Mol Biol Rep (2015) 42:303–310 DOI 10.1007/s11033-014-3773-6
Genetic association study between INSULIN pathway related genes and high myopia in a Han Chinese population Xiaoqi Liu • Pu Wang • Chao Qu • Hong Zheng Bo Gong • Shi Ma • He Lin • Jing Cheng • Zhenglin Yang • Fang Lu • Yi Shi
•
Received: 2 March 2013 / Accepted: 20 September 2014 / Published online: 30 September 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract To investigate the association between insulin (INS) pathway related genes, including INS, insulin receptor (INSR), insulin receptor substrate 1 (IRS1), insulin-like growth factor 2 (IGF2), IGF2 receptor (IGF2R) and IGF binding protein 1 (IGFBP1), and high myopia (HM) in a Han Chinese population, we have genotyped 24 single nucleotide polymorphisms (SNPs) of these genes in this cohort by Sequenom MassARRAY method. The genotyping data was analyzed by v2 test and the linkage disequilibrium block structure was examined by Haploview software. SNPs in the INS-IGF2 region (rs2070762 and rs1003483), and the INSR gene (rs3745551 and rs2229429) showed significant association with HM (allelic P = 0.0085, 0.0494, 0.0171 and 0.0238, respectively). Under the model of risk genotype combination of INSR and IRS1, carrying the variant allele (A) of the IRS1 Gly972Arg SNP (rs1801278) further increased the risk among the rs2229429T allele carriers (odds ratio 6.865,
Xiaoqi Liu, Pu Wang, Chao Qu and Hong Zheng have contributed equally to this work. X. Liu P. Wang Z. Yang Y. Shi School of materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China X. Liu P. Wang H. Zheng B. Gong S. Ma H. Lin J. Cheng Z. Yang F. Lu Y. Shi The Sichuan Key Laboratory for Human Disease Gene Study, Clinical Laboratory Department, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan, China e-mail: [email protected]
95 % confidence interval 1.533–30.745). None of the SNPs in the IGF2R and IGFBP1 genes were found to be significantly associated with HM. Genetic variants in the insulin signaling pathway genes may increase the susceptibility of high myopia in Han Chinese. Keywords INSULIN pathway related gene High myopia Case–control association study
Introduction Myopia is the most common eye disease worldwide. The global incidence of myopia has increased quickly in the past six decades [1–4]. In Asian populations, the prevalence of myopia is about 40–70 % [3, 5, 6], which is much higher than that in North American, Australian, and European populations (20–30 %) [7, 8]. Myopia is divided into three categories: low, medium and high. High myopia (HM) is defined with a refractive error of less than or equal to -6.0 diopters (D), and with an axial length longer than or equal to 26 mm. HM is an important risk factor for C. Qu Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan, China F. Lu Y. Shi (&) Center for Human Molecular Biology & Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, 32 The First Ring Road West 2, Chengdu 610072, Sichuan, China e-mail: [email protected]
X. Liu Z. Yang F. Lu Y. Shi School of Clinical Medicine, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
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many blinding diseases, such as macular hemorrhage, posterior scleral staphyloma, glaucoma, retinal detachment, choroidal neovascularization (CNV) and so on [8–11]. Myopia, in particular, is a complex disorder with largely unknown etiology. Genetic and environmental factors both contribute to the development of myopia. Well known environmental factors include near work, education level and reduced outdoor exposure [12–16]. Many studies have quantified a strong genetic basis for myopia, especially for HM [17]. Twenty-one loci have been identified for myopia (MYP1–MYP21) [18–21]. Additional chromosomal regions and candidate genes were reported to have association with myopia [22, 23]. However, the results can not be replicated in other study cohorts, and the applications to general population are limited [22]. Therefore, the identification of myopia genes is still ongoing. Recent studies suggested potential influences of insulin (INS) pathway genes on the development of myopia. It has been found that insulin had powerful effects on cell proliferation and eye axial elongation [24]. Insulin regulated apoptosis and cell growth by binding to the receptor (INSR) or to the structurally related insulin-like growth factor I receptor (IGF1R). Insulin can influence retinal metabolism or function by cooperating with INSR and insulin receptor substrate1 (IRS1) [25]. Single nucleotide polymorphisms (SNPs) of insulin-like growth factor1 (IGF1) have been reported to be associated with HM in Chinese and Caucasian populations [26–29]. Insulin-like growth factor2 (IGF2), member of the insulin family, can mediate growth hormone and insulin actions and influence the development of the refractive properties and axial length in animal models [30, 31]. However, except IGF1, very few studies have focus on the genetic variants of insulin pathway related genes and the susceptibility of HM. In order to further explore potential genetic influences of insulin pathway related genes, including INS, INSR, IRS1, IGF2, IGF2R and IGFBP1, on HM, we have conducted a case–control association study in a mainland Han Chinese cohort containing 1,244 HM patients HM and 1,380 normal controls.
Patients and methods Ethics statement Approval for this case–control association study was provided by the Institutional Review Board of Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital in Sichuan Province, China. All of the participants were Han Chinese. Each participant has signed the informed consent before participating this study. Study population All of the participants were recruited at the ophthalmology clinic at Sichuan Provincial People’s Hospital. The diagnosis for HM were underwent by experienced ophthalmologists and the inclusion criteria were that: (1) with the refractive error less than or equal to -6.0 D in one or two eyes, (2) the axial length of this eye greater than or equal to 26.0 mm. In this study, individuals have been excluded if they have eye problems or other symptoms which might influence dioptry. The criteria for the normal controls were that: (1) with the refractive error from –1.0 to ?1.0 D, 2) with no eye diseases. Clinical information of the participants in this study was showed in Table 1. SNP selection Tag SNPs approach were used to detect the association between the genetic variants of the insulin pathway related genes and the high myopic subjects. To select the most representative SNPs to capture the majority of genetic variation, SNPs involving the coding region of the INSIGF2 gene, as well as 11 kb upstream regions of the start codon and 7 kb downstream regions of the stop codon, were confirmed by HapMap Phase II ? III (February 2009) of CHB (Han Chinese in Beijing) database. Tag SNPs were selected by pairwise tagging using the Haploview software (version 4.2) [32]. Each tag SNP had to meet the following criteria: r2 [ 0.8 to capture 80 % of genotype
Table 1 Characteristics of high myopia cases and controls in the study Group
Total number
Male (%)
Female (%)
Average age (years)a
Cases
1,244
549 (44.1)
695 (55.9)
41.26 ± 13.51
Controls
1,380
590 (42.8)
790 (57.2)
58.39 ± 12.77
a
± Standard deviation
OD right eye, OS left eye
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Refractive errors (Diopter)
Axial length (mm)
OD
OS
OD
OS
-10.12 ± 3.45
-10.03 ± 3.16
28.18 ± 1.87
28.26 ± 1.95
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information in the region, and minor allele frequency (MAF) [ 10 % in CHB database. Ten tag SNPs were selected in the INS-IGF2 region. In IRS1, the rs1801278 (Gly972Arg) was selected because this non-synonymous SNP may possibly effect on p85 binding [33]. In the IGF2R, INSR and IGFBP1 genes, we have selected seven non-synonymous SNPs and 5 tag SNPs to be genotyped. Genotyping Venous blood of each participant was obtained from cubital vein and collected in an EDTA tube. The total genomic DNA was extracted by serial phenol–chloroform extraction and ethanol precipitation. Sequenom MassARRAY method (Sequenom iPLEX Assay; Sequenom, San Diego, CA, USA) was used to genotype the 24 SNPs according to the manufacturer’s instruction. In brief, PCR and single base extension primers were designed by using the MassARRAY design program (version 3.1), multiplex reaction were followed in the whole process, including PCR amplification, PCR production purification, SBE reactions, and clean-up. All the reaction products were dispensed onto a 384-element SpectroCHIP and assayed by MassARRAY platform. Data was processed and analyzed by the Sequenom MassARRAY Workstation software (version 3.3). Finally, the genotype results were analyzed by the Typer Analyzer software (version 4.2). Statistical analysis All of the statistical analyses were performed by using SPSS program (version 13). Hardy–Weinberg equilibrium (HWE) was tested by v2 analysis. Genotype and allele frequencies between HM cases and controls were compared by v2 test. The multifactor-dimensionality reduction method was applied to verify the possible gene–gene interactions among the insulin pathway related genes [34, 35]. Logistic regression was used to adjust for age and sex. The P values of the SNPs were estimated using an additive model. Statistical significance was defined as P \ 0.05. The odds ratios (ORs) of genotypes and alleles between the HM cases and controls were calculated by v2 analysis. Haplotype analysis Haploview software (version 4.2) was used to examine the linkage disequilibrium (LD) block structure [36]. Values of the D0 and r2 for each SNP pair were also calculated by Haploview software.
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Results Single nucleotide polymorphism analysis All of the 24 tested SNPs were within the HWE (P [ 0.001, Table 2). The genotype distribution of these SNPs were similar to those in the HapMap CHB database and the previously published studies in Han Chinese populations, which implied reliable genotyping data in this study. We found that rs2070762 and rs1003483 in the INS-IGF2 gene region showed nominally association with HM (allelic P = 0.0085, 0.0494, respectively; OR (95 % confidence interval (CI)) = 1.161(1.039–1.297), 1.178(1.030–1.348), respectively, Table 2). Rs3745551 and rs2229429 in the INSR gene were also significantly associatied with HM (allelic P = 0.0171, 0.0238, respectively; OR (95 % CI) = 1.178 (1.030–1.348), 1.290 (1.034–1.610), respectively, Table 2). Unfortunately, after Bonferroni multiplecorrection, all of the SNPs in the INS-IGF2 and INSR genes showed no significant difference between HM and control groups. However, none of the SNPs in the IGF2R, IRS1 and IGFBP1 genes was found to be significantly associated with HM in this study (P [ 0.05, Table 2). Haplotype analysis We have performed haplotype analysis by using Haploview 4.2 software to examine the LD block structure of these SNPs according to the block definition created by Gabriel [37]. As shown in Fig. 1, only one LD block was located in INS-IGF2 or INSR separately. Rs7113485 and rs3802971 were in the same LD block of the INS-IGF2 gene with D0 value of 0.982 (conf. bounds 0.66–1, Fig. 1a). Rs1003483 and rs2070762 of the INS-IGF2 gene were not in the same LD block with the D0 of 0.44 (Fig. 1a). Rs2229429 and rs2059807 were in the same LD block with the D0 of 0.971 (conf. bounds 0.93–0.99, Fig. 1b). Rs2229429 and rs3745551 were not in the same LD block with the D0 of 0.08 (Fig. 1b). The r2 of rs7113485 and rs3802971 was 0.276. The r2 of rs2229429 and rs2059807 was 0.135. We have tested the association between the haplotypes of rs7113485 and rs3802971 in the INS-IGF2 region with HM as well as rs2059807 and rs2229429 in the INSR gene. None of the haplotypes were significantly associated with HM in this study (P [ 0.05, Table 3). Furthermor, we have performed haplotype analysis by combining all of the tested SNPs in INS-IGF2 and INSR respectively (Table 4). The protective haplotype TCCCTCTCTA generated by the 10 SNPs in INS-IGF2 showed significant difference between the case and control groups (P = 3.48 9 10-21,
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Table 2 Association between high myopia and SNPs of the INS, INSR, IRS1, IGF2, IGF2R and IGFBP1 genes in a Han Chinese cohort Gene
SNP
Position
Location
Risk allele
P_HWE (case/ control)
