Document Detail

Meta-analysis identifies common variants associated with body mass index in east Asians.
Jump to Full Text
MedLine Citation:
PMID:  22344219     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Multiple genetic loci associated with obesity or body mass index (BMI) have been identified through genome-wide association studies conducted predominantly in populations of European ancestry. We performed a meta-analysis of associations between BMI and approximately 2.4 million SNPs in 27,715 east Asians, which was followed by in silico and de novo replication studies in 37,691 and 17,642 additional east Asians, respectively. We identified ten BMI-associated loci at genome-wide significance (P < 5.0 × 10(-8)), including seven previously identified loci (FTO, SEC16B, MC4R, GIPR-QPCTL, ADCY3-DNAJC27, BDNF and MAP2K5) and three novel loci in or near the CDKAL1, PCSK1 and GP2 genes. Three additional loci nearly reached the genome-wide significance threshold, including two previously identified loci in the GNPDA2 and TFAP2B genes and a newly identified signal near PAX6, all of which were associated with BMI with P < 5.0 × 10(-7). Findings from this study may shed light on new pathways involved in obesity and demonstrate the value of conducting genetic studies in non-European populations.
Authors:
Wanqing Wen; Yoon-Shin Cho; Wei Zheng; Rajkumar Dorajoo; Norihiro Kato; Lu Qi; Chien-Hsiun Chen; Ryan J Delahanty; Yukinori Okada; Yasuharu Tabara; Dongfeng Gu; Dingliang Zhu; Christopher A Haiman; Zengnan Mo; Yu-Tang Gao; Seang-Mei Saw; Min-Jin Go; Fumihiko Takeuchi; Li-Ching Chang; Yoshihiro Kokubo; Jun Liang; Mei Hao; Loïc Le Marchand; Yi Zhang; Yanling Hu; Tien-Yin Wong; Jirong Long; Bok-Ghee Han; Michiaki Kubo; Ken Yamamoto; Mei-Hsin Su; Tetsuro Miki; Brian E Henderson; Huaidong Song; Aihua Tan; Jiang He; Daniel P-K Ng; Qiuyin Cai; Tatsuhiko Tsunoda; Fuu-Jen Tsai; Naoharu Iwai; Gary K Chen; Jiajun Shi; Jianfeng Xu; Xueling Sim; Yong-Bing Xiang; Shiro Maeda; Rick T H Ong; Chun Li; Yusuke Nakamura; Tin Aung; Naoyuki Kamatani; Jian-Jun Liu; Wei Lu; Mitsuhiro Yokota; Mark Seielstad; Cathy S J Fann; ; Jer-Yuarn Wu; Jong-Young Lee; Frank B Hu; Toshihiro Tanaka; E Shyong Tai; Xiao-Ou Shu
Related Documents :
22414959 - Missense substitutions associated with behavioural disturbances in alzheimer's disease ...
12921159 - Molecular population genetic analysis of the spectacled bear (tremarctos ornatus) in th...
14739019 - Human transportation influences aedes aegypti gene flow in southeast asia.
22475779 - Interaction between endothelial nitric oxide synthase gene polymorphisms (-86t>c, 894g>...
15241459 - Small-scale clinal variation, genetic diversity and environmental heterogeneity in the ...
22593559 - The integrated phenotype.
25046559 - Whole exome sequencing for the identification of a new mutation in tgfb2 involved in a ...
21350909 - Meet me halfway: when genomics meets structural bioinformatics.
23776239 - Reversion of a fungal genetic code alteration links proteome instability with genomic a...
Publication Detail:
Type:  Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-02-19
Journal Detail:
Title:  Nature genetics     Volume:  44     ISSN:  1546-1718     ISO Abbreviation:  Nat. Genet.     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-02-27     Completed Date:  2012-04-20     Revised Date:  2014-09-13    
Medline Journal Info:
Nlm Unique ID:  9216904     Medline TA:  Nat Genet     Country:  United States    
Other Details:
Languages:  eng     Pagination:  307-11     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Asian Continental Ancestry Group / genetics*
Body Mass Index*
Far East
Genetic Predisposition to Disease / genetics*
Genome-Wide Association Study
Genotype
Humans
Obesity / genetics*
Polymorphism, Single Nucleotide / genetics
Quantitative Trait Loci / genetics*
Grant Support
ID/Acronym/Agency:
1 UL1 RR024975/RR/NCRR NIH HHS; CA054281/CA/NCI NIH HHS; CA063464/CA/NCI NIH HHS; CA132839/CA/NCI NIH HHS; DK46200/DK/NIDDK NIH HHS; HG004446/HG/NHGRI NIH HHS; HG004726/HG/NHGRI NIH HHS; HG04424/HG/NHGRI NIH HHS; HL071981/HL/NHLBI NIH HHS; HL072507/HL/NHLBI NIH HHS; HL087263/HL/NHLBI NIH HHS; HL090682/HL/NHLBI NIH HHS; KL2 RR024977/RR/NCRR NIH HHS; KL2 TR000446/TR/NCATS NIH HHS; R01 CA064277/CA/NCI NIH HHS; R01 CA064277/CA/NCI NIH HHS; R01 CA090899/CA/NCI NIH HHS; R01 CA092585/CA/NCI NIH HHS; R01 CA092585/CA/NCI NIH HHS; R01 CA092585-07/CA/NCI NIH HHS; R01 CA118229/CA/NCI NIH HHS; R01 CA118229/CA/NCI NIH HHS; R01 CA118229-04/CA/NCI NIH HHS; R01 CA122756/CA/NCI NIH HHS; R01 CA124558/CA/NCI NIH HHS; R01 CA148667/CA/NCI NIH HHS; R37 CA070867/CA/NCI NIH HHS; TL1 RR024978/RR/NCRR NIH HHS; TL1 TR000447/TR/NCATS NIH HHS; UL1 RR024975/RR/NCRR NIH HHS; UL1 TR000445/TR/NCATS NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-journal-id): 9216904
Journal ID (pubmed-jr-id): 2419
Journal ID (nlm-ta): Nat Genet
Journal ID (iso-abbrev): Nat. Genet.
ISSN: 1061-4036
ISSN: 1546-1718
Article Information
Download PDF

License:
nihms-submitted publication date: Day: 9 Month: 2 Year: 2012
Electronic publication date: Day: 19 Month: 2 Year: 2012
pmc-release publication date: Day: 19 Month: 8 Year: 2012
Volume: 44 Issue: 3
First Page: 307 Last Page: 311
ID: 3288728
PubMed Id: 22344219
DOI: 10.1038/ng.1087
ID: nihpa346739

Meta-analysis identifies common variants associated with body mass index in East Asians
Wanqing Wen1*
Yoon Shin Cho2*
Wei Zheng1*
Rajkumar Dorajoo34*
Norihiro Kato5*
Lu Qi6*
Chien-Hsiun Chen78*
Ryan J. Delahanty1
Yukinori Okada910
Yasuharu Tabara11
Dongfeng Gu12
Dingliang Zhu13141516
Christopher A. Haiman17
Zengnan Mo18
Yu-Tang Gao19
Seang Mei Saw20
Min Jin Go2
Fumihiko Takeuchi5
Li-Ching Chang7
Yoshihiro Kokubo21
Jun Liang22
Mei Hao23
Loic Le Marchand24
Yi Zhang131415
Yanling Hu25
Tien Yin Wong262728
Jirong Long1
Bok-Ghee Han2
Michiaki Kubo29
Ken Yamamoto30
Mei-Hsin Su7
Tetsuro Miki31
Brian E. Henderson17
Huaidong Song32
Aihua Tan33
Jiang He23
Daniel P.-K. Ng20
Qiuyin Cai1
Tatsuhiko Tsunoda34
Fuu-Jen Tsai8
Naoharu Iwai35
Gary K. Chen17
Jiajun Shi1
Jianfeng Xu36
Xueling Sim37
Yong-Bing Xiang19
Shiro Maeda38
Rick T.H. Ong339
Chun Li40
Yusuke Nakamura41
Tin Aung2627
Naoyuki Kamatani9
Jian Jun Liu3
Wei Lu42
Mitsuhiro Yokota43
Mark Seielstad344
Cathy S.J. Fann7
The GIANT Consortium
Jer-Yuarn Wu78#
Jong-Young Lee2#
Frank B. Hu46#
Toshihiro Tanaka47#
E. Shyong Tai204849#
Xiao Ou Shu1#
1Division of Epidemiology, Department of Medicine; Vanderbilt Epidemiology Center; and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
2Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea.
3Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore.
4Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, United Kingdom.
5Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.
6Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA.
7Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
8School of Chinese Medicine, China Medical University, Taichung, Taiwan.
9Laboratory for Statistical Analysis, Center for Genomic Medicine (CGM), RIKEN, Yokohama, Japan.
10Department of Allergy and Rheumatology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
11Department of Basic Medical Research and Education, Ehime University Graduate School of Medicine, Toon, Japan.
12Cardiovascular Institute and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
13State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
14Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
15Sino-French Research Center for Life Science and Genomics, Shanghai, China.
16Shanghai Key Laboratory of Vascular Biology, Shanghai, China.
17Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
18Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
19Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China.
20Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
21Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan.
22Department of Endocrinology, the Central Hospital of Xuzhou, Affiliated Hospital of Southeast University, Xuzhou, Jiangsu, China.
23Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
24Epidemiology Program, Cancer Research Center, University of Hawaii, Honolulu, Hawaii, USA.
25Medical Scientific Research Center, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
26Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
27Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
28Center for Eye Research Australia, University of Melbourne, East Melbourne, Australia.
29Laboratory for Genotyping Development, CGM, RIKEN, Yokohama, Japan.
30Division of Genome Analysis, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
31Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Japan.
32Ruijin Hospital, State Key Laboratory of Medical Genomics, Molecular Medical Center, Shanghai Institute of Endocrinology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
33Center for Metabolic Disease and Diabetes, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
34Laboratory for Medical Informatics, CGM, RIKEN, Yokohama, Japan.
35Department of Genomic Medicine, National Cerebral and Cardiovascular Center, Suita, Japan.
36Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
37Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore.
38Laboratory for Endocrinology and Metabolism, CGM, RIKEN, Yokohama, Japan.
39NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore.
40Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
41Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
42Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
43Department of Genome Science, Aichi-Gakuin University, School of Dentistry, Nagoya, Japan.
44Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA.
45Contributors to the GIANT consortium is provided in the supplementary material.
46Departments of Epidemiology and Nutrition, Harvard University School of Public Health, Boston, Massachusetts, USA.
47Laboratory for Cardiovascular Diseases, CGM, RIKEN, Yokohama, Japan.
48Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
49Duke-National University of Singapore Graduate Medical School, Singapore, Singapore.
Correspondence: Address for correspondence: Xiao Ou Shu, M.D., Ph.D., Professor of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, 2525 West End Avenue, Suite 600, IMPH, Nashville, TN37203-1738, Tel: 615-936-0713, Fax: 615-936-8291, xiao-ou.shu@vanderbilt.edu
*These authors contributed equally.
#These authors jointly directed this work.

Since 2007, genome-wide association studies (GWAS) have contributed to a major leap forward in understanding the genetic basis of obesity111. To date, 37 genetic loci associated with obesity or body mass index (BMI) have been identified through these GWAS. However, virtually all of these studies were conducted in populations of European ancestry and included limited data from Asian populations9, 11. Asians, which account for over 60% of the world’s population, have a greater percentage of body fat and higher metabolic disease risk than European-ancestry individuals with the same BMI12. Therefore, studies conducted in Asian populations not only allow an evaluation of whether genetic markers of obesity identified in North American and European populations can be generalized to Asians, but also facilitate the dissection of the genetic architecture of obesity and the identification of genetic variants of particular importance to Asians.

We began with an initial genome-wide association meta-analysis using BMI as the primary outcome based on approximately 2.4 million genotyped or imputed SNPs generated from eight GWAS including 27,715 East Asians (stage I). This was followed by an in silico replication analysis conducted among 37,691 East Asians from an additional seven GWAS (stage II) and subsequently a de novo replication conducted among 17,642 East Asians from three studies (stage III). All of these studies were conducted in populations of East Asian ancestry; details of the study designs are presented in Supplementary Figure 1 and described in the Supplementary Note and Supplementary Tables 1 to 3.

The stage I meta-analysis was performed using the METAL program (http://www.sph.umich.edu/csg/abecasis/Metal), and study-specific genomic control adjustment was applied (see ONLINE METHODS). The Stage I analysis revealed that three well established loci (FTO, SEC16B, and MC4R) were associated with BMI at or near the genome-wide significance level (P<5×10−8)13 (Table 1, Figure 1).

In stage II, we analyzed 798 SNPs with a P value <1.0×10−4 in stage I and 50 additional SNPs that were previously reported to be associated with BMI in studies conducted in European-ancestry populations but that did not reach P<1.0×10−4 in stage I. Seven additional GWAS conducted in East Asian populations participated in stage II and provided regression analysis results for the selected SNPs. These data, along with the stage I meta-analysis results, were combined again in meta-analyses using methods similar to stage I with adjustment for both study-specific genomic control inflation and estimated residual inflation for the stage I meta-analysis results, which was 1.056 (see ONLINE METHODS). Analysis of combined data from stages I and II revealed that the index SNPs in six previously reported loci (FTO, SEC16B, MC4R, GIPR/QPCTL, ADCY3/RBJ, and BDNF) were genome-wide significant (P<5.0×10−8) and in three other previously reported loci (GNPDA2, TFAP2B, and MAP2K5) were near genome-wide significant (P<5.0×10−7) in East Asians (Table 1, Supplementary Table 4). In addition, the index SNPs in nine other previously reported loci were associated with BMI in the East Asian data at the nominal significance level (P<0.05) (Supplementary Table 4).

We compared two SNPs at each of the three loci GIPR/QPCTL, ADCY3/RBJ, and MAP2K5 (Supplementary Table 5), one identified by our study and another by the GIANT consortium (published during the course of our study)8. The SNPs at ADCY3/RBJ and MAP2K5 identified in our study are in linkage disequilibrium(LD) with the ones identified by the GIANT consortium. At GIPR/QPCTL, the SNP identified by our study, rs11671664, is not in LD in Asians (r2 =0.026) and is in weak LD in Europeans (r2=0.264) with the SNP identified by the GIANT consortium, rs2287019. The latter was not in a statistically significant association with BMI in East Asians (see also Supplementary Table 4). Conditional analyses (see ONLINE METHODS) with the two SNPs in each locus included in the same model for mutual adjustment showed that a statistically significant association with BMI remained only for the SNP identified by our study (Supplementary Table 5), suggesting that the SNP we identified may represent an independent association signal at the same locus in Asians.

The reported effect sizes for BMI-related SNPs in studies of European ancestry populations are usually greater than 3% of the standard deviation of BMI 4. Given the sample sizes of our study (N=27,715 for stage I and N=65,406 for stages I and II combined), we had adequate statistical power (>0.8) to detect a SNP with such an effect size and with a MAF>0.2 in stage I or a MAF>0.08 in the combined stage I and II data at a significance level of P<0.05. The index SNPs in the 19 previously identified loci that were not replicated in our study at P<0.05 had either very small effect sizes or very low MAFs (two were not available, seven were monomorphic according to the HapMap Asian data) in East Asians (Supplementary Table 4).

We selected one representative SNP from each of seven loci for further replication, including the four loci at or near the CDKAL1, PCSK1, PAX6, and GP2 genes that have not previously been reported to be associated with BMI and the three loci at the GIPR/QPCTL,ADCY3/RBJ, and MAP2K5 genes that were reported by the GIANT consortium (the selection of these SNPs was completed before the publication of the GIANT paper)8(Supplementary Table 4). Replication for these seven SNPs was conducted in stage III using de novo genotyping data from three study sites that included a total of 17,642 subjects (Supplementary Table 1 and 2). SNPs at other reported BMI loci that were genome-wide significant in stage I and II data were not included in the stage III de novo replication study for cost saving purposes. Stage III analyses found that the direction of the associations between BMI and the seven SNPs were consistent with stages I and II. The final results derived from a meta-analysis of data from all three stages combined, with adjustment for both study-specific genomic control inflation and estimated residual inflation for the stage I meta-analysis results, showed that six SNPs at or near GIPR/QPCTL,ADCY3/RBJ, MAP2K5, CDKAL1,PCSK1, and GP2 were associated with BMI at the genome-wide significance level (P=1.02×10−8 to 5.93×10−14) (Table 1) and SNP rs652722 near the PAX6 gene nearly reached the genome-wide significance threshold (P=7.65×10−8) (Supplementary Table 6). The explained variances of these SNPs are also presented in Table 1.

We also evaluated the association of BMI with these seven SNPs in data obtained from the GIANT consortium. Three of these SNPs (rs654581, rs4776970, and rs1167166) at the three loci that were recently reported by the GIANT consortium8 (AGCY3/RBJ, MAP2K5, and GIPR/QPCTL) and one newly identified SNP (rs261967) near the PCSK1 gene exhibited a significant association with BMI at P<0.007 (=0.05/7, to account for seven tests for seven SNPs) (Supplementary Table 7). Although the effect sizes of these seven loci were smaller than those of the well established variants in the FTO, MC4R, and SEC16B loci (2.55–4.22 percentile of standard deviation of normal deviate versus 5.51–7.92, Table 1), their effect sizes were larger and the explained variances were bigger among East Asians than among Europeans (Supplementary Table 7, data obtained from the GIANT consortium), with the exception of SNP rs4776970 in the MAP2K5 gene, which was independently identified by both our study and the GIANT consortium. The explained variance of this SNP is 0.03% in Europeans (Supplementary Table 7) and 0.02% in Asians (Table 1).

As shown in Table 1, the FTO SNP had the biggest effect on BMI and accounted for the largest proportion of the variance (0.18%) in our study population, as compared with 0.34% estimated from the GIANT consortium8. Together, the 10 BMI loci that reached the genome-wide significance level explained 0.87% of the inter-individual variation in BMI. In order to provide a comparison with data from the GIANT consortium, we also estimated the inter-individual variation in BMI explained by all 22 loci that were associated with BMI at P<0.05, including the above 10 SNPs with a genome-wide significant association (Supplementary Table 4). These 22 loci explained 1.18% of the inter-individual variation in BMI in our study population (see ONLINE METHODS). These explained variances are lower than those reported by the GIANT consortium (1.45% for overall and 0.34% for FTO)8. Even after excluding SNPs within these 22 loci associated with BMI at P<0.05, the number of SNPs with small observed P values for an association with BMI still appeared to exceed the expected number (Figure 2), suggesting that additional BMI-related loci remain to be uncovered in these East Asian populations.

As shown in Supplementary Table 6, the associations with BMI for the SNPs in the four new loci at or near the CDKAL1, PCSK1, PAX6, and GP2 genes were consistent across studies. Stratified analyses by sex and population showed that associations for all four loci were similar between men and women (P for homogeneity test ≥0.0837) and across Chinese, Japanese, Korean, and Malay populations (P for homogeneity test ≥0.185). Meta-analyses performed after excluding 23,093 subjects with chronic disease (cancer or diabetes), found similar associations, although with less significant P values due to the decreased sample size. Meta-analyses of obesity as a dichotomous outcome (BMI≥27.5)14 also showed similar associations with odds ratios per allele ranging from 1.05 to 1.10, although the statistical power for this analysis was lower (Supplementary Table 8). Of the studies participating in our analyses, one stage II study (SCORM) was based on children (aged 9 years). Analysis of data from the SCROM study showed that all the four loci had an association with BMI consistent with the meta-analysis, and SNP rs652722 near the PAX6 gene was nominally significant (P=0.0335) (Supplementary Table 6). Additional analysis excluding the SCORM study showed little change in the results.

The consistency of the findings across studies and populations suggests that population structure alone cannot account for the significant associations we identified. In addition, multiple SNPs in LD with each other showed similar associations in the combined stage I and II data at each locus (Figure 3, Supplementary Table 9). This plus the finding of similar associations in the de novo replication suggest that our results are unlikely to have been caused by genotyping or imputation errors.

The locus represented by SNP rs9356744 (6p22.3) contains the CDKAL1 gene, which has been reported to affect type 2 diabetes risk in a number of studies1517. A recent study reported an association between a CDKAL1 SNP, rs4712526, and BMI at age 8 years18. SNP rs4712526 was not included in our stage II replication set, but our stage I data for this SNP showed results consistent with the previous report (the minor allele A was associated with lower BMI, P=1.75×10−4, Supplementary Table 10). The SNP we identified, rs9356744, is in strong LD with rs4712526 (r2 =0.87) in Asians. To date, no study has reported an association between CDKAL1 variants and adult BMI. Given the strong link between type 2 diabetes and obesity, we carried out additional analyses and reevaluated the association with BMI after excluding participants with type 2 diabetes. A similar association was observed, although the P value (P=4.01×10−8) was less significant (Supplementary Table 6). These results indicate that the association of rs9356744 with BMI cannot be explained by the inclusion of subjects with diabetes. Additionally, two SNPs in the CDKAL1 gene (rs9356744 and rs9368222, Supplementary Table 9) are cis-expression quantitative trait loci (eQTLs) for the nearby E2F3 gene, a transcription factor and tumor suppressor19. Okada et al20 identified another SNP (rs2206734) in the CDKAL1 gene. While the data obtained from the GIANT consortium showed no significant association of our identified SNP rs9356744 with BMI (P=0.186, Supplementary Table 7), a nominally significant association (P=0.0049, Table 1 in Okada et al20) between rs2206734 and BMI was observed in the GIANT consortium data. This discrepancy could be explained by differences in genetic architecture between East Asians and Europeans. SNPs rs9356744 and rs2206734 are in strong LD in Asians (r2=0.932) and in weaker LD in Europeans (r2=0.396). Taken together, the findings of our study and those of Okada et al, suggest that the functional SNP encoding risk for obesity is in LD with both rs9356744 and rs2206734 in East Asians but only with rs2206734 in populations of European ancestry. These differences in patterns of LD may facilitate further fine mapping to identify the functional variant by combining data across ethnic groups.

At the chromosome 5 locus (5q15), the top SNP, rs261967, along with 13 other SNPs that are in strong LD (r2=1.0) with it, all reached the genome-wide significance threshold in the combined stage I and II data (Supplementary Table 9). The nearest gene to this locus is PCSK1 (81.3kb away). A study using the candidate-gene approach reported two common non-synonymous coding variants (rs6234, rs6235) in the PCSK1 gene that were associated with obesity21. However, these two SNPs showed no association with BMI in our study (Supplementary Table 10). None of the 14 SNPs identified at this locus by our study are in LD with the previously reported PCSK1 SNPs (r2=0) according to HapMap Asian data. Although SNP rs261967 was not statistically significant in the stage III replication, it showed an association with BMI (P=0.00158, Supplementary Table 7) in the data provided by the GIANT consortium8. Therefore, we believe that 5q15 represents a novel genetic locus for BMI and the association is unlikely to be a false positive finding.

The nearest genes flanking the chromosome 16 locus (16p12.3) are GPR139 and GP2. Although only one SNP at this locus, rs12597579, reached the significance threshold of 1×10−4 for stage I screening and was therefore included in our stage II replication, multiple SNPs in this region showed an association with BMI that nearly met this significance threshold (Figure 3d). One of those SNPs, rs12598578 (P=1.63×10−4, Supplementary Table 10), which is in LD (r2=0.968 in Asians) with the identified SNP rs12597579, is highly conserved across species according to the TRANSFAC database22 and the common G allele creates a Ying-Yang transcription factor binding site (CONSITE http://www.phylogood.org/consite).

The top SNP at the chromosome 11 locus (11p13), rs652722, is approximately 66.0kb from the nearest gene, PAX6. However, SNP rs652722 exhibits no significant LD with SNPs in the PAX6 gene or its 5′ region according to HapMap and 1000 Genomes Project data. Nevertheless, rs652722 is in LD with several SNPs that are predicted to be eQTLs, according to the SCAN database23, for a number of genes potentially important in the regulation of body weight. Among them is expression of the MIF gene based on HapMap lymphoblastoid cell lines. High plasma levels of MIF are related to higher BMI24. Another gene associated with this eQTL is the PFKP gene, which, along with the FTO gene, has been associated with increased BMI, hip circumference, and weight2. The association of BMI with rs652722 did not reach the conventional genome-wide significance level; thus, additional replication is needed.

Among the multiple hits at the ADCY3/RBJ locus (Supplementary Table 4), SNP rs11676272 (P=5.88×10−10) is a predicted missense mutation and causes a Ser107Pro change in the ADCY3 gene. This change is predicted to be potentially deleterious by Polyphen (http://genetics.bwh.harvard.edu/pph/). This locus is also associated with expression of the POMC gene, which regulates energy balance, thus, the susceptibility to obesity8. In addition, SNPs rs11676272 and rs6545814 at this locus (r2 =0.98 for LD between the two SNPs in Asians) are both eQTLs for the ADCY3 gene25.

In conclusion, our study identified 10 BMI-associated loci at the genome-wide significance level (P<5.0×10−8), including seven loci previously identified by studies conducted among European-ancestry populations (FTO, SEC16B, MC4R, GIPR/QPCTL, ADCY3/RBJ, BDNF, and MAP2K5) and three novel loci in or near the CDKAL1,PCSK1, and GP2 genes. Three additional loci nearly reached the genome-wide significance threshold, including two previously identified loci in the GNPDA2 and TFAP2B genes and a new locus near PAX6, which all had P<5.0×10−7.Of the three previously reported loci at GIPR/QPCTL, ADCY3/RBJ, and MAP2K5), conditional analyses with both SNPs at the same locus included in the same models showed that only the SNPs identified by our study were associated with BMI in East Asian populations. The representative SNP (rs261967) near the newly identified PCSK1 gene exhibited a significant association (P=0.00158) with BMI in a European population. As expected, the explained variances of the previously reported loci were generally lower in East Asians compared with those in Europeans, while the explained variances for the newly identified loci from this study were generally larger in East Asians than in Europeans. Although the specific mechanisms through which these loci affect BMI and obesity require further study, the identification of new loci may shed light on new pathways involved in obesity. In addition, fine mapping of multi-ethnic populations could lead to identification of causal links.


Supplementary Material 1 2

Notes

FN3Author Contributions

(in alphabetical order for each contribution)

T.A., Y.-S.C., Y.-T.G., D.-F.G., B.-G.H., J.H., F.B.H, N. Kamatami, N. Kato, L. L.-M., J.-H.L., W.L., Z.-N.M., Y.N., D.P.-K.N., L.Q., S.-M.S., X.-O.S., E.-S.T., F.T., T. Tanaka, F.J.T., T.-Y.W., J.-Y.W., Y.-B.X., J.-F.X., W.Z., and D.-L.Z. supervised the research. Y.-S.C., D.-F.G., J.H., Y.-L.H., N. Kato, J. Liang, Z.-N.M., Y.N., L.Q., M.S., X.-O.S., H.-D.S., E.-S.T., T. Tanaka, T.-Y.W., W.Z., and D.-L.Z. conceived and designed the experiments. J.H., Y.-L.H., M.K., J. Liang, M.S., J.-J.S., M.Y., and Y.Z. performed the experiments. L.C.C., C.-H.C., G.-K.C., R.D., M.-J.G., M.H., Y.-L.H., C.L., J. Long, Y.O., L.Q., M.-H.S., Y.T., F.K., A.-H.T., T. Tsunoda, and W.W. performed the statistical analyses. The GIANT Consortium, Q.C., L.-C.C., C.-H.C., R.J.D., R.D., M.-J.G., M.H., Y.-L.H., N.I., J. Long, T.M., Y.O., R.T.H.O., L.Q, X.S., M.H.S., and Y.T. analyzed the data. T.A., Q.C., Y.-T.G., C.A.H., B.E.H., N.I., N. Kato, Y.K., L.L.-M., J. Liang, J.-J.L., W.L., D.P.-K.N., L.Q., S.-M.S., M.S., X.-O.S., H.-D.S., E.-S.T., F.-J.T., T.-Y.W., J.-Y.W., Y.-B.X., K.Y., M.Y., and W.Z. contributed reagents/materials/analysis tools. R.J.D., Y.O., X.-O.S., E.-S.T., T. Tanaka, W.W., and W.Z. wrote the paper. All authors reviewed and approved the final version of the manuscript.

FN4Competing Financial Interests: The authors declare that they have no competing financial interests.

ACKNOWLEDGEMENTS

The Shanghai Genome Wide Associations Studies (SGWAS) would like to thank the dedicated investigators and staff members from the research teams at Vanderbilt University, the Shanghai Cancer Institute, and the Shanghai Institute of Preventive Medicine, and most of all, the study participants for their contributions to the studies. Genotyping assays and statistical analyses for the SGWAS were supported primarily by NIH grants R01 CA064277, R37 CA070867, and R01 CA090899, R01 CA118229, R01 CA092585and R01 CA122756, as well as by Ingram professorship funds, Allen Foundation funds, and Vanderbilt CTSA grant 1 UL1 RR024975 from NCRR/NIH. Grant support for the participating studies include: the Shanghai Breast Cancer Study (R01 CA064277), the Shanghai Breast Cancer Survival Study (R01 CA118229), the Shanghai Endometrial Cancer Study (R01 CA092585). The KARE project was supported by grants from the Korea Centers for Disease Control and Prevention, Republic of Korea (4845-301, 4851-302, 4851-307). The Singapore Prospective Study Program (SP2) was funded through grants from the Biomedical Research Council of Singapore (BMRC 05/1/36/19/413 and 03/1/27/18/216) and the National Medical Research Council of Singapore (NMRC/1174/2008). E.S.T. also receives additional support from the National Medical Research Council through a clinician scientist award (NMRC/CSA/008/2009). The Singapore Malay Eye Study (SiMES) was funded by the National Medical Research Council (NMRC 0796/2003 and NMRC/STaR/0003/2008) and Biomedical Research Council (BMRC, 09/1/35/19/616). The CAGE Network Studies were supported by grants for the Core Research for Evolutional Science and Technology (CREST) from the Japan Science Technology Agency; the Program for Promotion of Fundamental Studies in Health Sciences, National Institute of Biomedical Innovation Organization (NIBIO); and the Grant of National Center for Global Health and Medicine (NCGM). Dr. Qi is supported by a grant from the National Institutes of Health (R01 HL071981), an American Heart Association Scientist Development Award, and the Boston Obesity Nutrition Research Center (DK46200). The Genetic Epidemiology Network of Salt Sensitivity (GenSalt) is supported by research grants (U01 HL072507, R01 HL087263, and R01 HL090682) from the National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA. SINDI was funded by grants from Biomedical Research Council of Singapore (BMRC 09/1/35/19/616), Biomedical Research Council of Singapore (BMRC 08/1/35/19/550, Singapore), and National Medical Research Council of Singapore (NMRC/STaR/0003/2008). SCORM was funded by National Medical Research Council of Singapore (NMRC/0975/2005), Biomedical Research Council of Singapore (BMRC 06/1/21/19/466), and the Centre for Molecular Epidemiology, National University of Singapore. The SIH was supported by the Chinese National Key Program for Basic Research (Grant 973:2004CB518603) and Chinese National High Tech Program (Grant 863:2009AA022703). The MEC was supported by National Cancer Institute (NCI) grants CA063464, CA054281 and CA132839, as well as the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004726). Assistance with genotype cleaning for the MEC Japanese prostate cancer study was provided by the GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the NIH GEI (U01 HG04424).


Reference List
1. Frayling TM,et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesityScienceYear: 200731688989417434869
2. Scuteri A,et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traitsPLoS. GenetYear: 20073e11517658951
3. Loos RJ,et al. Common variants near MC4R are associated with fat mass, weight and risk of obesityNat. GenetYear: 20084076877518454148
4. Thorleifsson G,et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesityNat. GenetYear: 200941182419079260
5. Willer CJ,et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulationNat. GenetYear: 200941253419079261
6. Meyre D,et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populationsNat. GenetYear: 20094115715919151714
7. Scherag A,et al. Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groupsPLoS. GenetYear: 20106e100091620421936
8. Speliotes EK,et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass indexNat. GenetYear: 2010
9. Chambers JC,et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistanceNat. GenetYear: 20084071671818454146
10. McCarthy MI. Genomics, type 2 diabetes, and obesityN. Engl. J. MedYear: 20103632339235021142536
11. Cho YS,et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traitsNat. GenetYear: 20094152753419396169
12. Deurenberg P,Deurenberg-Yap M,Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationshipObes. RevYear: 2002314114612164465
13. de Bakker PI,et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studiesHum. Mol. GenetYear: 200817R122R12818852200
14. WHO expert consultation Appropriate body-mass index for Asian populations and its implications for policy and intervention strategiesLancetYear: 200436315716314726171
15. Voight BF,et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysisNat. GenetYear: 20104257958920581827
16. Zeggini E,et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetesScienceYear: 20073161336134117463249
17. Steinthorsdottir V,et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetesNat. GenetYear: 20073977077517460697
18. Winkler C,et al. BMI at age 8 years is influenced by the type 2 diabetes susceptibility genes HHEX-IDE and CDKAL1DiabetesYear: 2010592063206720460429
19. Veyrieras JB,et al. High-resolution mapping of expression-QTLs yields insight into human gene regulationPLoS. GenetYear: 20084e100021418846210
20. Yukinori,Okada,et al. Common variants at CDKAL1 and KLF9 loci are associated with body mass index in East Asian populations (in submission)
21. Benzinou M,et al. Common nonsynonymous variants in PCSK1 confer risk of obesityNat. GenetYear: 20084094394518604207
22. Wingender E,et al. TRANSFAC: an integrated system for gene expression regulationNucleic Acids ResYear: 20002831631910592259
23. Gamazon ER,et al. SCAN: SNP and copy number annotationBioinformaticsYear: 20102625926219933162
24. Dandona P,et al. Increased plasma concentration of macrophage migration inhibitory factor (MIF) and MIF mRNA in mononuclear cells in the obese and the suppressive action of metforminJ. Clin. Endocrinol. MetabYear: 2004895043504715472203
25. Yang TP,et al. Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studiesBioinformaticsYear: 2010262474247620702402
26. Zeggini E,Ioannidis JP. Meta-analysis in genome-wide association studiesPharmacogenomicsYear: 20091019120119207020
27. Cochran WG. The Combination of Estimates from Different ExperimentsBiometricsYear: 195410101129
28. Devlin B,Roeder K. Genomic control for association studiesBiometricsYear: 199955997100411315092
29. Park JH,et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveriesNat. GenetYear: 20104257057520562874
30. Xu Z,Taylor JA. SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studiesNucleic Acids ResYear: 200937W600W60519417063
31. Ramensky V,Bork P,Sunyaev S. Human non-synonymous SNPs: server and surveyNucleic Acids ResYear: 2002303894390012202775
32. Montgomery SB,et al. Transcriptome genetics using second generation sequencing in a Caucasian populationNatureYear: 201046477377720220756
33. Fujita PA,et al. The UCSC Genome Browser database: update 2011Nucleic Acids ResYear: 201139D876D88220959295
34. Frazer KA,et al. A second generation human haplotype map of over 3.1 million SNPsNatureYear: 200744985186117943122
35. Feuk L,Carson AR,Scherer SW. Structural variation in the human genomeNat. Rev. GenetYear: 20067859716418744

Article Categories:
  • Article


Previous Document:  Computed Tomographic Scan Diagnosis of Appendicitis in Children by Pediatric and Adult Radiologists.
Next Document:  Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs.