Printer Friendly

Association between Nicotinamide N-Methyltransferase Gene Polymorphisms and Obesity in Chinese Han Male College Students.

1. Introduction

NNMT is an enzyme which catalyzes the methylation of nicotinamide (NAM) and produces methylnicotinamide (MNA) [1, 2]. Metabolomic works have showed that levels of MNA in urine were correlated with the body mass index (BMI) significantly [3, 4]; thus indicating that NNMT is involved in the regulation of the body fat. Recently, these findings have been supported by multiple evidences. For example, serum MNA was reportedly increased in Chinese obesity population [5] and urinary MNA was found elevated in humans with obesity and type 2 diabetes (T2D), in db/db mice and in obese Zucker rats [4]. These results implicate an increased NNMT activity in obesity. The more direct evidences came from Kraus et al. [6] and Lee et al. [7]. They found that Nnmt knockdown caused a47% reduction in relative fat mass of mice [6], and NNMT expression is increased in adipocytes of obese population [7].

More than two hundreds of single nucleotide polymorphisms (SNPs) have been identified within human NNMT gene in recent years, and two noncoding region SNPs (rs694539 and rs1941404) have been reported to be significantly associated with some related noncommunicable chronic diseases (NCD), such as hyperhomocysteinemia [8], congenital heart diseases [9], abdominal aortic diseases [10], migraine [11], nonalcoholic steatohepatitis [12], bipolar disorder [13], epilepsy [14], schizophrenia [15], and hyperlipidemia [16]. However, the association between NNMT gene SNPs and the body composition has not yet been reported to date.

Are there any SNPs in NNMT DNA sequence significantly associated with obesity? We check all the candidate genetic association studies (CGASs) on NNMT gene and all the genome-wide association studies (GWASs) on obesity. The results show that no CGAS concerning NNMT gene and obesity has been carried out to date and that no SNP in NNMT DNA sequence has been identified to be significantly associated with obesity in existing GWASs. Theoretically, it is not surprising that NNMT gene is significantly associated with obesity. As the precursor of the [NAD.SUP.+], NAM methylation can directly affect [NAD.SUP.+] levels, because NAM cannot be used in the synthesis of [NAD.SUP.+] if it is methylated by NNMT [17]. [NAD.SUP.+] is essential for the fuel oxidation in our bodies. The competition between NNMT and [NAD.SUP.+] salvage suggests that NNMT gene polymorphisms might affect the fuel oxidation and the storage of fat. Then, why no SNP in NNMT gene has been identified to be significantly associated with obesity in GWASs?

In fact, GWASs usually apply strictest genome-wide significance criterion to overcome type I error which makes the false negative discoveries inevitable, therefore a CGAS is still necessary to check whether there are any NNMT SNPs significantly associated with the body composition. Moreover, the most existing GWASs used SNP data from HapMap Project, but many more SNPs have been sequenced in 1000 Genome Project nowadays, so it is possible that some significant SNPs in NNMT gene were not detected in GWASs.

To test this speculation, a tagSNP based CGAS was carried out in this investigation. Within a candidate gene, the number of SNPs may be large, but direct analysis of all SNPs is inefficient, because the genotypes at many of these loci are strongly correlated. It is a common method to select the maximally informative set of SNPs (tagSNPs) to analyze in a CGAS, thus all known SNPs either are directly assayed or highly associated with a tagSNP [18]. Here, we identified 19 tagSNPs (including rs694539 and rs1941404) across the whole NNMT gene DNA sequence using Haploview software (Haploview 4.2) in the first place and then performed a case-control (fat versus thin) study to explore the association between these tagSNPs and obesity.

2. Subjects and Methods

2.1. Subjects and Grouping. 783 subjects were selected from Chinese Han male college students (aged from 17 to 23 years). According to their body fat percentage (BF%), the selected subjects were divided into two groups: the high body fat group (HBFG, 19 [less than or equal to] BF%, n = 289) and the low body fat group (LBFG, 3 [less than or equal to] BF% < 13.5, n = 494). The inclusion criteria were free of any diagnosed diseases (especially the anorexia, bulimia nervosa, and the diseases in the digestive system), without partiality for a particular kind of food and without exercise habit. The local ethics committee of Jiangxi Normal University approved this investigation, and the written informed consent was given to all the participants. This study conforms to the latest revision of the Declaration of Helsinki.

