920502, rs12631819 20 2.02 140.60 2 6.66 -0.45 140.60 H 0.038
    8 rs275646, rs2933249, rs1492100, rs2972164, rs1175543, rs3856806, rs2920502, rs2638360 25 1.59 147.30 1 2.72 -0.70 147.30 H 0.165
    Note. aThe merged number of cells of high-risk categories. bThe regression coefficient of high-risk categories. cThe Wald test value of high-risk categories. dThe merged number of cells of low-risk categories. eThe regression coefficient of low-risk categories. fThe Wald test value of low-risk categories. gWmax = max (WH, WL). hThe categories of combinatorial model tested by Perm. P (H: high-risk; L: low-risk). iAdjusted for age, sex, BMI, TC, TG, HDL-C, LDL-C, FBG, smoking, and drinking with 1, 000 times replacement.
    下载: 导出CSV
  • [1] Abou Ziki MD, Mani A. Metabolic syndrome:genetic insights into disease pathogenesis. Curr Opin Lipidol, 2016; 27, 162-71. doi:  10.1097/MOL.0000000000000276
    [2] Michel MC, Brunner HR, Foster C, et al. Angiotensin Ⅱ type 1 receptor antagonists in animal models of vascular, cardiac, metabolic and renal disease. Pharmacol Ther, 2016; 164, 1-81. doi:  10.1016/j.pharmthera.2016.03.019
    [3] Qian X, Guo D, Zhou H, et al. Interactions between PPARG and AGTR1 gene polymorphisms on the risk of hypertension in Chinese han population. Genet Test Mol Biomarkers, 2018; 22, 90-7. doi:  10.1089/gtmb.2017.0141
    [4] Martínezrodríguez N, Posadasromero C, Cardoso G, et al. Association of angiotensin Ⅱ type 1-receptor gene polymorphisms with the risk of developing hypertension in Mexican individuals. J Renin Angiotensin Aldosterone Syst, 2012; 13, 133-40. doi:  10.1177/1470320311419175
    [5] Chaves FJ, Pascual JM, Rovira E, et al. Angiotensin Ⅱ AT1 receptor gene polymorphism and microalbuminuria in essential hypertension. Am J Hypertens, 2001; 14, 364-70. doi:  10.1016/S0895-7061(00)01284-X
    [6] Zhu X, Yan D, Cooper RS, et al. Linkage disequilibrium and haplotype diversity in the genes of the renin-angiotensin system:findings from the family blood pressure program. Genome Res, 2003; 13, 173-81. doi:  10.1101/gr.302003
    [7] Hai B, Xie H, Guo Z, et al. Gene-Gene Interactions among Pparα/δ/γ Polymorphisms for Apolipoprotein (Apo) A-I/Apob Ratio in Chinese Han Population. Iran J Public Health, 2014; 43, 749-59. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000004067547
    [8] Chan KH, Niu T, Ma Y, et al. Common genetic variants in peroxisome proliferator-activated receptor-γ (PPARG) and type 2 diabetes risk among Women's Health Initiative postmenopausal women. J Clin Endocrinol Metab, 2013; 98, E600-4. doi:  10.1210/jc.2012-3644
    [9] Guo SX, Guo H, Ma RL, et al. Analysis of the haplotype and linkage disequilibrium of PPARγ gene polymorphisms rs3856806, rs12490265, rs1797912, and rs1175543 among patients with metabolic syndrome in Kazakh of Xinjiang Province. Genet Mol Res, 2014; 13, 8686-94. doi:  10.4238/2014.October.27.9
    [10] Auclair M, Vigouroux C, Boccara F, et al. Peroxisome proliferator-activated receptor-γ mutations responsible for lipodystrophy with severe hypertension activate the cellular renin-angiotensin system. Arterioscler Thromb Vasc Biol, 2013; 33, 829-38. doi:  10.1161/ATVBAHA.112.300962
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  • 收稿日期:  2018-04-23
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Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population

doi: 10.3967/bes2018.084
    基金项目:

    grants from the National Nature Science Foundation of China 81502869

    Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201605

    Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201706

    Suzhou Key technologies of prevention and control of major diseases and infectious diseases Gwzx201506

    作者简介:

    DING Yi, female, born in 1985, PHD candidate, majoring in epidemiology and health statistics

    通讯作者: ZHOU Hui, E-mail:childhealth@suda.edu.cnDONG Chen, E-mail:cdong@suda.edu.cn

English Abstract

DING Yi, GUO Dao Xia, JING Yang, WANG Jie, SHEN Chong, ZHOU Hui, DONG Chen. Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population[J]. Biomedical and Environmental Sciences, 2018, 31(8): 619-622. doi: 10.3967/bes2018.084
Citation: DING Yi, GUO Dao Xia, JING Yang, WANG Jie, SHEN Chong, ZHOU Hui, DONG Chen. Association of PPARγ and AGTR1 Polymorphisms with Hypertriglyceridemia in Chinese Population[J]. Biomedical and Environmental Sciences, 2018, 31(8): 619-622. doi: 10.3967/bes2018.084
  • Hypertriglyceridemia (HTG) is an important metabolic disease and strongly associated with the development of hypertension, atherosclerosis, coronary artery disease, and type 2 diabetes mellitus (T2DM). HTG risk is affected by various factors and might occur owing to the complex synergistic interaction between the genetic background and environmental factors[1].

    Among peroxisome proliferator-activated receptors (PPARs), PPARγ is an isotype that was recognized to play a major role in the regulation of fatty acid metabolism, probably in the adipose tissue storage and free fatty acids reduction. Several studies suggested that the single nucleotide polymorphisms (SNPs) in PPARγ including rs10865710, rs1805192, and rs709158 are associated with HTG related diseases. However, the results obtained from these studies remain controversial.

    Angiotensin Ⅱ type Ⅰ receptor (AGTR1) is a G-protein-coupled receptor of angiotensin Ⅱ that is a peptide hormone and plays a fundamental role as a vasoconstrictor in the regulation of cardiovascular function, renal homeostasis, oxidative stress, and lipid and cholesterol metabolism[2]. During the past decades, several studies reported the association between the AGTR1 polymorphisms and risk of HTG-related diseases. HTG risk is affected by several gene polymorphisms or interactions among several genes and PPARγ as well as AGTR1 are risk factors of HTG-associated diseases; however, to date, merely a few studies focused on the effects of PPARγ-AGTR1 interaction on HTG risk. Therefore, in this study, we aimed to investigate the effect of PPARγ and AGTR1 polymorphisms and synergistic interaction between these two genes on the HTG risk.

    In this study, we included a total of 1, 591 participants who were selected from a prospective cohort study of '135' in Soochow, China, that was performed during the period from August, 2012 to March, 2013 and designed to investigate the prevention strategy for chronic diseases[3]. In the current study, the age range of subjects was 53.95 ± 9.61 and the protocol was approved by the independent ethics committee of Suzhou Industrial Park (Soochow, China) in accordance with the Declaration of Helsinki (1975). A written informed consent was obtained from each participant.

    The body weight, height, and waist circumference (WC) were measured by following the standardized procedures. Blood pressure (BP) was measured thrice in a seated position with 1 min interval between measurements using a mercury sphygmomanometer. Blood samples were collected in the morning after fasting for at least 8 h. All the plasma and serum samples were frozen at -80 ℃ until their use in the laboratory experiments. Plasma glucose was measured using oxidase enzymatic method.

    The triglyceride (TG) levels were quantified and HTG was established according to the criteria defined by the National Cholesterol Education Program (NCEP) Adult Treatment Panel Ⅲ (ATP Ⅲ) that is TG ≥ 1.7 mmol/L.

    In this study, all the SNPs were selected based on the criteria of minor allele frequency (MAF) ≥ 0.05 and r2 ≥ 0.8 according to the linkage disequilibrium (LD) values obtained using haploview version 4.2 software. The functional SNPs were analyzed by using the websites of selection tool for SNPs and SNPs with predictive biological effects were retuned as tag-SNPs (Supplementary Table S1, available in www.besjournal.com).

    Table Supplementary Table S1.  Biological Information about Candidate SNPs of PPARG Gene and AGTR1 Gene

    Gene tagSNPs HGVS Nomenclature Chromosome Allelesa Region Biological Effect Transcription Factor Binding Site MAFb, c
    PPARG rs12631819 NC_000003.12: g.12301362G > T 3 G/T 12301362 intron variant cap 0.378/0.402
    rs2920502 NC_000003.12: g.12287696G > C 3 G/C 12287696 upstream variant 2KB cap 0.273/0.244
    rs3856806 NC_000003.12: g.12434058C > 3 C/T 12434058 intron variant, synonymous codon, utr variant 3 prime - 0.268/0.250
    rs13433696 NC_000003.12: g.12316993G > A 3 G/A 12316993 intron variant Skn-1, cap 0.378/0.390
    rs1175543 NC_000003.12: g.12424934A > G 3 A/G 12424934 intron variant CdxA 0.450/0.484
    rs9817428 NC_000003.12: g.12298768C > A 3 A/C 12298768 intron variant HSF, SRY 0.435/0.402
    rs2972164 NC_000003.12: g.12292917T > C 3 C/T 12292917 intron variant CdxA, Abd-B 0.090/0.073
    AGTR1 rs2638360 NC_000003.12: g.148710569G > A 3 T/C 148710569 intron variant CdxA, Dfd, Oct-1, Skn-1, cap, STATx, C/EBPa 0.077/0.156
    rs1492100 NC_000003.12: g.148719640T > A 3 A/T 148719640 intron variant Hb 0.122/0.098
    rs5182 NC_000003.12: g.148741608C > T 3 T/C 148741608 synonymous codon - 0.306/0.433
    rs2933249 NC_000003.12: g.148698733G > A 3 C/T 148698733 intron variant HSF 0.128/0.098
    rs275646 NC_000003.12: g.148745735T > C 3 C/T 148745735 - - 0.120/0.110
    Note. aMajor/minor allele. bMAF in the control. cMAF in CHB.

    The DNA extraction and genotyping were performed by following the previously described method[3]. (Supplementary Table S2, available in www.besjournal.com)

    Table Supplementary Table S2.  The Informations about the Primers and Probes of the Candidate SNPs of PPARG Gene and AGTR1 Gene

    Gene SNP Primers and Probes
    PPARG rs12631819 forward sequence AAATGAGGCCAAAACTTGATAGTGT
    reverse sequence AAGGTTTACAATAATGCCCAGTACAA
    probes 1 FAM-AAGTTTAAGAAGAGAACCAG-MGB
    probes 2 HEX-AGTTTAAGAAGAGAACAAGT-MGB
    rs2920502 forward sequence GCACAGTAGGGCCCACG
    reverse sequence GGATCCCTCCTCGGAAATG
    probes 1 FAM-CCACTCTCTGCCC-MGB
    probes 2 HEX-CCACTGTCTGCCC-MGB
    rs3856806 forward sequence CGTCTTCTTGATCACCTGCAGTA
    reverse sequence AAAATGACAGACCTCAGACAGATTGT
    probes 1 FAM-CTGCACGTGTTCC-MGB
    probes 2 HEX-CTGCACATGTTCC-MGB
    rs13433696 forward sequence GAGGGAGAAAAGGGTTTAGATAAAAGA
    reverse sequence TGCTCCATCCAGTACATCTATAATTGA
    probes 1 FAM-AACTTGTTTGGTCTCAGTG-MGB
    probes 2 VIC-ACTTGTTTGGTCTCAATGA-MGB
    rs1175543 forward sequence ATGTGAAGCCTCTGGCACAAT
    reverse sequence ATATAGGGCAAAAGGGAAAATTAGC
    probes 1 FAM-TTCAGCACACAGTAAA-MGB
    probes 2 VIC-TTCAGCACACAATAA-MGB
    rs9817428 forward sequence AAAATAAAACGCATCAGTCTCAGTAGAT
    reverse sequence GCCAAGACAAACTTCAGCTAACAA
    probes 1 FAM-ATCATCACATCGAGTTT-MGB
    probes 2 VIC-TATCATCACATCGAGGTT-MGB
    rs2972164 forward sequence CTGGACTGGCAAGCCACTCT
    reverse sequence GCATCCTTTTAGTGAAGTCCCTACTT
    probes 1 FAM-AGTGTGGAGCTATAAA-MGB
    probes 2 VIC-AGTGTGGAGCTACAAA-MGB
    AGTR1 rs2638360 forward sequence GCCAATATTTTCTTCCTTACTCATTACC
    reverse sequence GTTTGGCTCTCCAACTGCTTAAA
    probes 1 FAM-TTTCTTTAGTTTTCCAGTAAT-MGB
    probes 2 HEX-TCTTTAGTTTTCCAATAAT-MGB
    rs1492100 forward sequence CCTGTGCTGTTCTCAGGTTCTG
    reverse sequence CACATGGAGTTTCCCTCTCATG
    probes 1 FAM-ATTGGATGGCTTTTT-MGB
    probes 2 VIC-ATTGGATGGCTATTTAG-MGB
    rs5182 forward sequence TGCTTTCCATTATGAGTCCCAAA
    reverse sequence GAAAAGGAAACAGGAAACCCAGTA
    probes 1 FAM-CAACCCTTCCGATAGG-MGB
    probes 2 VIC-TTCAACCCTCCCGATAG-MGB
    rs2933249 forward sequence GGCTAAGGCTGTAGGGATTGG
    reverse sequence TCCCAGATGTCCTTTGAATAATCA
    probes 1 FAM-TGCTTCTCCTTCTTCAGT-MGB
    probes 2 VIC-TGCTTCTCCTTCCTC-MGB
    rs275646 forward sequence GGAAATTCATCTTTTTGGACATCA
    reverse sequence CAACAAGAGTGAAACTCCATCTCAA
    probes 1 FAM-ATCATTTTTCAAGTATGGTGAG-MGB
    probes 2 VIC-CATCATTTTTCAAGTACGG-MGB

    Linear regression was applied to analyze the association between the gene polymorphisms and HTG risk. The association between the tag-SNPs and HTG risk was analyzed using SNPAssoc package of R. The potential gene-gene interactions among the selected polymorphisms were validated using model-based multifactor dimensionality reduction (MB-MDR) method[20]. All the statistical analyses were performed using R (X64 3.2.5) and SAS 9.4. P ≤ 0.05 was considered to be statistically significant.