Case, control frequencies
Allelic P
OR (95 % CI)
INSIGF2
rs7113485
chr11:2102344
30 region
C
0.110/0.528
0.468, 0.455
0.3577
NS
rs3802971
chr11:2108628
intron
C
0.633/0.170
0.807, 0.804
0.7998
NS
rs3213225
chr11:2113112
intron
T
0.669/0.635
0.261, 0.242
0.123
NS
rs3741212
chr11:2118434
intron
C
0.002/0.001
0.897, 0.893
0.6928
NS
rs1003483
chr11:2124119
intron
G
0.336/0.465
0.337, 0.312
0.0494
1.123 (1.000–1.261)
rs1004446
chr11:2126719
intron
T
0.761/0.520
0.302, 0.294
0.4864
NS
rs7924316
chr11:2130023
intron
G
0.703/0.995
0.392, 0.376
0.2401
NS
rs3842748
chr11:2137971
intron
C
0.001/0.493
0.961, 0.954
0.2193
NS
rs2070762
chr11:2142911
intron
C
0.066/0.980
0.417, 0.381
0.0085
1.161 (1.039–1.297)
rs6356
chr11:2147527
50 region
G
0.442/0.999
0.175, 0.168
0.5016
NS
rs3745551
chr19:7065288
30 UTR
A
0.005/0.173
0.808, 0.782
0.0171
1.178 (1.030–1.348)
rs1051690
chr19:7067963
30 UTR
A
0.001/0.837
0.042, 0.036
0.2091
NS
rs1799817
chr19: 7076297
His1085His
C
0.101/0.994
0.605, 0.597
0.5338
NS
rs2059807
chr19:7117109
intron
T
0.008/0.567
0.680, 0.671
0.4672
NS
rs2229429
chr19:7117388
Asp546Glu
T
0.006/0.077
0.072, 0.057
0.0238
1.290 (1.034–1.610)
rs8108622
chr19:7133753
intron
T
0.287/0.915
0.862, 0.851
0.2455
NS
rs891087
chr19:7135518
Asp216Asp
A
0.001/0.009
0.051, 0.045
0.3047
NS
INSR
IRS1
rs1801278
chr2:227368788
Gly972Arg
A
0.544/0.001
0.017, 0.012
0.1672
NS
IGFBP1
rs4619
chr7:45932669
Ile253Met
A
0.232/0.180
0.452, 0.450
0.9051
NS
IGF2R
rs8191754
chr6:160448324
Leu252Val
C
0.901/0.210
0.785, 0.784
0.9898
NS
rs6413491 rs8191859
chr6:160468309 chr6:160485490
Ala724Thr Gly1315Glu
A A
– 0.687/0.672
0.000, 0.000 0.011, 0.011
– 0.9935
– NS
rs1805075
chr6:160505207
Asn2020Thr
A
0.795/0.897
0.680, 0.679
0.9023
NS
rs17847658
chr6:160511055
Asn2192Ser
G
–
0.000, 0.000
–
–
SNP single nucleotide polymorphism, HWE Hardy–Weinberg equilibrium, OR Odds Ratio, CI confidence interval, UTR untranslated region, NS No Significance
Fig. 1 Linkage disequilibrium (LD) block structures in the INS-IGF2 region (a) and the INSR gene (b). D0 values have been shown (white, D0 = 0; grey, 0 \ D0 \ 1; black, D0 = 1). The LD block structure was examined using the program Haploview (version 4.2). The D0
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values and r2 values for all pairs of single nucleotide polymorphisms (SNPs) were calculated, and the haplotype blocks were estimated using the program Haploview (version 4.2). (Color figure online)
Mol Biol Rep (2015) 42:303–310 Table 3 Haplotype analysis of the INS-IGF2 region and INSR gene in a Han Chinese population
* The haplotypes were generated from the SNPs: rs7113485 and rs3802971, or rs2059807 and rs2229429 in order
Table 4 Haplotype association of all tested SNPs in INS-IGF2 and INSR with high myopia in this study
307
Gene (SNPs)
Haplotype*
INS-IGF2 (rs7113485 and rs3802971)
INSR (rs2059807 and rs2229429)
Frequency
Case, control frequencies
P values
TC
0.538
0.531, 0.544
0.3461
CC
0.267
0.275, 0.260
0.1983
CT
0.193
0.191, 0.194
0.7685
TC
0.674
0.678, 0.670
0.5713
CC
0.262
0.250, 0.273
0.0632
CT
0.063
0.070, 0.057
0.0518
Gene
Haplotype
Frequency
Case, control frequencies
P values
OR (95 % CI)
INS-IGF2
TCCCTCTCTA
0.349
0.292, 0.419
3.48E-21
0.571 (0.508–0.642)
CTCCGTGCCA
0.08
0.062, 0.100
1.33E-06
0.603 (0.489–0.742)
TCCCTCTCCA
0.06
0.097, 0.031
4.81E-22
3.306 (2.559–4.272)
CCTCGCGCCA
0.044
0.044, 0.047
0.683
CCTCGCGCTA
0.043
0.067, 0.025
5.09E-13
2.840 (2.121–3.804)
CCTCGCGCCG
0.042
0.028, 0.057
5.82E-07
0.484 (0.362–0.647)
CTCCGTGCTA
0.031
0.050, 0.016
1.12E-11
3.212 (2.256–4.573)
TCCCTCTCCG
0.028
0.049, 0.012
2.25E-15
4.436 (2.975–6.615)
CCTTTTGCCA
0.026
0.024, 0.029
0.2345
INSR
TCCCTTTCTA
0.023
0.022, 0.025
0.5533
CCTCTCTCTA
0.019
0.019, 0.019
0.997
TCCCTCTCTG
0.015
0.012, 0.018
0.0789
CCTTTTTGTG
0.014
0.004, 0.023
9.12E-09
TCCCTCGCCA CTCTTTGCCA
0.011 0.011
0.009, 0.014 0.008, 0.014
0.1418 0.0298
0.557 (0.320–0.969)
CTCCTCTCTA
0.01
0.007, 0.014
0.0214
0.512 (0.287–0.911)
CCTTTTTCTA
0.01
0.013, 0.008
0.1093
AGCTCTG
0.348
0.350, 0.347
0.8234
AGTTCTG
0.169
0.177, 0.162
0.1416
GGTTCTG
0.098
0.086, 0.109
0.0053
AGCCCTG
0.074
0.072, 0.076
0.5318
AGCCCAG
0.058
0.055, 0.061
0.3268
AGTCCTG
0.035
0.036, 0.034
0.6637
GGTCCTG
0.028
0.027, 0.029
0.5877
AGTCCAG
0.023
0.025, 0.021
0.4292
AGCCTAG
0.022
0.021, 0.023
0.6897
OR odds ratio, CI confidence interval
GGCTCTG
0.02
0.022, 0.019
0.4135
AGCCTTA
0.02
0.023, 0.016
0.0923
The odds ratio was not calculated when the P value was higher than 0.05
GACTCTG
0.014
0.019, 0.009
0.0028
AGCCCTA
0.01
0.011, 0.010
0.558
Table 4). An individual who carried this protective haplotype has a 0.429-fold decrease in susceptibility to HM. However, th individual with the risk haplotype TCCCTCTCCA generated by these 10 SNPs has a 2.306fold increase in susceptibility to HM (P = 4.81 9 10-22,
0.177 (0.091–0.347)
0.772 (0.641–0.928)
2.027 (1.252–3.284)
Table 4). In addition, the protective haplotype GGTTCTG generated by the seven SNPs in INSR also proved to be significantly different between the case and control groups (P = 0.0053, Table 4). With this protective haplotype, an individual has a 0.228-fold decrease in susceptibility to
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Table 5 Joint effect of the polymorphisms in the INSR and IRS1 genes in high myopia cases and controls Genotype
Number
INSR rs3745551
rs2229429
IRS1 (rs1801278) GG
GG
GG
AA/AG
AG/AA
GG
AG/AA
AA/AG
CC
GG
Cases
P value
P (after Bonferroni correction)
OR (95 % CI)*
Controls
61
57
0
0
*
*
–
1,141
1,287
0.318
1
0.828 (0.572–1.199)
42
32
0.494
1
1.226 (0.683–2.201)
1,047
1,198
CC
AA/AG
30
30
0.606
1
1.144 (0.685–1.911)
CT/TT CT/TT
GG AA/AG
155 12
147 2
0.126 0.003
0.377 0.010
1.206 (0.949–1.534) 6.865 (1.533–30.745)
* OR Odds ratio, CI confidence interval
HM. The individual with the risk haplotype GACTCTG generated by these seven SNPs has a 1.027-fold increase in susceptibility to HM (P = 0.0028, Table 4). Gene–gene interaction In these insulin pathway-related genes, INS can regulate the biology function of IGFBP1 and it can also start the downstream signaling pathway by binding to INSR through IRS1. This suggested that the mutual effect of the genotypes in these genes might be much more important for the susceptibility to HM than single gene genotypes. Therefore, we have assessed the joint effect of these genes by using the genotype data. The genotypes of rs3745551 and rs2229429 in the INSR gene were stratified based on the dominant model and according to the genotypes of the IRS1 Gly972Arg SNP (rs1801278, GG and AA/GA, Table 5). Under the model of risk genotype combination of INSR and IRS1, carriers of the AA/GA genotype for rs1801278 further increased the risk among the carriers of the TT/CT genotype for rs2229429 (P = 0.01 afer Bonferroni correction, OR (95 % CI) = 6.865 (1.533–30.745), Table 5). This indicated a significant interaction between INSR and IRS1 genotypes on the risk of HM. We also used the multi-factor-dimensionality reduction method to investigate the gene–gene interactions between other insulin pathway related genes, but no associations were detected.
Discussion Recently, insulin pathway related genes, including INS, INSR, IRS1, IGF2, IGF2R and IGFBP1, have been suggested to take functional effects on the development of myopia [18, 24, 30, 31, 38, 39]. However, the genetic association between these genes and HM was unclear [24,
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30, 31]. In this study, we have verified that the genetic variants in insulin pathway genes were susceptible to HM in Han Chinese. IGF2 is insulin family member of polypeptide growth factors. It mediates growth hormone action, stimulates cultured cells’ growth and the action of insulin. The 50 region of IGF2 gene overlapped the 30 region of INS gene and formed a read-through gene, INS-IGF2. Feldkaemper et al. reported that insulin injection to chick eyes could enhance myopia and induce high amounts of axial myopia [24]. In form-deprivation myopia (FDM) chicks, the expression of IGF2 in the posterior sclera of myopic eyes was higher than that in controls [38]. Recombinant human IGF-2 (rhIGF-2) can promote the development of FDM on the condition of FD (form-deprivation) [31], and intravitreous injection with IGF2 antisense oligonucleotides can inhibit the development of myopia in guinea pig [30]. In this study, we found that the carrier status of G allele in rs1003483 and C allele in rs2070762 in the INS-IGF2 region could increase the risk of HM. Taken previous studies and our results together, it is suggested that INS and IGF2 may play important roles in the development of myopia. In this study, we have investigated seven SNPs of the INSR gene and two SNPs (rs3745551and rs2229429) showed significant association with HM in the tested cohort. Rs3745551 located in the 30 -UTR of INSR and might regulate the expression of INSR. Rs3745551 was also implicated in insulin action and was associated with the insulin resistant phenotype [40]. The allelic P values of all the tested SNPs in this study were higher than 0.05 after multiple testing by Bonferroni correction. Therefore, we considered that rs1003483, rs2070762 in INS-IGF2 and rs3745551, rs2229429 in INSR were tendency correlation with HM, but not show statistical significance. The results suggested the association trend among these genes with HM. To avoid filtering real
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significant SNPs, a much larger case–control study of these significant SNPs and appropriate multiple-correction criteria should be applied, which would be more proper and accurate to determine a real association in a separate population. In this study, the haplotypes which frequency is higher than 5 % include three haplotypes in gene INS-IGF2 and five haplotypes in gene INSR. Haplotypes of TCCCTC TCTA, CTCCGTGCCA in gene INS-IGF2 were found to be protective haplotypes, they significantly decreased the susceptibility to HM. P values of these two haplotypes were 5.916E-20 and 2.261E-05 (after Bonferroni correction) with ORs of 0.571 (0.508–0.642) and 0.603 (0.489–0.742), suggesting a protective function of these haplotypes. The haplotype of TCCCTCTCCA in this gene significantly increased the susceptibility to HM with an odds ratio of 3.306 (2.559–4.272). Haplotype of GGT TCTG in the INSR gene showed tendency correlation to HM. P value of this haplotype was 0.0053 with an odds ratio of 0.772 (0.641–0.928). But after Bonferroni correction, the p value increased to 0.0689. So we considered this haplotype maybe decreased the susceptibility of HM. None of the five non-synonymous SNPs in the IGF2R gene showed significant association with HM in this study. IGF2 can bind not only with IGF2R but also with IGF1R and INSR, and the binding affinity of IGF2-IGF1R and IGF2-INSR are higher than IGF2-IGF2R [41]. IGF2 can activate IGF1R to result in cell growth (proliferation) and resistance to cell death (apoptosis). However, IGF2R does not trigger signaling, it plays a major role in controlling IGF signaling by directing IGF2 to lysosomes [42, 43]. This might be one of the reasons for that not the genetic variants of IGF2R but IGF1R and INSR were associated with HM in this study. IRS1 affects insulin-like growth factor and insulin signaling. Rs1801278 (Gly972Arg) of the IRS1 gene lies between two tyrosine residues, which are involved in a further interaction with the downstream signaling molecules [33]. This variant can decrease the binding to the p85 regulatory subunit of Phosphatidylinositol (PI) 3-kinase, leading to impaired insulin-stimulated signaling [44]. Our results showed that the INSR genotypes stratification had a gene–gene interaction with the IRS1 gene. The individuals who had the risk genotype of CT/TT for rs2229429 in the INSR gene and the AA/GA genotype for rs1801278 in the IRS1 gene had higher risk for HM. This effect can be explained by the assumed enhance insulin signaling due to the variant alleles of these two genes [44, 45]. Our study confirmed the significant interaction between INSR and IRS1 genotypes on the risk for HM. In conclusion, we have genotyped 24 SNPs of the insulin pathway related genes including INS, INSR, IRS1, IGF2, IGF2R and IGFBP1. We found that rs1003483,
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rs2070762 in INS-IGF2 and rs3745551, rs2229429 in INSR showed a suggestive association with HM in the Han Chinese population. The risk effect was even stronger among carriers of both the risk allele in INSR and IRS1 (T allele of rs2229429 in INSR and A allele of rs1801278 in IRS1). Our results suggested that genetic variants of the insulin signaling pathway genes might be potential HM candidate genes in Han Chinese and should be further investigated. Acknowledgments We thank the patients and their families for their participation. This work was supported by grants from the National Basic Research Program of China (973 Program, 2011CB504604 to ZY); the Natural Science Foundation of China (81271047 to XL; 81170882 to YS; 81170883 to ZY; 81100693 to CQ; 81070761 and 81241001 to FL); the Department of Science and Technology of Sichuan Province (2012JQ0023 to YS); the Department of Sichuan Provincial Health (120121 to XL). Conflict of Interest conflict of interest.
All authors have declared that they have no
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Genetic association study between INSULIN pathway related genes and high myopia in a Han Chinese population Xiaoqi Liu • Pu Wang • Chao Qu • Hong Zheng Bo Gong • Shi Ma • He Lin • Jing Cheng • Zhenglin Yang • Fang Lu • Yi Shi
•
Received: 2 March 2013 / Accepted: 20 September 2014 / Published online: 30 September 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract To investigate the association between insulin (INS) pathway related genes, including INS, insulin receptor (INSR), insulin receptor substrate 1 (IRS1), insulin-like growth factor 2 (IGF2), IGF2 receptor (IGF2R) and IGF binding protein 1 (IGFBP1), and high myopia (HM) in a Han Chinese population, we have genotyped 24 single nucleotide polymorphisms (SNPs) of these genes in this cohort by Sequenom MassARRAY method. The genotyping data was analyzed by v2 test and the linkage disequilibrium block structure was examined by Haploview software. SNPs in the INS-IGF2 region (rs2070762 and rs1003483), and the INSR gene (rs3745551 and rs2229429) showed significant association with HM (allelic P = 0.0085, 0.0494, 0.0171 and 0.0238, respectively). Under the model of risk genotype combination of INSR and IRS1, carrying the variant allele (A) of the IRS1 Gly972Arg SNP (rs1801278) further increased the risk among the rs2229429T allele carriers (odds ratio 6.865,
Xiaoqi Liu, Pu Wang, Chao Qu and Hong Zheng have contributed equally to this work. X. Liu P. Wang Z. Yang Y. Shi School of materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China X. Liu P. Wang H. Zheng B. Gong S. Ma H. Lin J. Cheng Z. Yang F. Lu Y. Shi The Sichuan Key Laboratory for Human Disease Gene Study, Clinical Laboratory Department, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan, China e-mail: [email protected]
95 % confidence interval 1.533–30.745). None of the SNPs in the IGF2R and IGFBP1 genes were found to be significantly associated with HM. Genetic variants in the insulin signaling pathway genes may increase the susceptibility of high myopia in Han Chinese. Keywords INSULIN pathway related gene High myopia Case–control association study
Introduction Myopia is the most common eye disease worldwide. The global incidence of myopia has increased quickly in the past six decades [1–4]. In Asian populations, the prevalence of myopia is about 40–70 % [3, 5, 6], which is much higher than that in North American, Australian, and European populations (20–30 %) [7, 8]. Myopia is divided into three categories: low, medium and high. High myopia (HM) is defined with a refractive error of less than or equal to -6.0 diopters (D), and with an axial length longer than or equal to 26 mm. HM is an important risk factor for C. Qu Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan, China F. Lu Y. Shi (&) Center for Human Molecular Biology & Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, 32 The First Ring Road West 2, Chengdu 610072, Sichuan, China e-mail: [email protected]
X. Liu Z. Yang F. Lu Y. Shi School of Clinical Medicine, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
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many blinding diseases, such as macular hemorrhage, posterior scleral staphyloma, glaucoma, retinal detachment, choroidal neovascularization (CNV) and so on [8–11]. Myopia, in particular, is a complex disorder with largely unknown etiology. Genetic and environmental factors both contribute to the development of myopia. Well known environmental factors include near work, education level and reduced outdoor exposure [12–16]. Many studies have quantified a strong genetic basis for myopia, especially for HM [17]. Twenty-one loci have been identified for myopia (MYP1–MYP21) [18–21]. Additional chromosomal regions and candidate genes were reported to have association with myopia [22, 23]. However, the results can not be replicated in other study cohorts, and the applications to general population are limited [22]. Therefore, the identification of myopia genes is still ongoing. Recent studies suggested potential influences of insulin (INS) pathway genes on the development of myopia. It has been found that insulin had powerful effects on cell proliferation and eye axial elongation [24]. Insulin regulated apoptosis and cell growth by binding to the receptor (INSR) or to the structurally related insulin-like growth factor I receptor (IGF1R). Insulin can influence retinal metabolism or function by cooperating with INSR and insulin receptor substrate1 (IRS1) [25]. Single nucleotide polymorphisms (SNPs) of insulin-like growth factor1 (IGF1) have been reported to be associated with HM in Chinese and Caucasian populations [26–29]. Insulin-like growth factor2 (IGF2), member of the insulin family, can mediate growth hormone and insulin actions and influence the development of the refractive properties and axial length in animal models [30, 31]. However, except IGF1, very few studies have focus on the genetic variants of insulin pathway related genes and the susceptibility of HM. In order to further explore potential genetic influences of insulin pathway related genes, including INS, INSR, IRS1, IGF2, IGF2R and IGFBP1, on HM, we have conducted a case–control association study in a mainland Han Chinese cohort containing 1,244 HM patients HM and 1,380 normal controls.
Patients and methods Ethics statement Approval for this case–control association study was provided by the Institutional Review Board of Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital in Sichuan Province, China. All of the participants were Han Chinese. Each participant has signed the informed consent before participating this study. Study population All of the participants were recruited at the ophthalmology clinic at Sichuan Provincial People’s Hospital. The diagnosis for HM were underwent by experienced ophthalmologists and the inclusion criteria were that: (1) with the refractive error less than or equal to -6.0 D in one or two eyes, (2) the axial length of this eye greater than or equal to 26.0 mm. In this study, individuals have been excluded if they have eye problems or other symptoms which might influence dioptry. The criteria for the normal controls were that: (1) with the refractive error from –1.0 to ?1.0 D, 2) with no eye diseases. Clinical information of the participants in this study was showed in Table 1. SNP selection Tag SNPs approach were used to detect the association between the genetic variants of the insulin pathway related genes and the high myopic subjects. To select the most representative SNPs to capture the majority of genetic variation, SNPs involving the coding region of the INSIGF2 gene, as well as 11 kb upstream regions of the start codon and 7 kb downstream regions of the stop codon, were confirmed by HapMap Phase II ? III (February 2009) of CHB (Han Chinese in Beijing) database. Tag SNPs were selected by pairwise tagging using the Haploview software (version 4.2) [32]. Each tag SNP had to meet the following criteria: r2 [ 0.8 to capture 80 % of genotype
Table 1 Characteristics of high myopia cases and controls in the study Group
Total number
Male (%)
Female (%)
Average age (years)a
Cases
1,244
549 (44.1)
695 (55.9)
41.26 ± 13.51
Controls
1,380
590 (42.8)
790 (57.2)
58.39 ± 12.77
a
± Standard deviation
OD right eye, OS left eye
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Refractive errors (Diopter)
Axial length (mm)
OD
OS
OD
OS
-10.12 ± 3.45
-10.03 ± 3.16
28.18 ± 1.87
28.26 ± 1.95
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information in the region, and minor allele frequency (MAF) [ 10 % in CHB database. Ten tag SNPs were selected in the INS-IGF2 region. In IRS1, the rs1801278 (Gly972Arg) was selected because this non-synonymous SNP may possibly effect on p85 binding [33]. In the IGF2R, INSR and IGFBP1 genes, we have selected seven non-synonymous SNPs and 5 tag SNPs to be genotyped. Genotyping Venous blood of each participant was obtained from cubital vein and collected in an EDTA tube. The total genomic DNA was extracted by serial phenol–chloroform extraction and ethanol precipitation. Sequenom MassARRAY method (Sequenom iPLEX Assay; Sequenom, San Diego, CA, USA) was used to genotype the 24 SNPs according to the manufacturer’s instruction. In brief, PCR and single base extension primers were designed by using the MassARRAY design program (version 3.1), multiplex reaction were followed in the whole process, including PCR amplification, PCR production purification, SBE reactions, and clean-up. All the reaction products were dispensed onto a 384-element SpectroCHIP and assayed by MassARRAY platform. Data was processed and analyzed by the Sequenom MassARRAY Workstation software (version 3.3). Finally, the genotype results were analyzed by the Typer Analyzer software (version 4.2). Statistical analysis All of the statistical analyses were performed by using SPSS program (version 13). Hardy–Weinberg equilibrium (HWE) was tested by v2 analysis. Genotype and allele frequencies between HM cases and controls were compared by v2 test. The multifactor-dimensionality reduction method was applied to verify the possible gene–gene interactions among the insulin pathway related genes [34, 35]. Logistic regression was used to adjust for age and sex. The P values of the SNPs were estimated using an additive model. Statistical significance was defined as P \ 0.05. The odds ratios (ORs) of genotypes and alleles between the HM cases and controls were calculated by v2 analysis. Haplotype analysis Haploview software (version 4.2) was used to examine the linkage disequilibrium (LD) block structure [36]. Values of the D0 and r2 for each SNP pair were also calculated by Haploview software.
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Results Single nucleotide polymorphism analysis All of the 24 tested SNPs were within the HWE (P [ 0.001, Table 2). The genotype distribution of these SNPs were similar to those in the HapMap CHB database and the previously published studies in Han Chinese populations, which implied reliable genotyping data in this study. We found that rs2070762 and rs1003483 in the INS-IGF2 gene region showed nominally association with HM (allelic P = 0.0085, 0.0494, respectively; OR (95 % confidence interval (CI)) = 1.161(1.039–1.297), 1.178(1.030–1.348), respectively, Table 2). Rs3745551 and rs2229429 in the INSR gene were also significantly associatied with HM (allelic P = 0.0171, 0.0238, respectively; OR (95 % CI) = 1.178 (1.030–1.348), 1.290 (1.034–1.610), respectively, Table 2). Unfortunately, after Bonferroni multiplecorrection, all of the SNPs in the INS-IGF2 and INSR genes showed no significant difference between HM and control groups. However, none of the SNPs in the IGF2R, IRS1 and IGFBP1 genes was found to be significantly associated with HM in this study (P [ 0.05, Table 2). Haplotype analysis We have performed haplotype analysis by using Haploview 4.2 software to examine the LD block structure of these SNPs according to the block definition created by Gabriel [37]. As shown in Fig. 1, only one LD block was located in INS-IGF2 or INSR separately. Rs7113485 and rs3802971 were in the same LD block of the INS-IGF2 gene with D0 value of 0.982 (conf. bounds 0.66–1, Fig. 1a). Rs1003483 and rs2070762 of the INS-IGF2 gene were not in the same LD block with the D0 of 0.44 (Fig. 1a). Rs2229429 and rs2059807 were in the same LD block with the D0 of 0.971 (conf. bounds 0.93–0.99, Fig. 1b). Rs2229429 and rs3745551 were not in the same LD block with the D0 of 0.08 (Fig. 1b). The r2 of rs7113485 and rs3802971 was 0.276. The r2 of rs2229429 and rs2059807 was 0.135. We have tested the association between the haplotypes of rs7113485 and rs3802971 in the INS-IGF2 region with HM as well as rs2059807 and rs2229429 in the INSR gene. None of the haplotypes were significantly associated with HM in this study (P [ 0.05, Table 3). Furthermor, we have performed haplotype analysis by combining all of the tested SNPs in INS-IGF2 and INSR respectively (Table 4). The protective haplotype TCCCTCTCTA generated by the 10 SNPs in INS-IGF2 showed significant difference between the case and control groups (P = 3.48 9 10-21,
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Table 2 Association between high myopia and SNPs of the INS, INSR, IRS1, IGF2, IGF2R and IGFBP1 genes in a Han Chinese cohort Gene
SNP
Position
Location
Risk allele
P_HWE (case/ control)
Case, control frequencies
Allelic P
OR (95 % CI)
INSIGF2
rs7113485
chr11:2102344
30 region
C
0.110/0.528
0.468, 0.455
0.3577
NS
rs3802971
chr11:2108628
intron
C
0.633/0.170
0.807, 0.804
0.7998
NS
rs3213225
chr11:2113112
intron
T
0.669/0.635
0.261, 0.242
0.123
NS
rs3741212
chr11:2118434
intron
C
0.002/0.001
0.897, 0.893
0.6928
NS
rs1003483
chr11:2124119
intron
G
0.336/0.465
0.337, 0.312
0.0494
1.123 (1.000–1.261)
rs1004446
chr11:2126719
intron
T
0.761/0.520
0.302, 0.294
0.4864
NS
rs7924316
chr11:2130023
intron
G
0.703/0.995
0.392, 0.376
0.2401
NS
rs3842748
chr11:2137971
intron
C
0.001/0.493
0.961, 0.954
0.2193
NS
rs2070762
chr11:2142911
intron
C
0.066/0.980
0.417, 0.381
0.0085
1.161 (1.039–1.297)
rs6356
chr11:2147527
50 region
G
0.442/0.999
0.175, 0.168
0.5016
NS
rs3745551
chr19:7065288
30 UTR
A
0.005/0.173
0.808, 0.782
0.0171
1.178 (1.030–1.348)
rs1051690
chr19:7067963
30 UTR
A
0.001/0.837
0.042, 0.036
0.2091
NS
rs1799817
chr19: 7076297
His1085His
C
0.101/0.994
0.605, 0.597
0.5338
NS
rs2059807
chr19:7117109
intron
T
0.008/0.567
0.680, 0.671
0.4672
NS
rs2229429
chr19:7117388
Asp546Glu
T
0.006/0.077
0.072, 0.057
0.0238
1.290 (1.034–1.610)
rs8108622
chr19:7133753
intron
T
0.287/0.915
0.862, 0.851
0.2455
NS
rs891087
chr19:7135518
Asp216Asp
A
0.001/0.009
0.051, 0.045
0.3047
NS
INSR
IRS1
rs1801278
chr2:227368788
Gly972Arg
A
0.544/0.001
0.017, 0.012
0.1672
NS
IGFBP1
rs4619
chr7:45932669
Ile253Met
A
0.232/0.180
0.452, 0.450
0.9051
NS
IGF2R
rs8191754
chr6:160448324
Leu252Val
C
0.901/0.210
0.785, 0.784
0.9898
NS
rs6413491 rs8191859
chr6:160468309 chr6:160485490
Ala724Thr Gly1315Glu
A A
– 0.687/0.672
0.000, 0.000 0.011, 0.011
– 0.9935
– NS
rs1805075
chr6:160505207
Asn2020Thr
A
0.795/0.897
0.680, 0.679
0.9023
NS
rs17847658
chr6:160511055
Asn2192Ser
G
–
0.000, 0.000
–
–
SNP single nucleotide polymorphism, HWE Hardy–Weinberg equilibrium, OR Odds Ratio, CI confidence interval, UTR untranslated region, NS No Significance
Fig. 1 Linkage disequilibrium (LD) block structures in the INS-IGF2 region (a) and the INSR gene (b). D0 values have been shown (white, D0 = 0; grey, 0 \ D0 \ 1; black, D0 = 1). The LD block structure was examined using the program Haploview (version 4.2). The D0
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values and r2 values for all pairs of single nucleotide polymorphisms (SNPs) were calculated, and the haplotype blocks were estimated using the program Haploview (version 4.2). (Color figure online)
Mol Biol Rep (2015) 42:303–310 Table 3 Haplotype analysis of the INS-IGF2 region and INSR gene in a Han Chinese population
* The haplotypes were generated from the SNPs: rs7113485 and rs3802971, or rs2059807 and rs2229429 in order
Table 4 Haplotype association of all tested SNPs in INS-IGF2 and INSR with high myopia in this study
307
Gene (SNPs)
Haplotype*
INS-IGF2 (rs7113485 and rs3802971)
INSR (rs2059807 and rs2229429)
Frequency
Case, control frequencies
P values
TC
0.538
0.531, 0.544
0.3461
CC
0.267
0.275, 0.260
0.1983
CT
0.193
0.191, 0.194
0.7685
TC
0.674
0.678, 0.670
0.5713
CC
0.262
0.250, 0.273
0.0632
CT
0.063
0.070, 0.057
0.0518
Gene
Haplotype
Frequency
Case, control frequencies
P values
OR (95 % CI)
INS-IGF2
TCCCTCTCTA
0.349
0.292, 0.419
3.48E-21
0.571 (0.508–0.642)
CTCCGTGCCA
0.08
0.062, 0.100
1.33E-06
0.603 (0.489–0.742)
TCCCTCTCCA
0.06
0.097, 0.031
4.81E-22
3.306 (2.559–4.272)
CCTCGCGCCA
0.044
0.044, 0.047
0.683
CCTCGCGCTA
0.043
0.067, 0.025
5.09E-13
2.840 (2.121–3.804)
CCTCGCGCCG
0.042
0.028, 0.057
5.82E-07
0.484 (0.362–0.647)
CTCCGTGCTA
0.031
0.050, 0.016
1.12E-11
3.212 (2.256–4.573)
TCCCTCTCCG
0.028
0.049, 0.012
2.25E-15
4.436 (2.975–6.615)
CCTTTTGCCA
0.026
0.024, 0.029
0.2345
INSR
TCCCTTTCTA
0.023
0.022, 0.025
0.5533
CCTCTCTCTA
0.019
0.019, 0.019
0.997
TCCCTCTCTG
0.015
0.012, 0.018
0.0789
CCTTTTTGTG
0.014
0.004, 0.023
9.12E-09
TCCCTCGCCA CTCTTTGCCA
0.011 0.011
0.009, 0.014 0.008, 0.014
0.1418 0.0298
0.557 (0.320–0.969)
CTCCTCTCTA
0.01
0.007, 0.014
0.0214
0.512 (0.287–0.911)
CCTTTTTCTA
0.01
0.013, 0.008
0.1093
AGCTCTG
0.348
0.350, 0.347
0.8234
AGTTCTG
0.169
0.177, 0.162
0.1416
GGTTCTG
0.098
0.086, 0.109
0.0053
AGCCCTG
0.074
0.072, 0.076
0.5318
AGCCCAG
0.058
0.055, 0.061
0.3268
AGTCCTG
0.035
0.036, 0.034
0.6637
GGTCCTG
0.028
0.027, 0.029
0.5877
AGTCCAG
0.023
0.025, 0.021
0.4292
AGCCTAG
0.022
0.021, 0.023
0.6897
OR odds ratio, CI confidence interval
GGCTCTG
0.02
0.022, 0.019
0.4135
AGCCTTA
0.02
0.023, 0.016
0.0923
The odds ratio was not calculated when the P value was higher than 0.05
GACTCTG
0.014
0.019, 0.009
0.0028
AGCCCTA
0.01
0.011, 0.010
0.558
Table 4). An individual who carried this protective haplotype has a 0.429-fold decrease in susceptibility to HM. However, th individual with the risk haplotype TCCCTCTCCA generated by these 10 SNPs has a 2.306fold increase in susceptibility to HM (P = 4.81 9 10-22,
0.177 (0.091–0.347)
0.772 (0.641–0.928)
2.027 (1.252–3.284)
Table 4). In addition, the protective haplotype GGTTCTG generated by the seven SNPs in INSR also proved to be significantly different between the case and control groups (P = 0.0053, Table 4). With this protective haplotype, an individual has a 0.228-fold decrease in susceptibility to
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Table 5 Joint effect of the polymorphisms in the INSR and IRS1 genes in high myopia cases and controls Genotype
Number
INSR rs3745551
rs2229429
IRS1 (rs1801278) GG
GG
GG
AA/AG
AG/AA
GG
AG/AA
AA/AG
CC
GG
Cases
P value
P (after Bonferroni correction)
OR (95 % CI)*
Controls
61
57
0
0
*
*
–
1,141
1,287
0.318
1
0.828 (0.572–1.199)
42
32
0.494
1
1.226 (0.683–2.201)
1,047
1,198
CC
AA/AG
30
30
0.606
1
1.144 (0.685–1.911)
CT/TT CT/TT
GG AA/AG
155 12
147 2
0.126 0.003
0.377 0.010
1.206 (0.949–1.534) 6.865 (1.533–30.745)
* OR Odds ratio, CI confidence interval
HM. The individual with the risk haplotype GACTCTG generated by these seven SNPs has a 1.027-fold increase in susceptibility to HM (P = 0.0028, Table 4). Gene–gene interaction In these insulin pathway-related genes, INS can regulate the biology function of IGFBP1 and it can also start the downstream signaling pathway by binding to INSR through IRS1. This suggested that the mutual effect of the genotypes in these genes might be much more important for the susceptibility to HM than single gene genotypes. Therefore, we have assessed the joint effect of these genes by using the genotype data. The genotypes of rs3745551 and rs2229429 in the INSR gene were stratified based on the dominant model and according to the genotypes of the IRS1 Gly972Arg SNP (rs1801278, GG and AA/GA, Table 5). Under the model of risk genotype combination of INSR and IRS1, carriers of the AA/GA genotype for rs1801278 further increased the risk among the carriers of the TT/CT genotype for rs2229429 (P = 0.01 afer Bonferroni correction, OR (95 % CI) = 6.865 (1.533–30.745), Table 5). This indicated a significant interaction between INSR and IRS1 genotypes on the risk of HM. We also used the multi-factor-dimensionality reduction method to investigate the gene–gene interactions between other insulin pathway related genes, but no associations were detected.
Discussion Recently, insulin pathway related genes, including INS, INSR, IRS1, IGF2, IGF2R and IGFBP1, have been suggested to take functional effects on the development of myopia [18, 24, 30, 31, 38, 39]. However, the genetic association between these genes and HM was unclear [24,
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30, 31]. In this study, we have verified that the genetic variants in insulin pathway genes were susceptible to HM in Han Chinese. IGF2 is insulin family member of polypeptide growth factors. It mediates growth hormone action, stimulates cultured cells’ growth and the action of insulin. The 50 region of IGF2 gene overlapped the 30 region of INS gene and formed a read-through gene, INS-IGF2. Feldkaemper et al. reported that insulin injection to chick eyes could enhance myopia and induce high amounts of axial myopia [24]. In form-deprivation myopia (FDM) chicks, the expression of IGF2 in the posterior sclera of myopic eyes was higher than that in controls [38]. Recombinant human IGF-2 (rhIGF-2) can promote the development of FDM on the condition of FD (form-deprivation) [31], and intravitreous injection with IGF2 antisense oligonucleotides can inhibit the development of myopia in guinea pig [30]. In this study, we found that the carrier status of G allele in rs1003483 and C allele in rs2070762 in the INS-IGF2 region could increase the risk of HM. Taken previous studies and our results together, it is suggested that INS and IGF2 may play important roles in the development of myopia. In this study, we have investigated seven SNPs of the INSR gene and two SNPs (rs3745551and rs2229429) showed significant association with HM in the tested cohort. Rs3745551 located in the 30 -UTR of INSR and might regulate the expression of INSR. Rs3745551 was also implicated in insulin action and was associated with the insulin resistant phenotype [40]. The allelic P values of all the tested SNPs in this study were higher than 0.05 after multiple testing by Bonferroni correction. Therefore, we considered that rs1003483, rs2070762 in INS-IGF2 and rs3745551, rs2229429 in INSR were tendency correlation with HM, but not show statistical significance. The results suggested the association trend among these genes with HM. To avoid filtering real
Mol Biol Rep (2015) 42:303–310
significant SNPs, a much larger case–control study of these significant SNPs and appropriate multiple-correction criteria should be applied, which would be more proper and accurate to determine a real association in a separate population. In this study, the haplotypes which frequency is higher than 5 % include three haplotypes in gene INS-IGF2 and five haplotypes in gene INSR. Haplotypes of TCCCTC TCTA, CTCCGTGCCA in gene INS-IGF2 were found to be protective haplotypes, they significantly decreased the susceptibility to HM. P values of these two haplotypes were 5.916E-20 and 2.261E-05 (after Bonferroni correction) with ORs of 0.571 (0.508–0.642) and 0.603 (0.489–0.742), suggesting a protective function of these haplotypes. The haplotype of TCCCTCTCCA in this gene significantly increased the susceptibility to HM with an odds ratio of 3.306 (2.559–4.272). Haplotype of GGT TCTG in the INSR gene showed tendency correlation to HM. P value of this haplotype was 0.0053 with an odds ratio of 0.772 (0.641–0.928). But after Bonferroni correction, the p value increased to 0.0689. So we considered this haplotype maybe decreased the susceptibility of HM. None of the five non-synonymous SNPs in the IGF2R gene showed significant association with HM in this study. IGF2 can bind not only with IGF2R but also with IGF1R and INSR, and the binding affinity of IGF2-IGF1R and IGF2-INSR are higher than IGF2-IGF2R [41]. IGF2 can activate IGF1R to result in cell growth (proliferation) and resistance to cell death (apoptosis). However, IGF2R does not trigger signaling, it plays a major role in controlling IGF signaling by directing IGF2 to lysosomes [42, 43]. This might be one of the reasons for that not the genetic variants of IGF2R but IGF1R and INSR were associated with HM in this study. IRS1 affects insulin-like growth factor and insulin signaling. Rs1801278 (Gly972Arg) of the IRS1 gene lies between two tyrosine residues, which are involved in a further interaction with the downstream signaling molecules [33]. This variant can decrease the binding to the p85 regulatory subunit of Phosphatidylinositol (PI) 3-kinase, leading to impaired insulin-stimulated signaling [44]. Our results showed that the INSR genotypes stratification had a gene–gene interaction with the IRS1 gene. The individuals who had the risk genotype of CT/TT for rs2229429 in the INSR gene and the AA/GA genotype for rs1801278 in the IRS1 gene had higher risk for HM. This effect can be explained by the assumed enhance insulin signaling due to the variant alleles of these two genes [44, 45]. Our study confirmed the significant interaction between INSR and IRS1 genotypes on the risk for HM. In conclusion, we have genotyped 24 SNPs of the insulin pathway related genes including INS, INSR, IRS1, IGF2, IGF2R and IGFBP1. We found that rs1003483,
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rs2070762 in INS-IGF2 and rs3745551, rs2229429 in INSR showed a suggestive association with HM in the Han Chinese population. The risk effect was even stronger among carriers of both the risk allele in INSR and IRS1 (T allele of rs2229429 in INSR and A allele of rs1801278 in IRS1). Our results suggested that genetic variants of the insulin signaling pathway genes might be potential HM candidate genes in Han Chinese and should be further investigated. Acknowledgments We thank the patients and their families for their participation. This work was supported by grants from the National Basic Research Program of China (973 Program, 2011CB504604 to ZY); the Natural Science Foundation of China (81271047 to XL; 81170882 to YS; 81170883 to ZY; 81100693 to CQ; 81070761 and 81241001 to FL); the Department of Science and Technology of Sichuan Province (2012JQ0023 to YS); the Department of Sichuan Provincial Health (120121 to XL). Conflict of Interest conflict of interest.
All authors have declared that they have no
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