2.2. Body Component Measurement. Body component was measured using bioimpedance measurement with an XSCAN PLUS body composition analyzer (X-SCAN PLUSII, Jawon Medical Co., Ltd., South Korea). Measurements were performed in the morning with empty stomachs.

2.3. SNP Selection and Genotyping. As mentioned above, there are many SNPs in NNMT gene. To determine the investigation of loci, a tagSNP approach was used [18]. With the criteria (MAF > 0.10 and r2 > 0.8), nineteen tagSNPs were selected using Haploview software (Haploview 4.2) from the known SNP data in the Chinese Han population (CHB + CHS), which were downloaded from the database, 1000 Genomes Project ( Genomic DNA was extracted from blood samples with DNA extraction kits (Promega, USA). The gene sequence was downloaded from the database of National Center for Biotechnology Information (NCBI). Both probes and primers were designed with online software Primer 3 (http://bioinfo Genotypes of the SNPs were detected by polymerase chain reaction-ligase detection reaction (PCRLDR) [17]. Ten percent of the PCR-LDR reactions were performed in duplicate to check the reliability of the genotyping, and more than 99.5% of them had the matching results. Additionally, Sanger sequencing method was used to genotype the significantly associated tagSNP (rs10891644) of 30 samples randomly selected, and 100% had the matching results with the PCR-LDR method.

2.4. Statistics. The frequency distributions of genotype and allele and Hardy-Weinberg equilibrium (HWE) were analyzed online ( The HWE tests only performed in the control group. Four genetic models provided by Zintzaras and Santos [19] and two classification logistic regressions were used in the genotype effects analyses. The mean values were compared with one-way ANOVA with IBM SPSS Statistics 20.0 (SPSS Inc., Chicago, IL, USA). P value < 0.05 was considered statistically significant. Bonferroni correction was performed for the analysis of the genotype and allele frequency distributions, and the corrected significance level was P < 0.0026. The P values and the odds ratios were adjusted for the age in the analysis of the genotype effects.

3. Results and Discussions

3.1. The Distributions of Alleles and Genotypes of the 19 Tag SNPs. The allele and genotype distributions of the 19 tagSNPs are shown in the Table 1. Among these SNPs, rs10891644 was the only one significantly associated SNP after Bonferroni correction (P < 0.0026) and qualified with HWE test (P > 0.05). At this locus, the HBlG exhibited a higher allele T frequency and a higher genotype GT frequency than the LBFG did. Thus the rs10891644 variation was focused in the rest analyses and the other SNPs were not analyzed any longer.

3.2. Genotype Effects and Genetic Models of rs10891644 Variation. Genetic models (dominant, recessive, additive, and codominant) are often used in genotype effect analyses. However, these models are not totally independent. To avoid hash of these models, a whole solution and the degree of dominance (h) were offered by Zintzaras and Santos [19]. As shown in the Table 2, chances of subjects of the genotypes GG, GT, and TT to be the HBFG were 31%, 44%, and 35%, respectively. Two classification logistic regression analyses suggested that the dominant model and the codominance model were both significant ([P.sub.adjusted] < 0.05) after the adjustment for age, and the adjusted degree of dominance ([h.sub.adjusted]) was less than -1 ([absolute value of h] > 1 indicates that the phenotype of heterozygotes lies outside the phenotypical range of both homozygotes). Based on the [P.sub.adjusted] values, these results indicate that the possible inheritance modes of the rs10891644 variation are dominant or codominant. However, the [P.sub.adjusted] value of the additive model (GG versus TT) demonstrated that the difference between the homozygous GG and TT was not significant ([h.sub.adjusted] > 0.05), which denied the dominant mode, because in the dominant mode the difference between the homozygous genotypes must be significantly different. To determine whether the inheritance mode is overdominant, the [h.sub.adjusted] value has been calculated, and the [h.sub.adjusted] = 2.57 strongly suggests that the inheritance mode of the rs10891644 variation is overdominant. Overdominant inheritance is a condition in genetics where the phenotype of the heterozygote lies outside the phenotypical range of both homozygotes, which usually is described as heterosis, wherein heterozygous individuals have advantages in the natural selection. In this study, the [OR.sub.adjusted] value of the GT versus (GG + TT) was 1.716, which means that the chance of the GT being of HBFG is 1.716 times that of the homozygous (GG + TT), thus indicating that the heterozygous individuals (GT carriers) are the susceptible population to obesity. It is worth noting that although many diseases are associated with obesity nowadays, fat storage might be very helpful to enhance survival chances when lack of food in the long history of mankind. Therefore, it may be a result from the natural selection that the heterozygous individuals (GT carriers) are the susceptible population to obesity.

