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AnalysisDescription
ARIC: inverse normal transformation of the beta values were performed fro all analyses; a linear mixed effect model was used with methylation chip number as a random effect and plate, chip row, and imputed cell counts of neutrophils, lymphocytes, monocytes and eosinophils as fixed effects to generate residuals of the inverse normal transformed beta values for association with CKD. To evaluate the association between DNA methylation and CKD, logistic regression was used with prevalent CKD as the outcome. For chromosome X, sex stratified analyses were conducted. FHS: the inverse-quantile normalized values of the methylation beta-values were residualized adjusting for laboratory batch, plate, column, row and Houseman imputed cell counts. For analysis of DNA methylation and CKD, regression models were counducted using normalized and residualized methyation beta-values as the independent variable and prevalent CKD as the dependent variable. Two models were assessed: model 1 adjusted for age, sex, diabetes status, and hypertension; model 2 adjusted for model 1 covariates and included albuminuria. To account for family relatedness, linear mixed models were used and GEE methods were employed. All models were adjusted for laboratory batch (fixed effect) and family structure (random effect). The data are in the public ftp site .
The SNP most associated with COPD in the 19q13.2 region was rs7937 (C>T, MAF(T)~0.54) with OR=1.37; this SNP was used to perform all subsequent analyses. meQTL analyses were preformed in RSdisc cohort using linear regression with rs7937 as independent variable and DNA methylation sites as dependent variable. two models were fit (covariates specified) with FDR < 0.05 as threshold for significance. Significant sites in Model 1 were tested for replication in RSrep with same models for discovery. significant CpG sites were also included in a third model with an rs7937 x smoking interaction term. Another model included COPD status for direction of effect between methylation and COPD. For blood eQTL analysis, a linear regression analysis with rs7937 as independent variable and genome-wide normalized gene expression as dependent variable. For significant probes, a second model additionally adjusted for DNA methylation levels. for lung eQTL analysis, a genome-wdie linear regression with SNP as independent variable and gene expression levels as dependent variable. The data are in the public ftp site .
ARIC: inverse normal transformation of the beta values were performed fro all analyses; a linear mixed effect model was used with methylation chip number as a random effect and plate, chip row, and imputed cell counts of neutrophils, lymphocytes, monocytes and eosinophils as fixed effects to generate residuals of the inverse normal transformed beta values for association with CKD. To evaluate the association between DNA methylation and iCKD, Cox proportional hazards regression with iCKD as the outcome was performed; in the primary analysis, covariates also included eGFR at Visit 2. For chromosome X, sex stratified analyses were conducted. FHS: the inverse-quantile normalized values of the methylation beta-values were residualized adjusting for laboratory batch, plate, column, row and Houseman imputed cell counts. For analysis of DNA methylation and iCKD, regression models were counducted using normalized and residualized methyation beta-values as the independent variable and prevalent CKD as the dependent variable; eGFR at baseline was also included as a covariate. Two models were assessed: model 1 adjusted for age, sex, diabetes status, and hypertension; model 2 adjusted for model 1 covariates and included albuminuria. To account for family relatedness, linear mixed models were used and GEE methods were employed. All models were adjusted for laboratory batch (fixed effect) and family structure (random effect). The data are in the public ftp site .
ARIC: inverse normal transformation of the beta values were performed fro all analyses; a linear mixed effect model was used with methylation chip number as a random effect and plate, chip row, and imputed cell counts of neutrophils, lymphocytes, monocytes and eosinophils as fixed effects to generate residuals of the inverse normal transformed beta values for association with CKD. To evaluate the association between DNA methylation and eGFR, linear regression with natural log-transformed eGFR as the outcome. For chromosome X, sex stratified analyses were conducted. FHS: the inverse-quantile normalized values of the methylation beta-values were residualized adjusting for laboratory batch, plate, column, row and Houseman imputed cell counts. For analysis of DNA methylation and eGFR, regression models were counducted using normalized and residualized methyation beta-values as the independent variable and natural log-transformed eGFR as the dependent variable. Two models were assessed: model 1 adjusted for age, sex, diabetes status, and hypertension; model 2 adjusted for model 1 covariates and included albuminuria. To account for family relatedness, linear mixed models were used and GEE methods were employed. All models were adjusted for laboratory batch (fixed effect) and family structure (random effect). The data are in the public ftp site .
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of brain volume in 6 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the HapMap CEU reference panel was performed obtain ~2.2M non-monomorphic autosomal SNPs for analysis. Each study performed GWAS tests using linear regression with the same basic additive genetic model of dosages of the risk allele with a 1-df trend test. Meta-analyses used inverse-variance weighting to combine GWAS summary statistics.
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of verbal declarative memory (combining multiple tests of word list recall or paragraph recall) in 19 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the HapMap CEU reference panel was performed obtain ~2.7M non-monomorphic autosomal SNPs for analysis. Meta-analyses with METAL using inverse-variance weighted meta-analysis to combine GWAS for the same memory test and effective sample size weighted meta-analysis to combine GWAS for nonidentical memory tests. For each SNP, the Z-statistic was weighted by the effective sample size. A combined estimate was obtained by summing the weighted Z statistics and dividing by the summed weights.
Genome-wide association analyses were conducted by each study, with BMI regressed on covariates to obtain residuals, and the residuals were inverse-normally transformed to obtain a standard normal distribution with mean zero and standard deviation one. For studies with unrelated subjects, each SNP was tested assuming an additive genetic model by regressing the transformed residuals on the number of copies of the SNP effect allele. The analyses were stratified by sex and case-control status (if needed). For studies with related subjects, family-based association tests were used, with sex-stratified, case-control stratified and combined analyses performed. Association results were combined across studies in sex-combined and sex-stratified samples using inverse-variance weighted fixed effect meta-analysis (METAL). BMI analyses used ~18M SNPs for BMI, with genome-wide significance attaining a P<5x10-8; variants with P<10-4 were carried to replication. Separate analyses were conducted for all subjects, men only, and women only.
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of heart rate variability from 12-lead ECG in 3 discovery cohorts of Hispanic/Latino ancestry. The phenotype is the standard deviation of normal-to-normal interbeat intervals (SDNN, ms), natural log-transformed. The genotyping was conducted with different platforms, so imputation to the 1000 Genomes Project reference panels was performed obtain ~17M non-monomorphic autosomal SNPs for analysis. Each study performed GWAS tests using mixed models accounting for familial relationships (HCHS/SOL using R/Bioconductor GENESIS) and linear regression (MESA using SNPTEST2, WHI using ProbAbel). Results were combined using summary statistics from each cohort employing inverse variance-weighted meta-analysis.
A maximum of 17,586,686 imputed SNPs were examined for associations with QT under an additive genetic model using linear regression (MESA, Starr County, and WHI) or linear mixed models (HCHS/SOL). Genome wide signifcant associations were defned as SNPs with P<5 10?8. Suggestive associations were those with P<5 10?6.
Meta-analyses were conducted using the METAL package with inverse variance weighting and fixed-effect models. Prior to meta-analysis, SNPs were filtered out with low minor allele frequency, MAF (<1%) and poor imputation quality (proper_info<0.4 for IMPUTE and rsq_hat<0.3); genomic control correction was applied when ?GC was >1.0. Quantile-quantile (Q-Q) plots of observed vs. expected -log10 (p-value) were used to examine the genome-wide distribution of p-values for signs of excessive false-positive results. Manhattan plots were generated to report genomewide p-values, regional plots for genomic regions within 100 Kb of top hits, and forest plots for meta-analyses and study-specific results of the most significant SNP associations. A threshold of p < 5 10?8 was pre-specified as being genome-wide significant, while a threshold of p < 2.3 10?6 was used to select SNPs for a replication study (suggestive genome-wide significant, sGWS).
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of heart rate variability from 12-lead ECG in 3 discovery cohorts of Hispanic/Latino ancestry. The phenotype is the root mean squared difference in successive, normal-to-normal interbeat intervals (RMSSD, ms), natural log-transformed. The genotyping was conducted with different platforms, so imputation to the 1000 Genomes Project reference panels was performed obtain ~17M non-monomorphic autosomal SNPs for analysis. Each study performed GWAS tests using mixed models accounting for familial relationships (HCHS/SOL using R/Bioconductor GENESIS) and linear regression (MESA using SNPTEST2, WHI using ProbAbel). Results were combined using summary statistics from each cohort employing inverse variance-weighted meta-analysis.
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. CHARGE has incorporated many genomic and phenotypic data from other cohorts, depending on the phenotype. The basic design was estimation of single variant tests on incident ischemic stroke in 16 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the 1000 Genomes Project reference panel to obtain ~14M non-monomorphic autosomal SNPs for analysis. Within each study, genome-wide multivariable Cox regression was used to test the association of SNPs with incident ischemic stroke under an additive model, with covariates. Meta-analysis of study-specific association statistics used inverse variance weighted approach using METAL
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of SNP effect on paragraph recall in 8 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the HapMap CEU reference panel was performed obtain ~2.7M non-monomorphic autosomal SNPs for analysis. Meta-analyses with METAL using inverse-variance weighted meta-analysis to combine GWAS for the paragraph recall memory test.
Genome-wide association analyses were conducted by each study, with WHR regressed on covariates to obtain residuals, and the residuals were inverse-normally transformed to obtain a standard normal distribution with mean zero and standard deviation one. For studies with unrelated subjects, each SNP was tested assuming an additive genetic model by regressing the transformed residuals on the number of copies of the SNP effect allele. The analyses were stratified by sex and case-control status (if needed). For studies with related subjects, family-based association tests were used, with sex-stratified, case-control stratified and combined analyses performed. Association results were combined across studies in sex-combined and sex-stratified samples using inverse-variance weighted fixed effect meta-analysis (METAL). Analyses used ~20.5M SNPs for WHRadjBMI with genome-wide significance attaining a P<5x10-8; variants with P<10-4 were carried to replication. Separate analyses were conducted for all subjects, men only, and women only.
Stage 1 discovery samples came from 17 T2D studies (FIND and WFSM were combined into a single analysis group), with association results combined by a fixed effect model with inverse variance weighted method (METAL). Genomic control correction was applied to each study prior to meta-analysis. Results from SNPs genotyped in <10,000 samples and those with minor allele frequency difference >0.3 across studies were excluded, resulting in ~2.5M SNPs for analysis. Following discovery, replications samples were used and combined in meta-analysis, with a final effect esimation performed with all samples. Genome-wide significance was based upon P<5x10-8 criterion from meta-analysis of the combined discovery and replication samples. Logistic regression was used for samples from unrelated individuals; GEE or SOLAR were used for samles of related individuals.
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of intracranial volume in 5 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the HapMap CEU reference panel was performed obtain ~2.2M non-monomorphic autosomal SNPs for analysis. Each study performed GWAS tests using linear regression with the same basic additive genetic model of dosages of the risk allele with a 1-df trend test. Meta-analyses used inverse-variance weighting to combine GWAS summary statistics.
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. CHARGE has incorporated many genomic and phenotypic data from other cohorts, depending on the phenotype. The basic design was estimation of single variant tests on incident ischemic stroke in 4 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation was made to the ~2.5M non-monomorphic autosomal SNPs in the HapMap CEU panel. Within each study, a Cox proportional hazards model was used to evaluate time to first ischemic stroke; each study fit an additive genetic model with genotype dosage to total stroke; summary statistics were combined using inverse-variance weighting fixed-effects meta-analysis; SNPs with P < 5x10-8 was considered genome-wide significance
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. The basic design was estimation of hippocampal volume in 10 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the HapMap CEU reference panel was performed obtain ~2.5M non-monomorphic autosomal SNPs for analysis, as long as the minor allele frequency was > 1% and the SNP was reported in at least 2 studies. Meta-analyses with METAL using inverse-variance weighted meta-analysis to combine GWAS for the same memory test and effective sample size weighted meta-analysis to combine GWAS for nonidentical memory tests. For each SNP, the Z-statistic was weighted by the effective sample size. A combined estimate was obtained by summing the weighted Z statistics and dividing by the summed weights.
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. CHARGE has incorporated many genomic and phenotypic data from other cohorts, depending on the phenotype. The basic design was estimation of single variant tests on cerebral white matter hyperintensities in 20 discovery cohorts of European ancestry. The genotyping was conducted with different platforms, so imputation to the 1000 Genomes Project reference panel to obtain ~14M non-monomorphic autosomal SNPs for analysis. Within each study, a linear regression model was used with covariates to estimate the effect of each SNP on WMH assuming an additive genetic effect, with phenotype expressed as ln(WMH+1). Analysis was conducted separately within each study cohort according to a pre-specified plan; cohort-specific results were combined with fixed-effects meta-analysis using a z-score method with a genomic control parameter to remove residual population stratification.
The analysis of WHRadjBMI in African ancestry male participants utilized GWAS data imputed to 1000 Genomes Project Phase 1 v3 data. Discovery was based upon 8 studies. Genotyping in each study was conducted with Affymetrix or Illumina arrays, with quality control and imputation performed by each study (~20.5M variants). WHR residuals were normalized and were included in inverse-variance weighted fixed effect meta-analysis. A novel locus was declared if teh most significant SNP was >500 kb from an established lead SNP in a previous study. A locus was named by the closest gene to the most associated variant.
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