Background Alcohol dependence （AD） is a complex disorder characterized by impaired control over drinking. It is determined by both genetic and environmental factors. The recent approach of genome-wide association st...Background Alcohol dependence （AD） is a complex disorder characterized by impaired control over drinking. It is determined by both genetic and environmental factors. The recent approach of genome-wide association study （GWAS） is a powerful tool for identifying complex disease-associated susceptibility alleles, however, a few GWASs have been conducted for AD, and their results are largely inconsistent. The present study aimed to screen the loci associated with alcohol-related phenotypes using GWAS technology. Methods A genome-wide association study with the behavior of regular alcohol drinking and alcohol consumption was performed to identify susceptibility genes associated with AD, using the Affymetrix 500K SNP array in an initial sample consisting of 904 unrelated Caucasian subjects. Then, the initial results in GWAS were replicated in three independent samples： 1972 Caucasians in 593 nuclear families, 761 unrelated Caucasian subjects, and 2955 unrelated Chinese Hans. Results Several genes were associated with the alcohol-related phenotypes at the genome-wide significance level, with the ankyrin repeat domain 7 gene （ANKRDT） showing the strongest statistical evidence for regular alcohol drinking and suggestive statistical evidence for alcohol consumption. In addition, certain haplotypes within the ANKRD7 and cytokine-likel （CYTL 1） genes were significantly associated with regular drinking behavior, such as one ANKRD7 block composed of the SNPs rs6466686-rs4295599-rs12531066 （P = 6.51×10^-8）. The association of alcohol consumption was successfully replicated with rs4295599 in ANKRD7 gene in independent Caucasian nuclear families and independent unrelated Chinese Hans, and with rs16836497 in CYTL1 gene in independent unrelated Caucasians. Meta-analyses based on both the GWAS and replication samples further supported the observed significant associations between the ANKRDTor CYTL1 gene and alcohol consumption. Conclusion The evidence suggests that ANKRD7 and CYTL 1 genes may play an important role in the variance in AD risk.展开更多
To quantify the genetic correlations between total body fat mass （TBFM） and femoral neck geometric parameters （FNGPs） and, if pos- sible, to detect the specific genomic regions shared by them, bivariate genetic an...To quantify the genetic correlations between total body fat mass （TBFM） and femoral neck geometric parameters （FNGPs） and, if pos- sible, 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 link- ages 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.展开更多
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 n...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 plei- otropic 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 ana- lytical 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 pre-set nominal significance level. Our real data analyses attested to the usefulness of the method.展开更多
基金This work was supported by grants from NIH （No. R21 AA015973, No. R21 AGO27110, No. R01 AR050496-01, No. R01 AG026564, and No. P50 AR055081）, National Science Foundation of China （No. 30771222 and No. 30731160618）, Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China （（2010） 609） and Innovative Program of Hunan Province （No. 2011TF1004）.
文摘Background Alcohol dependence （AD） is a complex disorder characterized by impaired control over drinking. It is determined by both genetic and environmental factors. The recent approach of genome-wide association study （GWAS） is a powerful tool for identifying complex disease-associated susceptibility alleles, however, a few GWASs have been conducted for AD, and their results are largely inconsistent. The present study aimed to screen the loci associated with alcohol-related phenotypes using GWAS technology. Methods A genome-wide association study with the behavior of regular alcohol drinking and alcohol consumption was performed to identify susceptibility genes associated with AD, using the Affymetrix 500K SNP array in an initial sample consisting of 904 unrelated Caucasian subjects. Then, the initial results in GWAS were replicated in three independent samples： 1972 Caucasians in 593 nuclear families, 761 unrelated Caucasian subjects, and 2955 unrelated Chinese Hans. Results Several genes were associated with the alcohol-related phenotypes at the genome-wide significance level, with the ankyrin repeat domain 7 gene （ANKRDT） showing the strongest statistical evidence for regular alcohol drinking and suggestive statistical evidence for alcohol consumption. In addition, certain haplotypes within the ANKRD7 and cytokine-likel （CYTL 1） genes were significantly associated with regular drinking behavior, such as one ANKRD7 block composed of the SNPs rs6466686-rs4295599-rs12531066 （P = 6.51×10^-8）. The association of alcohol consumption was successfully replicated with rs4295599 in ANKRD7 gene in independent Caucasian nuclear families and independent unrelated Chinese Hans, and with rs16836497 in CYTL1 gene in independent unrelated Caucasians. Meta-analyses based on both the GWAS and replication samples further supported the observed significant associations between the ANKRDTor CYTL1 gene and alcohol consumption. Conclusion The evidence suggests that ANKRD7 and CYTL 1 genes may play an important role in the variance in AD risk.
基金supported by grants from NIH in USA (No. K01 AR02170-01, R01 AR45349-01, R01 GM60402-01 A1, R01 AG026564-01A2, and R21 AG027110-01A1)the Natural Science Foundation o China (NSFC) (No. 30600364)The genotyping experiment was performed by Marshfield Center for Medical Genetics and supported by NHLB Mammalian Genotyping Service (Contract No. HV48141)
文摘To quantify the genetic correlations between total body fat mass （TBFM） and femoral neck geometric parameters （FNGPs） and, if pos- sible, 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 link- ages 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.
基金supported by grants from the Natural Science Foundation of China (No.30600364,30470534,and 30230210)the NSFC-Canadian Institutes of Health Research (CIHR) Joint Health Research Initia-tive Proposal (No.30811120436)+3 种基金the NSFC/RGC Joint Research Scheme (No.30731160618)Shanghai Leading Academic Discipline Project (No.S30501)supported by grants from NIH (No.P50AR055081,R01AG026564,R01AR050496,and R01AR057049)the Dickson/Missouri endowment
文摘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 plei- otropic 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 ana- lytical 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 pre-set nominal significance level. Our real data analyses attested to the usefulness of the method.
