Objective Obesity is becoming a worldwide health problem. The genome wide association(GWA) study particularly for body mass index(BMI) has not been successfully conducted in the Chinese. In order to identify novel genes for BMI variation in the Chinese, an initial GWA study and a follow up replication study were performed. Methods Affymetrix 500K SNPs were genotyped for initial GWA of 597 Northern Chinese. After quality control, 281 533 SNPs were included in the association analysis. Three SNPs were genotyped in a Southern Chinese replication sample containing 2 955 Chinese Han subjects. Association analyses were performed by Plink software. Results Eight SNPs were significantly associated with BMI variation after false discovery rate(FDR) correction(P=5.45×10-7-7.26×10-6, FDR q=0.033-0.048). Two adjacent SNPs(rs4432245 & rs711906) in the eukaryotic translation initiation factor 2 alpha kinase 4(EIF2AK4) gene were significantly associated with BMI(P=6.38×10-6 & 4.39×10-6, FDR q=0.048). In the follow-up replication study, we confirmed the associations between BMI and rs4432245, rs711906 in the EIF2AKE gene(P=0.03 & 0.01, respectively). Conclusion Our study suggests novel mechanisms for BMI, where EIF2AK4 has exerted a profound effect on the synthesis and storage of triglycerides and may impact on overall energy homeostasis associated with obesity. The minor allele frequencies for the two SNPs in the EIF2AK4 gene have marked ethnic differences between Caucasians and the Chinese. The association of the EIF2AK4 gene with BMI is suggested to be ‘ethnic specific' in the Chinese.
YANG FangCHEN Xiang DingTAN Li JunSHEN JieLI Ding YouZHANG FangSHA Bao YongDENG Hong Wen
To quantify the genetic correlations between total body fat mass(TBFM) and femoral neck geometric parameters(FNGPs) and, if possible, to detect the specific genomic regions shared by them, bivariate genetic analysis and bivariate whole-genome linkage scan were carried out in a large Caucasian population.All the phenotypes studied were significantly controlled by genetic factors(P < 0.001) with the heritabilities ranging from 0.45 to 0.68.Significantly genetic correlations were found between TBFM and CSA(cross-section area), W(sub-periosteal diameter), Z(section modulus) and CT(cortical thickness) except between TBFM and BR(buckling ratio).The peak bivariate LOD scores were 3.23(20q12), 2.47(20p11), 3.19(6q27), 1.68(20p12), and 2.47(7q11) for the five pairs of TBFM and BR, CSA, CT, W, and Z in the entire sample, respectively.Gender-specific bivariate linkage evidences were also found for the five pairs.6p25 had complete pleiotropic effects on the variations of TBFM & Z in the female sub-population, and 6q27 and 17q11 had coincident linkages for TBFM & CSA and TBFM & Z in the entire population.We identified moderate genetic correlations and several shared genomic regions between TBFM and FNGPs in a large Caucasian population.
Shufeng LeiFeiyan DengPeng XiaoKai ZhongHongyi DengRobert R. ReckerHongwen Deng
Quantitative traits often underlie risk for complex diseases.Many studies collect multiple correlated quantitative phenotypes and perform univariate analyses on each of them respectively.However,this strategy may not be powerful and has limitations to detect pleiotropic genes that may underlie correlated quantitative traits.In addition,testing multiple traits individually will exacerbate perplexing problem of multiple testing.In this study,generalized estimating equation 2(GEE2) is applied to association mapping of two correlated quantitative traits.We suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits.In that region,multiple SNPs are genotyped.Genotypes of these SNPs and the two quantitative traits affected by a causal SNP were simulated under various parameter values:residual correlation coefficient between two traits,causal SNP heritability,minor allele frequency of the causal SNP,extent of linkage disequilibrium with the causal SNP,and the test sample size.By power analytical analyses,it is showed that the bivariate method is generally more powerful than the univariate method.This method is robust and yields false-positive rates close to the preset nominal significance level.Our real data analyses attested to the usefulness of the method.
