Rare variants and NHLBI traits in deeply phenotyped cohorts

描述(由申请人提供)：尽管全基因组关联研究(GWAS)已经确定了常见遗传变异与各种 复杂的疾病和风险因素，这些常见的变异通常只解释5%-10%的遗传贡献的表型差异。残留的遗传方差，即“缺失的遗传力”，可能有几个来源，部分归因于罕见的变异。作为在大量人群中进行关联研究的一种手段，从超过12,000名受试者的外显子组测序中获得的一组变异已被用于创建Illumina Infinium基因分型阵列ExomeChip，其特征是非同义、无义和剪接位点编码区罕见或罕见的变异。对这些罕见的编码区变体进行高质量分析所面临的分析挑战和样本量要求将需要新颖的组织结构和协作。在Gwas时代，最成功和最有成效的合作之一是Charge Consortium，它促进了在大型队列研究中对多种表型进行前瞻性计划的Gwas荟萃分析。最初的五个指控队列和四个合作队列拥有超过5万名参与者的联合称为ExomeChip的基因数据。虽然这些队列已经能够重新分配资金(经研究所批准)来生成罕见的变异数据，但现有来源中没有额外的资金可用于解决分析问题、协调努力或实施分析 ExomeChip数据。Charge合作利用了在这些队列研究上投入的数亿美元，为基因研究提供了独特的资源。表型工作组在选择和协调表型方面起到了带头作用。指控分析委员会成员不仅为分析方法提供建议，而且他们还解决分析问题，进行特定队列分析，并实施财团范围内前瞻性计划的荟萃分析。在Gwas时代，这些方法和这种组织结构使指控调查人员能够加快发现常见变异的遗传关联。利用从9个表型良好的队列中获得的ExomeChip编码区基因数据，主要目的是在多种族队列中发现心脏、肺和血液高优先表型的新候选基因和假定的功能变异。主要活动包括选择高度优先的表型，评估和传播分析方法，在每个群体内进行罕见变异分析，在族裔群体内部和群体之间对群体具体结果进行荟萃分析，分析家庭数据，通径分析，努力查明分析问题，公布研究结果，以及公开发布源代码和方法评估。我们预计每年至少完成3-4次主要分析。这些发现将被用来更好地理解可能确定治疗靶点的生物途径。

DESCRIPTION (provided by applicant): 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.