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遗传学特征的统计学显著关联， 尽管这些常见变异体是复杂疾病和风险因素的一部分，但它们通常只能解释表型变异的5-10%的遗传贡献。剩余的遗传变异，“缺失的遗传力”，可能有几个来源，部分归因于罕见的变异。作为在大群体中进行关联研究的一种手段，来自超过12，000名受试者的外显子组测序的一组变体已用于创建Illumina Infinium基因分型阵列ExomeChip，其特征在于非同义、无义和剪接位点编码区罕见或罕见变体。这些罕见的编码区变体的高质量分析的分析挑战和样本量要求将需要新的组织结构和合作。在GWAS时代，最成功和富有成效的合作之一是CHARGE联盟，它促进了大型队列研究中多种表型的前瞻性计划GWAS荟萃分析。最初的五个CHARGE队列和四个合作队列共同拥有超过5万名参与者的ExomeChip基因型数据。虽然这些队列能够重新分配资金（经研究所批准）以生成罕见变异数据，但现有来源中没有额外的资金可用于解决分析问题，协调工作或实施分析。 ExomeChip数据CHARGE合作项目利用了在这些队列研究中投入的数亿美元，代表了遗传研究的独特资源。表型特异性工作组牵头选择和协调表型。CHARGE分析委员会成员不仅为分析方法提供建议，而且还解决分析问题，进行特定队列分析，并实施联盟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CHARGE研究人员能够加速发现常见变异的遗传关联。使用来自9个表型良好的队列的可用ExomeChip编码区基因型数据，主要目的是发现多种族队列中高优先级心脏，肺和血液表型的新候选基因和推定的功能变体。主要活动包括选择高度优先的表型，评估和传播分析方法，在每个群组内进行罕见变异分析，在族群内和族群之间对特定群组的研究结果进行荟萃分析，分析家庭数据，途径分析，努力确定分析问题，发表研究结果，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项主要分析。这些发现将用于更好地理解可能识别治疗靶点的生物学途径。

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.