    In this study, among the 1, 591 participants 29.4% were identified as HTG patients and 44.4% of these HTG patients were males. The HTG group exhibited a higher systolic BP (SBP) and diastolic BP (DBP), body mass index (BMI), WC, TG, total cholesterol (TC), low-density lipoprotein cholesterol LDL-C, and fast blood glucose (FBG) levels as well as propensity for smoking and drinking than those in the normal-TG group. Contradictorily, the high-density lipoprotein cholesterol (HDL-C) levels in the HTG group were lower than those in the normal-TG group (Table 1).

    Table 1.  Baseline Characteristics of the Participants in This Study

    Variables Group HTG (n = 482) Normal-TG (n = 1, 109) t/χ2 P
    Gender Male 214 (44.4) 468 (42.2) 0.66 0.421
    Female 268 (55.6) 641 (57.8)
    Age (year) 56.10 ± 9.95 53.90 ± 9.59 0.38 0.886
    Blood pressure (mmHg) SBP 127.51 ± 16.32 123.81 ± 16.41 4.13 < 0.001
    DBP 80.72 ± 11.76 78.42 ± 11.20 3.71 < 0.001
    TC (mmol/L) 5.23 ± 1.03 4.70 ± 0.82 9.95 < 0.001
    TG (mmol/L) 2.97 ± 1.40 1.03 ± 0.34 29.92 < 0.001
    HDL-C (mmol/L) 1.31 ± 0.33 1.43 ± 0.28 6.53 < 0.001
    LDL-C (mmol/L) 2.74 ± 0.85 2.58 ± 0.61 3.70 < 0.001
    FBG (mmol/L) 5.81 ± 1.04 5.57 ± 0.79 4.47 < 0.001
    BMI (kg/m2) 24.63 ± 2.87 23.07 ± 2.95 9.78 < 0.001
    WC (cm) 83.16 ± 8.06 79.32 ± 8.11 6.76 < 0.001
    Smoking Yes 156 (32.4) 300 (27.1) 4.64 0.032
    No 326 (67.6) 809 (72.9)
    Drinking Yes 100 (20.7) 183 (16.5) 4.14 0.041
    No 382 (79.3) 926 (83.5)

    After adjustment based on the gender, age, BMI, and drinking and smoking propensity, the homozygous wild type A allele (AA, recessive model) of rs13433696 was associated with a decreased HTG risk compared to the carriers of (GA+AA) genotype (difference =-0.186, 95% CI =-0.362 to-0.011, P = 0.038). However, the homozygous wild type C allele (CC, recessive model) of rs5182 was associated with an increased HTG risk compared to the carriers of (TT+TC) genotype (difference = 0.208, 95% CI = 0.001 -0.415, P = 0.049) (Table 2). However, the remaining ten SNPs did not exhibit any association with HTG after the covariates adjustment (Supplementary Tables S3-S4, available in www.besjournal.com). The results of Hardy-Weinberg equilibrium (HWE) test to identify candidate SNPs was presented in Supplementary Table S5 (available in www.besjournal.com).

    Table 2.  Association of the Selected SNP Genotypes with HTG

    SNP Model Genotype n(%) Me Se dif 95% CI P AIC
    rs13433696 Codominant G/G 658 (41.6) 1.639 0.048 0.000 1 0.112 5, 099
    G/A 714 (45.1) 1.650 0.047 0.016 -0.112, 0.145
    A/A 210 (13.3) 1.461 0.067 -0.178 -0.366, 0.010
    Dominant G/G 658 (41.6) 1.639 0.048 0.000 1 0.652 5, 101
    G/A-A/A 924 (58.4) 1.607 0.040 -0.028 -0.149, 0.093
    Recessive G/G-G/A 1, 372 (86.7) 1.645 0.034 0.000 1 0.038 5, 097
    A/A 210 (13.3) 1.461 0.067 -0.186 -0.362, -0.011
    Overdominant G/G-A/A 868 (54.9) 1.596 0.040 0.000 1 0.332 5, 100
    G/A 714 (45.1) 1.650 0.047 0.059 -0.061, 0.179
    rs5182 Codominant T/T 754 (47.4) 1.638 0.045 0.000 1 0.069 5, 121
    T/C 692 (43.5) 1.563 0.044 -0.077 -0.202, 0.048
    C/C 144 (9.1) 1.811 0.108 0.171 -0.044, 0.387
    Dominant T/T 754 (47.4) 1.638 0.045 0.000 1 0.577 5, 124
    T/C-C/C 836 (52.6) 1.606 0.041 -0.034 -0.153, 0.085
    Recessive T/T-T/C 1, 446 (91.9) 1.602 0.032 0.000 1 0.049 5, 120
    C/C 144 (9.1) 1.811 0.108 0.208 0.001, 0.415
    Overdominant T/T-C/C 898 (76.7) 1.666 0.042 0.000 1 0.657 5, 124
    T/C 692 (43.5) 1.563 0.044 -0.105 -0.225, 0.016
    Note. Adjusted for age, sex, BMI, drinking and smoking propensities. Dif, difference. AIC, Akake information criterion.

    Table Supplementary Table S3.  Associations of the Selected SNPs Genotypes in PPARγ Gene with HTG

    SNP Model Genotype n(%) me se dif Lower, upper P AIC
    rs12631819 Codominant G/G 623 (39.3) 1.674 0.052 0.000 1 0.186 5104
    G/T 759 (47.9) 1.609 0.042 -0.006 -0.193, 0.064
    T/T 202 (12.8) 1.505 0.070 -0.176 -0.368, 0.016
    Dominant G/G 623 (39.3) 1.674 0.000 0.000 1 0.156 5103
    G/T-T/T 961 (60.7) 1.587 0.037 -0.088 -0.211, 0.034
    Recessive G/G-G/T 1, 382 (87.2) 1.638 0.033 0.000 1 0.123 5103
    T/T 202 (12.8) 1.505 0.070 -0.141 -0.320, -0.038
    Overdominant G/G-T/T 825 (52.1) 1.632 0.043 0.000 1 0.719 5105
    G/T 759 (47.9) 1.609 0.043 -0.022 -0.142, 0.098
    rs12920502 Codominant G/G 817 (51.6) 1.603 0.040 0.000 1 0.713 5106
    G/C 659 (41.6) 1.632 0.048 0.032 -0.093, 0.156
    C/C 107 (6.8) 1.701 0.149 0.094 -0.150, 0.338
    Dominant G/G 817 (51.6) 1.603 0.040 0.000 1 0.509 5104
    G/C-C/C 766 (48.4) 1.642 0.046 0.040 -0.079, 0.160
    Recessive G/G-G/C 1, 476 (83.2) 1.616 0.031 0.000 1 0.511 5104
    C/C 107 (6.8) 1.701 0.149 0.080 -0.158, 0.317
    Overdominant G/G-T/T 924 (58.4) 1.614 0.039 0.000 1 0.738 5105
    G/T 659 (41.6) 1.609 0.048 0.021 -0.100, 0.142
    rs3656806 Codominant C/C 882 (55.9) 1.654 0.042 0.000 1 0.391 5066
    C/T 588 (37.3) 1.583 0.048 -0.066 -0.192, 0.059
    T/T 107 (6.8) 1.515 0.093 -0.135 -0.377, 0.106
    Dominant C/C 882 (55.9) 1.615 0.034 0.000 1 0.689 5126
    C/T-T/T 695 (44.1) 1.573 0.043 -0.077 -0.197, 0.043
    Recessive C/C-C/T 1, 470 (93.2) 1.625 0.032 0.000 1 0.367 5065
    T/T 107 (6.8) 1.515 0.093 -0.109 -0.345, 0.127
    Overdominant C/C-T/T 989 (62.7) 1.639 0.039 0.000 1 0.411 5065
    C/T 588 (37.3) 1.583 0.048 -0.052 -0.174, 0.071
    rs1175543 Codominant A/A 479 (30.2) 1.606 0.057 0.000 1 0.551 5114
    A/G 800 (50.4) 1.601 0.038 0.003 -0.134, 0.140
    G/G 307 (19.4) 1.696 0.083 0.086 -0.088, 0.259
    Dominant A/A 479 (30.2) 1.606 0.057 0.000 1 0.692 5113
    A/G-G/G 1, 107 (69.8) 1.627 0.036 0.026 -0.104, 0.156
    Recessive A/A-A/G 1, 279 (80.6) 1.603 0.032 0.000 1 0.275 5112
    G/G 307 (19.4) 1.696 0.083 0.084 -0.067, 0.235
    Overdominant A/A-G/G 786 (49.6) 1.641 0.047 0.000 1 0.618 5113
    A/G 800 (50.4) 1.601 0.038 -0.030 -0.150, 0.089
    rs9817428 Codominant A/A 477 (30.1) 1.617 0.059 0.000 1 0.259 5116
    A/C 786 (49.5) 1.585 0.038 0.003 -0.162, 0.114
    C/C 324 (20.4) 1.720 0.078 0.086 -0.064, 0.277
    Dominant A/A 477 (30.1) 1.617 0.059 0.000 1 0.829 5117
    A/C-C/C 1, 110 (69.9) 1.625 0.035 0.014 -0.116, 0.144
    Recessive A/A-A/C 1, 263 (79.6) 1.597 0.033 0.000 1 0.108 5114
    C/C 324 (20.4) 1.720 0.077 0.121 -0.026, 0.269
    Overdominant A/A-C/C 801 (49.6) 1.659 0.047 0.000 1 0.271 5116
    A/C 786 (50.4) 1.585 0.038 -0.067 -0.186, 0.052
    rs2972164 Codominant C/C 1, 344 (84.5) 1.625 0.033 0.000 1 0.778 5126
    C/T 237 (14.9) 1.610 0.081 -0.017 -0.185, 0.150
    T/T 9 (0.6) 1.324 0.156 -0.277 -1.071, 0.516
    Dominant C/C 1, 344 (84.5) 1.625 0.033 0.000 1 0.749 5124
    C/T-T/T 246 (15.5) 1.600 0.078 -0.027 -0.191, 0.138
    Recessive C/C-C/T 1, 581 (99.4) 1.623 0.031 0.000 1 0.497 5124
    T/T 9 (0.6) 1.324 0.156 -0.275 -1.068, 0.518
    Overdominant C/C-T/T 1, 353 (85.1) 1.623 0.033 0.000 1 0.856 5124
    C/T 237 (14.9) 1.610 0.081 -0.016 -0.183, 0.152
    Note. Adjusted for age, sex, BMI, drinking and smoking.

    Table Supplementary Table S4.  Associations of the Selected SNPs Genotypes in AGTR1 Gene with HTG

    SNP Model Genotype n(%) me se dif Lower, upper P AIC
    rs2638360 Codominant T/T 1, 294 (81.3) 1.615 0.034 0.000 1 0.544 5127
    T/C 286 (18.0) 1.633 0.068 0.017 -0.138, 0.172
    C/C 11 (0.7) 2.011 0.326 0.399 -0.319, 1.117
    Dominant T/T 1, 294 (81.9) 1.615 0.034 0.000 1 0.689 5126
    T/C-C/C 297 (18.7) 1.647 0.067 -0.031 -0.121, 0.184
    Recessive T/T-T/C 1, 580 (99.3) 1.618 0.030 0.000 1 0.279 5125
    C/C 11 (0.7) 2.011 0.326 0.396 -0.321, 1.113
    Overdominant T/T-C/C 1, 305 (82.0) 1.618 0.034 0.000 1 0.863 5127
    T/C 286 (18.0) 1.633 0.068 0.014 -0.141, 0.168
    rs1492100 Codominant A/A 1, 200 (76.1) 1.651 0.035 0.000 1 0.148 5088
    A/T 347 (22.0) 1.515 0.063 -0.133 -0.278, 0.012
    T/T 30 (1.9) 1.781 0.234 0.140 -0.299, 0.579
    Dominant A/A 1, 200 (76.1) 1.651 0.035 0.000 1 0.120 5088
    A/T-T/T 377 (23.9) 1.536 0.061 -0.111 -0.252, 0.029
    Recessive A/A-A/T 1, 547 (90.1) 1.620 0.031 0.000 1 0.448 5090
    T/T 30 (1.9) 1.781 0.233 0.170 -0.269, 0.608
    Overdominant A/A-T/T 1, 230 (88.0) 1.654 0.035 0.000 1 0.064 5087
    A/T 347 (22.0) 1.515 0.063 -0.137 -0.281, 0.008
    rs293249 Codominant C/C 1, 198 (75.6) 1.608 0.035 0.000 1 0.458 5108
    C/T 356 (22.5) 1.651 0.065 0.044 -0.100, 0.187
    T/T 30 (1.9) 1.851 0.212 0.255 -0.184, 0.694
    Dominant C/C 1, 198 (75.6) 1.608 0.035 0.000 1 0.397 5106
    C/T-T/T 386 (24.4) 1.666 0.624 -0.060 -0.079, 0.199
    Recessive C/C-C/T 1, 554 (98.1) 1.618 0.031 0.000 1 0.272 5106
    T/T 30 (1.9) 1.851 0.212 0.245 -0.192, 0.683
    Overdominant C/C-T/T 1, 228 (77.5) 1.614 0.034 0.000 1 0.608 5107
    C/T 356 (22.5) 1.651 0.065 0.037 -0.105, 0.180
    rs275646 Codominant C/C 1, 212 (76.8) 1.619 0.034 0.000 1 0.290 5071
    C/T 344 (21.8) 1.647 0.069 0.032 -0.112, 0.176
    T/T 23 (1.4) 1.267 0.150 -0.376 -0.872, 0.121
    Dominant C/C 1, 212 (76.8) 1.619 0.034 0.000 1 0.930 5071
    C/T-T/T 367 (23.2) 1.623 0.066 0.006 -0.134, 0.147
    Recessive C/C-C/T 1, 556 (98.6) 1.625 0.031 0.000 1 0.131 5069
    T/T 23 (1.4) 1.267 0.150 -0.382 -0.878, 0.113
    Overdominant C/C-T/T 1, 235 (78.2) 1.613 0.034 0.000 1 0.597 5071
    C/T 344 (21.8) 1.647 0.069 0.039 -0.105, 0.183
    Note. Adjusted for age, sex, BMI, drinking and smoking.

