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The role of height-associated loci identified in genome wide association studies in the determination of pediatric stature

  • Jianhua Zhao1,
  • Mingyao Li2,
  • Jonathan P Bradfield3,
  • Haitao Zhang3,
  • Frank D Mentch3,
  • Kai Wang3,
  • Patrick M Sleiman3,
  • Cecilia E Kim3,
  • Joseph T Glessner3,
  • Cuiping Hou3,
  • Brendan J Keating3,
  • Kelly A Thomas3,
  • Maria L Garris3,
  • Sandra Deliard1,
  • Edward C Frackelton3,
  • F George Otieno3,
  • Rosetta M Chiavacci3,
  • Robert I Berkowitz4, 5,
  • Hakon Hakonarson1, 2, 6Email author and
  • Struan FA Grant1, 2, 6Email author
Contributed equally
BMC Medical Genetics201011:96

DOI: 10.1186/1471-2350-11-96

Received: 20 January 2010

Accepted: 14 June 2010

Published: 14 June 2010

Abstract

Background

Human height is considered highly heritable and correlated with certain disorders, such as type 2 diabetes and cancer. Despite environmental influences, genetic factors are known to play an important role in stature determination. A number of genetic determinants of adult height have already been established through genome wide association studies.

Methods

To examine 51 single nucleotide polymorphisms (SNPs) corresponding to the 46 previously reported genomic loci for height in 8,184 European American children with height measurements. We leveraged genotyping data from our ongoing GWA study of height variation in children in order to query the 51 SNPs in this pediatric cohort.

Results

Sixteen of these SNPs yielded at least nominally significant association to height, representing fifteen different loci including EFEMP1-PNPT1, GPR126, C6orf173, SPAG17, Histone class 1, HLA class III and GDF5-UQCC. Other loci revealed no evidence for association, including HMGA1 and HMGA2. For the 16 associated variants, the genotype score explained 1.64% of the total variation for height z-score.

Conclusion

Among 46 loci that have been reported to associate with adult height to date, at least 15 also contribute to the determination of height in childhood.

Background

Height has been correlated with various disorders, including the observations that taller people are at a higher risk of developing cancer and shorter people are more likely to present with type 2 diabetes [13]. Determination of height in humans has long been considered to be largely influenced by genetic factors; indeed, twin and family studies have suggested that as much as 90% of variation in human height is genetically determined[48].

For many years, studies have attempted to identify genetic factors influencing human height in order to provide insights into human growth and development. Prior to 2007, genome-wide linkage and candidate-gene association studies had limited success in this regard; however, with the recent emergence of genome wide association (GWA) studies, tens of common genetics variants influencing height have now been uncovered, primarily in adults[914].

Weedon et al published the first GWA study of height using the Affymetrix GeneChip Human Mapping 500 K platform on nearly 5,000 individuals of self-reported European ancestry[9]. As a consequence, they observed association to common variation in the mobility group-A2 (HMGA2) oncogene. Follow-up analyses in approximately 19,000 more individuals (both adults and children) revealed strong replication of this observation. A subsequent GWA study uncovered another height locus, GDF5-UQCC, using data from the FUSION and SardiNIA cohorts[10].

These initial discoveries were followed by four meta-analyses with larger sample sizes, which collectively revealed 44 additional height loci [1114]. However, some lack of overlap between the results of these GWA studies has been observed, which may be partly explained by the different statistical powers of the studies[15].

Although the causal variants at these loci have still to be elucidated, it has been shown that many of the implicated genes are involved in pathways influencing bone and cartilage development, including skeletal development signaling (PTCH1, HHIP, BMPs, GDF5), the extracellular matrix (ACAN, FBLN5, EFEMP1, ADAMTS17, ADAMTSL3), chromatin structure and regulation (DOT1L, SCMH1, HMGA2) and cell cycle regulation and mitosis (CDK6, ANAPC13, NCAPG)[15]. In addition, some of the loci were novel and are now a clear focus of attention in height biology.

In this study we aimed at examining these initial and meta-analysis findings that were previously reported to be genome wide significant in a large European American pediatric cohort with height measurements to determine the relative impact of these variants on childhood stature. For this purpose, we leveraged genotyping data from our ongoing GWA study of height variation in children.

Methods

Study population

All subjects were consecutively recruited from the Greater Philadelphia area from 2006 to 2009 at the Children's Hospital of Philadelphia and its Primary Care Centers. Our study cohort consisted of 8,184 children of European ancestry with height information. All subjects were biologically unrelated and were aged between 0 and 18 years old. The basic characteristics of the study subjects are outlined in Table 1. This study was approved by the Institutional Review Board of the Children's Hospital of Philadelphia. Parental informed consent was given for each study participant for both the blood collection and subsequent genotyping.
Table 1

Basic characteristics of the study subjects, including sample size and mean height plus standard deviation (S.D.) for each age and gender separately

 

MALE

FEMALE

Age

N

Average Height (cm)

S.D.

N

Average Height (cm)

S.D.

Under 2

673

73.51

10.33

424

73.04

8.86

2

319

88.92

5.79

200

87.87

6.40

3

318

97.72

5.50

244

96.17

5.58

4

279

104.22

5.97

183

103.66

6.30

5

215

110.75

7.05

175

110.40

6.88

6

218

119.01

7.23

177

117.98

6.52

7

219

125.14

7.42

159

124.19

7.23

8

197

130.01

7.81

157

128.87

7.56

9

196

135.56

8.20

145

133.52

9.99

10

184

139.98

8.73

177

139.64

8.62

11

188

145.59

9.39

188

147.47

9.06

12

220

150.60

10.64

181

152.92

9.17

13

221

157.79

10.28

243

157.21

8.40

14

237

164.66

9.40

248

160.25

7.25

15

252

169.09

9.48

260

161.39

7.69

16

201

172.90

7.90

275

162.64

6.77

17

171

174.38

8.52

216

162.89

7.29

18

113

174.60

7.01

111

163.47

7.58

Genotyping

We performed high throughput genome-wide SNP genotyping using either the Illumina Infinium™ II HumanHap550 or Human 610 BeadChip technology in the same manner as our center has reported previously[16]. The SNPs analyzed survived the filtering of the genome wide dataset for SNPs with call rates < 95%, minor allele frequency < 1%, missing rate per person < 2% and Hardy-Weinberg equilibrium P < 10-5.

Loci described from GWA studies published to date have been found using either the Affymetrix or Illumina platform. In the event a locus was reported using both the Illumina and Affymetrix arrays, we used the SNPs present on the Illumina array. In the event of a signal only being described on the Affymetrix array, we either already had that SNP on our Illumina array or we identified and used the best surrogate SNP available (see Additional file 1: Supplemental Table S1 for the surrogates employed).

Statistical analyses

From our database of heights for our multi-dimensional scaling (MDS) determined Caucasians, as previously described[1719] and resulting in a low genomic inflation factor, we eliminated height outliers using 2% cutoff for each age category in order to remove potential measurement error. As height values vary widely across pediatric age groups and gender, we calculated the Z-scores using inverse-normal transformation for each age (one year bin) and gender category, and conducted association analysis with the Z-scores as the outcome variable.

We queried the data for the indicated SNPs in our pediatric samples. All statistical analyses were carried out using the software package plink[20]. By treating the Z-score for height as a quantitative trait, association analysis for each SNP was carried out using linear regression with the SNP included as an independent variable (coded as 0, 1, and 2, counting the number of minor alleles at the SNP).

The results for Figure 1 were generated by summing the number of height increasing alleles across all 16 height-associated SNPs in our study to in order to produce a scatter plot showing the impact of the genotype score on the cumulative height Z-score.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-11-96/MediaObjects/12881_2010_Article_657_Fig1_HTML.jpg
Figure 1

Scatter plot for association between height z-score and the genotype score by summing the number of height increasing alleles across all 16 height-associated SNPs.

Results

The 51 SNPs corresponding to the 46 previously reported height loci were investigated with respect to their association to normalized pediatric height in MDS-determined European Americans (Table 2; also Additional file 2: Supplemental Table S2 for analyses by age categories).
Table 2

Quantitative association results for the candidate loci in the European American height cohort (n = 8,184), sorted by chromosomal location.

Chr

Minor Allele

SNP

Position (Build 36)

Nearby genes(s)

NMISS

MAF

BETA

SE

R2

T

P

1

A

rs11809207

26205282

CATSPER4

8106

0.1730

0.02775

0.0213

0.0002095

1.303

0.1926

1

C

rs6663565

41232781

SCMH1

8184

0.4297

0.03744

0.01587

0.0006802

2.36

0.0183

1

C

rs17038164

118574711

SPAG17

8182

0.2601

-0.06029

0.01784

0.001395

-3.38

0.0007274

1

G

rs11205277

146705945

Histone class 2A, MTMR11, SV2A, SF3B4

8182

0.4195

0.0109

0.01579

5.83E-05

0.6906

0.4898

1

G

rs678962

168921546

DNM3

8178

0.2183

0.01272

0.0192

5.37E-05

0.6625

0.5077

1

A

rs2274432

180752602

C1orf19, GLT25D2

7965

0.3237

0.04568

0.01722

0.0008828

2.653

0.008003

1

A

rs3942992

224079131

ZNF678

8181

0.1725

9.96E-05

0.02072

2.83E-09

0.004809

0.9962

2

G

rs3791679

56008543

EFEMP1, PNPT1

8179

0.2539

-0.0782

0.01798

0.002308

-4.349

1.39×10 -5

2

T

rs1052483

219759853

IHH, CRYBA2, FEV, SLC23A3, TUBA1

8110

0.0956

-0.04701

0.02686

0.0003777

-1.75

0.08009

3

C

rs9841212

135674636

ANAPC13, CEP63

8154

0.3289

-0.006868

0.01676

2.06E-05

-0.4098

0.682

3

A

rs6763931

142585531

ZBTB38

8174

0.4091

0.04634

0.01587

0.001042

2.92

0.003513

4

T

rs6842303

17530324

LCORL, NCAPG

8173

0.2466

0.02231

0.01823

0.0001834

1.224

0.2209

4

C

rs6830062

17693999

LCORL, NCAPG

8184

0.1883

-0.05215

0.02014

0.0008192

-2.59

0.009613

4

A

rs1812175

145932449

HHIP

8172

0.1639

-0.03329

0.02125

0.0003002

-1.566

0.1173

5

T

rs10472828

32924575

NPR3

8182

0.4585

-0.005366

0.01589

1.39E-05

-0.3376

0.7357

6

A

rs12198986

7665058

BMP6

8183

0.4516

-0.0003072

0.01598

4.52E-08

-0.01922

0.9847

6

G

rs10946808

26341366

Histone class 1, Butyrophilin genes

8164

0.2923

-0.05734

0.01736

0.001336

-3.304

0.0009572

6

C

rs2844479

31680935

HLA class III

8183

0.3890

-0.03031

0.01605

0.0004355

-1.888

0.05907

6

G

rs3130050

31726740

HLA class III

8178

0.1249

0.06717

0.02369

0.0009819

2.835

0.004598

6

T

rs185819

32158045

HLA class III

8178

0.4576

0.0516

0.01576

0.001309

3.274

0.001066

6

G

rs1776897

34302989

HMGA1, LBH

8183

0.0940

0.02524

0.02701

0.0001068

0.9348

0.3499

6

A

rs2814993

34726871

C6orf106

8091

0.1390

0.06941

0.02284

0.001141

3.039

0.002378

6

A

rs4713858

35510763

ANKS1, TCP11, ZNF76, DEF6, SCUBE3

8184

0.1730

-0.02058

0.02103

0.000117

-0.9786

0.3278

6

C

rs314263

105499438

LIN28B, HACE1, BVES, POPDC3

8184

0.3129

0.02821

0.01698

0.0003373

1.661

0.09665

6

T

rs1490388

126877348

C6orf173/LOC387103

8179

0.4784

0.05315

0.01571

0.001398

3.383

0.0007196

6

G

rs3748069

142809326

GPR126

8184

0.3126

-0.06047

0.01695

0.001552

-3.566

0.0003641

7

T

rs798544

2536343

GNA12

8184

0.2897

0.009501

0.01746

3.62E-05

0.5441

0.5864

7

C

rs1182188

2643226

GNA12

8184

0.2948

0.01528

0.01742

9.40E-05

0.8769

0.3806

7

A

rs849141

27958331

JAZF1

8180

0.2729

0.05199

0.01769

0.001055

2.939

0.0033

7

C

rs2282978

91909061

CDK6, PEX1, GATAD1, ERVWE1

8180

0.3562

-0.0002285

0.01639

2.38E-08

-0.01394

0.9889

7

C

rs11765954

91925346

CDK6, PEX1, GATAD1, ERVWE1

8183

0.2866

0.006002

0.01744

1.45E-05

0.3441

0.7307

8

C

rs10958476

57258362

PLAG1, MOS, CHCHD7, RDHE2, RPS20, LYN, TGS1, PENK

8158

0.2015

0.05689

0.01979

0.001012

2.874

0.004062

8

C

rs7846385

78322734

PXMP3, ZFHX4

8175

0.2753

0.004128

0.01761

6.72E-06

0.2344

0.8147

9

G

rs4448343

95345925

PTCH1

8182

0.3217

-0.007508

0.01686

2.42E-05

-0.4453

0.6561

9

A

rs4743034

106711908

ZNF462

8183

0.2250

0.01859

0.01872

0.0001206

0.9933

0.3206

12

C

rs8756

64646019

HMGA2

8175

0.4607

0.02308

0.01586

0.0002588

1.455

0.1458

12

G

rs3825199

92479422

SOCS2, MRPL42, CRADD, UBE2N

8183

0.2088

0.02109

0.0194

0.0001445

1.087

0.2769

13

C

rs1239947

50004556

DLEU7

8183

0.3287

0.03243

0.01678

0.0004563

1.933

0.05333

14

C

rs910316

74695795

TMED10

8184

0.4863

-0.02021

0.01577

0.0002006

-1.281

0.2002

14

C

rs7153027

91496975

TRIP11, FBLN5, ATXN3, CPSF2

8149

0.4316

-0.02966

0.0158

0.0004323

-1.877

0.06054

15

C

rs2554380

82106888

ADAMTSL3, SH3GL3

8067

0.1991

0.01455

0.02029

6.38E-05

0.7171

0.4734

15

T

rs11633371

87157836

ACAN

8184

0.4636

0.02862

0.01575

0.0004032

1.817

0.06929

15

A

rs4533267

98603794

ADAMTS17

8184

0.2933

-0.001093

0.01737

4.84E-07

-0.06294

0.9498

17

A

rs3760318

26271841

CRLF3, ATAD5, CENTA2, RNF135

8184

0.3821

-0.03009

0.01627

0.0004178

-1.849

0.06444

17

A

rs4794665

52205328

NOG, DGKE, TRIM25, COIL, RISK

8183

0.4756

0.006489

0.01569

2.09E-05

0.4135

0.6792

17

A

rs757608

56852059

BCAS3, NACA2, TBX2, TBX4

8126

0.3322

0.02008

0.01676

0.0001767

1.198

0.2309

18

G

rs4800148

18978326

CABLES1, RBBP8, C18orf45

8183

0.2043

-0.04987

0.01943

0.0008048

-2.567

0.01028

18

T

rs530550

45105636

DYM

8182

0.3572

-0.01124

0.01631

5.81E-05

-0.6892

0.4907

19

G

rs12459350

2127586

DOT1L

8179

0.4744

0.02751

0.01579

0.0003711

1.742

0.08149

20

A

rs967417

6568893

BMP2

8184

0.4495

-0.02479

0.01591

0.0002964

-1.558

0.1194

20

C

rs4911494

33435328

UQCC, GDF5, CEP250, EIF6, MMP24

8182

0.3864

0.05107

0.01621

0.001212

3.151

0.001633

The SNPs in bold are those that survived correction for multiple testing.

NMISS: number of individuals tested; MAF: minor allele frequency; BETA: regression coefficient for the test SNP; SE: standard error of the regression coefficient; R2: r2 value in linear regression; T: test statistic; P: two-sided trend test P-value. The direction of effect is shown for the minor allele in each case.

In summary, sixteen of these SNPs yielded at least nominally significant association to height (P < 0.05), representing fifteen different loci with the same direction of effect as previously reported. Of these fifteen loci, variation at the EFEMP1-PNPT1 locus yielded the strongest association with P = 1.39×10-5, namely rs3791679.

With a slightly lower magnitude of association was GPR126 with rs3748069 yielding a P = 3.64×10-4, C6orf173 (also known as LOC387103) with rs1490388 yielding a P = 7.20×10-4, SPAG17 with 118574711 yielding a P = 7.27×10-4 and the Histone class 1 gene cluster with rs10946808 yielding a P = 9.57×10-4.

Overall, in addition to these loci, we found evidence for association at the HLA class III region, UQCC-GDF5, C6orf106, JAZF1, ZBTB38, PLAG1, C1orf19-GLT25D2, LCORL-NCAPG, CABLES1-RBBP8-C18orf45 and SCMH1 loci. One could argue that we have carried out multiple testing in our height cohort for these previously reported SNPs, albeit at a number of magnitudes less than for a full GWA study. If we were to apply the strictest correction, i.e. the Bonferroni correction based on 51 SNPs, then EFEMP1-PNPT1, GPR126, C6orf173, SPAG17 and the Histone class 1 gene cluster would still be considered significant and their effects are consistent with the outcomes of the adult GWA studies.

It was also observed that SNPs residing at the 31 other loci did not reveal any evidence of association with height in our pediatric cohort, most notably HMGA2.

Finally, we investigated the sixteen significant SNPs further by testing for association between height Z-score and the genotype score, by summing the number of height increasing alleles across all these SNPs. The resulting P-value for the genotype score was < 2×10-16 (Figure 1). The genotype score explains 1.64% of the total variation for height z-score. We also tested pair-wise interactions between the sixteen significant SNPs, but none of the interaction effects were significant, suggesting that these sixteen SNPs act additively on pediatric height.

Discussion

We queried the existing dataset from our ongoing GWAS of pediatric height in European Americans for adult height loci uncovered in GWAS to date. We examined 51 single nucleotide polymorphisms (SNPs) corresponding to 46 genomic loci in 8,184 children with height measurements. Sixteen of these SNPs yielded at least nominally significant association to the trait, representing fifteen different loci.

One of the more notable results is the negative association with HMGA2. This gene is one of the most strongly associated loci with adult height[9] so its lack of association with childhood stature in this study is striking. We previously published a replication attempt with this locus and pediatric height when our cohort was substantially smaller[21]; at that time, we observed nominal association but it is clear that as our cohort has grown, this signal has failed to strengthen. Despite the wealth the evidence from adult GWA studies and from previous work with knock-out mouse models, it is of surprise not to observe association with HMGA2. However, when considering the age bins presented in Additional file 2: Supplemental Table S2, the T statistic generally increases with age, with the strongest value being for the 15-18 age group. Although none of these observations are significant, it may point to an age-specific effect at a particular point during childhood that is undetected in the overall analysis; however our large cohort size may still not be powered enough to tease out this effect.

For the loci we did not observe any evidence for association at all may be due to power issues, but could also indicate that they have a less pronounced role in a pediatric setting. In addition, only a portion of the published adult height loci have been independently and robustly replicated to date[22]. It should also be noted that childhood growth is an ongoing process where development factors may cloud detection at certain loci, including at the two rapid growth stages, where nutrition plays a major role in infant growth and hormone signaling impacts at puberty. Our study may lack power to detect stage specific association when using a mixed age childhood cohort; however we have presented the association results for specific age bins in Additional file 2: Supplemental Table S2.

From this analysis, it is clear that a number of loci previously reported from GWA analyses of adult height also play a role in our phenotype of interest. While these recently discovered loci unveil several new biomolecular pathways not previously associated with height, it is important to note that these well established genetic associations with stature explain very little of the genetic contribution for this pediatric phenotype, suggesting the existence of additional loci whose number and effect size remain unknown.

Conclusions

Among 46 loci that have been reported to associate with adult height to date, at least 15 also contribute to the determination of height in childhood. Once our GWA study is complete, we will have the opportunity to look for other variants in the genome that are associated with height in childhood.

Notes

Declarations

Acknowledgements

We would like to thank all participating subjects and families. Hope Thomas, Kisha Harden, Andrew Hill, Kenya Fain, Crystal Johnson-Honesty, Alex Moy, Cynthia Drummond, Shanell Harrison and Sarah Wildrick provided expert assistance with genotyping or data collection and management. We would also like to thank Smari Kristinsson, Larus Arni Hermannsson and Asbjörn Krisbjörnsson of Raförninn ehf for their extensive software design and contribution. This research was financially supported by the Children's Hospital of Philadelphia. The study is supported in part by a Research Development Award from the Cotswold Foundation (H.H. & S.F.A.G.) and NIH grant 1R01HD056465-01A1.

Authors’ Affiliations

(1)
Division of Human Genetics, The Children's Hospital of Philadelphia
(2)
Department of Biostatistics and Epidemiology, University of Pennsylvania
(3)
Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia
(4)
Behavioral Health Center and Department of Child and Adolescent Psychiatry, The Children's Hospital of Philadelphia
(5)
Center for Weight and Eating Disorders, Department of Psychiatry, University of Pennsylvania
(6)
Department of Pediatrics, University of Pennsylvania

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  23. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/11/96/prepub

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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