Although genome-wide association studies (GWAS) have identified statistically significant associations of common genetic variants with a variety of complex diseases and risk factors, these common variants typically explain only 5-10% of the genetic contribution to the phenotypic variance. The residual genetic variance, """"""""the missing heritability,"""""""" may have several sources and is in part attributed to rare variants. As a means of performing association studies in large populations, a panel of variants derived from the exome sequencing of over 12,000 subjects has been used to create an Illumina Infinium genotyping array, the ExomeChip, which features non-synonymous, non-sense, and splice-site coding-region rare or infrequent variants. The analytic challenges and the sample size requirements for the high-quality analysis of these rare coding-region variants will require novel organizational structures and collaborations. In the GWAS era, one of the most successful and productive collaborations has been the CHARGE Consortium, which facilitated prospectively planned GWAS meta-analyses of multiple phenotypes among large cohort studies. The original five CHARGE cohorts and four collaborating cohorts have jointly-called ExomeChip genotype data on more than 50,000 participants. While the cohorts have been able to reallocate funding (with Institute approval) to generate the rare variant data, there are no additional funds available in existing sources to address analytic issues, to coordinate efforts, or to implement the analysis of the ExomeChip data. The CHARGE collaboration, which takes advantage of the hundreds of millions of dollars invested in these cohort studies, represents a unique resource for genetic studies. The phenotype-specific Working Groups take the lead in choosing and harmonizing phenotypes. The CHARGE Analysis Committee members not only provide recommendations for analytic methods, but they also solve analytic problems, conduct cohort-specific analysis, and implement consortium- wide prospectively planned meta-analyses. In the GWAS era, these methods and this organizational structure enabled the CHARGE investigators to accelerate the discovery of genetic association for common variants. Using the available ExomeChip coding-region genotype data from 9 well-phenotyped cohorts, the primary aim is to discover novel candidate genes and putative functional variants for high-priority heart, lung and blood phenotypes in multi-ethnic cohorts. The main activities include the selection of high-priority phenotypes, the appraisal and dissemination of analytic methods, the conduct of rare-variant analyses within each cohort, the meta-analysis of cohort-specific findings, both within and between ethnic groups, analysis of family data, pathway analyses, efforts to identify analytic problems, publication of findings, and the public release of source code and methods appraisals. We expect to complete at least 3-4 major analyses each year. The findings will be used to better understand biological pathways that may identify therapeutic targets.
The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium has helped to accelerate the discovery of genetic variants associated with common diseases and risk factors. In the proposed application from the original 5 CHARGE cohorts and 4 collaborating cohorts-all with jointly- called ExomeChip genotype data, the analysis of rare and uncommon variants will identify new genetic loci associated with a variety of heart, lung, and blood-related phenotypes. The main contribution of these genetic studies will be the discovery of new biology, which may lead to future treatments. ecarlv5.doc
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