    Table Supplementary Table S5.  HWE Test for Candidate SNPs of PPARG Gene and AGTR1 Gene for Both HTG and Normal-TG Group

    Gene SNP WT/HT/MT HTG P Normal-TG P
    PPARG rs12631819 GG/GT/TT 191/233/56 0.235 432/526/146 0.476
    rs2920502 GG/GC/CC 246/200/33 0.371 541/459/74 0.154
    rs3856806 CC/CT/TT 271/174/33 0.572 611/414/75 0.669
    rs13433696 GG/GA/AA 205/220/53 0.599 493/494/157 0.235
    rs1175543 AA/AG/GG 141/246/92 0.406 338/554/215 0.655
    rs9817428 AA/AC/CC 210/225/103 0.100 267/531/221 0.157
    rs2972164 CC/CT/TT 416/64/2 0.782 928/173/7 0.729
    AGTR1 rs2638360 TT/TC/CC 384/92/6 0.854 910/194/5 0.115
    rs1492100 AA/AT/TT 330/136/12 0.648 870/211/18 0.213
    rs5182 TT/TC/CC 221/205/56 0.423 533/487/88 0.109
    rs2933249 CC/CT/TT 353/111/15 0.092 845/245/15 0.560
    rs275646 CC/CT/TT 364/109/6 0.497 848/235/17 0.876
    Note. WT wild type, HT heterozygote, MT mutant type.

    We observed that 2 to 9-locus models were significantly associated with HTG by quantitative trait that indicated a potential gene-gene interaction among rs5182, rs1492100, rs2972164, rs9817428, rs1175543, rs3856806, rs2920502, rs2638360, and rs12631819 after adjustment for gender, age, drinking and smoking status (Table 3). As rs13433696 in PPARγ as well as rs5182 in AGTR1 were potentially associated with HTG risk, we further analyzed the gene-gene interactions among the remaining ten SNPs using MB-MDR method (Supplementary Table S6, available in www.besjournal.com).

    Table 3.  Best Gene-gene Interaction Models Identified Using Model-based Multifactor Dimensionality Reduction Method

    Locus No. Best Model NHa βHb WHc NLd WLe βLf Wmaxg Riskh Permi
    2 rs9817428, rs1175543 1 3.76 19.54 0 NA NA 19.54 H 0.003
    3 rs9817428, rs13433696, rs2638360 2 2.81 27.38 1 3.57 -0.19 27.38 H 0.015
    4 rs2972164, rs13433696, rs6817428, rs2638360 3 4.39 54.37 2 5.41 -0.23 54.37 H 0.004
    5 rs5182, rs1175543, rs13433696, rs3856806, rs2920502 14 1.18 85.75 3 8.93 -0.51 85.75 H 0.014
    6 rs2972164, rs5182, rs9817428, rs1175543, rs3856806, rs2920502 17 1.87 144.20 3 12.22 -0.57 144.20 H 0.012
    7 rs2972164, rs5182, rs9817428, rs1175543, rs3856806, rs2920502, rs2638360 25 2.27 216.70 2 6.43 6.43 216.70 H 0.004
    8 rs275646, rs5182, rs9817428, rs1175543, rs13433696, rs3856806, rs2920502, rs2638360 32 2.31 262.00 1 3.41 -0.67 262.00 H 0.037
    9 rs5182, rs1492100, rs2972164, rs9817428, rs1175543, rs3856806, rs2920502, rs2638360, rs12631819 33 2.58 291.60 1 4.11 -0.74 291.60 H 0.030
    10 rs275646, rs5182, rs1492100, rs9817428, rs1175543, rs13433696, rs3856806, rs2920502, rs2638360, rs12631819 47 2.13 342.40 2 6.59 -0.77 342.40 H 0.139
    Note. aThe merged number of cells of high-risk categories. bThe regression coefficient of high-risk categories. cThe Wald test value of high-risk categories. dThe merged number of cells of low-risk categories. eThe regression coefficient of low-risk categories. fThe Wald test value of low-risk categories. gWmax = max(WH, WL). hThe categories of combinatorial model tested using Perm. P (H: high-risk; L: low-risk). iAdjusted for age, sex, BMI, TC, TG, HDL-C, LDL-C, FBG, smoking, and drinking with 1, 000 times replacement.

    Table Supplementary Table S6.  Best Gene-gene Interaction Models Identified by the Model-based Multifactor Dimensionality Reduction Method

    Locus No. Best model NHa betaHb WHc NLd WLe betaLf Wmaxg Riskh Permi
    2 rs9817428, rs1175543 1 3.76 19.54 0 NA NA 19.54 H 0.004
    3 rs9817428, rs1175543, rs2638360 2 3.50 25.44 0 NA NA 25.44 H 0.013
    4 rs9817428, rs1175543, rs2920502, rs2638360 3 3.80 51.31 2 10.86 -0.33 51.31 H 0.005
    5 rs2933249, rs9817428, rs1175543 rs3856806, rs2920502 10 1.36 71.60 3 8.26 -0.34 71.60 H 0.018
    6 rs275646, rs9817428, rs1175543, rs2638360, rs3856806, rs2920502 16 1.65 116.10 2 6.90 -0.43 116.10 H 0.010
    7 rs275646, rs9817428, rs1175543, rs2638360, rs3856806, rs2920502, rs12631819 20 2.02 140.60 2 6.66 -0.45 140.60 H 0.038
    8 rs275646, rs2933249, rs1492100, rs2972164, rs1175543, rs3856806, rs2920502, rs2638360 25 1.59 147.30 1 2.72 -0.70 147.30 H 0.165
    Note. aThe merged number of cells of high-risk categories. bThe regression coefficient of high-risk categories. cThe Wald test value of high-risk categories. dThe merged number of cells of low-risk categories. eThe regression coefficient of low-risk categories. fThe Wald test value of low-risk categories. gWmax = max (WH, WL). hThe categories of combinatorial model tested by Perm. P (H: high-risk; L: low-risk). iAdjusted for age, sex, BMI, TC, TG, HDL-C, LDL-C, FBG, smoking, and drinking with 1, 000 times replacement.

    In the present study, our results demonstrated that the AA genotype individuals with rs13433696 in PPARγ exhibited a decreased HTG risk, while the CC genotype individuals with rs5182 in ATGR1 exhibited an increased HTG risk. Furthermore, we observed that the gene-gene interactions existed in the HTG-associated SNPs as well as HTG-non-associated SNPs of PPARγ and AGTR1.

    Although the studies that analyzed the association between AGTR1 polymorphisms and risk of HTG are rare, the association between rs5182 SNP and BP was extensively discussed[4-6]. Additionally, in a case-control study, screening the exon 5 and 3x-untranslated region of ATGR1 demonstrated that solely the +1166 SNP in the 3x-untranslated region was significantly associated with hypertension among the five polymorphisms that are +573 (rs5182), +1062, +1166, +1517, and +1878. Therefore, although our results from the present study suggest that rs5182 in C allele is a risk factor for HTG, further studies are necessary to investigate the functions of ATGR1 polymorphisms.

    Our previous studies suggested that PPARγ polymorphisms such as rs3856806 allele are significantly associated with the apoA-I/apoB ratio in the Chinese Han population[7]. Additionally, Chan et al. found a borderline significant association between the Pro12Ala (rs1801282) variant in PPARγ and risk of T2DM in women's health initiative-observational study (WHI-OS)[8]. Moreover, the results of a study in Kazakh population suggested that PPARγ polymorphism rs1175543 is significantly associated with metabolic syndromes[9]. In this study, we reported that a novel variant of AA genotype with rs13433696 in PPARγ is significantly associated with HTG susceptibility indicating that the polymorphisms of PPARγ might play a critical role in dyslipidemia associated diseases.

    PPARγ inactivation leads to familial partial lipodystrophy (FPLD) syndrome associated with early-onset severe hypertension[10]. Considering that PPARγ and AGTR1 are located on the chromosome 3, it is not surprising that the gene-gene interactions exist not only in HTG-associated SNPs but also in HTG-non-associated SNPs.

    In conclusion, the present study suggested that the polymorphisms of PPARγ and AGTRI contribute to the HTG risk either independently or in an interactive manner in the Chinese population. Further multiple comprehensive studies must be performed to confirm this genetic association using large sample size and to analyze the probable interactions of these SNPs with other gene variants.

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