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. Author manuscript; available in PMC: 2009 Aug 23.
Published in final edited form as: Hum Genet. 2008 Nov 23;125(1):1–9. doi: 10.1007/s00439-008-0590-9

Genome-wide association scan for stature in Chinese: evidence for ethnic specific loci

Shu-Feng Lei 1,2, Tie-Lin Yang 3, Li-Jun Tan 4, Xiang-Ding Chen 5, Yan Guo 6, Yan-Fang Guo 7, Liang Zhang 8, Xiao-Gang Liu 9,10, Han Yan 11, Feng Pan 12, Zhi-Xin Zhang 13, Yu-Mei Peng 14, Qi Zhou 15, Li-Na He 16, Xue-Zhen Zhu 17, Jing Cheng 18, Yao-Zhong Liu 19, Christopher J Papasian 20, Hong-Wen Deng 21,22,✉
PMCID: PMC2730511  NIHMSID: NIHMS131802  PMID: 19030899

Abstract

In Caucasian, several studies have identified some common variants associated with human stature variation. However, no such study was performed in Chinese, which is the largest population in the world and evidently differs from Caucasian in genetic background. To identify common or ethnic specific genes for stature in Chinese, an initial GWAS and follow-up replication study were performed. Our initial GWAS study found that a group of 13 contiguous SNPs, which span a region of ∼150 kb containing two neighboring genes, zinc finger protein (ZNP) 510 and ZNP782, achieved strong signals for association with stature, with P values ranging from 9.71 × 10−5 to 3.11 × 10−6. After false discovery rate correction for multiple testing, 9 of the 13 SNPs remain significant (FDR q = 0.036–0.046). The follow-up replication study in an independent 2,953 unrelated southern Chinese confirmed the association of rs10816533 with stature (P = 0.029). All the13 SNPs were in consistently strong linkage disequilibrium (D′ > 0.99) and formed a single perfect haplotype block. The minor allele frequencies for the 13 contiguous SNPs have evidently ethnic difference, which range from 0.21 to 0.33 in Chinese but have as low as ∼0.017 reported in dbSNP database in Caucasian. The present results suggest that the genomic region containing the ZNP510 and ZNP782 genes is an ethnic specific locus associated with stature variation in Chinese.

Introduction

Human stature has long been recognized as an index of physical growth and development. Final stature is closely associated with the earlier bone growth and differentiation. In gene mapping for bone disorders, human stature is often used as an important covariate. Genetic analyses for bone diseases may lead to false-positive results if genetic determination of human statue is not appropriately identified and adjusted for (Goring and Terwilliger 2000). Understanding the biological process and genetic determination of stature will provide insights into human development and growth and the genetic architecture of other human complex traits/diseases in general.

Stature is under strong genetic influence, with heritability generally above 0.75 (Carmichael and McGue 1995; Luo et al. 1998; Phillips and Matheny 1990). We identified a high heritability of 0.65 by segregation analyses on a Chinese sample of 1,169 informative individuals from 385 nuclear families (Li et al. 2004). In Caucasian, extensive genetic studies via candidate gene association analyses and whole genome linkage scans were performed to identify genes contributing to the variation of human stature (Deng et al. 2002; Hirschhorn et al. 2001;Liu et al. 2006a; Liu et al. 2004, 2006b; Mukhopadhyay et al. 2003; Perola et al. 2001; Wiltshire et al. 2002; Wu et al. 2003; Xu et al. 2002). Recent developing microarray technology of SNP genotyping provides powerful tool for genome-wide association scan (GWAS) studies to rapidly and systematically identify/confirm functional loci underlying human stature variation. In Caucasian, several GWAS studies have identified some common variants associated with human stature variation (Gudbjartsson et al. 2008; Lettre et al. 2008; Sanna et al. 2008; Weedon et al. 2007, 2008). However, in Chinese, which is the largest population in the world, and apparently differs from Caucasian in ethnic genetic background, no such GWAS study on stature was performed to dissect the genetic basis of stature at the genome-wide scale, or to compare the results for finding ethnic specific genes.

