An analysis of the association between the vitamin D pathway and serum 25-hydroxyvitamin D levels in a healthy Chinese population

Authors

  • Zeng Zhang,

    1. Department of Orthopedic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China
    2. Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China
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    • ZZ and JWH contributed equally to this work.
  • Jin-Wei He,

    1. Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China
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    • ZZ and JWH contributed equally to this work.
  • Wen-Zhen Fu,

    1. Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China
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  • Chang-Qing Zhang,

    Corresponding author
    • Department of Orthopedic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China
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  • Zhen-Lin Zhang

    1. Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Diseases, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China
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Address correspondence to: Zhen-Lin Zhang, MD, and Chang-Qing Zhang, MD, Shanghai 200233, 600 Yi-Shan Rd., P.R. China. E-mail: zzl2002@medmail.com.cn and zhangchangq@yahoo.com.cn

ABSTRACT

Vitamin D deficiency has been recognized as a major public health issue worldwide. Recent studies have indicated that genetic factors might play an important role in determining serum 25-hydroxyvitamin D [25(OH)D] levels in Caucasians and African Americans. However, the genes that contribute to the variation in serum 25(OH)D levels in Chinese are unknown. In this study, we screened 15 key genes within the vitamin D metabolic pathway using 96 single-nucleotide polymorphism (SNP) markers in a group of 2897 unrelated healthy Chinese subjects. Significant confounding factors that may influence the variability in serum 25(OH)D levels were used as covariates for association analyses. An association test for quantitative traits was performed to evaluate the association between candidate genes and serum 25(OH)D levels. In the present study, variants and/or haplotypes in GC, CYP2R1, and DHCR7/NADSYN1 were identified as being associated with 25(OH)D levels. Participants with three or four risk alleles of the two variants (GC-rs4588 and CYP2R1-rs10766197) had an increased chance of presenting with a 25(OH)D concentration lower than 20 ng/mL (odds ratio 2.121, 95% confidence interval 1.586–2.836, p = 6.1 × 10−8) compared with those lacking the risk alleles. Each additional copy of a risk allele was significantly associated with a 0.12-fold decrease in the log-25(OH)D concentration (p = 3.7 × 10−12). Haplotype TGA of GC rs705117-rs2282679-rs1491710, haplotype GAGTAC of GC rs842999-rs705120-rs222040-rs4588-rs7041-rs10488854, haplotype CA of GC rs1155563-rs222029, and haplotype AAGA of CYP2R1 rs7936142-rs12794714-rs2060793-rs16930609 were genetic risk factors toward a lower 25(OH)D concentration. In contrast, haplotype TGGGCCC of DHCR7/NADSYN1 rs1790349-rs7122671-rs1790329-rs11606033-rs2276360-rs1629220-rs2282618 were genetic protective factors. The results suggest that the GC, CYP2R1, and DHCR7/NADSYN1 genes might contribute to variability in the serum 25(OH)D levels in a healthy Chinese population in Shanghai. These markers could be used as tools in Mendelian randomization analyses of vitamin D, and they could potentially be drug targets in the Chinese population in Shanghai.

Introduction

Vitamin D deficiency has been recognized as a major public health issue worldwide. This condition has been linked to bone disorders such as rickets and osteoporosis[1-4] as well as extraskeletal diseases that include cancer,[5, 6] cardiovascular disease,[7] and diabetes.[8] It has been reported that 40% to 100% of elderly U.S. and European men and women suffer from vitamin D deficiency.[9] A high prevalence of vitamin D deficiency has also been described in Asian populations.[10-12]

The serum concentration of 25-hydroxy vitamin D [25(OH)D] is the established clinical marker for vitamin D. The impact of heritable factors on the 25(OH)D concentration is estimated to range from 29% to 80%.[13-15] Because several genes control pathways that synthesize, transport, and degrade forms of vitamin D, common genetic variants may play a role in individual (and potentially population) differences in vitamin D status; thus, a more complete understanding of the determinants of vitamin D status is necessary and must take into consideration inherited characteristics.[16] The identification of high-risk groups will allow for effective prevention and treatment and is a topic of great interest in vitamin D research. Information on genetic variants that affect 25(OH)D levels could be used in Mendelian randomization analyses on vitamin D status and disease outcomes; moreover, these variants have potential as drug targets.[17]