To further justify the genotype effects of the rs10891644 variation shown in Table 2, we compared the BF% between the subjects with different genotypes. As shown in Figure 1, the BF% of the GT, TT, and GG carriers were 14.56 [+ or -] 8.35,

13.47[+ or -]8.11, and 12.42 [+ or -] 7.50, the highest was the GT carriers followed by the TT and the GG carriers, and there were highly significant differences (P < 0.01) between the GT and the GG carriers, between the GT and the GG + TT carriers (codominant model), and between the GT + TT and the GG carriers (dominant model), respectively, while there were no significant differences between the GG and TT carriers (additive model) and between the TT and the GG + GT carriers (recessive model) (P > 0.05). These results further demonstrated that the body composition is significantly affected by the rs10891644 variation, and the inheritance mode of the rs10891644 variation is overdominant.

As mentioned above, although no NNMT gene SNP had been reported to be significantly associated with obesity before this paper, numerous reports have confirmed the roles of NNMT in regulation of the body composition. Among individuals, NNMT activity varies fivefold and has a bimodal frequency distribution in livers [20], but when the cDNAs of individuals with high and low NNMT activity were compared, no sequence differences were seen [21]. Thus the differences of phenotypes are due to the differences at transcriptional level and not because of NNMT SNPs in the coding regions [21]. NNMT gene is highly polymorphic in humans, and most of which are in the noncoding regions of this gene. The tagSNP (rs10891644), which was found significantly associated with obesity in this study, is also in the noncoding region (57 near gene). Therefore, it presumably affects the transcription of NNMT gene, thus causing the genetic risk for fat storage.

The existing reports have shown that NMT plays roles in the regulation of energy metabolism [6, 22, 23] and the body composition [3-7]. Therefore it is reasonable that NNMT gene polymorphism is associated with obesity. However, besides the genetic factor, many other factors are also related to obesity, such as race, gender, age, diet, exercise, and the gut microbiome [23-29]. To maximally eliminate the influences from other factors, we recruited the subjects from Chinese Han male college students, who were free of any diagnosed diseases (especially the anorexia, bulimia nervosa, and the diseases in the digestive system), without partiality for a particular kind of food and without exercise habit, and did the adjustment for age. The limitations of this investigation are that all participants were not entirely on the same controlled diet and that the influences of gut microbiome could not be eliminated.

In summary, for the first time we found that a tagSNP (rs10891644) in NNMT gene is significantly associated with obesity and the heterozygous individuals (GT carriers at this locus) are the susceptible population. Although the precise mechanism of the regulation process still needs further investigation, our findings suggest that the variant of a tagSNP (rs10891644) in NNMT gene is involved in the etiopathology of obesity in Chinese Han male college students.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Authors' Contributions

Qiong Zhou and Xiao-Juan Zhu contributed equally to this work.


This investigation was supported by the National Science Foundation of China (21365013) and by the Graduate Student Innovation Special Fund Project of Jiangxi Province (YC2016S138).


[1] P. Pissios, "Nicotinamide N-methyltransferase: more than a vitamin B3 clearance enzyme," Trends in Endocrinology and Metabolism, vol. 28, no. 5, pp. 340-353, 2017

[2] J.-H. Li, W. Chen, X.-J. Zhu et al., "Associations of nicotinamide N-methyltransferase gene single nucleotide polymorphisms with sport performance and relative maximal oxygen uptake," Journal of Sports Sciences, pp. 1-6, 2016.

[3] J. H. Li and Z. H. Wang, "Association between urinary low-molecular-weight metabolites and body mass index," International Journal of Obesity, vol. 35, no. S2, 554 pages, 2011.

[4] R. M. Salek, M. L. Maguire, E. Bentley et al., "A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human," Physiological Genomics, vol. 29, no. 2, pp. 99-108, 2007.

[5] M. Liu, L. Li, J. Chu et al., "Serum [N.sup.1]-methylnicotinamide is associated with obesity and diabetes in Chinese," The Journal of Clinical Endocrinology & Metabolism, vol. 100, no. 8, pp. 3112-3117, 2015.

[6] D. Kraus, Q. Yang, D. Kong et al., "Nicotinamide N-methyltransferase knockdown protects against diet-induced obesity," Nature, vol. 508, no. 7495, pp. 258-262, 2014.

[7] Y. H. Lee, S. Nair, E. Rousseauet al., "Microarray profiling of isolated abdominal subcutaneous adipocytes from obese vs non-obese Pima Indians: Increased expression of inflammation-related genes," Diabetologia, vol. 48, no. 9, pp. 1776-1783, 2005.

[8] J. C. Souto, F. Blanco-Vaca, J. M. Soria et al., "A genomewide exploration suggests a new candidate gene at chromosome 11q23 as the major determinant of plasma homocysteine levels: results from the GAIT project," American Journal of Human Genetics, vol. 76, no. 6, pp. 925-933, 2005.

[9] L. M. J. W. van Driel, H. P. M. Smedts, W. A. Helbing et al., "Eight-fold increased risk for congenital heart defects in children carrying the nicotinamide N-methyltransferase polymorphism and exposed to medicines and low nicotinamide," European Heart Journal, vol. 29, no. 11, pp. 1424-1431, 2008.

[10] B. Giusti, C. Saracini, P Bolli et al., "Genetic analysis of 56 polymorphisms in 17 genes involved in methionine metabolism in patients with abdominal aortic aneurysm," Journal of Medical Genetics, vol. 45, no. 11, pp. 721-730, 2008.

[11] A. Sazci, G. Sazci, B. Sazci, E. Ergul, and H. A. Idrisoglu, "Nicotinamide-N-Methyltransferase gene rs694539 variant and migraine risk," Journal of Headache and Pain, vol. 17, no. 1, article no. 93, 2016.

[12] A. Sazci, M. D. Ozel, E. Ergul, and C. Aygun, "Association of nicotinamide-N-methyltransferase gene rs694539 variant with patients with nonalcoholic steatohepatitis," Genetic Testing and Molecular Biomarkers, vol. 17, no. 11, pp. 849-853, 2013.

[13] A. Sazci, M. D. Ozel, E. Ergul, and M. E. Onder, "Association of nicotinamide-N-methyltransferase (NNMT) gene rs694539 variant with bipolar disorder," Gene, vol. 532, no. 2, pp. 272-275, 2013.

[14] G. Sazci, B. Sazci, A. Sazci, and H. A. Idrisoglu, "Association of nicotinamide-N-methyltransferase gene rs694539 variant with epilepsy," Molecular Neurobiology, vol. 53, no. 6, pp. 4197-4200, 2016.

[15] A. Bromberg, E. Lerer, M. Udawela et al., "Nicotinamide-N-methyltransferase (NNMT) in schizophrenia: genetic association and decreased frontal cortex mRNA levels," International Journal of Neuropsychopharmacology, vol. 15, no. 6, pp. 727-737, 2012.

[16] X.-J. Zhu, Y.-J. Lin, W. Chen et al., "Physiological study on association between nicotinamide N-methyltransferase gene polymorphisms and hyperlipidemia," BioMed Research International, vol. 2016, Article ID 7521942, 2016.

[17] S. A. J. Trammell and C. Brenner, "NNMT: a bad actor in fat makes good in liver," Cell Metabolism, vol. 22, no. 2, pp. 200-201, 2015.

[18] C. S. Carlson, M. A. Eberle, M. J. Rieder, Q. Yi, L. Kruglyak, and D. A. Nickerson, "Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium," The American Journal of Human Genetics, vol. 74, no. 1, pp. 106-120, 2004.

[19] E. Zintzaras and M. Santos, "Estimatingthe mode ofinheritance in genetic association studies of qualitative traits based on the degree of dominance index," BMC Medical Research Methodology, vol. 11, article 171, 2011.

[20] J. Rini, C. Szumlanski, R. Guerciolini, and R. M. Weinshilboum, "Human liver nicotinamide N-methyltransferase: ion-pairing radiochemical assay, biochemical properties and individual variation," Clinica Chimica Acta, vol. 186, no. 3, pp. 359-374, 1990.

[21] M.-L. Smith, D. Burnett, P Bennett et al., "A direct correlation between nicotinamide N-methyltransferase activity and protein levels in human liver cytosol," Biochimica et Biophysica Acta Gene Structure and Expression, vol. 1442, no. 2-3, pp. 238-244, 1998.

[22] J.-H. Li, L.-Q. Qiu, X.-J. Zhu, and C.-X. Cai, "Influence of exercises using different energy metabolism systems on NNMT expression in different types of skeletal muscle fibers," Science and Sports, vol. 32, no. 1, pp. 27-32, 2017

[23] H. Tilg, "Obesity, metabolic syndrome and microbiota: multiple interactions," Journal of Clinical Gastroenterology, vol. 44, supplement 1, pp. S16--S18, 2010.

[24] Y.-D. Gao, Y. Zhao, and J. Huang, "Metabolic modeling of common Escherichia coli strains in human gut microbiome," BioMed Research International, vol. 2014, Article ID 694967, 11 pages, 2014.

[25] F. K. Videhult, I. Ohlund, H. Stenlund, O. Hernell, and C. E. West, "Probiotics during weaning: a follow-up study on effects on body composition and metabolic markers at school age," European Journal of Nutrition, vol. 54, no. 3, pp. 355-363, 2016.

[26] A. Damms-Machado, S. Mitra, A. E. Schollenberger et al., "Effects of surgical and dietary weight loss therapy for obesity on gut microbiota composition and nutrient absorption," BioMed Research International, vol. 2015, Article ID 806248, 12 pages, 2015.

[27] L. Wang, B. Zeng, X. Zhang et al., "The effect of green tea polyphenols on gut microbial diversity and fat deposition in C57BL/6J HFA mice," Food and Function, vol. 7, no. 12, pp. 4956-4966, 2016.

[28] M. C. Dao, A. Everard, J. Aron-Wisnewsky et al., "Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology," Gut, vol. 65, no. 3, pp. 426-436, 2016.

[29] A. C. Vaughn, E. M. Cooper, P. M. Dilorenzo et al., "Energydense diet triggers changes in gut microbiota, reorganization of gut-brain vagal communication and increases body fat accumulation," Acta Neurobiologiae Experimental, vol. 77, no. 1, pp. 18-30, 2017.

Qiong Zhou, (1,2) Xiao-Juan Zhu, (1,2) and Jiang-Hua Li (1,2)

(1) Key Laboratory of Functional Small Organic Molecule, Ministry of Education, Jiangxi Normal University, Nanchang China

(2) Institute of Physical Education, Jiangxi Normal University, Nanchang, China

Correspondence should be addressed to Jiang-Hua Li;

Received 28 June 2017; Revised 6 August 2017; Accepted 14 August 2017; Published 18 September 2017

Academic Editor: Hai-Feng Pan

Caption: Figure 1: Comparisons of the body fat percentage between the different genotypes. GT, the genotype GT (n = 336); TT, the genotype TT (n = 82); GG, the genotype GG (n = 354); GT + TT, the genotypes (GT + TT) (n = 418); GG + GT, the genotypes (GG + GT) (n = 690); GG + TT, the genotypes (GG + TT) (n = 436). ** P < 0.01 compared with GT; ## P < 0.01 compared with GG. Error bars, [+ or -] standard deviation.
Table 1: The distribution of alleles and genotypes of the 19 tag SNPs.

SNPs                             Allele frequency             P

rs2511153      Case     C: 352 (0.63)     T: 206 (0.37)     0.708
              Ctrl.     C: 625 (0.64)     T: 351 (0.36)
rs505978       Case     A: 307 (0.54)     C: 261 (0.46)     0.668
              Ctrl.     A: 544 (0.55)     C: 442 (0.45)
rs694539       Case     A: 192 (0.34)     G: 374 (0.66)     0.613
              Ctrl.     A: 347 (0.35)     G: 639 (0.65)
rs12285641     Case     C: 349 (0.62)     T: 217 (0.38)     0.817
              Ctrl.     C: 585 (0.61)     T: 373 (0.39)
rs11214926     Case     A: 154 (0.27)     G: 410 (0.73)     0.871
              Ctrl.     A: 273 (0.28)     G: 713 (0.72)
rs7109984      Case     C: 502 (0.89)     T: 64 (0.11)      0.221
              Ctrl.     C: 829 (0.87)     T: 129 (0.14)
rs10891644     Case     G: 370 (0.64)     T: 206 (0.36)    0.029 *
              Ctrl.     G: 674 (0.70)     T: 294 (0.30)
rs55675450     Case     A: 88 (0.16)      G: 470 (0.84)     0.368
              Ctrl.     A: 138 (0.14)     G: 842 (0.86)
rs2244175      Case     A: 274 (0.48)     G: 292 (0.52)     0.147
              Ctrl.     A: 515 (0.52)     G: 471 (0.48)
rs2847492      Case     A: 198 (0.35)     G: 368 (0.65)     0.162
              Ctrl.     A: 380 (0.39)     G: 606 (0.62)
rs2852432      Case     C: 336 (0.59)     T: 232 (0.41)     0.209
              Ctrl.     C: 551 (0.56)     T: 435 (0.44)
rs4646335      Case     A: 326 (0.57)     T: 242 (0.43)     0.387
              Ctrl.     A: 588 (0.60)     T: 398 (0.40)
rs3819100      Case     A: 282 (0.50)     G: 284 (0.50)     0.597
              Ctrl.     A: 505 (0.51)     G: 481 (0.49)
rs2256292      Case     C: 234 (0.41)     G: 338 (0.59)     0.417
              Ctrl.     C: 382 (0.39)     G: 602 (0.61)
rs2301128      Case     A: 81 (0.15)      G: 477 (0.86)     0.071
              Ctrl.     A: 111 (0.11)     G: 867 (0.89)
rs1941404      Case     C: 265 (0.47)     T: 301 (0.53)     0.472
              Ctrl.     C: 443 (0.45)     T: 543 (0.55)
rs2155806      Case     C: 59 (0.11)      T: 499 (0.89)     0.200
              Ctrl.     C: 125 (0.13)     T: 853 (0.87)
rs1941399      Case     A: 102 (0.18)     C: 464 (0.82)     0.425
              Ctrl.     A: 194 (0.20)     C: 792 (0.80)
rs4646337      Case     A: 479 (0.86)     G: 79 (0.14)      0.935
              Ctrl.     A: 841 (0.86)     G: 137 (0.14)

