June 2006
Volume 47, Issue 6
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Biochemistry and Molecular Biology  |   June 2006
Family-Based Association Analysis of Hepatocyte Growth Factor (HGF) Gene Polymorphisms in High Myopia
Author Affiliations
  • Wei Han
    From the Department of Ophthalmology, The First Affiliated Hospital, Medical College, Zhejiang University, Hangzhou, China.; the
    School of Optometry, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.; the
  • Maurice K. H. Yap
    School of Optometry, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.; the
  • Jing Wang
    From the Department of Ophthalmology, The First Affiliated Hospital, Medical College, Zhejiang University, Hangzhou, China.; the
  • Shea Ping Yip
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
Investigative Ophthalmology & Visual Science June 2006, Vol.47, 2291-2299. doi:10.1167/iovs.05-1344
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      Wei Han, Maurice K. H. Yap, Jing Wang, Shea Ping Yip; Family-Based Association Analysis of Hepatocyte Growth Factor (HGF) Gene Polymorphisms in High Myopia. Invest. Ophthalmol. Vis. Sci. 2006;47(6):2291-2299. doi: 10.1167/iovs.05-1344.

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      © 2016 Association for Research in Vision and Ophthalmology.

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Abstract

purpose. To investigate the association of high myopia with polymorphisms in the hepatocyte growth factor (HGF) gene, a potential candidate for myopia development.

methods. Single nucleotide polymorphisms (SNPs) were screened and identified in the HGF gene region with denaturing high-performance liquid chromatography, and their linkage disequilibrium pattern was established in a Han Chinese population (n = 150). Tag SNPs were selected and genotyped using restriction digestion and fluorescence polarization assays for 128 nuclear families with 133 severely myopic (mean spherical equivalent [MSE] ≤ −10.0 D) offspring. A family-based association study was performed using FBAT and GenAssoc (Cambridge University, Cambridge, UK).

results. Of three tag SNPs (HGF5-5b, HGFe9, and HGFe10b) selected for association study, HGF5-5b, located in the upstream region, was found to be associated with high myopia considered as a quantitative trait (MSE) in additive, dominant, and recessive models (P = 0.0157, 0.0108, and 0.0108, respectively). The genotype relative risk was 2.19 for the genotype C/T, and 2.14 for T/T with reference to C/C of HGF5-5b. Significantly reduced transmission was demonstrated for the haplotypes C-A-C (HGF5-5b, HGFe9, and HGFe10b; P = 0.0031) and C-A (HGF5-5b and HGFe9; P = 0.0015) in the recessive model, whereas significantly increased transmission was found for haplotype T-C (HGF5-5b and HGFe10b; P = 0.0040) under the dominant model. Preferential transmission of haplotypes remained significant even after correction for multiple comparisons. Analysis gave similar results, with myopia considered to be a qualitative trait.

conclusions. HGF is a potential locus associated with high myopia in the Han Chinese population. This is the first study reporting the association of an HGF gene polymorphism with high myopia.

Myopia, a very common ocular refractive condition, has a high prevalence all over the world, particularly in Asian populations, and has become a public health problem in modern society. 1 2 High myopia, typically in excess of −6 D, 3 could result in severe ocular morbidity, visual impairment, and even blindness (Chiang LM, et al. IOVS 1993;34:ARVO Abstract 937). 4  
Myopia is a complex trait in which multiple genes, multiple environmental factors, and their interactions have been all implicated. 5 6 7 8 The much higher prevalence of myopia in Asian populations than in white and African populations suggests a higher genetic susceptibility to myopia in Asian populations. 1 Twin studies also have shown high concordance of myopia traits in monozygotic twins. 9 10 Although whether low to moderate myopia, also known as school myopia, is genetic is still controversial, 11 high myopia seems to have an obvious genetic background. 12 Because the first myopia locus was mapped to Xq28, 13 many more myopia loci have been found, including the two latest loci at 2q and 4q. 14 15 However, the common nonsyndromic high myopia might be multifactorial or complex. 16 Genetic association study is currently regarded as the most powerful approach to mapping the genes underlying such complex traits. 17 The transmission disequilibrium test (TDT) 18 and its modifications are based on families instead of unrelated cases and controls and are effective for detecting the association of traits and disease-susceptibility genes with modest impact on disease. 19 They are also robust in population stratification, which may lead to a false-positive association in conventional case–control studies. 
Hepatocyte growth factor (HGF) is an important multifunctional cytokine for cellular scattering and proliferation. 20 HGF and its receptor are broadly expressed in the eye and play a critical role in many ocular physiological and pathologic processes. 21 22 23 Linkage analysis of mouse eye size showed that the HGF gene may be a strong positional candidate responsible for myopia. 24 Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are critical in sclera remolding, which is the characteristic change in axial myopia and is particularly remarkable in high myopia. 25 26 27 28 HGF was found to be closely related to the biological activities of MMPs and TIMPs. 22 29 30 31 Moreover, HGF can also induce the expression of egr-1/ZENK, 32 33 which was recently found to be associated with myopia. 34 Therefore, we hypothesize that the HGF gene may be a potential candidate susceptibility gene for human high myopia. 
The HGF gene maps to chromosome 7, region q21.1, spans approximately 70-kb and has 18 exons. In this study, we identified the single nucleotide polymorphisms (SNPs) within and around the HGF coding region and established the pattern of linkage disequilibrium (LD) among the identified SNPs in a Han Chinese population. SNP markers for association analysis were then selected on the basis of the LD pattern. Using the approach of family-based association study, we investigated the genetic association between high myopia and SNP markers of the HGF gene in a group of Han Chinese nuclear families with highly myopic sibs. 
Materials and Methods
Subjects
Blood samples used in the SNP screening and genotyping stage were collected as described by Han et al. 35 In contrast, high-myopia families were recruited from the Department of Ophthalmology, the First Affiliated Hospital in Hangzhou, China, with written informed consent being given. Blood samples were also collected from all recruited family members. All study subjects were ethnic Han Chinese from Southern China. The study was approved by the Human Subject Ethics Subcommittees of the Hong Kong Polytechnic University and Zhejiang University and adhered to the tenets of the Declaration of Helsinki. 
Each nuclear family consisted of two parents and at least one affected sib with high myopia. Refractive error was determined with cycloplegic refraction for affected sibs and only noncycloplegic autorefraction was used in the parents. For myopic sibs, the entry criterion was a spherical equivalent of −10.0 D or worse for both eyes, where spherical equivalent was calculated as sphere diopters plus half-cylinder diopters. Mean spherical equivalent (MSE) of the two eyes for each sib was used for analysis. All subjects received keratometric measurement with an autorefractor (Humphrey, Carl Zeiss Meditec, Inc., Dublin, CA). Every affected sib also received the measurement of intraocular pressure with a noncontact tonometer (Reichert Ophthalmic Instruments, Depew, NY) and corneal curvature (Obscan II; Orbtek, Bausch & Lomb, Tampa, FL). A-ultrasonography (Scanner A2500; Sonomed, Lake Success, NY) was used to measure the ocular axial length (AXL), anterior chamber depth (ACD), and lens thickness (LT). The earliest time when myopia was first diagnosed was recorded and used as the surrogate for the onset age of myopia. Myopic sibs were excluded from the study if they had a premature birth history, early-age refractive media opacity, known genetic diseases (such as Stickler or Marfan syndrome) with myopia as one of the presenting features, a history of ocular trauma, or increased intraocular pressure (≥20 mm Hg). Myopic sibs were also excluded if they had AXL <26.0 mm or an average corneal power (CP) of two meridians >47.0 D in either eye. 
DNA Extraction
DNA from university students and healthy blood donors was extracted as described previously. 35 For blood samples collected from high-myopia families, DNA was extracted (NucleoSpin Blood L kit; Macherey-Nagel, Düren, Germany), according to the manufacturer’s instructions. 
Polymerase Chain Reaction
Forty-four primer pairs were designed with the software Oligo (ver. 6.57; Molecular Biology Insights, Cascade, WA) to amplify the 18 HGF exons and their immediate flanking regions (within 100 bp), and noncoding sequences approximately 3.0 kb upstream of the start codon and 5.0 kb downstream of the stop codon. Primer sequences are available on request. Touchdown PCR was used to amplify the pooled or individual DNA samples, as described previously. 35 In the screening stage, the same DNA pooling strategy for identifying SNPs was applied again. 35 For genotyping purposes, DNA samples were amplified individually. 
SNP Identification and Genotyping for Healthy Blood Donors
In the screening stage, the WAVE DNA Fragment Analysis System (Transgenomic, Omaha, NE) was used to analyze the PCR products. The details of denaturing high performance liquid chromatography (DHPLC) analysis for SNP identification were described in our previous report. 35 For each identified SNP, 150 Chinese samples were genotyped to establish the allele frequencies using the same DHPLC-based approach. 35 The software ElDorado (http://www.genomatix.de/ Genomatix GmbH, Munich, Germany) was used to search the potential transcription factor binding sites and the promoter region of the HGF gene. 
SNP Genotyping for Myopia Families
Three SNPs (HGF5-5b, HGFe9, and HGFe10b) were selected for association study on the basis of their LD pattern. Because of the logistic arrangement in the use of instruments in our laboratory, we switched to different platforms for genotyping DNA samples from myopia families. The SNP HGF5-5b, located within the recognition sequence of BglII, was genotyped with restriction analysis. A 10-μL reaction mix consisting of 5 μL PCR product, 1× NEBuffer 3, and 5 U BglII (New England Biolabs, Beverly, MA) was prepared and incubated at 37°C for 16 hours. The restriction products were analyzed by agarose gel electrophoresis. 
The other two SNPs HGFe9 and HGFe10b were genotyped an SNP detection kit (AcycloPrime-FP; PerkinElmer, Boston, MA) according to the manufacturer’s instructions. The method was a modification of template-directed dye-terminator incorporation with fluorescence polarization detection. 36 The sequences of the SNP primers were 5′-GTTATCGCTATTCTGAGTCCAAAA-3′ for HGFe9 and 5′-AAGTCCAATGAATATCAAGGC-3′ for HGFe10b. The optimal number of thermal cycles of the AcycloPrime-FP protocol was 25 cycles for HGFe9 and 35 cycles for HGFe10b. Fluorescence polarization was measured (Victor3V Multilabel Reader; PerkinElmer), and genotypes were called automatically using the provided allele-calling software. 
LD Analysis of Common SNPs
The genotype data of SNPs with the minor allele frequency (MAF) >0.10 were input to the software Haploview 37 (version 3.11; http://www.broad.mit.edu/mpg/haploview/ provided in the public domain by the Massachusetts Institute of Technology, Cambridge, MA) which then performed the Hardy-Weinberg equilibrium test, allelic association, and haplotype block analysis. Haploview calculated Lewontin’s original and standardized LD parameters (D and D′), r 2, and the 95% confidence intervals (CI) of D′ (using a bootstrap algorithm). Haploview defined SNP pairs to be in “strong LD” if the upper 95% confidence limit on D′ was ≥0.98, and the lower confidence limit was ≥0.7. The SNP pairs were described as showing “strong evidence of historical recombination” if the upper 95% confidence limit was <0.9. Other SNP pairs were categorized as “uninformative.” A haplotype block was defined if the outermost pair of SNPs was in strong LD, and within the block region the number of pairs in strong LD was at least 19 times greater than those in weak LD. 38  
Statistical Analysis of Ocular Data
The software package SPSS (ver. 11.0; SPSS Inc., Chicago, IL) was used to test the partial correlation between MSE of the affected sibs and other ocular components (AXL, ACD, LT, and CP). 
Genetic Association Study
Genetic association study was performed using the Family-Based Association Test software package (FBAT, ver. 1.5.5; http://www.biostat.harvard.edu/∼fbat/default.html/ provided in the public domain by Harvard Medical School, Boston, MA) which is a generalized approach derived from original TDT method. 18 FBAT compares the genotype distribution observed in offspring with its expected distribution. 39 This approach allows the user to dictate a genetic model for association analysis. 40 Association tests for single loci and haplotypes under additive, dominant, and recessive models were performed using FBAT with high myopia being treated as a quantitative or a qualitative trait. 41 When high myopia was analyzed as a quantitative trait, MSE was taken as the measured trait. When high myopia was analyzed as a qualitative trait, the phenotypes were simplified to a dichotomous trait: affected with high myopia (MSE ≤ −10.0 D) or unaffected. The parental affection status, also included in the input pedigree files, did not affect the FBAT analysis because the high myopia pedigrees in this study were all nuclear families consisting of two parents and their children only. The null hypothesis was no linkage and no association, and the alternative hypothesis was that there was both linkage and association. 
The multiple-comparison problems for the alleles of a given marker were solved by the global statistic for each SNP tested under any given genetic model. For bi-allelic markers, dominant and recessive models give reciprocal results and thus are equivalent to one test for the purpose of accounting for multiple testing. Therefore, for the global statistics, there were three SNPs, each tested under two different genetic models (additive and dominant/recessive) and thus six tests of global association. The more powerful false-discovery rate (FDR) 42 was used to control for multiple-hypothesis testing, instead of the conventional Bonferroni adjustment. The FDR is the expected proportion of the true null hypotheses rejected out of the total number of null hypotheses rejected. Multiple-comparison procedures controlling FDR can be regarded as post hoc maximizing procedures, and are more powerful than the commonly used multiple-comparison procedures based on the family-wise error rate. After adjustment for multiple comparisons and with an FDR level of 0.05, the cutoff for significant global association was 0.0167. 
Similarly, the multiple-comparison issues for the haplotypes of two or three SNPs were solved by the global statistic under any given genetic model. For the global statistics, there were four groups of haplotypes each tested under three different genetic models and thus 12 tests of global association. After adjustment for multiple comparisons with an FDR of 0.05, the cutoff for significant global association was 0.0125. 
A matched case–control dataset was generated with each affected (myopic) sib matched to three possible pseudocontrol subjects created from the untransmitted parental allele. 43 44 Conditional logistic regression was used to analyze this case–pseudocontrol dataset, to calculate the effect size of the marker genotype on the disease risk as the genotype relative risk (GRR) and the corresponding 95% CIs. Analysis was performed with the GenAssoc package (http://www-gene.cimr.cam.ac.uk/clayton/software/ provided in the public domain by the Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK) and executed within the software (STATA, ver. 8.2; Stata Corp., College Station, TX). 
Results
LD Pattern of the SNPs Identified
Of the total 18 SNPs identified in 11 fragments (Table 1) , nine SNPs were common with MAF > 0.10 and were selected as the markers for LD analysis. Two of these nine SNPs were novel. The Han Chinese population under study was in Hardy-Weinberg equilibrium for all common SNPs. According to the definition of LD blocks proposed by Gabriel et al., 38 one block covering HGFe10a, HGFe10b, HGF3-12a, HGF3-12b, HGF3-16b, and HFG3-17b was defined in the HGF gene region (Fig. 1) . Meanwhile, HGFe8 was also in strong LD with this block of SNPs. The SNP HGFe9 showed no LD with all other SNPs. The LD between HGF5-5b and all other SNPs was “uninformative.” Based on these LD data, three SNPs in HGF gene region—namely, HGF5-5b, HGFe9, and HGFe10b—were selected as markers for further association study. 
Clinical Myopia Data Analysis
The detailed information of the myopic siblings is listed in Table 2 . The MSE of high myopia offspring is −12.08 ± 3.37 D with single eye’s spherical diopter ranging from −9.5 to −22.5 D and cylinder diopter from 0 to −4.25 D. Of the total 128 families (256 parents and 133 highly myopic offspring) studied, 52 (40.6%) had one myopic parent with both eyes’ spherical equivalent < −0.75 D and 26 (20.3%) had two myopic parents, whereas 37 (28.9%) had one highly myopic parent with both eyes’ spherical equivalent < −6.0 D, and 4 (3.1%) had two highly myopic parents. No significant sex association for the prevalence of high myopia was observed in the myopic sib group (P > 0.05). For all affected sibs, corneal curvature examination (Obscan; Bausch & Lomb) showed no corneal shape anomaly, such as keratoconus and posterior ectasia. Partial correlation analysis showed significant correlation between MSE and ocular refractive components of AXL, ACD, and CP, with the exception of LT. In agreement with previous studies, 46 47 AXL had the strongest correlation (r = −0.70; P < 0.001) to refractive error. 
Genetic Association Study
With high myopia as a quantitative trait measured as MSE, no significant association of the SNPs HGFe9 and HGFe10b with high myopia was found under all the genetic models tested (Table 3) . In contrast, SNP HGF5-5b showed a significant association under additive and dominant/recessive models with the quantitative trait MSE (Table 3) . The major and minor alleles of HGF5-5b showed opposite preferential transmission under the additive model. The minor allele (2 or T) showed significantly increased transmission under the dominant model (z = 3.008, P = 0.0026), whereas the major allele (1 or C) exhibited significantly reduced transmission under the recessive model (z = −3.008, P = 0.0026). It is interesting to note the reciprocal relationship when the marker is bi-allelic. The global statistics (P = 0.0157 or 0.0108) were significant under the three models tested. They were still statistically significant, even on correction for multiple comparisons (n = 6) based on FDR. 
For the sake of easy discussion, each haplotype is indicated by three digits representing the alleles of the SNPs HGF5-5b, HGFe9, and HGFe10b (in that order), and a zero is inserted when a particular SNP is not involved. No association of any haplotypes involving only HGFe9 and HGFe10b (e.g., 0-1-1 and 0-2-1) with the measured trait MSE was found under all three genetic models tested with FBAT (Table 4) . However, significant association was demonstrated for haplotypes involving HGF5-5b (Table 4) . When individual haplotypes were considered, significantly increased transmission (z > 2 and P < 0.05) was found for haplotypes carrying allele 2 of HGF5-5b (2-1-1, 2-1-0, and 2-0-1) under additive and dominant models. When a group of haplotypes involving two or three SNPs were considered as a whole, a similar significant association was maintained except for haplotypes involving only HGF5-5b and HGFe9 under the dominant model (P = 0.0883). The haplotype 2-0-1 was still transmitted in significant excess (z = 3.308, global P = 0.0040) to the myopia offspring when the global statistic was adjusted for multiple comparisons based on FDR. 
Significantly reduced transmission (Z < −2 and P < 0.05) was observed for haplotypes carrying allele 1 of HGF5-5b (1-1-1, 1-1-0, and 1-0-1) under the recessive model, and haplotype 1-1-0 under the additive model, no matter whether the haplotypes were considered individually or globally with other haplotypes within the same group (Table 4) . With correction for multiple comparisons for global statistics, reduced transmission of haplotypes 1-1-1 and 1-1-0 to the myopic offspring was still significant (z = −3.139, global P = 0.0031; and z = −3.445, global P = 0.0015, respectively). 
With high myopia considered as a dichotomous qualitative trait, similar results were also obtained except that most probabilities were slightly larger than when high myopia was treated as a quantitative trait (MSE). Detailed results are available online in Supplementary Tables S1, S2. In particular, the three critical global probabilities for haplotype analysis were 0.0061 for three-locus analysis and 0.0025 for two-locus (HGF5-5b and HGFe9) analysis under the recessive model, and 0.0076 for two-locus (HGF5-5b and HGFe10b) analysis under the dominant model. In comparison, the respective probabilities were 0.0031, 0.0015, and 0.0040 when analysis was performed with the measured trait MSE (Table 4) . Both sets of probabilities remained significant after correction for multiple testing based on FDR. However, with high myopia considered as a qualitative trait, the initial significant results for single marker analysis did not survive correction for multiple testing. 
Also treating high myopia as a qualitative trait, GenAssoc generated a case–pseudocontrol dataset. Analysis of HGF5-5b with GenAssoc gave a GRR of 2.19 (95% CI = 1.29–3.72, P = 0.004) for genotype 1/2 and 2.14 (95% CI = 1.02–4.49, P = 0.043) for 2/2 with reference to 1/1. This is consistent with the increased transmission of allele 2 (T) under the dominant model. 
Discussion
LD Pattern
Haplotype pattern in the HGF locus was partitioned according to the CI of D′ in this study. On the basis of the LD map (Fig. 1) , three SNPs—HGF5-5b, HGFe9, and HGFe10b—were selected as the markers for subsequent association test. SNP HGFe9 was categorized as a “hole” inserting in the haplotype block, a type of departure from strict haplotype criteria, 48 and should be tested individually in association study. Simply selecting markers for a genetic association test may substantially decrease the efficacy of the TDT test, as TDT crucially depends on the LD between the markers and disease loci. 49 The “hole” HGFe9, as categorized by the LD pattern, could be missed if selecting markers for analysis was arbitrary. It is also desirable to select markers based on homogeneous ethnic population as ethnic population heterogeneity for high myopia could confound the results of genetic analysis. 50 We also calculated the LD for these three marker SNPs in the parents of high myopic nuclear families. The SNP pair of HGF5-5b and HGFe10b showed a higher LD level but still remained “uninformative,” whereas SNP HGFe9 still behaved like a “hole” (data not shown). The results were in agreement with those (Fig. 1)for random population. The similar LD pattern in the parental population suggests the homogeneity of the populations in our study and the usefulness of establishing LD patterns in advance. A recent Japanese study also showed a similar LD pattern in the HGF locus. 51 Moreover, online data of the Human Haplotype Map Project also showed that LD patterns in the HGF locus for the Chinese, Japanese and European populations (http://www.hapmap.org) are largely similar to the LD patterns reported herein. No coding SNPs in the HGF gene were identified in our random Chinese population, and the SNPs identified in the 5′ upstream region were not involved in the promoter region and regulatory sites documented in ElDorado. We speculate that other potential common polymorphisms of interest may give rise to the variation of gene regulation, but not amino acid substitution in the Chinese population. 
Genetic Association Analysis
The etiology of myopia is still not well understood. Finding the susceptibility genes will lead to a better understanding of the mechanisms underlying myopia and hence finding effective ways to control or treat myopia. Up to now, mapping genes predisposing to myopia is still a great challenge. This work is the first effort to assess the relationship between the HGF gene and human high myopia. 
According to the results of FBAT with the quantitative trait MSE, no significant association was observed for HGFe9 and HGFe10b for either allele or haplotype (Tables 3 4) . In contrast, the 5′ upstream side seems to be an interesting region of the HGF locus, as the marker HGF5-5b is always implicated in the significant results for either allele or haplotype. FBAT analysis showed that HGF5-5b was associated with high myopia under the three genetic models tested, even after correction for multiple testing (Table 3) . The GRR estimates indicated that the genotypes 1/2 (C/T) and 2/2 (T/T) were risk factors for high myopia with reference to 1/1 (C/C): 2.19 (P = 0.004) and 2.14 (P = 0.043), respectively. This is compatible with FBAT results for HGF5-5b under the dominant–recessive models. Such analysis highlights the complexity of myopia inheritance, as has been suggested by previous studies. 16 52 The fact that 50 families had no myopic parent in our study also reflects the complexity of inheritance modes of high myopia. In line with the results of single-marker association, tests for haplotypes also showed similar patterns. Haplotypes 1-1-1 or 1-1-0 showed significant association under the recessive model, whereas haplotype 2-0-1 demonstrated significant association in the dominant model (Table 4) . The association was also significant after correction for multiple comparisons. 
In general, the same conclusion was reached no matter whether high myopia was considered as a quantitative trait (MSE) or a qualitative trait (affected or unaffected). But, our results suggest that analysis based on the measure trait MSE is slightly more powerful than that based on the dichotomous trait. Because AXL correlated highly with MSE (r = −0.70; Table 2 ), we expect that analysis based on AXL should give similar results. This is in fact the case: the results paralleled those when myopia was considered a qualitative trait, except that the probabilities were in general slightly larger (data not shown). 
It is tempting to speculate that other SNPs of interest may exist in proximity to HGF5-5b and on its implicated haplotypes. HGF is an inducible cytokine and the promoter and some regulatory factors of the HGF gene have been characterized. 53 54 SNP HGF5-5b is not located in these experimentally verified regulatory sites. Other nearby sequence variations are worthy of further investigation to identify the functional SNPs that may play a role in the development of myopia. Recently, two case−control studies in Japanese populations reported that the polymorphisms in intron 8 (43839A→T; see Table 1 ) and intron 13 (not reported in our study) of the HGF locus may be associated with vascular diseases including hypertension and atherosclerosis. 51 55 Our results suggest that the HGF5-5b and its adjacent polymorphisms may be the interesting loci for high myopia. The finding that the HGF locus is associated with different diseases may not be too surprising, considering the multifunctional roles of HGF. 20 21 22 23 29 30 31 32 33  
Less frequent SNPs like HGFe14b and HGFe18-1a (Table 1)may also contribute to a common trait like myopia. Our preliminary data showed that these two SNPs were also not common in our high myopia population (MAF = 0.03 for HGFe14b and 0.06 for HGFe18-1a) and were not associated with high myopia (data not shown). 
Myopia is a delicate change in refractive error with ocular components of AXL, ACD, CP, and LT all being involved. 56 The profile of genetic and/or environmental components for myopic subjects is highly variable. These could confound the association study of myopia. Strict entry criteria and careful phenotyping of myopic subjects are crucially important for the success of myopia association study. The price to pay here is the corresponding increased difficulty in subject recruitment. With the TDT-based approach in our study, the spurious association caused by population stratification in conventional case–control study was effectively eliminated. Additional efforts were made to enhance the efficacy of FBAT in our study. First, only typical high myopia (≤ −10.0D) was included, and the onset age of myopia for all affected subjects was less than 12 years. Such criteria may enhance the contribution of the genetic component to the myopia trait in the subjects studied. 57 Second, the ocular components ACD and CP were factored out, and AXL was taken into account purposely in myopic subject recruitment to diminish the complexity of genetic background. 56 This is because typical high myopia is mainly due to the elongation of ocular AXL, and inheritance has a significant impact on AXL but not on ACD or CP. 12 58 The most significant correlation between AXL and refractive error (Table 2)demonstrated that the posterior axial myopia was studied and the confounding due to other ocular components was largely ruled out. Third, establishing the LD pattern in the same ethnic population beforehand in this study also enhanced the validity of association test. Nevertheless, the high complexity of genetic background for our high myopic population still exists, and each potential candidate gene may only have a mild effect on myopia onset and severity. A larger sample size and replication with independent sample sets are always instructive in drawing a clearer conclusion regarding the relation between the HGF gene and high myopia. 
As a part of our ongoing joint effort to identify myopia susceptibility genes, this study established the LD pattern in the HGF locus and investigated the association between three tag SNPs and high myopia in a group of Chinese families with high myopia. Our study followed a logical approach to association test on the basis of characterizing the LD patterns, since the selection of markers based on LD pattern in candidate genes in advance was a critical part of the association study. 59 Our results at this stage showed the evidence of association of the HGF locus with early-onset high myopia in a Han Chinese population. According to the results of this exploratory work, we consider that the 5′ region of the HGF gene may contain potential polymorphisms affecting myopia susceptibility in the Han Chinese population. Study with a larger sample size and denser SNP markers or in other populations is needed to further elucidate the relationship between the HGF gene and high myopia and to identify functional SNPs that play a role in high myopia. 
 
Table 1.
 
Details of the SNPs Identified in the HGF Gene in a Han Chinese Population
Table 1.
 
Details of the SNPs Identified in the HGF Gene in a Han Chinese Population
SNP* Designation* Location Reference SNP ID No. (NCBI) Flanking Sequence (5′→3′) and Alleles Major/Minor, † MAF HWE P
−1680delA, ‡ HGF5-5a 5′ Flanking region Novel AAGGATTAGC [A/–] ATAGAAACGG 0.0033
−1652C→T HGF5-5b 5′ Flanking region rs3735520 AAAATAGATC [C/T] CTCAAAAGGA 0.3433 0.985
40171delT HGFe8 Intron 7 rs5745686 AGTTTTTTTT [–/T] GTTGTTGTTTT 0.2067 0.907
43839A→T HGFe9 Intron 8 rs2286194 GAGTCCAAAA [A/T] GTTAGAACTC 0.1467 1.00
49065C→T HGFe10a Intron 9 rs10272030 TTGTAAAAAA [T/C] CTTTTTGTTT 0.1967 1.00
49080T→C HGFe10b Intron 9 Novel TTGTTTTATC [C/T] GCCTTGATAT 0.1967 1.00
62704T→C, ‡ HGFe14a Intron 14 Novel TTAAAACTAG [T/C] ATTATTTTGA 0.0100
62753G→T HGFe14b Intron 14 Novel TGCTCTTAAG [G/T] TTATAATATG 0.0500 1.00
64588A→G HGFe17 Intron 17 Novel GTGAGGTAAA [A/G] AGGAAGTTCT 0.0033
67075A→G HGFe18-1a Intron 17 rs5745765 TAGTACACTA [A/G] TTTTTATATC 0.0600 1.00
67183T→G, ‡ HGFe18-1b Intron 17 rs5745767 TAATTCCTAA [T/G] AATACTTTGT 0.0067
67787A→T HGFE18-3 3′ UTR Novel CCTCACCAAA [A/T] CAATTTATAC 0.0033
71188T→C HGF3-12a 3′ Flanking region rs5745787 AATCTCTATG [C/T] ACTTTAGTTT 0.2295 1.00
71207G→A HGF3-12b 3′ Flanking region rs5745788 TTCTCCCACC [A/G] TAAAATGTAA 0.2295 1.00
72122T→C, ‡ HGF3-16a 3′ Flanking region Novel AAGCCTCTCA [T/C] GTATAATTCA 0.0033
72223A→G HGF3-16b 3′ Flanking region Novel TTTAAAAGCC [G/A] AGAATTAAAA 0.2448 0.934
72403T→A, ‡ HGF3-17a 3′ Flanking region Novel GGTTATATGT [T/A] TATTTATGGA 0.0033
72433-72434insAAC HGF3-17b 3′ Flanking region rs10664024 AGTAAATAAT [AAC/–] AACATCAAAA 0.2245 1.00
Figure 1.
 
Pair-wise LD measures of D′, the 95% CI, and r 2 for common SNPs of the HGF locus. For each cell, the number on the top line is D′; the second line (in parentheses) the 95% CI of D′; and the bottom line r 2. The cells in black denote complete LD according to the confidence intervals of D′, the cells in gray the uninformative LD, and the other cells strong historical recombination, as defined by Gabriel et al. 38 The diagonal scale shows the sites of each SNP around the HGF gene region.
Figure 1.
 
Pair-wise LD measures of D′, the 95% CI, and r 2 for common SNPs of the HGF locus. For each cell, the number on the top line is D′; the second line (in parentheses) the 95% CI of D′; and the bottom line r 2. The cells in black denote complete LD according to the confidence intervals of D′, the cells in gray the uninformative LD, and the other cells strong historical recombination, as defined by Gabriel et al. 38 The diagonal scale shows the sites of each SNP around the HGF gene region.
Table 2.
 
Clinical and Demographic Information of High-Myopia Siblings*
Table 2.
 
Clinical and Demographic Information of High-Myopia Siblings*
Age at entry ± SD (y) 22.32 ± 11.18
Sex ratio (male/female), † 70/63 (p > 0.05)
Onset age of myopia SD (y) 7.54 ± 2.95
Families with no myopic parents 50
Families with one myopic parent, ‡ 52
 Families with one high-myopic parent, ‡ 37
Families with two myopic parents, ‡ 26
 Families with two high myopic parents, ‡ 4
MSE ± SD (D) −12.08 ± 3.37
 Range of spherical diopter −9.50 to −22.50
 Range of cylinder diopter 0 to −4.25
AXL ± SD (mm) 28.07 ± 1.64 (r = −0.70, p < 0.001)
CP ± SD (D) 42.79 ± 1.90 (r = −0.26, p < 0.001)
ACD ± SD (mm) 3.69 ± 0.29 (r = −0.29, p < 0.001)
LT ± SD (mm) 3.65 ± 0.27 (r = −0.05, p = 0.385)
Table 3.
 
Single Locus Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
Table 3.
 
Single Locus Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
HGF5-5b HGFe9 HGFe10b
Allele 1 (C) 2 (T) 1 (A) 2 (T) 1 (C) 2 (T)
Frequency in parents 0.578 0.422 0.816 0.184 0.848 0.152
FBAT: additive model
n 98 98 73 73 59 59
Z score −2.415 2.415 −0.789 0.789 −1.707 1.707
P 0.0157 0.0157 0.4299 0.4299 0.0877 0.0877
 Global statistic df = 1, χ2 = 5.831, P = 0.0157, † df = 1, χ2 = 0.623, P = 0.4299 df = 1, χ2 = 2.915, P = 0.0877
FBAT: dominant model
n 53 77 11 71 12 54
Z score −0.457 3.008 0.375 0.984 0.871 −1.543
P 0.6479 0.0026 0.7074 0.3253 0.3839 0.1229
 Global statistic df = 2, χ2 = 9.051, P = 0.0108, † df = 2, χ2 = 1.187, P = 0.5524 df = 2, χ2 = 2.941, P = 0.2298
FBAT: recessive model
n 77 53 71 11 54 12
Z score −3.008 0.457 −0.984 −0.375 1.543 −0.871
P value 0.0026 0.6479 0.3253 0.7074 0.1229 0.3839
 Global statistic df = 2, χ2 = 9.051, P = 0.0108, † df = 2, χ2 = 1.187, P = 0.5524 df = 2, χ2 = 2.941, P = 0.2298
Table 4.
 
Haplotype Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
Table 4.
 
Haplotype Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
Haplotype HF Additive Model Dominant Model Recessive Model
HGF5-5b HGFe9 HGFe10b n Z Score P Global Statistic n Z Score P Global Statistic n Z Score P Global Statistic
1 1 1 0.406 101 −1.633 0.1026 df = 5, 77 0.282 0.7777 df = 5, 47 −3.139 0.0017 df = 2,
χ2 = 12.148, χ2 = 11.77, χ2 = 11.56,
P = 0.0328 P = 0.0381 P = 0.0031 , †
2 1 1 0.257 78 2.562 0.0104 74 2.326 0.0200 22 1.478 0.1395
1 1 0 0.554 97 −2.548 0.0108 df = 3, 57 −0.490 0.6240 df = 4, 63 −3.445 0.0006 df = 2,
χ2 = 7.634, χ2 = 8.091, χ2 = 13.06,
P = 0.0542 P = 0.0883 P = 0.0015 , †
2 1 0 0.263 81 2.344 0.0191 77 2.100 0.0357 25 1.331 0.1830
1 0 1 0.432 100 −1.449 0.1472 df = 3, 71 0.480 0.6314 df = 3, 60 −2.802 0.0051 df = 2,
χ2 = 9.739, χ2 = 13.215, χ2 = 7.90,
P = 0.0209 P = 0.0040 , † P = 0.0192
2 0 1 0.416 93 2.551 0.0107 73 3.308 0.0009 47 0.518 0.6046
0 1 1 0.664 96 0.603 0.5468 df = 2, 40 0.610 0.5419 df = 3, 80 0.361 0.7181 df = 2,
0 2 1 0.184 73 0.782 0.4340 χ2 = 3.077, 71 1.011 0.3119 χ2 = 3.084, <10 χ2 = 1.21,
P = 0.2147 P = 0.3788 P = 0.5460
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Figure 1.
 
Pair-wise LD measures of D′, the 95% CI, and r 2 for common SNPs of the HGF locus. For each cell, the number on the top line is D′; the second line (in parentheses) the 95% CI of D′; and the bottom line r 2. The cells in black denote complete LD according to the confidence intervals of D′, the cells in gray the uninformative LD, and the other cells strong historical recombination, as defined by Gabriel et al. 38 The diagonal scale shows the sites of each SNP around the HGF gene region.
Figure 1.
 
Pair-wise LD measures of D′, the 95% CI, and r 2 for common SNPs of the HGF locus. For each cell, the number on the top line is D′; the second line (in parentheses) the 95% CI of D′; and the bottom line r 2. The cells in black denote complete LD according to the confidence intervals of D′, the cells in gray the uninformative LD, and the other cells strong historical recombination, as defined by Gabriel et al. 38 The diagonal scale shows the sites of each SNP around the HGF gene region.
Table 1.
 
Details of the SNPs Identified in the HGF Gene in a Han Chinese Population
Table 1.
 
Details of the SNPs Identified in the HGF Gene in a Han Chinese Population
SNP* Designation* Location Reference SNP ID No. (NCBI) Flanking Sequence (5′→3′) and Alleles Major/Minor, † MAF HWE P
−1680delA, ‡ HGF5-5a 5′ Flanking region Novel AAGGATTAGC [A/–] ATAGAAACGG 0.0033
−1652C→T HGF5-5b 5′ Flanking region rs3735520 AAAATAGATC [C/T] CTCAAAAGGA 0.3433 0.985
40171delT HGFe8 Intron 7 rs5745686 AGTTTTTTTT [–/T] GTTGTTGTTTT 0.2067 0.907
43839A→T HGFe9 Intron 8 rs2286194 GAGTCCAAAA [A/T] GTTAGAACTC 0.1467 1.00
49065C→T HGFe10a Intron 9 rs10272030 TTGTAAAAAA [T/C] CTTTTTGTTT 0.1967 1.00
49080T→C HGFe10b Intron 9 Novel TTGTTTTATC [C/T] GCCTTGATAT 0.1967 1.00
62704T→C, ‡ HGFe14a Intron 14 Novel TTAAAACTAG [T/C] ATTATTTTGA 0.0100
62753G→T HGFe14b Intron 14 Novel TGCTCTTAAG [G/T] TTATAATATG 0.0500 1.00
64588A→G HGFe17 Intron 17 Novel GTGAGGTAAA [A/G] AGGAAGTTCT 0.0033
67075A→G HGFe18-1a Intron 17 rs5745765 TAGTACACTA [A/G] TTTTTATATC 0.0600 1.00
67183T→G, ‡ HGFe18-1b Intron 17 rs5745767 TAATTCCTAA [T/G] AATACTTTGT 0.0067
67787A→T HGFE18-3 3′ UTR Novel CCTCACCAAA [A/T] CAATTTATAC 0.0033
71188T→C HGF3-12a 3′ Flanking region rs5745787 AATCTCTATG [C/T] ACTTTAGTTT 0.2295 1.00
71207G→A HGF3-12b 3′ Flanking region rs5745788 TTCTCCCACC [A/G] TAAAATGTAA 0.2295 1.00
72122T→C, ‡ HGF3-16a 3′ Flanking region Novel AAGCCTCTCA [T/C] GTATAATTCA 0.0033
72223A→G HGF3-16b 3′ Flanking region Novel TTTAAAAGCC [G/A] AGAATTAAAA 0.2448 0.934
72403T→A, ‡ HGF3-17a 3′ Flanking region Novel GGTTATATGT [T/A] TATTTATGGA 0.0033
72433-72434insAAC HGF3-17b 3′ Flanking region rs10664024 AGTAAATAAT [AAC/–] AACATCAAAA 0.2245 1.00
Table 2.
 
Clinical and Demographic Information of High-Myopia Siblings*
Table 2.
 
Clinical and Demographic Information of High-Myopia Siblings*
Age at entry ± SD (y) 22.32 ± 11.18
Sex ratio (male/female), † 70/63 (p > 0.05)
Onset age of myopia SD (y) 7.54 ± 2.95
Families with no myopic parents 50
Families with one myopic parent, ‡ 52
 Families with one high-myopic parent, ‡ 37
Families with two myopic parents, ‡ 26
 Families with two high myopic parents, ‡ 4
MSE ± SD (D) −12.08 ± 3.37
 Range of spherical diopter −9.50 to −22.50
 Range of cylinder diopter 0 to −4.25
AXL ± SD (mm) 28.07 ± 1.64 (r = −0.70, p < 0.001)
CP ± SD (D) 42.79 ± 1.90 (r = −0.26, p < 0.001)
ACD ± SD (mm) 3.69 ± 0.29 (r = −0.29, p < 0.001)
LT ± SD (mm) 3.65 ± 0.27 (r = −0.05, p = 0.385)
Table 3.
 
Single Locus Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
Table 3.
 
Single Locus Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
HGF5-5b HGFe9 HGFe10b
Allele 1 (C) 2 (T) 1 (A) 2 (T) 1 (C) 2 (T)
Frequency in parents 0.578 0.422 0.816 0.184 0.848 0.152
FBAT: additive model
n 98 98 73 73 59 59
Z score −2.415 2.415 −0.789 0.789 −1.707 1.707
P 0.0157 0.0157 0.4299 0.4299 0.0877 0.0877
 Global statistic df = 1, χ2 = 5.831, P = 0.0157, † df = 1, χ2 = 0.623, P = 0.4299 df = 1, χ2 = 2.915, P = 0.0877
FBAT: dominant model
n 53 77 11 71 12 54
Z score −0.457 3.008 0.375 0.984 0.871 −1.543
P 0.6479 0.0026 0.7074 0.3253 0.3839 0.1229
 Global statistic df = 2, χ2 = 9.051, P = 0.0108, † df = 2, χ2 = 1.187, P = 0.5524 df = 2, χ2 = 2.941, P = 0.2298
FBAT: recessive model
n 77 53 71 11 54 12
Z score −3.008 0.457 −0.984 −0.375 1.543 −0.871
P value 0.0026 0.6479 0.3253 0.7074 0.1229 0.3839
 Global statistic df = 2, χ2 = 9.051, P = 0.0108, † df = 2, χ2 = 1.187, P = 0.5524 df = 2, χ2 = 2.941, P = 0.2298
Table 4.
 
Haplotype Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
Table 4.
 
Haplotype Association Tests for the HGF Gene by FBAT Analysis with the Measured Trait MSE*
Haplotype HF Additive Model Dominant Model Recessive Model
HGF5-5b HGFe9 HGFe10b n Z Score P Global Statistic n Z Score P Global Statistic n Z Score P Global Statistic
1 1 1 0.406 101 −1.633 0.1026 df = 5, 77 0.282 0.7777 df = 5, 47 −3.139 0.0017 df = 2,
χ2 = 12.148, χ2 = 11.77, χ2 = 11.56,
P = 0.0328 P = 0.0381 P = 0.0031 , †
2 1 1 0.257 78 2.562 0.0104 74 2.326 0.0200 22 1.478 0.1395
1 1 0 0.554 97 −2.548 0.0108 df = 3, 57 −0.490 0.6240 df = 4, 63 −3.445 0.0006 df = 2,
χ2 = 7.634, χ2 = 8.091, χ2 = 13.06,
P = 0.0542 P = 0.0883 P = 0.0015 , †
2 1 0 0.263 81 2.344 0.0191 77 2.100 0.0357 25 1.331 0.1830
1 0 1 0.432 100 −1.449 0.1472 df = 3, 71 0.480 0.6314 df = 3, 60 −2.802 0.0051 df = 2,
χ2 = 9.739, χ2 = 13.215, χ2 = 7.90,
P = 0.0209 P = 0.0040 , † P = 0.0192
2 0 1 0.416 93 2.551 0.0107 73 3.308 0.0009 47 0.518 0.6046
0 1 1 0.664 96 0.603 0.5468 df = 2, 40 0.610 0.5419 df = 3, 80 0.361 0.7181 df = 2,
0 2 1 0.184 73 0.782 0.4340 χ2 = 3.077, 71 1.011 0.3119 χ2 = 3.084, <10 χ2 = 1.21,
P = 0.2147 P = 0.3788 P = 0.5460
Supplementary Table S1
Supplementary Table S2
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