Recent genomewide association studies (GWASs) and candidate gene studies on 25(OH)D have provided important insights into the influence of common genetic variations on vitamin D levels, primarily in Caucasians.[18, 19] However, there are ethnic differences in vitamin D status.[20] Furthermore, there is true population variation, which arises if: (1) different disease-causing alleles predominate in different study populations or variation exists in the degree of linkage disequilibrium between the marker and the disease alleles; or (2) allele frequencies are similar, but the magnitude of the effect of the disease gene is heterogeneous between study settings.[21] Recently, Lu and colleagues[22] screened seven common variants in four genes in a mixed Chinese population from Beijing and Shanghai and showed that different genetic variants might be associated with serum 25(OH)D concentrations, indicating that different genetic backgrounds between these two subpopulations might exist. To investigate the genetic variants affecting 25(OH)D levels in a Chinese population from Shanghai, we screened 96 common variants in 15 genes that regulate the vitamin D metabolic pathway in 2897 Chinese from Shanghai and identified CYP2R1, GC, and DHCR7/NADSYN1 as potential contributors to the variation in serum 25(OH)D levels in this healthy Chinese population.

Materials and Methods

Study population

From February 2009 to March 2009, 2243 women (aged 20–96 years) and 865 men (aged 23–94 years)—a total of 3108 healthy Chinese people living in Shanghai—were recruited from several community centers. The study subjects were identical to those included in the Shanghai Osteoporosis Study (SOS).[23] The study subjects were recruited from 10 communities within Shanghai. After stratifying the population of each selected community by age, we randomly sampled participants. Selected participants were called and persuaded to come to the hospital in the corresponding district center. All of the participants were of Han ethnicity. The participants' age, body weight, height, and age at both menarche and amenorrhea were recorded. Height (cm) was measured using a wall-mounted stadiometer. The participants were weighed (kg) while wearing indoor clothing without shoes on a balance-beam scale. Both the stadiometer and the balance-beam scale were regularly calibrated during the study. All of the participants were subjected to blood counts, fasting plasma glucose tests, serum lipid tests, and liver and kidney function tests.

All of the healthy Han subjects included in the present study had (1) normal blood counts, (2) normal results for liver and kidney function tests, and (3) normal values for serum calcium, phosphorus, alkaline phosphatase (ALP), parathyroid hormone (PTH), glucose, insulin, and triglycerides. Participants were excluded from the study if they had diseases deemed to affect vitamin D metabolism, such as cancer, hyperthyroidism, diabetes mellitus, primary hyperparathyroidism, pituitary, or adrenal and rheumatic diseases. Participants who had taken vitamin D and/or calcium supplements within the past 3 months were also excluded. After these exclusions, 3108 participants entered the study. The study was approved by the Ethics Committee of the Shanghai Jiao Tong University Affiliated Sixth People's Hospital. All of the participants signed informed consent forms before entering the study.

Measuring serum 25(OH)D and vitamin D binding protein levels

The serum levels of 25(OH)D were determined using an automated Roche electrochemiluminescence system (E170; Roche Diagnostic GmbH, Mannheim, Germany). The intraassay coefficients of variation (CVs) for 25(OH)D were 5.7% at a level of 25.2 ng/mL, 5.7% at a level of 39.9 ng/mL, and 5.4% at a level of 65.6 ng/mL, respectively. The interassay CVs for 25(OH)D were 9.9% at a level of 25.2 ng/mL, 7.3% at a level of 39.9 ng/mL, and 6.9% at a level of 65.6 ng/mL, respectively. The lower detection limit of 25(OH)D was 4 ng/mL. The serum levels of vitamin D binding protein (DBP) were determined using Human Vitamin D-binding protein enzyme-linked immunosorbent assay (ELISA) Kits (Cusabio Biotech Co., Ltd., Hubei, PR China; Catalog Number: CSB-E11859h). The detection range was 0.156 to 10 mg/L. The intraassay CV was <8% and the interassay CV was <10%. All of the serum samples were taken in the morning (fasting blood) during the winter season (from February 2009 to March 2009).

Candidate genes and tag SNP selection

We selected 15 candidate genes according to the following criteria: (1) biological importance in vitamin D metabolism, transportation, or degradation; and (2) evidence of a significant association in previous GWASs. The selected genes were GC, CYP2R1, CYP27A1, CYP27B1, CYP24A1, DHCR7, NADSYN1, CYP3A4, CYP2J2, PTH, ACADSB, CUBN, CYP11A1, CYP1A1, and CYP2C9. The basic characteristics of these 15 genes are shown in Table 1. The roles of these selected genes in the vitamin D cascade are shown in Fig. 1. The detailed functions of the candidate genes are described in the Discussion section.

Table 1. The Basic Characteristics of the Candidate Genes
GeneLocationFull nameSelected SNPs
  1. SNP = single-nucleotide polymorphism; Acyl-CoA = acetyl coenzyme A.
GC4q12–q13Vitamin D binding protein15
CYP2R111p15.2Vitamin D 25-hydroxylase5
CYP27A12q33–qterVitamin D(3) 25-hydroxylase2
CYP27B112q13.1–q13.325 Hydroxyvitamin D-1-alpha hydroxylase2
CYP24A120q13Vitamin D 24-hydroxylase14
DHCR711q13.47-Dehydrocholesterol reductase5
NADSYN111q13.4NAD synthetase 16
CYP3A47q21.1Cytochrome P450 3A43
CYP2J21p31.3–p31.2Cytochrome P450 2J26
PTH11p15.3–p15.1Parathyroid hormone4
ACADSB10q26.13Acyl-CoA dehydrogenase, short/branched chain7
CUBN10p12.31Cubilin15
CYP11A115q23–q24Cytochrome P450 11A15
CYP1A115q24.1Cytochrome P450 1A13
CYP2C910q24Cytochrome P450 2C94
Figure 1.

The roles of the selected candidate genes in the vitamin D cascade are depicted. Humans obtain vitamin D mainly from exposure to sunlight. Solar ultraviolet B radiation penetrates the skin and converts 7-dehydrocholesterol (7-DHC) to previtamin D3, which is rapidly converted to vitamin D3. DHCR7/NADSYN1 removes 7-DHC from the vitamin D pathway, thereby reducing the amount of substrate available for 25(OH)D synthesis. Vitamin D3 is metabolized in the liver to 25(OH)D3 primarily by CYP2R1, but CYP2J2, CYP3A4, CYP27A1, CYP1A1, and CYP2C9 might also contribute. Subsequently, 25(OH)D3 is transported by vitamin D binding protein (DBP), encoded by GC, to the kidney. In the proximal tubule cells, cubilin, encoded by CUBN, facilitates the endocytic retrieval of the filtered 25(OH)D3-DBP complex. Once transported into the proximal tubule cells, 25(OH)D3 is metabolized by CYP27B1 to its active form, 1,25(OH)2D3. Finally, CYP24A1 catabolizes both 25(OH)D3 and 1,25(OH)2D3 into the biologically inactive, water-soluble calcitroic acid. In addition, the renal production of 1,25(OH)2D3 is tightly regulated by plasma parathyroid hormone. In the metabolic bypass of vitamin D3, CYP11A1 can hydroxylate vitamin D3, producing 20S-hydroxyvitamin D3 [20(OH)D3] and 20S,23-dihydroxyvitamin D3 [20,23(OH)2D3] as the major metabolites instead of 25(OH)D3.

For the studied genes, tagging SNPs were selected from the International HapMap Project (http://www.hapmap.org/cgi-perl/gbrowse/hapmap3_B36). The SNPs were selected based on the following criteria: (1) validation status, especially in Chinese; (2) degree of heterozygosity (minor allele frequencies [MAFs] > 0.1); (3) binned by the algorithm such that the pairwise linkage disequilibrium (LD) exceeds a threshold r2 (r2 = 0.05); and (4) requirement for tag-SNPs. SNPs reported in GWASs or potentially functional SNPs in candidate genes were forced into the SNP selection process.