SNPs                          Genotype frequency

rs2511153      Case    CC: 108 (0.39)     CT: 57 (0.44)
              Ctrl.    CC: 199 (0.41)    CT: 227 (0.47)
rs505978       Case     AA: 80 (0.28)    AC: 254 (0.52)
              Ctrl.    AA: 145 (0.29)     AC: 66 (0.50)
rs694539       Case     AA: 30 (0.11)    AG: 132 (0.47)
              Ctrl.     AA: 55 (0.11)    AG: 237 (0.48)
rs12285641     Case    CC: 102 (0.36)    CT: 145 (0.51)
              Ctrl.    CC: 174 (0.36)    CT: 237 (0.50)
rs11214926     Case     AA: 18 (0.06)    AG: 118 (0.42)
              Ctrl.     AA: 28 (0.06)    AG: 217 (0.44)
rs7109984      Case    CC: 224 (0.79)     CT: 54 (0.19)
              Ctrl.    CC: 359 (0.75)    CT: 111 (0.23)
rs10891644     Case    GG: 111 (0.39)    GT: 148 (0.51)
              Ctrl.    GG: 243 (0.50)    GT: 188 (0.39)
rs55675450     Case     AA: 5 (0.02)      AG: 78 (0.28)
              Ctrl.     AA: 17 (0.04)    AG: 104 (0.21)
rs2244175      Case     AA: 64 (0.23)    AG: 146 (0.52)
              Ctrl.    AA: 132 (0.27)    AG: 251 (0.51)
rs2847492      Case     AA: 33 (0.12)    AG: 132 (0.47)
              Ctrl.     AA: 72 (0.15)    AG: 236 (0.48)
rs2852432      Case     CC: 94 (0.33)    CT: 148 (0.52)
              Ctrl.    CC: 153 (0.31)    CT: 245 (0.50)
rs4646335      Case     AA: 89 (0.31)    AT: 148 (0.52)
              Ctrl.    AA: 169 (0.34)    AT: 250 (0.51)
rs3819100      Case     AA: 70 (0.25)    AG: 142 (0.50)
              Ctrl.    AA: 126 (0.26)    AG: 253 (0.51)
rs2256292      Case     CC: 50 (0.18)    CG: 134 (0.47)
              Ctrl.     CC: 71 (0.14)    CG: 240 (0.49)
rs2301128      Case     AA: 3 (0.01)      AG: 75 (0.27)
              Ctrl.     AA: 8 (0.02)      AG: 95 (0.19)
rs1941404      Case     CC: 59 (0.21)    CT: 147 (0.52)
              Ctrl.     CC: 94 (0.19)    CT: 255 (0.52)
rs2155806      Case     CC: 1 (0.00)      CT: 57 (0.20)
              Ctrl.     CC: 4 (0.01)     CT: 117 (0.24)
rs1941399      Case     AA: 4 (0.01)      AC: 94 (0.33)
              Ctrl.     AA: 19 (0.04)    AC: 156 (0.32)
rs4646337      Case    AA: 201 (0.72)     AG: 77 (0.28)
              Ctrl.    AA: 362 (0.74)    AG: 117 (0.24)

SNPs                      frequency        HWE         P

rs2511153      Case     TT: 20 (0.16)                0.827
              Ctrl.     TT: 62 (0.13)      0.83
rs505978       Case     CC: 57 (0.20)                0.909
              Ctrl.     CC: 94 (0.19)      0.36
rs694539       Case    GG: 121 (0.43)                0.861
              Ctrl.    GG: 201 (0.41)      0.23
rs12285641     Case     TT: 36 (0.13)                0.820
              Ctrl.     TT: 68 (0.14)      0.38
rs11214926     Case    GG: 146 (0.52)                0.810
              Ctrl.    GG: 248 (0.50)      0.03
rs7109984      Case     TT: 5 (0.02)                 0.407
              Ctrl.     TT: 9 (0.02)       0.90
rs10891644     Case     TT: 29 (0.10)               0.002 **
              Ctrl.     TT: 53 (0.11)      0.07
rs55675450     Case    GG: 196 (0.70)                0.056
              Ctrl.    GG: 369 (0.75)      0.01
rs2244175      Case     GG: 73 (0.26)                0.337
              Ctrl.    GG: 110 (0.22)      0.65
rs2847492      Case    GG: 118 (0.42)                0.366
              Ctrl.    GG: 185 (0.38)      0.82
rs2852432      Case     TT: 42 (0.15)                0.286
              Ctrl.     TT: 95 (0.19)      0.86
rs4646335      Case     TT: 47 (0.17)                0.666
              Ctrl.     TT: 74 (0.15)      0.24
rs3819100      Case     GG: 71 (0.25)                0.825
              Ctrl.    GG: 114 (0.23)      0.55
rs2256292      Case    GG: 102 (0.36)                0.526
              Ctrl.    GG: 181 (0.37)      0.55
rs2301128      Case    GG: 201 (0.72)                0.051
              Ctrl.    GG: 386 (0.79)      0.44
rs1941404      Case     TT: 77 (0.27)                0.762
              Ctrl.    TT: 144 (0.29)      0.32
rs2155806      Case    TT: 221 (0.79)                0.387
              Ctrl.    TT: 3 68 (0.75)     0.11
rs1941399      Case    CC: 185 (0.65)                0.152
              Ctrl.    CC: 318 (0.65)      0.98
rs4646337      Case     GG: 1 (0.00)                 0.102
              Ctrl.     GG: 10 (0.02)      0.88

HWE, Hardy-Weinberg equilibrium; Case, the high body fat group; Ctrl.,
the low body fat group. HWE, P value of Hardy-Weinberg equilibrium
test on the control group; the values of allele and genotype are the
number of individuals (frequency); * P < 0.05; ** P < 0.01.

Table 2: Genotype effects and genetic models of the rs10891644

Model          Genotype       HBFG          LBFG

Recessive         TT        29 (0.35)     53 (0.65)
              (GT + GG)    259 (0.38)    431 (0.62)
Dominant      (TT + GT)    177 (0.42)    241 (0.58)
                  GG       111 (0.31)    243 (0.69)
Additive          TT        29 (0.35)     53 (0.65)
                  GG       111 (0.31)    243 (0.69)
Codominant        GT       148 (0.44)    188 (0.56)
              (TT + GG)    140 (0.32)    296 (0.68)

Model          Genotype      OR (95% CI)        Adjusted
                                               OR (95% CI)

Recessive         TT            0.911             0.932
              (GT + GG)     (0.564,1.469)    (0.538, 1.614)
Dominant      (TT + GT)         1.608             1.669
                  GG       (1.195, 2.163)    (1.188, 2.345)
Additive          TT            1.198             1.239
                  GG       (0.723, 1.985)    (0.695, 2.208)
Codominant        GT            1.664             1.716
              (TT + GG)    (1.240, 2.235)    (1.220, 2.413)

Model          Genotype            P                    h
                           ([P.sub.adjusted])   ([h.sub.adjusted])

Recessive         TT         0.701 (0.802)
              (GT + GG)
Dominant      (TT + GT)     0.002 (0.003) **
                  GG                               2.83 (2.57)
Additive          TT         0.484 (0.468)
Codominant        GT        0.001 (0.002) **
              (TT + GG)

HBFG, the high body fat group; LBFG, the low body fat group; the
values of HBFG and LBFG are the number of individuals (ratio) from
different genotypes; OR, odds ratio; CI, confidence interval; h
(dominance degree) = ln([])/ln([OR.sub.a]), [], OR
of the codominant model, [OR.sub.a], OR of the additive model,
[absolute value of h] > 1 indicates that the phenotype of the
heterozygotes lies outside the phenotypical range of both homozygotes;
Adjusted, adjustment for age. ** P < 0.01.
COPYRIGHT 2017 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Zhou, Qiong; Zhu, Xiao-Juan; Li, Jiang-Hua
Publication:BioMed Research International
Article Type:Report
Geographic Code:9CHIN
Date:Jan 1, 2017
Previous Article:Posttraumatic Psychiatric Disorders and Resilience in Healthcare Providers following a Disastrous Earthquake: An Interventional Study in Taiwan.
Next Article:Microglia-Synapse Pathways: Promising Therapeutic Strategy for Alzheimer's Disease.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |