The article discusses a study which aimed to examine the association between body mass index and several genes, including FTO, TMEM18 and PCSK1, by genotyping the previously reported 11 single-nucleotide polymorphism (SNP) in southern Chinese. Topics include SNP selection and genotyping, the linear regression between single SNP and age, and the association of the genes FTO, TMEM18-associated SNPs, and PCSK1 with obesity.published_or_final_versio

Chen, J

Huang, Q

Xu, A

Yang, M

Zhao, K

Journal of Genetics

JIE CHEN

MEI YANG

KEHUI ZHAO

AIMIN XU

QINGYANG HUANG

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Polymorphisms in FTO, TMEM18 and PCSK1 are associated with BMI in southern Chinese population

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Title Polymorphisms in FTO, TMEM18 and PCSK1 are associated withBMI in southern Chinese populationAuthor(s) Chen, J; Yang, M; Zhao, K; Xu, A; Huang, QCitation Journal of Genetics, 2014, v. 93 n. 2, p. 509-512Issued Date 2014URL http://hdl.handle.net/10722/214316Rights Creative Commons: Attribution 3.0 Hong Kong Licensec© Indian Academy of SciencesRESEARCH NOTEPolymorphisms in FTO, TMEM18 and PCSK1 are associated with BMIin southern Chinese populationJIE CHEN1, MEI YANG1, KEHUI ZHAO1, AIMIN XU2 and QINGYANG HUANG1∗1College of Life Sciences, Central China Normal University, Wuhan 430079, People’s Republic of China2Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong[Chen J., Yang M., Zhao K., Xu A. and Huang Q. 2014 Polymorphisms in FTO, TMEM18 and PCSK1 are associated with BMI in southernChinese population. J. Genet. 93, 509–512]IntroductionObesity is a major health problem in the world, and is definedas excessive fat accumulation and quantified by body massindex (BMI). Genetic factors play a key role in obesity. Inthe last few years, genomewide association studies (GWAS)have produced a number of novel genes and polygenic vari-ants associated putatively with obesity. Common variants atFTO, INSIG2 and MC4R loci were previously reported tobe associated with fat mass, weight and obesity in severalpopulations (Sandholt et al. 2010). Yanagiya et al. (2007)revealed that genetic variations in MTMR9 may confer a pre-disposition towards obesity. PCSK1 was also firmly placedon the short list of genes reproducibly associated with com-mon obesity (Benzinou et al. 2008). A meta-analysis of 15GWAS performed by the GIANT consortium on total 32,387individuals of European ancestry discovered that six previ-ously unreported loci in or near KCTD15, SH2B1, GNPDA2,TMEM18, NEGR1 and MTCH2 are associated with BMI(Willer et al. 2009).In this study, we attempt to examine the associationbetween BMI and the genes mentioned here by genotypingthe previously reported 11 single-nucleotide polymorphism(SNP) in southern Chinese.Materials and methodsSubjectsThis study included 1560 southern Chinese individuals (288men and 1272 women) with mean age of 49.8 ± 15.9 years(women, 49.5 ± 15.9, men, 50.9 ± 15.9), mean height of1.57 ± 0.08 m (women, 1.55 ± 0.07, men, 1.67 ± 0.07),∗For correspondence. E-mail: huangqy@mail.ccnu.edu.cn.and mean weight of 55.7 ± 10.8 kg (women, 53.4 ± 9.3,men, 53.4 ± 9.3). A detailed description of subject ascer-tainment, inclusion and exclusion criteria has been describedpreviously (Ng et al. 2006). BMI was calculated as weight(kg)/height2 (m2).SNP selection and genotypingEleven SNPs from 10 candidate genes (FTO, KCTD15,INISG2, SH2B1, PCSK1, MTMR9, GNPDA2, TMEM18,MC4R and NEGR1), which have been reported to be asso-ciated with BMI, were selected for genotyping in 1560 sub-jects using the high-throughput mass array technology. Fivepercent of samples were duplicated for quality check in thegenotyping process and the reproducibility rate exceeded99.8%.Statistical analysisSince the raw data of BMI deviated from normal distribu-tion, a square root transformation was conducted. Genotype–BMI association analyses were performed by linear regres-sion, SNP being coded as 0, 1 and 2 according to genotypes.Analyses included linear regression of single SNP (adjustedwith age) with BMI and multiple stepwise regressions of 11SNPs, and age with BMI. Statistical analyses were performedusing SPSS 11.5 for Windows software. A P value less than0.05 was considered to be statistically significant.Results and discussionThe BMI in our samples ranged from 14.97 to 37.95 kg/m2.Kolmogorov–Smirnov test suggested that the distribution ofBMI deviated from normal distribution (P = 0.001), butsquare root of BMI (sqrt BMI) followed normal distribution(P = 0.056) (figure 1).Keywords. association; FTO; TMEM18; PCSK1; BMI.Journal of Genetics, Vol. 93, No. 2, August 2014 509Jie Chen et al.Figure 1. Histogram graph with normal curve of sqrt (BMI).Linear regression between single SNP and age, and squareroot of BMI was performed (table 1). Significant par-tial regression coefficient were found for FTO rs9939609(P = 0.004) and TMEM18 rs6548238 (P = 0.003).KCTD15 (rs11084753), PCSK1 (rs6234, rs6235), MTMR9(rs2293855) and NEGR1 (rs2815752) showed a weak cor-relation with BMI. For FTO rs9939609, BMI of AA(23.25 kg/m2) and AT (22.87 kg/m2) genotypes were sig-nificantly higher than those of TT (22.35 kg/m2) genotype(P = 0.010). It seems that the A allele increases BMI indominant model. For rs6548238 (TMEM18), there is sig-nificant difference in BMI between CC (22.60 kg/m2) andTC (21.98 kg/m2) genotype (P = 0.015). TT genotype hadthe lowest BMI (20.84 kg/m2). Multiple stepwise regres-sion equation of BMI on 11 SNPs and age included FTO,TMEM18 and PCSK1 genes: sqrt BMI = 4.216 + 0.065x1(rs6548238, P = 0.007) +0.058x2 (rs9939609, P = 0.002)+0.030x3 (rs6234, P = 0.031) +0.007x4 (age), R2 = 10.4%.Subgroup analyses were also performed to examine gen-der difference of obesity genetics. For women, apart fromFTO rs9939609 (P = 0.006) and TMEM18 rs6548238(P = 0.007), an association was found between GNPDA2rs10938397 (P = 0.014) and BMI. For men, only MC4Rrs17782313 was associated with BMI (P = 0.034). Causesfor gender difference included sex-specific genetic controland small sample size of men’s subgroup.FTO, located on chromosome 16q12.2, was earliest identi-fied as a susceptible locus for obesity. rs9939609 in FTO wasidentified to be predisposed to diabetes through an effect onBMI in a genomewide search for type 2 diabetes, and thenfirst reported as obesity susceptibility gene (Sandholt et al.2012). In two ongoing longitudinal studies, Liu et al. (2011)assessed the influence of rs9939609 on development of adi-posity in European-American and African-American youth,and found that rs9939609 was associated with logBMI. Sofar, six studies have been published on rs9939609 locus ofFTO in Chinese populations. Although two studies found noassociation of rs9939609 variant with obesity (Li et al. 2008;Yan et al. 2009), rs9939609 is associated with BMI in eth-nic Chinese in Singapore (Tan et al. 2008), Chinese children(Fang et al. 2010), Taiwan Chinese Han populations (Changet al. 2008) and Hubei Han Chinese aged from 50–70 years(Huang et al. 2011). Our current study successfully repli-cated association between FTO rs9939609 and BMI in south-ern Chinese from Hong Kong. Four meta-analyses of asso-ciations between rs9939609 and obesity / BMI have beenpublished in recent years and FTO rs9939609 was signifi-cantly associated with BMI (Xi and Mi 2009; Liu et al. 2010;Chauhan et al. 2011; Li et al. 2012).TMEM18-associated SNPs were first found to be asso-ciated with obesity by two GWAS (Willer et al. 2009;Thorleifsson et al. 2009). From then on, the association withobesity was confirmed on Swedish children (Almén et al.2010), Greek (Rouskas et al. 2012) and Japanese populations(Takeuchi et al. 2011) and children and adolescents fromDenmark and Estonia (Den Hoed et al. 2010). Although twoprevious studies found no association of TMEM18 polymor-phisms with BMI in Chinese populations (Ng et al. 2010;Huang et al. 2012), our study demonstrated that rs6548238was associated with BMI in southern Chinese aged 18–85Table 1. Association of 11 loci with BMI in southern Chinese population.Gene SNP (normal allele / risk allele) Regression equation R2 PGNPDA2 rs10938397(A/G) y = 4.367 + 0.023x1 + 0.007x2 8.9% 0.107FTO rs9939609(T/A) y = 4.367 + 0.054x1 + 0.007x2 9.2% 0.004*INSIG2 rs7566605(G/C) y = 4.374 + 0.009x1 + 0.007x2 8.8% 0.505KCTD15 rs11084753 (A/G) y = 4.362 + 0.028x1 + 0.007x2 9.0% 0.053MC4R rs17782313(T/C) y = 4.371 + 0.027x1 + 0.007x2 9.0% 0.126MTMR9 rs2293855(A/G) y = 4.367 + 0.025x1 + 0.007x2 9.0% 0.078NEGR1 rs2815752(C/T) y = 4.315 + 0.037x1 + 0.007x2 9.0% 0.088PCSK1 rs6234(C/G) y = 4.362 + 0.024x1 + 0.007x2 9.1% 0.080PCSK1 rs6235(G/C) y = 4.361 + 0.026x1 + 0.007x2 9.1% 0.054SH2B1 rs7498665(G/A) y = 4.356 + 0.014x1 + 0.007x2 8.8% 0.544TMEM18 rs6548238(T/C) y = 4, 248 + 0.071x1 + 0.007x2 9.4% 0.003*x1(SNP), x2(age), * P < 0.05.510 Journal of Genetics, Vol. 93, No. 2, August 2014FTO, TMEM18, PCSK1 polymorphisms and BMI in southern Chinayears. In addition, a study on Chinese children (10–20 yearsaged boys) from Shanghai also validated that rs6548238in TMEM18 has an association with obesity-related indicesduring puberty (Wang et al. 2012). A recent research hasshown that TMEM18 plays a regulatory role in humanadipocyte differentiation (Bernhard et al. 2013).PCSK1 encodes prohormone convertase 1/3 which canconvert inactive prohormones into biologically active pep-tide hormones. Loss-of-function mutations in PCSK1 giverise to monogenic obesity (Jackson et al. 1997). Benzinouet al. (2008) initially uncovered rs6232 and rs6234–rs6235in PCSK1 strongly associated with obesity risk among adultsand children in European populations. Association of rs6232and rs6235 with obesity, and BMI were also replicated inEuropean (Kilpeläinen et al. 2009; Sandholt et al. 2010;Rouskas et al. 2012) and Mexican populations (Villaloboset al. 2012). 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Polymorphisms in FTO, TMEM18 and PCSK1 are associated with BMI in southern Chinese population

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