SNP genotyping

Blood samples were collected from all of the study subjects. Genomic DNA was isolated from peripheral blood leukocytes using the conventional phenol-chloroform extraction method. Genotyping was performed using the high-throughput Sequenom genotyping platform (MassARRAY matrix-assisted laser desorption/ionization-time of flight mass spectroscopy [MALDI-TOF MS] system; Sequenom, San Diego, CA, USA).

Genotype frequencies were tested against the Hardy-Weinberg equilibrium (HWE) using the χ2 test to estimate the laboratory error.

Statistical analysis

We compared the baseline characteristics of the men and women using the t test for continuous variables. The mean serum 25(OH)D values were computed within each group of homozygous referent (HR), heterozygous (HET), and homozygous variant (HV) genotypes for each SNP. HR was selected as the most common homozygous genotype. The serum 25(OH)D levels were adjusted by the covariates (age, gender, and body mass index [BMI]) using a linear regression approach (SPSS, version 13.0; SPSS Inc., Chicago, IL, USA). The adjusted serum 25(OH)D levels were used for the subsequent data analyses. All of the SNPs that passed the quality control checks were tested for quantitative trait association interaction using the linear model implemented in PLINK (http://pngu.mgh.harvard.edu/purcell/plink/). Out of the four possible models (additive, codominant, dominant, and recessive) in the linear model, we selected the additive model because it generally reflects the additive contribution to risk for complex diseases. The degree of variation in the log-25(OH)D levels imparted by the SNPs was generated from the adjusted general linear regression models as the ratio of the type II sum of squares to the total sum of squares. The genetic risk score (GRS) was calculated by counting the number of CYP2R1-rs10766197 and GC-rs4588 risk alleles. The participants without risk alleles served as the reference group. Logistic regression was used to calculate the odds ratio of vitamin D deficiency according to the GRS. General linear regression was applied for associations between GRS and serum 25(OH)D levels. Vitamin D deficiency is defined as a 25(OH)D level of less than 20 ng/mL.[9] The statistical analyses were performed using SPSS version 11.0 (SPSS Inc., Chicago, IL, USA).

We performed haplotype interaction analysis on the genes containing the significant SNPs. The linkage disequilibrium structure was examined using Haploview 4.2 (http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview) with the Gabriel confidence interval method. PLINK was used to test for the significance of each haplotype (with frequency ≥10%) within the defined blocks.

To control for the familywise error rate, the Bonferroni correction was used to adjust for multiple testing. The results were regarded as statistically significant at a value of p < 0.05.

Results

Of the 3108 participants selected for this study, 192 (6.2%) were excluded because the serum 25(OH)D level could not be measured or was below the lower detection limit, and 119 (3.8%) were excluded because less than 95% of the markers were successfully genotyped across all of the SNPs. This exclusion process left 2897 participants (2304 women and 593 men) for analysis. The clinical characteristics and mean laboratory values of the 2304 women and 593 men are shown in Table 2. Generally, the men had higher BMI and serum 25(OH)D levels than the women. By Pearson correlation analysis, 25(OH)D levels were significantly correlated with age (r = −0.166; p < 0.001) but not with BMI (r = −0.046; p = 0.064). Age and gender accounted for 14.4% of the variation in serum 25(OH)D levels.

Table 2. The Characteristics of the Study Participants
 All (n = 2897)Males (n = 593)Females (n = 2304)p
  1. The data are presented as the means ± SD, median (interquartile range), or n (%). Skewed data were natural log–transformed before analysis to approximate normality. Student's t test was used to compare the means between males and females.
  2. BMI = body mass index; 25(OH)D = 25-hydroxyvitamin D; DBP = vitamin D binding protein.
Age (years)46 (32–60)43 (33–56)48 (31–61)0.126
<50 years1550 (53.5%)366 (61.7%)1184 (51.4%) 
≥50 years1347 (46.5%)227 (38.3%)1120 (48.6%) 
Height (cm)161.6 ± 8.2173.1 ± 6.2159.1 ± 6.2<0.001
Weight (kg)61.0 ± 10.874.8 ± 10.758.0 ± 8.1<0.001
BMI (kg/m2)23.3 ± 3.424.9 ± 3.323.0 ± 3.3<0.001
Serum 25(OH)D (ng/mL)19.5 (14.9–23.9)22.6 (19.0–26.8)18.5 (14.0–22.9)<0.001
Serum DBP (mg/L)312 ± 56308 ± 57313 ± 560.592

The basic characteristics of the SNPs are listed in Supporting Information Table S1. The MAFs of all of the SNPs examined in this study were comparable to those reported for the HapMap-CHB sample. Two SNPs (rs10493270 in the CYP2J2 gene and rs4646437 in the CYP3A4 gene) were excluded based on the HWE test (p ≤ 0.001). None of the SNPs failed the missingness test (genotyping > 0.05) or the frequency test (MAF < 0.01). After removing the SNPs that did not pass the quality control measures, 94 SNPs, with a mean call rate of 0.998%, remained available for analysis, indicating a very high rate of successful genotyping.

The SNP genotype frequencies and the mean serum 25(OH)D levels grouped by genotype are presented in Supporting Information Table S2, with the significant results shown in Table 3. Three SNPs, one in the CYP2R1 gene (rs10766197) and two in the GC gene (rs4588 and rs2282679), were identified as being significantly associated with serum 25(OH)D levels. Rs10766197 is located in the promoter of the CYP2R1 gene. Rs2282679 is located in the intron of GC and rs4588 is located in exon 11 of GC, which leads to a Thr/Lys amino acid change at codon 420. The two SNPs in GC are in high LD. The strongest association was observed for GC-rs4588, which accounted for 0.7% of the variation in serum 25(OH)D levels. To analyze the combined effect of CYP2R1-rs10766197 and GC-rs4588 on 25(OH)D levels, we calculated the GRS by counting the number of risk alleles of these two SNPs. Table 4 shows the results for the variants both individually and in combination. As shown in Fig. 2, using a general linear regression model, each additional copy of a risk allele was significantly associated with a 0.12-fold decrease in the log-25(OH)D concentration (p = 3.7 × 10−12). The combined effect of the two SNPs, as measured by GRS, accounted for 1.7% of the variance in the log-25(OH)D concentration.

Table 3. SNPs Significantly Associated With Serum 25(OH)D Levels
GeneSNPMean concentrations of 25(OH)DFrequencyBetappa
HVHETHRHVHETHR
  1. HR was selected to be the most common homozygous genotype.
  2. SNP = single nucleotide polymorphism; 25(OH)D = 25-hydroxyvitamin D; HV = homozygous variant genotype; HET = heterozygous genotype; HR = homozygous referent genotype; Beta represents the regression coefficient.
  3. ap after Bonferroni correction.
GCrs458817.8819.5520.30.098320.44170.46−0.16352.15 × 10−50.002016
CYP2R1rs1076619717.8718.1719.480.13170.45580.4125−0.14844.69 × 10−50.004411
GCrs228267916.8718.3319.320.091360.43640.4722−0.15296.60 × 10−50.006206
Table 4. The Genetic Variants and the Risk of Vitamin D Deficiency (25(OH)D < 20 ng/mL)
 Odds ratio (95% CI)p
  1. For individual variants, the odds ratios are per copy of the risk allele. The p value for the trend in odds ratio based on the number of risk alleles was 6.1 × 10−8. All of the logistic regressions were adjusted for age, gender, and BMI.
  2. 25(OH)D = 25-hydroxyvitamin D; CI = confidence interval; BMI = body mass index.
Individual variants
GC-rs4458 (G)1.403 (1.245–1.583)2.7 × 10−8
CYP2R1-rs10766197 (A)1.215 (1.037–1.424)0.016
Number of risk alleles from GC-rs4458 and CYP2R1-rs10766197
01.0 (Reference)6.1 × 10−8
11.200 (0.964–1.493) 
21.456 (1.157–1.833) 
3 + 42.121 (1.586–2.836) 
Figure 2.

The association between genotype risk score and serum 25(OH)D concentrations are reported after adjusting for age, sex, and BMI. The genotype risk score (GRS) equals the sum of the number of risk alleles (G for GC-rs4588 and A for CYP2R1-rs10766197).

By haplotype analysis, we found five candidate haplotype blocks, three in GC, one in CYP2R1, and one in DHCR7/NADSYN1 (Fig. 3). The frequencies and beta values of haplotypes with a frequency over 0.1 in each block are shown in Table 5. Among them, haplotype TGA of GC rs705117-rs2282679-rs1491710, haplotype GAGTAC of GC rs842999-rs705120-rs222040-rs4588-rs7041-rs10488854, haplotype CA of GC rs1155563-rs222029, and haplotype AAGA of CYP2R1 rs7936142-rs12794714-rs2060793-rs16930609 were genetic risk factors toward a lower 25(OH)D concentration. In contrary, haplotype TGGGCCC of DHCR7/NADSYN1 rs1790349-rs7122671-rs1790329-rs11606033-rs2276360-rs1629220-rs2282618 were genetic protective factors.

Figure 3.

LD plots with r2 values were generated using Haploview for the GC, CYP2R1, and DHCR7/NADSYN1 genes. The classic D′ measurement for all pairs of SNP makers was calculated to construct the LD matrix. D′ values are indicated by the dark depth. The r2 values multiplied by 100 are shown as numbers in the diamonds.

Table 5. The Association of Candidate Haplotypes With Serum 25(OH)D Concentration
HaplotypeFrequencyBetaap
  1. The analyses were performed under an additive model adjusted for age, sex, and BMI.
  2. 25(OH)D = 25-hydroxyvitamin D; BMI = body mass index. Significant values (p < 0.05) are in bold.
  3. aBeta represents the regression coefficient.
GC: rs705117-rs2282679-rs1491710
CTC0.32480.024210.5114
TGA0.3083−0.15296.6 × 10−5
TTA0.19070.080.07077
CTA0.17580.10.03234
GC: rs842999-rs705120-rs222040-rs7041-rs10488854
GCGGAT0.17270.02080.5435
CCAGCC0.18790.074370.1066
GAGTAC0.299−0.16632.0 × 10−5
GAGGAC0.17730.10290.02784
GC: rs1155563-rs222029
TG0.19060.11490.01124
CA0.409−0.09510.008782
TA0.39780.024360.5032
CYP2R1: rs7936142-rs12794714-rs2060793-rs16930609
AGGC0.14130.024410.6304
AGAA0.36370.065820.07223
AAGA0.3599−0.11050.002534
TGGA0.13320.07530.1449
DHCR7/NADSYN1: rs1790349-rs7122671-rs1790329-rs11606033-rs2276360-rs1629220-rs2282618
TGAAGCT0.1204−0.095070.08167
CGGACTC0.2815−0.021620.5839
TGGGCCC0.43150.08990.01123

In view of the strong association of genetic variants at GC with 25(OH)D concentrations, we also performed a correlative analysis to assess the associations between 25(OH)D and circulating DBP and examined whether these variants were also associated with serum concentrations of DBP. Pearson correlations indicated that 25(OH)D levels were positively correlated with circulating DBP (r = 0.161, p < 0.001). As Fig. 4 shows, both rs4588 and rs2282679 were associated with concentrations of DBP, with the minor alleles (T for rs4588 and G for rs2282679) related to reduced protein concentrations.

Figure 4.

The associations of genotypes of rs4588 and rs2282679 in GC with serum 25(OH)D and DBP concentrations. The minor alleles (T for rs4588 and G for rs2282679) are related to reduced protein concentrations.

Discussion

The present study investigated the association of 15 candidate genes with serum 25(OH)D levels. Figure 1 depicts the placement of these candidate genes within the vitamin D cascade. Humans obtain vitamin D primarily from exposure to sunlight. ACADSB is involved in cholesterol synthesis, and DHCR7/NADSYN1 removes 7-dehydrocholesterol (7-DHC) from the vitamin D pathway, thereby reducing the amount of substrate available for 25(OH)D synthesis. The association of these three genes with vitamin D levels was recently identified in GWASs.[18, 19] Solar ultraviolet B radiation (wavelength, 290–315 nm) penetrates the skin and converts 7-DHC to previtamin D3, which is rapidly converted to vitamin D3.[24] Vitamin D3 is primarily metabolized in the liver to 25(OH)D3 by CYP2R1,[25] but CYP2J2,[26] CYP3A4,[27] CYP27A1,[28] CYP1A1,[27] and CYP2C9[27] also contribute. Subsequently, 25(OH)D3 is transported by vitamin D binding protein (DBP), encoded by the GC gene, to the kidney. In the proximal tubule cells, cubilin, encoded by the CUBN gene, facilitates the endocytic retrieval of the filtered 25(OH)D3-DBP complex.[29] Once transported into the proximal tubule cells, 25(OH)D3 is metabolized by the enzyme CYP27B1 to its active form, 1,25(OH)2D3.[30] Finally, CYP24A1 catabolizes both 25(OH)D3 and 1,25(OH)2D3 into the biologically inactive, water-soluble calcitroic acid.[31, 32] In addition, the renal production of 1,25(OH)2D3 is tightly regulated by the plasma levels of parathyroid hormone and fibroblast growth factor 23. In the metabolic bypass of vitamin D3, CYP11A1 can hydroxylate vitamin D3, producing 20S-hydroxyvitamin D3 [20(OH)D3] and 20S,23-dihydroxyvitamin D3 [20,23(OH)2D3] as the major metabolites instead of 25(OH)D3,[33] which may also influence the serum 25(OH)D levels.

The identified associations between SNPs in GC and 25(OH)D concentration are consistent with previous studies.[16, 18, 19, 34-41] In addition, we showed that GC variants associated with low 25(OH)D concentrations were strongly related to reduced DBP concentrations, which were also consistent with previous studies.[19, 42] DBP binds to vitamin D sterol metabolites and transports them via the circulation to target organs. Changes in quantity or function of DBP could be accompanied by changes in the relative proportions of free and bound 25(OH)D, with the free proportion being the potential rate-limiting factor for 1,25-(OH)2D production,[19] or the free 25(OH)D itself may be the active form. Analysis of the GC knockout mouse demonstrated that DBP stabilized and maintained serum levels of vitamin D under conditions of variable vitamin D availability, markedly prolonged the serum half-life of 25(OH)D, and slightly prolonged the half-life of vitamin D by slowing its hepatic uptake and increasing the efficiency of its conversion to 25(OH)D in the liver.[43] Verboven and colleagues[44] reported the 2.3-Å crystal structure of DBP in complex with 25-hydroxyvitamin D3, which revealed that the vitamin D binding site is in the N-terminal part of domain I. Because GC-rs2282679 is located in the intron region near actin subdomain III, which is distant from the N-terminal part of domain I,[45] the SNP itself is unlikely to be the disease-causing variant. The possible causal variant is rs4588, which leads to a Thr/Lys amino acid change at codon 420.

CYP2R1 is a member of the CYP2 family that encodes cytochrome P450 proteins. This enzyme is a key vitamin D 25-hydroxylase that hydroxylates vitamin D at the 25-C position to form 25(OH)D in the liver,[25, 46] although many enzymes with in vitro 25-hydroxylase activity have been described. Previous human genetic studies demonstrated that CYP2R1 gene mutations caused vitamin D–dependent rickets type 1B, further indicating the importance of CYP2R1 in vitamin D 25 hydroxylation.[47, 48] However, Tosson and Rose[49] reported a proband and his family with apparent autosomal dominant 25-hydroxylase enzyme deficiency without a mutation in the coding region of the CYP2R1 gene, thereby questioning the importance of different tributary pathways that affect the function of the 25-hydroxylase enzyme. Our results showed that the CYP2R1 gene polymorphism was significantly associated with serum 25(OH)D levels and strongly support the hypothesis that CYP2R1 is the crucial 25-hydroxylase enzyme, rather than CYP2J2, CYP3A4, CYP27A1, CYP1A1, or CYP2C9. The association of CYP2R1 polymorphisms with 25(OH)D levels is supported by previous studies.[18, 19, 41, 50-52] However, the variant in CYP2R1 that is most strongly associated with 25(OH)D levels was not consistent among studies. In two large GWASs in European populations, Ahn and colleagues[18] found that rs2060793 in CYP2R1 was associated with serum 25(OH)D levels, whereas Wang and colleagues[19] reported that rs10741657 was associated with serum 25(OH)D levels. Wjst and colleagues[50] reported that SNP rs10766197 in the CYP2R1 gene was significantly associated with 25(OH)D levels in 872 participants in the German Asthma Family Study. Recently, Lu and colleagues[22] demonstrated that the CYP2R1-rs2060793 variant trended toward an association with serum 25(OH)D levels in the Shanghai subpopulation (p = 0.08) but not in the Beijing subpopulation (p = 0.82), suggesting that genetic variation or environmental factors differ between these two subpopulations. Indeed, heterogeneity was observed in the association of 25(OH)D levels with CYP2R1 in the different cohorts in the GWAS by Ahn and colleagues.[18] In the present study, we found that rs10766197, not rs2060793, was associated with serum 25(OH)D levels in a Chinese population in Shanghai. SNP rs10766197 is located in the promoter region of the CYP2R1 gene. Therefore, studies are necessary to assess whether rs10766197 or unidentified causal variants in LD with it influence the production of CYP2R1.

DHCR7/NADSYN1 is a locus that was identified in recent GWASs on vitamin D deficiency.[18, 19] DHCR7/NADSYN1 removes 7-DHC from the vitamin D pathway, thus reducing the substrate available for 25(OH)D synthesis. Mutations in DHCR7 might confer a competitive advantage to heterozygous carriers because high concentrations of 7-DHC could provide protection against rickets and osteomalacia from hypovitaminosis D.[53] Recently, Lu and colleagues[22] found that the DHCR7-rs1790349 variant had a milder association with serum 25(OH)D levels in the Shanghai subpopulation (p = 0.0363) than in the Beijing subpopulation (p = 0.00017), suggesting that genetic variation or environmental factors differ between these two subpopulations. Therefore, the role of common variants in DHCR7/NADSYN1 in the regulation of vitamin D levels needs to be further studied in the Shanghai population.

Compared with previous studies, our study has several strengths: (1) by conducting this study in a healthy population, the analysis of the genetic impact on serum 25(OH)D levels is not confounded by the potential impact of disease; (2) dense markers and those previously reported in GWASs within 15 candidate genes involved in vitamin D metabolism were selected; and (3) all of the serum samples were taken during the winter season (from February 2009 to March 2009) to minimize the impact of sun exposure. Although participants who had taken vitamin D and/or calcium supplements within 3 months were excluded, the dietary intake of vitamin D and calcium for the participants was not recorded.

Insights gained from studying the circulating levels of vitamin D are likely to have implications in complex diseases, such as osteoporosis, asthma, type 2 diabetes, and cancer. Because the genotype is assigned randomly, the genetic association, unlike the directly observed association of vitamin D intake/status itself, will be less prone to confounding and free from reverse causation because the genotype is not modifiable by disease.[54] In addition, rs10766197 is located in the promoter of the CYP2R1 gene that encodes the key vitamin D 25-hydroxylase. Therefore, with further investigation of the biological mechanisms, this marker may become a potential drug target.

Disclosures

All authors state that they have no conflicts of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (81070692, 81000360, and 81170803), the Program of the Shanghai Subject Chief Scientist (08XD1403000), STCSM (10DZ1950100), Academic Leaders in Health Sciences in Shanghai (XBR2011014), and Shanghai Leading Talent Plan (051). We gratefully acknowledge the support of the participants in the study.

Authors' roles: Study design: ZLZ and CQZ. Study conduct: ZZ. Data collection: JWH and WZF. Data analysis: ZZ. Data interpretation: ZZ. Drafting of the manuscript: ZZ. Approving final version of the manuscript: ZLZ and CQZ. ZZ takes reponsibiliy for the integrity of the data analysis.

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