Here, we performed an initial WGAS study in 618 unrelated Chinese subjects, using high density Affymetrix 500 K SNP arrays examining ∼500,000 SNPs genome-wide, and the follow-up replication study in an independent 2,953 unrelated southern Chinese. The results suggest a genomic region containing ZNP510 and ZNP782 specifically important to stature in Chinese.

Materials and methods

Samples

The study was approved by the necessary Institutional Review Board or Research Administration of the involved institutions. All study volunteers signed the informed-consent forms before they entered the project.

GWAS sample (Northern Chinese)

A total of 618 Han adult unrelated subjects were recruited from the Xi'an City and its surrounding areas in northern China. The stature for each subject was measured using a stadiometer.

Replication study sample (Sothern Chinese)

Totally, 2,953 healthy unrelated adult subjects including 1,517 females and 1,436 males were recruited from Chinese Han adults living in Changsha, Hunan province, which is more than 1,000 km from Xi'an where the northern Chinese sample was recruited. The basic characteristics of the study samples were given in Table 1.

Table 1.

Basic characteristics of the study subjects

Region Total Female Male
GWAS Northern Chinese Sample size 618 351 267
Age (years) 70.6 (7.8) 70.2 (8.2) 71.1 (7.1)
Stature (cm) 160.9 (9.0) 155.5 (6.2) 168.0 (6.9)
Replication study Southern Chinese Sample size 2953 1517 1436
Age (years) 33.0 (14.5) 35.4 (15.7) 30.5 (12.5)
Stature (cm) 163.7 (7.9) 158.1 (5.3) 169.6 (5.6)
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Note: Presented are mean (SD)

Genotyping

For GWAS sample

Genomic DNA was extracted from whole human blood using a commercial isolation kit (Gentra systems, Minneapolis, MN, USA) according to the protocols of the kit. For each sample, genotyping with GeneChip® Human Mapping 500 K set containing 250 K Nsp array and 250 K Sty array (Affymetrix, Santa Clara, CA, USA) was performed using the standardized protocols recommended by the manufacturer. Briefly, for each array, 250 ng of DNA was digested with restriction enzyme (NspI or StyI) and ligated to adapters. A single PCR primer that recognizes the adapter sequence was used to amplify the ligated product. The amplified DNA (200–1,100 bp) was fragmentized into approximately 50 bp size, then labeled with biotin and hybridized to the arrays. After 16–18 h of hybridization, the arrays were washed with Wash Buffer A (6 × SSPE, 0.1% Tween 20) and Wash Buffer B (0.6 × SSPE, 0.1% Tween 20), in turn, on an Affymetrix Fluidics Station FS450. Then they were stained subsequently with the Streptavidin Phycoerythrin (SAPE, 10 μg/ml) and Anti-streptavidin antibody. The stained arrays were scanned with an Affymetrix GeneChip® 3000 7G scanner at 0.7 μm solution. SNPs genotypes from the scanned images were extracted using GCOS and GTYPE software (Affymetrix).

Quality control procedures were as follows. First, only samples with a minimum of 95% call rate were included. The final mean BRLMM call rate of the entire sample reached a high level of 99.02%. Second, out of the initial full-set of 500,568 SNPs, we discarded: (1) SNPs with a call rate <90% in both cases and controls (n = 54,845); (2) those deviating from Hardy–Weinberg equilibrium (HWE) in controls (P < 0.001, n = 22,002); and; (3) those having a minor allele frequency (MAF) < 0.05 in the total sample (n = 142,188). Therefore, 281,533 SNPs were available for the subsequent analyses.

For replication study sample

SNP genotyping in this sample was carried out with Mass-ARRAY system (Sequenom, San Diego, CA, USA) using matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (Braun et al. 2003) for separation and detection. The entire process has been designed for complete automation including assay design, PCR setup, post-PCR treatment, nanoliter transfer of diagnostic products onto silicon chips, serial reading of chip positions in the mass spectrometer, and final analytical interpretation. The SNP genotyping success rate was 97% and the duplicate concordance rate was 99%.

Statistical analysis

GWAS analysis

Because gender and age are two significant covariates associated with stature, they were used to adjust the raw stature in subsequent analyses. HelixTree 5.3.1 (Golden Helix, Bozeman, MT, USA) performed two types of association analyses (genotypic association analyses and haplotype association analyses). Genotypic association analyses compared the difference of mean stature values among three genotypic groups for each SNP. Haplotype association or block association analyses compared the mean stature values among different haplotype groups formed by a group of SNPs.

Haplotype analysis

Haploview software (Barrett et al. 2005) (available at http:// www.broad.mit.edu/mpg/haploview/) was used to analyze the linkage disequilibrium (LD) [standardized D′ (D/Dmax)] patterns of the interesting SNPs and to plot the haplotype block map.

Multiple testing correction

We used the QVALUE software (http://genomine.org/qvalue/) (Storey and Tibshirani 2003) to calculate false discovery rate (FDR)-based q values to measure the statistical significance at the genomewide level for the association results. The cutoff for significant association at the genome-wide level was set at FDR q value <0.05. We did not use Bonferroni correction for overly conservation for multiple testing adjustment in a GWAS study, since it does not take into account the intrinsic correlations between a large number of tests, which is caused by the extensive LD among SNP markers genomewide.

Population stratification

To detect potential population stratification that may lead to spurious association results, we used STRUCTURE 2.2 program (http://pritch.bsd.uchicago.edu/software.html) to investigate the potential substructure of our sample. The program uses a Markov chain Monte Carlo (MCMC) algorithm to cluster individuals into different cryptic sub-populations on the basis of multi-locus genotype data (Pritchard et al. 2000). We performed the analysis assuming the number of population strata k = 2, and using a set of 1,000 un-linked markers randomly selected genomewide. EIGENSTRAT (Price et al. 2006) was employed to perform principal component analysis to correct for stratification in genome-wide association studies. We used ∼280,000 SNPs to calculate the principal components and the ten default main eigenvectors were used in the association analysis with the EIGENSTRAT program.

Genotype imputation

Different genotyping platforms were used in our current and previous GWAS studies for stature. To compare the associations at the same SNPs, we impute genotypes for ∼2,500,000 HapMap SNPs in our GWAS sample upon a set of known haplotypes and an estimated fine-scale recombination map using a program IMPUTE (Marchini et al. 2007). The imputed genotype for each SNP is expressed as genotype probability. Association analysis was performed between the imputed SNPs (genotype dosage score) and stature using sex and age as covariates using a program called SNPTEST(Marchini et al. 2007).

Functional prediction

In order to analyze and predict the potential functions of the interesting SNPs, we utilized the FASTSNP program (http://fastsnp.ibms.sinica.edu.tw) to analyze SNP functions based on up-to-date information extracted from 11 external bioinformatic databases at query time (Yuan et al. 2006).

Results

Initial GWAS study

From a quantile–quantile (Q–Q) plot for the distribution of P values involving ∼280,000 eligible SNPs in our sample (Fig. 1), the observed P values matched the expected P values over the range of 1 < −log10(P) < 4.0, but departed at the extreme tail [−log10(P) > 4.0]. This suggests that the associations identified are likely due to true variants rather than potential biases such as genotyping error.

Fig. 1.

Fig. 1

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Quantile–quantile (Q–Q) plot for stature. From the Q–Q plot, the observed P values of stature match the expected P values under the null distributions over the range of [1 < −log10(P) < 4.0] and an excess of low P values is observed above 4.0 of −log10(P) for stature

We identified 5 genes that contained SNPs significantly associated with stature at genomewide level (q value < 0.05) (Table 2). In particular, a group of 13 contiguous SNPs, which span a region of ∼150 kb containing two neighboring genes (zinc finger protein (ZNP) 510 and ZNP782), achieved strong association signals for stature, with P values ranging from 9.71 × 10−5 to 3.11 × 10−6 (Table 3). Nine of the 13 SNPs (rs10978781, rs10816533, rs35334289, rs10978953, rs10117921, rs10118617, rs10119556, rs7859940, and rs10124033) remain significant at the genomewide significance threshold (FDR q < 0.05). The MAFs for the 13 contiguous SNPs have evidently ethnic difference, which range from 0.21 to 0.33 in Chinese but have as low as ∼0.017 reported in dbSNP database in Caucasian.

Table 2.

Five genes that contained SNPs significantly associated with stature at genomewide level (q value < 0.05)

Associated gene dbSNP ID Physical position Chromosomal position P valuea q value
KRT33A rs16966703 36755924 17q21 1.74 × 10−8 0.002
ZNF510 rs10816533 98578959 9q22 3.11 × 10−6 0.036
ZNF782 rs7859940 98624701 9q22 5.77 × 10−6 0.039
RAP2A rs2793701 97198607 13q32 7.85 × 10−6 0.038
SLC8A1 rs7606245 41871622 2p21 9.59 × 10−6 0.041
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a

If there are multiple SNPs with significant association with stature in a gene, we only list the most significant SNP

Table 3.

SNPs with strong association signals (q < 0.1) in the region of ZNP510 and ZNP782 genes

Associated gene dbSNP Physical position Role Allelea MAFb MAFc MAFd P valuee FDR q value
ZNP510 (9q22) rs10978781 98548301 3′ downstream C/T 0.30 0.33 0.017 1.19 × 10−5 0.046
rs10816533 98578959 Intron 1 C/G 0.29 0.32 0.017 3.11 × 10−6 0.036
rs35334289 98580112 Intron 1 C/T 0.29 0.33 0.017 7.00 × 10−6 0.038
rs10978953 98583622 5′ upstream T/G 0.30 0.33 0.017 6.41 × 10−6 0.038
rs4344199 98589270 5′ upstream G/A 0.21 0.22 0.017 8.83 × 10−5 0.079
rs10117921 98590255 5′ upstream G/A 0.30 0.33 0.017 6.35 × 10−6 0.038
rs10118617 98590290 5′ upstream C/T 0.30 0.33 0.017 7.19 × 10−6 0.038
rs10119466 98591446 5′ upstream G/A 0.21 0.22 0.017 9.71 × 10−5 0.080
ZNP782 (9q22) rs10119556 98591807 3′ downstream G/A 0.30 0.33 0.017 8.97 × 10−6 0.040
rs10124911 98607205 3′ downstream A/C 0.21 0.22 0.017 9.71 × 10−5 0.080
rs12236125 98623289 Intron 5 C/G 0.23 0.21 0.017 4.96 × 10−5 0.064
rs7859940 98624701 Intron 5 A/G 0.31 0.33 0.017 5.77 × 10−6 0.038
rs10124033 98653925 Intron 3 A/G 0.31 0.32 0.017 6.90 × 10−6 0.038
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a

The former allele represents the minor one of each locus

b

MAF (minor allele frequency) calculated in the present Chinese sample

c

MAF reported in Chinese or Asian in dbSNP public database (http://www.ncbi.nlm.nih.gov/SNP/)

d

MAF reported in Caucasian in dbSNP public database

e

P value for genotypic association using single-SNP marker test

Figure 2 shows the LD pattern and the haplotype block structure for the SNPs in the region of ZNP 510 and ZNP782 genes. These SNPs were in consistently strong LD (D′ > 0.99) and formed a single perfect haplotype block (i.e., Block 1 in Fig. 2). The haplotype in this block was still significantly associated with stature (P = 1.01 × 10−3). Analyses using FASTSNP program (Yuan et al. 2006) suggest that rs35334289 of the ZNP510 gene and rs7859940 of the ZNP782 gene are located as potential transcription factor binding sites. The rs10978953 in the promoter region of ZNP782 may be a potential regulatory factor for the expression of this gene.

Fig. 2.

Fig. 2

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Association signals of SNPs in the region of ZNP510 and ZNP782 genes. The Y axis is the negative Log10 P value. The LD between two SNPs is standardized D′ (D/Dmax). Within the block, the LD signal is very strong

We used the STRUCTURE program (Pritchard et al. 2000) to cluster 618 subjects based on 1,000 or 10,000 SNPs randomly selected genomewide to test the potential population stratification of our sample. According to the results generated by the program, all the subjects were clustered together as a single group by 1,000 or 10,000 SNPs, suggesting no detectable population stratification in our sample. The analyses by EIGENSTRAT confirmed, qualitatively, our main results presented above and consequently, results of the EIGENSTRAT analyses are not detailed here. The “inflation factor” λ calculated by Genomic Control (Devlin and Roeder 1999) is 1.02, indicating that potential population stratification in this homogeneous Chinese population is very minimal.

Replication study

The most significant SNP (rs10816533) in the SNP group is located in intron 1 of ZNP10 gene. In the follow-up replication study, the rs10816533 was significantly (P = 0.029) associated with stature in 2,953 unrelated southern Chinese subjects. The subjects with homozygous GG had higher stature (4.26 cm) than those with homozygous CC (Fig. 3). Fisher's combined P analyses (Fisher and Miles 2008), which combined the P values from association tests in the study populations, showed that rs10816533 has more strong association signal (P = 1.55 × 10−6).

Fig. 3.

Fig. 3

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The distribution of stature in different genotype groups of rs10816533 in Chinese in GWAS study [mean (SE)]

Comparison of the current study to the published GWAS studies

Table 4 lists 44 significant or suggestive association SNPs identified in previous GWAS studies in Caucasian and the corresponding association results in present study. Among these SNPs, we impute genotypes for 19 SNPs, which are not included in Affymetrix 500 K SNP Array. If the direction of association effect is the same between the previous GWAS studies and current study, a significant replication threshold can be set at P = 0.1. Even in this situation, only six SNPs were replicated their associations, and most of SNP associations identified in previous GWAS studies in Caucasian are unable to be confirmed by the current GWAS results in Chinese. Moreover, the MAFs reported from the dbSNP public database (http://www.ncbi.nlm.nih.gov/SNP/) for most of these SNPs are evidently different; particularly, the alleles with minor frequency for six SNPs (e.g., rs10946808, rs4794665) are different between Caucasian and Chinese. Collectively, these observations seem to suggest there are ethnic specific loci regulating variation of stature.

Table 4.

Comparison of the current study to the published GWAS studies for stature

Present GWAS Published GWAS
SNP Associated gene Chromosome Position P value MAFa P value MAFb Reference
rs757608 BCAS3 17 56852059 0.011 0.26/T 3.00 × 10−7 0.30/T Gudbjartsson et al. (2008)
rs12198986 BMP6 6 7665058 0.056 0.24/A 6.40 × 10−11 0.50/A Gudbjartsson et al. (2008)
rs4800148 CABLES1 18 18978326 0.061 0.23/G 9.40 × 10−8 0.26/G Gudbjartsson et al. (2008)
rs11107116 SOCS2 12 92502635 0.086 0.30/T 2.50 × 10−5 0.20/T Weedon et al. (2008)
rs10946808 HIST1H1D 6 26341366 0.093 0.24/A 3.30 × 10−8 0.28/G Lettre et al. (2008)
rs4713858 ANKS1 6 35510763 0.097 0.18/A 4.90 × 10−8 0.16/A Gudbjartsson et al. (2008)
rs798544 GNA12 7 2729628 0.11 0.30/A 4.50 × 10−12 0.28/A Gudbjartsson et al. (2008)
rs1812175 HHIP 4 145794294 0.11 0.40/T 2.80 × 10−10 0.17/T Gudbjartsson et al. (2008)
rs4794665 NOG 17 52205328 0.25 0.31/G 9.20 × 10−6 0.39/A Gudbjartsson et al. (2008)
rs3760318 CRLF3 17 26271841 0.25 0.22/T 1.80 × 10−9 0.36/T Gudbjartsson et al. (2008)
rs10906982 ADAMTSL3 15 82359162 0.26 0.29/T 5.40 × 10−7 0.48/A Weedon et al. (2008)
rs185819 HLA class III 6 32158045 0.26 0.33/T 1.90 × 10−6 0.44/T Gudbjartsson et al. (2008)
rs6060369 GDF5 20 33370575 0.27 0.24/C 1.00 × 10−11 0.36/C Sanna et al. (2008)
rs12986413 DOT1L 19 2121954 0.29 0.47/T 2.00 × 10−5 0.45/T Lettre et al. (2008)
rs967417 BMP2 20 6568893 0.35 0.25/G 4.60 × 10−6 0.41/A Gudbjartsson et al. (2008)
rs4743034 ZNF462 9 108672174 0.35 0.17/A 5.20 × 10−7 0.24/A Gudbjartsson et al. (2008)
rs9650315 CHCHD7 8 57318152 0.37 0.04/T 9.60 × 10−6 0.13/T Lettre et al. (2008)
rs8041863 ACAN 15 87160693 0.38 0.13/T 2.20 × 10−5 0.49/A Weedon et al. (2008)
rs3116602 DLEU7 13 50009356 0.39 0.01/G 5.60 × 10−6 0.18/G Weedon et al. (2008)
rs1042725 HMGA2 12 64644614 0.39 0.23/C 4.00 × 10−8 0.49/T Weedon et al. (2007)
rs10935120 ANAPC13 3 135715782 0.41 0.19/A 2.20 × 10−6 0.36/A Weedon et al. (2008)
rs7846385 PXMP3 8 78322734 0.46 0.26/C 5.30 × 10−7 0.34/C Gudbjartsson et al. (2008)
rs2562784 SH3GL3- 15 82077496 0.49 0.17/C 2.90 × 10−5 0.37/C Lettre et al. (2008)
rs4533267 ADAMTS17 15 98603794 0.49 0.19/A 3.80 × 10−7 0.29/A Gudbjartsson et al. (2008)
rs6724465 IHH 2 219652090 0.49 0.12/A 3.10 × 10−5 0.16/A Weedon et al. (2008)
rs2844479 HLA class III 6 31680935 0.51 0.40/G 8.40 × 10−9 0.32/G Gudbjartsson et al. (2008)
rs3791675 EFEMP1 2 55964813 0.60 0.22/G 7.10 × 10−8 0.28/A Weedon et al. (2008)
rs6440003 ZBTB38 3 142576899 0.60 0.35/A 1.30 × 10−14 0.48/A Weedon et al. (2008)
rs4549631 LOC387103 6 127008001 0.62 0.01/T 1.20 × 10−8 0.43/C Weedon et al. (2008)
rs10512248 PTCH1 9 97299524 0.65 0.32/C 1.50 × 10−6 0.32/C Weedon et al. (2008)
rs6686842 SCMH1 1 41303458 0.70 0.41/T 8.60 × 10−6 0.39/T Weedon et al. (2008)
rs3748069 GPR126 6 142809326 0.73 0.40/G 4.90 × 10−11 0.27/G Gudbjartsson et al. (2008)
rs7153027 TRIP11 14 91496975 0.75 0.28/C 1.10 × 10−10 0.39/C Gudbjartsson et al. (2008)
rs10958476 PLAG1 8 57258362 0.82 0.17/C 1.40 × 10−6 0.13/C Gudbjartsson et al. (2008)
rs12735613 SPAG17 1 118685496 0.83 0.17/A 3.40 × 10−8 0.31/A Weedon et al. (2008)
rs1390401 ZNF678 1 225864573 0.86 0.09/G 4.30 × 10−6 0.23/G Weedon et al. (2008)
rs12449568 ANKFN1 17 51785154 0.88 0.49/C 4.70 × 10−6 0.47/C Lettre et al. (2008)
rs6830062 LCORL 4 17626828 0.89 0.11/C 7.60 × 10−8 0.16/C Gudbjartsson et al. (2008)
rs314277 LIN28B 6 105514355 0.90 0.02/A 5.90 × 10−9 0.13/A Lettre et al. (2008)
rs4896582 GPR126 6 142745570 0.91 0.17/G 3.20 × 10−8 0.27/A Lettre et al. (2008)
rs3130050 HLA class III 6 31726740 0.92 0.02/G 5.10 × 10−7 0.23/G Gudbjartsson et al. (2008)
rs8756 HMGA2 12 64646019 0.93 0.11/C 2.20 × 10−13 0.47/C Gudbjartsson et al. (2008)
rs16896068 LCORL 4 17553938 0.99 0.11/A 1.00 × 10−4 0.16/A Weedon et al. (2008)
rs2814993 C6orf106 6 34726871 0.99 0/A 8.90 × 10−9 0.10/A Weedon et al. (2008)
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a

MAF: minor allele frequency (Frequency/Allele) in Chinese reported from dbSNP public database (http://www.ncbi.nlm.nih.gov/SNP/)

b

MAF: minor allele frequency (Frequency/Allele) in Caucasian reported from dbSNP public database (http://www.ncbi.nlm.nih.gov/SNP/)

Discussion

ZNP510 and ZNP782 are two newly identified genes with unclear functions. However, ZNPs are one of the largest families of transcription regulators in humans and play very important roles in a variety of processes associated with skeletal development (Ganss and Jheon 2004). ZNPs are extensively expressed in osteoblasts and chondrocytes. ZNPs by binding to DNA zinc finger motifs regulate transcription processes associated with skeletal development (Ganss and Jheon, 2004). Mutations in ZNPs can cause abnormal stature or skeletal abnormalities (Momeni et al. 2000; Mundlos et al. 1997). For example, mutations of the RUNX2 (Runt-related transcription factor 2) gene, a well-known ZNP, may cause cleidocranial dysplasia characterized by short stature and other anomalies in skeletal growth and development (Mundlos et al. 1997).

In Caucasian, extensive genetic studies using GWLS (Deng et al. 2002; Hirschhorn et al. 2001; Liu et al. 2006a, b, 2004; Mukhopadhyay et al. 2003; Perola et al. 2001; Phillips and Matheny 1990; Wiltshire et al. 2002; Wu et al. 2003; Xu et al. 2002) or GWAS (Gudbjartsson et al. 2008; Lettre et al. 2008; Sanna et al. 2008; Weedon et al. 2007, 2008) studies identified quite a few chromosomal regions potentially harboring genes underlying variation of human stature, or some common variants associated with human stature variation. However, in Chinese, genetics studies on stature still remain a largely uncharted territory. Currently, only a segregation analysis study was performed for stature in Chinese, which suggested a high heritability (Li et al. 2004). The present GWAS for stature in Chinese provided interesting genes for future detailed functional characterization.

The present study along with other genetic studies on stature suggests that there are ethnic specific loci regulating variation of stature. Direct evidences may come from: First, the MAFs for a cluster of SNPs significantly associated with stature in the present study have evidently ethnic difference between in Chinese and in Caucasian; Second, most of loci identified in previous GWAS studies in Caucasian (Gudbjartsson et al. 2008; Lettre et al. 2008; Sanna et al. 2008; Weedon et al. 2007, 2008) do not overlap their associations in Chinese. Moreover, the MAFs reported from the dbSNP public database (http://www.ncbi.nlm.nih.gov/SNP/) for most of these SNPs are evidently different; particularly, the alleles with minor frequency for six SNPs (e.g., rs10946808, rs4794665) are different between Caucasian and Chinese; Third, most of the linkage regions identified in previous GWLS studies can not be confirmed by the present association results. On the other hand, indirect evidences may also support this. It is well-known that the stature in Caucasian is apparently higher than in Chinese, probably suggesting a different genetic background to determinate stature. Some phenotypes (such as bone) closely related to stature are clearly under ethnic-specific genetic determination (Dvornyk et al. 2005; Dvornyk et al. 2003; Lei et al. 2006, 2003). For example, five candidate genes (Dvornyk et al. 2005) were tested for their contribution of ethnicity to bone mineral density variation in Caucasian and Chinese. The frequencies and distribution patterns of SNPs of some prominent bone candidate genes were different between Caucasian and Chinese (Lei et al. 2003), The above observed inconsistencies between the two ethnic populations may be partially attributed to ethnic difference in genetic background that may play an important role in genetic determination of stature.

It is well recognized that population stratification may yield spurious association results. Therefore, potential population stratification should be rigorously tested and excluded in the sample for association analyses. Using the STRUCTURE program (Pritchard et al. 2000) to analyze our sample, all the subjects were clustered into a single group, suggesting no significant population stratification in our sample. Therefore, our association results are unlikely to be plagued by spurious associations due to population stratification and thus robust.

Using a threshold P = 0.05, the statistical power in a sample size of 618, estimated by the software Genetic Power Calculator (http://pngu.mgh.harvard.edu/∼purcell/gpc/qtlassoc.html), is less than 50% for detecting a gene that accounts for 0.3% of stature variation. (Most of the previously published loci explained <0.3% of stature variation.). Therefore, the limited power in our GWAS study may another possible reason for the failure of replicating most of previously identified height loci. Another potential explanation, as discussed in the “Results”, for such failure is that some of the previously identified loci may be population specific.

In conclusion, the present study reported the first GWAS for adult stature in Chinese population which was often neglected by genetics community. The results of the present study suggest that a region is significantly associated with stature and interethnic differentiation at some loci may contribute to the interethnic difference in stature.

Acknowledgments

The study was partially supported by grants from Natural Science Foundation of China (30600364, 30470534, and 30230210). HWD was partially supported by grants from NIH (R01 AR050496-01, R21 AG027110, R01 AG026564, and P50 AR055081) and the Dickson/Missouri endowment.

Contributor Information

Shu-Feng Lei, Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, 410081 Changsha, Hunan, People's Republic of China; School of Medicine, University of Missouri-Kansas City, Kansas City 64108, MO, USA.

Tie-Lin Yang, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Li-Jun Tan, Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, 410081 Changsha, Hunan, People's Republic of China.

Xiang-Ding Chen, Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, 410081 Changsha, Hunan, People's Republic of China.

Yan Guo, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Yan-Fang Guo, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Liang Zhang, National Engineering Research Center for Beijing Biochip Technology, Beijing 102206, People's Republic of China.

Xiao-Gang Liu, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China; School of Medicine, University of Missouri-Kansas City, Kansas City 64108, MO, USA.

Han Yan, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Feng Pan, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Zhi-Xin Zhang, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Yu-Mei Peng, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Qi Zhou, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Li-Na He, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Xue-Zhen Zhu, The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, Shanxi, People's Republic of China.

Jing Cheng, National Engineering Research Center for Beijing Biochip Technology, Beijing 102206, People's Republic of China.

Yao-Zhong Liu, School of Medicine, University of Missouri-Kansas City, Kansas City 64108, MO, USA.

Christopher J. Papasian, School of Medicine, University of Missouri-Kansas City, Kansas City 64108, MO, USA

Hong-Wen Deng, Laboratory of Molecular and Statistical Genetics and Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, 410081 Changsha, Hunan, People's Republic of China; School of Medicine, University of Missouri-Kansas City, Kansas City 64108, MO, USA.

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