obesity is a severe social problem in korea ( kang , 2013 ) . not only social and economic factors ( chung et al . , 2016 ) but also other various risk factors are important factors affecting the pathophysiologic and developing process of obesity . and life style and psychological factors are also related to hormone changes in obesity ( azuma et al . , 2015 ; kim et al . , 2016 ; nordkap et al . , growth hormone ( gh ) is affected by psychological stress and many physiological stresses ( he et al . , 2015 ; karaca et al . , 2016 ) . and gh action is related to body energy expenditure and body weight ( glad et al . , 2015 ; therefore , gh is widely studied in many field of studies ( huang et al . , 2016 ; imran et al . however , hormone signaling of gh needs growth hormone receptor ( ghr ) ( carter - su et al . , 2016 ) because gh is not a steroid hormone ( carter - su et al . , 2016 ) , and its main function is related to signaling of ghr into a cell . it may show a kind of decreased pituitary hormone situation , which cause higher risk of metabolic syndrome ( khang et al . , 2016 ) , because the studies suggest that gh function is related with ghr function . one of the famous example is ghr - null ( ghr[/ ] ) mouse ( sackmann - sala et al . , 2014 ) , which showed insulin sensitivity and increased adiposity . insulin sensitivity is another problem in obesity , and gh is a major hormone in insulin sensitivity ( galescu et al . , 2016 ; poggiogalle et al . , 2016 ; those are closely related keywords to obesity , and these studies suggest that polymorphisms of ghr gene may associate with obesity . however , genetic effect such as common variation of functional gene may also affect its function ( barrie et al . therefore , in this study , we investigated the relationship between genetic polymorphisms of ghr gene and obese vs. nonobese in a korean population , to compare polymorphism effect of ghr on obesity . three hundred sixty - eight subjects who participated in a general health check - up program were enrolled in this study . the biochemical characteristics including fasting plasma glucose , fasted glycated hemoglobin , total cholesterol , and low - density lipoprotein cholesterol were measured . body mass index ( bmi ) is calculated based on the weight ( kg ) divided by the square of height ( m ) ( yang , 2015 ) . all subjects were classified according to the classification of korean society for the study of obesity ( underweight , bmi<18 kg / m ; normal , bmi 18 to < 23 kg / m ; moderately obese , bmi 23 to < 25 kg / m ; obesity i , bmi 25 to < 30 kg / m ; obesity ii , bmi30 kg / m ) . two hundred eleven subjects with a bmi23 kg / m were included in overweight / obese group ( mean agestandard deviation , 44.76.4 years ) and 157 subjects with a bmi of 18.523.0 kg / m in nonoverweight / obese control group ( mean agestandard deviation , 43.66.2 years ) . genomic dnas were extracted from peripheral bloods of all subjects by roche dna extraction kit ( roche diagnostics corp . , we selected seven snps including the rs6451620 ( intron ) , rs4130114 ( intron ) , rs4410646 ( intron ) , rs6898743 ( intron ) , rs4394131 ( intron ) , rs6182 ( cys440phe ) , and rs6184 ( pro579thr ) and rs2229765 in ghr gene . after that , each genotype of snps was analyzed using targeted genotyping analysis software gcos software ( affymetrix ) . to evaluate the odds ratio ( or ) , 95% confidence interval ( ci ) , and p - value , snpstats ( http://bioinfo.iconcologia.net/index.php ) and ibm spss statistics ver . genetic models ( dominant [ major homozygous vs. heterozygous+minor homozygous ] , recessive [ major homozygous+heterozygous vs. minor homozygous ] , and log - additive [ major homozygous vs. heterozygous vs. minor homozygous ] models ) were applied ( kim et al . , 2014 ; zare et al . , 2013 ) . three hundred sixty - eight subjects who participated in a general health check - up program were enrolled in this study . the biochemical characteristics including fasting plasma glucose , fasted glycated hemoglobin , total cholesterol , and low - density lipoprotein cholesterol were measured . body mass index ( bmi ) is calculated based on the weight ( kg ) divided by the square of height ( m ) ( yang , 2015 ) . all subjects were classified according to the classification of korean society for the study of obesity ( underweight , bmi<18 kg / m ; normal , bmi 18 to < 23 kg / m ; moderately obese , bmi 23 to < 25 kg / m ; obesity i , bmi 25 to < 30 kg / m ; obesity ii , bmi30 kg / m ) . two hundred eleven subjects with a bmi23 kg / m were included in overweight / obese group ( mean agestandard deviation , 44.76.4 years ) and 157 subjects with a bmi of 18.523.0 kg / m in nonoverweight / obese control group ( mean agestandard deviation , 43.66.2 years ) . genomic dnas were extracted from peripheral bloods of all subjects by roche dna extraction kit ( roche diagnostics corp . , indianapolis , in , usa ) and quantified using nanodrop machine . we selected seven snps including the rs6451620 ( intron ) , rs4130114 ( intron ) , rs4410646 ( intron ) , rs6898743 ( intron ) , rs4394131 ( intron ) , rs6182 ( cys440phe ) , and rs6184 ( pro579thr ) and rs2229765 in ghr gene . after that , each genotype of snps was analyzed using targeted genotyping analysis software gcos software ( affymetrix ) . to evaluate the odds ratio ( or ) , 95% confidence interval ( ci ) , and p - value , snpstats ( http://bioinfo.iconcologia.net/index.php ) and ibm spss statistics ver . genetic models ( dominant [ major homozygous vs. heterozygous+minor homozygous ] , recessive [ major homozygous+heterozygous vs. minor homozygous ] , and log - additive [ major homozygous vs. heterozygous vs. minor homozygous ] models ) were applied ( kim et al . , 2014 ; zare et al . , 2013 ) . to find genetic factors involved in the development of obesity , we performed an association study between polymorphisms of ghr gene and overweight / obese . firstly , we calculated hardy - weinberg equilibrium ( hwe ) in tested polymorphisms of ghr gene . the genotype distributions of the tested seven snps of ghr gene were consistent with the hwe ( rs6451620 , p=1.00 ; rs4130114 , p=1.00 ; rs4410646 , p=0.16 ; rs6898743 , p=1.00 ; rs4394131 , p=1.00 ; rs6182 , and rs6184 , p=1.00 ; rs2229765 , p=1.00 , data not shown ) . tables 2 and 3 showed genotype and allele distributions of seven snps in ghr gene . among tested seven snps of ghr gene , two snps ( rs4410646 and rs6898743 ) revealed the significant association with obesity ( rs4410646 , or , 1.68 , 95% ci , 1.092.60 , p=0.02 in dominant model [ a / a genotype vs. a / c genotype+c / c genotype ] and or , 1.38 , 95% ci , 1.021.87 , p=0.036 in log - additive model [ a / a genotype vs. a / c genotype vs. c / c genotype ] rs6898743 , or , 0.63 , 95% ci , 0.410.98 , p=0.039 in dominant model [ c / c genotype vs. c / g genotype+g / g genotype ] and or , 0.73 , 95% ci , 0.540.99 , p=0.044 in log - additive model [ c / c genotype vs. c / g genotype vs. g / g gentoype ] ) . however , the other five snps did not show any statistical significant association with susceptibility of obesity . in haplotype analysis , we found two haplotypes ( gc and ta haplotype ) of one ld block in ghr gene , which consisted of rs6182 and rs6184 . but two haplotypes of ghr gene showed no statistical significant difference between the control group and the overweight / obese group ( gc haplotype , p=0.22 and ta haplotype , p=0.30 ) ( table 4 ) . in present study , seven snps in ghr gene were studied , however , only two intron snps of them were weakly associated with obesity ( rs4410646 and rs6898743 ) . the two snps were not similar in major / minor allele frequencies , however , both of them showed association to obesity in the dominant and log - additive models , and not in recessive model . among the other snps , four snps ( rs4130114 , rs4394131 , rs6182 , and rs6184 ) displayed similar major / minor allele frequencies , and all of them showed no association with obesity . association between rs4410646 snp of ghr gene and obesity was previously found in previous study ( mong et al . , 2010 ) . they reported that rs4410646 was significantly associated with the body composition ( p=0.0044 ) and blood pressure factor scores ( p=0.00017 ) ( mong et al . , 2010 ) . in addition , the association between rs6898743 snp of ghr gene and cancer were studied , and rs6898743 was associated with esophageal adenocarcinoma ( mcelholm et al . , 2010 ; ong et al . , 2014 ) . but there was no obesity study was found . in general , our result may show only weak association of ghr snps and obesity susceptibility . a variation in ghr gene was studied in various anthropometry ( glad et al . , 2015 ) , and relationship to central obesity was found . however , in their discussion , deficit in ghr does not always directly show gh deficiency , because they found responses to exogeneous gh in deficient ghr . they suggested the complex and continuous lifelong relationship among gh sensitivity , gh secretion , and other metabolic mechanisms in normal gh another example of complex gh sensitivity may be the white adipose tissue of ghr - null mice ( sackmann - sala et al . , 2014 ) . they presented the insulin , leptin , adiponectin , and protein changes in enhanced insulin sensitivity ( sackmann - sala et al . , 2014 ) . in addition , beattie et al . ( 2002 ) reported that decreased binding affinity may not reflect biological activities in the protein of living organism . to our knowledge , this is the first study to report the relationship between snps in the ghr gene and obesity by bmi . our findings support that ghr may play a role in pathophysiologic signaling of obesity or other related diseases . and further study in a large numbers with more anthropometry will be needed to verify our findings .