肥胖的遗传流行病学研究以及双变量关联定位的理论探讨和应用
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摘要
最近的研究数据表明,白种人群的肥胖诊断标准可能不适用于亚洲人。虽然在白种人群中已经开展了许多关于肥胖相关表型的全基因组关联研究,但是在中国人群中至今没有成功运用全基因组关联研究定位肥胖基因的报道。在统计遗传学领域,已有大量关于单个性状的关联分析方法研究,但是对于两个相关性状的关联分析方法研究,还少有开展。
本文首先使用全身脂肪含量(PBF)作为金标准,在1109个男性个体和879个女性个体中采用受试者操作特征曲线(ROC)分析估计了三个简易测量指标,即体重指数(BMI)、腰围(WC)、腰髋比(WHR)预测肥胖的能力。分析表明,BMI、WC、WHR都与PBF高度相关,相关系数为0.45-0.75。真阳性率在男性中是82.4%到94.1%,在女性中是68.8%到86.3%。真阴性率在男性中是64.1%到84.7%,在女性中是56.9%到79.0%。BMI和WC两个指标在男性和女性不同年龄组中曲线下的面积比较高(0.76-0.92),而WHR这个指标稍微低-些(0.74-0.88)。研究表明,BMI和WC是两个很好的预测肥胖的指标,WHR次之。该研究同时发现,用BMI在中国人群中预测肥胖的标准要明显低于白种人群。
接下来我们在597个中国北方人群中运用Affymetrix 500k芯片开展了全基因组关联研究定位影响BMI变异的新基因,此研究进一步在2955个中国南方人中进行了重复验证。通过一系列的质量控制程序后,281,533个单核苷酸多态性(SNP)可以进行关联分析。经过FDR方法进行多重校正后,8个SNP仍然与BMI关联(FDRq=0.033-0.048)。最显著的SNP是rs4633,位于catechol-0-methyltransferase (COMT)基因的外显子上(p=5.45×10-7,FDRq=0.033)。另外,位于EIF2AK4(eukaryotic translation initiation factor 2 alpha kinase 4)基因内的两个临近的SNP rs4432245和rs711906也与BMI显著关联(?)值分别为4.38×10-6，6.39×10-6,FDRq=0.048)。在南方人群的验证研究中,这两个位点与BMI的关联获得了证实(p=0.03和0.01)。
除此之外,我们通过大量的计算机模拟,运用广义估计方程2(GEE2)对两个相关性状的关联定位方法进行研究。通过与单个性状关联研究的功效比较,发现双变量模型比单变量模型统计功效更高。而且双变量关联分析的假阳性率与单变量分析相比没有差别,都在设定的显著性水平0.05左右。所以我们提出的双变量分析模型是合理的。进一步对肥胖和骨质疏松两个相关性状的实验数据进行的双变量关联分析验证了该方法的优越性。
本研究的创新性在于：1)第一次在中国南方人群中对衡量全身肥胖的简易指标进行评估；2)第一次在中国人群中运用全基因组关联分析方法定位影响BMI变异的新基因；3)第一次尝试运用广义估计方程2拟合基于群体设计的两个数量性状的关联定位研究。
Recent data suggest that current obesity diagnostic criterion for Caucasians may not be appropriate for Asian populations. Genome wide association studies for obesity have been successfully conducted in Caucassians but none was done in Chinese. In the area of statistical genetics, univariate association analyses have been widely investigated, however, multivariate association analyses for population design have not received sufficient attention.
Here, in this study, firstly, we utilized receiver operating characteristic analyses to evaluate body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) in predicting obestiy using percentage body fat (PBF) as gold standard in 1109 males and 879 females. BMI, WC and WHR showed strong positive correlation with PBF (r=0.47-0.75) in both males and females within both age groups. True-positive rates ranged from 82.4% to 94.1% and 68.8% to 86.3% in males and females, respectively. True-negative rates ranged from 64.1% to 84.7% and from 56.9% to 79.0%, respectively. The areas under the curves (AUCs) for WC and BMI were high (0.76-0.92) in both sexes and divided age groups (20-30-y and 31-45-y), and those for WHR were a little lower (0.74-0.88). Our study suggested that BMI and WC are two important predictors for obesity in Chinese, and WHR is an alternative. We also suggested an obviously lower cut of BMI to define obestity in young Chinese compared with that in Caucasians.
Than we conducted a genome wide association study for BMI using Affymetrix 500k chip in 597 northern Chinese and a replication study in 2955 southeren Chinese. After quality control,281,533 single nucleotide polymorphisms (SNPs) were included in the association analysis. Eight SNPs were significantly associated with BMI variation after false discovery rate (FDR) correction (FDR q=0.033-0.048). Among these eight SNPs, rs4633 which is located at the exon of catechol-O-methyltransferase (COMT) gene is the most significant SNP associated with BMI (FDR q=0.033). Two additional contiguous SNPs (rs4432245 & rs711906) in the eukaryotic translation initiation factor 2 alpha kinase 4 (EIF2AK4) gene were significantly associated with BMI (FDR q=0.048). In the follow-up replication study (southern Chinese sample) containing 2955 Chinese Han subjects, we confirmed the associations between BMI and rs4432245, rs711906 in the EIF2AKE gene (p=0.03 & 0.01, respectively).
Besides, we performed extensive simulation studies and introduced generalized estimating equation 2 (GEE2) to association mapping for two traits. Through power comparison with univariate analyses, we found that bivariate models can be more powerful than univariate model. The introduced method leads to false-positive rates around 0.05 at the nominal significant level of 0.05, and so the introduced method is reasonably robust. Our real data analyses for obesity and osteoporosis confirmed these findings.
The present study is the first attempt to evaluate the diagnostic indices for obesity in southern Chinese, the first genome wide association study for Chinese and identified a new gene for BMI. It is also the first effort to introduce a method applied to association mapping for two traits for population-based design.
本文首先使用全身脂肪含量(PBF)作为金标准,在1109个男性个体和879个女性个体中采用受试者操作特征曲线(ROC)分析估计了三个简易测量指标,即体重指数(BMI)、腰围(WC)、腰髋比(WHR)预测肥胖的能力。分析表明,BMI、WC、WHR都与PBF高度相关,相关系数为0.45-0.75。真阳性率在男性中是82.4%到94.1%,在女性中是68.8%到86.3%。真阴性率在男性中是64.1%到84.7%,在女性中是56.9%到79.0%。BMI和WC两个指标在男性和女性不同年龄组中曲线下的面积比较高(0.76-0.92),而WHR这个指标稍微低-些(0.74-0.88)。研究表明,BMI和WC是两个很好的预测肥胖的指标,WHR次之。该研究同时发现,用BMI在中国人群中预测肥胖的标准要明显低于白种人群。
接下来我们在597个中国北方人群中运用Affymetrix 500k芯片开展了全基因组关联研究定位影响BMI变异的新基因,此研究进一步在2955个中国南方人中进行了重复验证。通过一系列的质量控制程序后,281,533个单核苷酸多态性(SNP)可以进行关联分析。经过FDR方法进行多重校正后,8个SNP仍然与BMI关联(FDRq=0.033-0.048)。最显著的SNP是rs4633,位于catechol-0-methyltransferase (COMT)基因的外显子上(p=5.45×10-7,FDRq=0.033)。另外,位于EIF2AK4(eukaryotic translation initiation factor 2 alpha kinase 4)基因内的两个临近的SNP rs4432245和rs711906也与BMI显著关联(?)值分别为4.38×10-6，6.39×10-6,FDRq=0.048)。在南方人群的验证研究中,这两个位点与BMI的关联获得了证实(p=0.03和0.01)。
除此之外,我们通过大量的计算机模拟,运用广义估计方程2(GEE2)对两个相关性状的关联定位方法进行研究。通过与单个性状关联研究的功效比较,发现双变量模型比单变量模型统计功效更高。而且双变量关联分析的假阳性率与单变量分析相比没有差别,都在设定的显著性水平0.05左右。所以我们提出的双变量分析模型是合理的。进一步对肥胖和骨质疏松两个相关性状的实验数据进行的双变量关联分析验证了该方法的优越性。
本研究的创新性在于：1)第一次在中国南方人群中对衡量全身肥胖的简易指标进行评估；2)第一次在中国人群中运用全基因组关联分析方法定位影响BMI变异的新基因；3)第一次尝试运用广义估计方程2拟合基于群体设计的两个数量性状的关联定位研究。
Recent data suggest that current obesity diagnostic criterion for Caucasians may not be appropriate for Asian populations. Genome wide association studies for obesity have been successfully conducted in Caucassians but none was done in Chinese. In the area of statistical genetics, univariate association analyses have been widely investigated, however, multivariate association analyses for population design have not received sufficient attention.
Here, in this study, firstly, we utilized receiver operating characteristic analyses to evaluate body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) in predicting obestiy using percentage body fat (PBF) as gold standard in 1109 males and 879 females. BMI, WC and WHR showed strong positive correlation with PBF (r=0.47-0.75) in both males and females within both age groups. True-positive rates ranged from 82.4% to 94.1% and 68.8% to 86.3% in males and females, respectively. True-negative rates ranged from 64.1% to 84.7% and from 56.9% to 79.0%, respectively. The areas under the curves (AUCs) for WC and BMI were high (0.76-0.92) in both sexes and divided age groups (20-30-y and 31-45-y), and those for WHR were a little lower (0.74-0.88). Our study suggested that BMI and WC are two important predictors for obesity in Chinese, and WHR is an alternative. We also suggested an obviously lower cut of BMI to define obestity in young Chinese compared with that in Caucasians.
Than we conducted a genome wide association study for BMI using Affymetrix 500k chip in 597 northern Chinese and a replication study in 2955 southeren Chinese. After quality control,281,533 single nucleotide polymorphisms (SNPs) were included in the association analysis. Eight SNPs were significantly associated with BMI variation after false discovery rate (FDR) correction (FDR q=0.033-0.048). Among these eight SNPs, rs4633 which is located at the exon of catechol-O-methyltransferase (COMT) gene is the most significant SNP associated with BMI (FDR q=0.033). Two additional contiguous SNPs (rs4432245 & rs711906) in the eukaryotic translation initiation factor 2 alpha kinase 4 (EIF2AK4) gene were significantly associated with BMI (FDR q=0.048). In the follow-up replication study (southern Chinese sample) containing 2955 Chinese Han subjects, we confirmed the associations between BMI and rs4432245, rs711906 in the EIF2AKE gene (p=0.03 & 0.01, respectively).
Besides, we performed extensive simulation studies and introduced generalized estimating equation 2 (GEE2) to association mapping for two traits. Through power comparison with univariate analyses, we found that bivariate models can be more powerful than univariate model. The introduced method leads to false-positive rates around 0.05 at the nominal significant level of 0.05, and so the introduced method is reasonably robust. Our real data analyses for obesity and osteoporosis confirmed these findings.
The present study is the first attempt to evaluate the diagnostic indices for obesity in southern Chinese, the first genome wide association study for Chinese and identified a new gene for BMI. It is also the first effort to introduce a method applied to association mapping for two traits for population-based design.
引文
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