Leveraging Family Data to Identify Genetic Variants for Sleep Apnea

描述（由申请人提供）：利用家族数据识别睡眠呼吸暂停的遗传变异项目摘要（摘要）阻塞性睡眠呼吸暂停（OSA）影响超过10%的人群，特别是西班牙裔和非洲裔美国人，并与严重的心脏代谢发病率相关。通过克利夫兰家族研究（CFS），一项通过OSA先证者确定的严格表型家族的遗传流行病学研究，我们已经确定OSA具有强大的遗传基础，并确定了有希望的连锁领域，以告知遗传关联分析和测序工作。为了实现本RFA的目标，我们打算有效地利用CFS的现有数据以及主要NHLBI队列（ARIC、CHS和Framingham Heart的睡眠心脏健康研究队列;梅萨; MrOS-睡眠、斯塔尔县健康研究和西班牙裔社区健康研究）的新可用基因型和睡眠表型数据。我们的主要表型是连续性特征，即呼吸暂停低通气指数（AHI），它来自于我们的中央睡眠阅读中心严格分析和存档的睡眠研究。总的来说，样本包括1200名CFS家族成员和来自NHLBI队列的约11，000名成员，包括OSA高风险和可能携带罕见变异的混合人群。利用家族设计的力量，结合重点测序工作和现代统计工具，包括我们领先的遗传统计学家，遗传学家和睡眠流行病学家研究团队的方法学进步，我们建议使用旨在识别与OSA相关的常见以及低频和罕见变异的互补策略。我们将：1）利用来自与AHI连锁的区域的信息来优先考虑基因以进行进一步测试和靶向外显子测序;以及2）在从我们最具信息性的家族和极端OSA表型中选择的个体中进行全外显子组测序，该样本可能富含罕见变体。我们的假设和目标将使用先进的基于基因的分析（SNP集内核关联测试）和荟萃分析，在一个多阶段的设计，包括发现和验证阶段。连锁信息将被纳入关联分析，以提高真正发现的机会。加权方法和生物信息学方法（使用基因网络）将用于提高检测罕见变异的分析能力。分析将控制协变量，包括使用主成分的人群分层、当地血统和家族相关性。将进行多重比较调整，以控制总体I类错误。此外，我们将开发新的统计方法来应对本研究以及本RFA支持的其他研究的挑战，包括当多种罕见和常见变异导致表型变异时分析家族和无关样本的方法。RFA HL-07-012提供了一个关键的机会，可以利用CFS的家族数据、大型队列的新数据、统计学进展和先进的测序技术，共同满足发现和复制OSA功能重要变体的主要需求，这些变体可能作为这种严重健康状况的新疗法的靶点。

DESCRIPTION (provided by applicant): Leveraging Family Data to Identify Genetic Variants for Sleep Apnea PROJECT SUMMARY (ABSTRACT) Obstructive Sleep Apnea (OSA) affects more than 10% of the population, especially Hispanic- and African- Americans, and is associated with profound cardio-metabolic morbidity. Through the Cleveland Family Study (CFS), a genetic epidemiological study of rigorously phenotyped families ascertained through probands with OSA, we have established that OSA has a strong genetic basis and have identified promising areas of linkage to inform genetic association analysis and sequencing efforts. To meet the objectives of this RFA, we intend to efficiently utilize existing data from th CFS as well as newly available genotype and sleep phenotype data from major NHLBI cohorts (Sleep Heart Health Study cohorts of ARIC, CHS and Framingham Heart; MESA; MrOS- Sleep, Starr County Health Study, and the Hispanic Community Health Study). Our primary phenotype is the continuous trait, the apnea hypopnea index (AHI), derived from sleep studies rigorously analyzed and archived at our central Sleep Reading Center. In toto, the sample includes 1200 CFS family members and ~11,000 members from NHLBI cohorts, including admixed populations at high risk for OSA and likely to harbor rare variants. Capitalizing on the power of family designs combined with focused sequencing efforts and modern statistical tools, including methodological advances by our research team of leading genetic statisticians, geneticists and sleep epidemiologists, we propose to use complementary strategies designed to identify common as well as low frequency and rare variants associated with OSA. We will: 1) leverage information from areas of linkage to the AHI to prioritize genes for further testing and for targeted exon sequencing; and 2) perform whole exome sequencing in individuals selected from our most informative families and extreme OSA phenotypes, a sample likely enriched with rare variants. Our hypotheses and aims will be addressed using advanced gene-based analysis (SNP-set kernel association tests) and meta-analysis in a multi-stage design consisting of discovery and validation phases. Linkage information will be incorporated into association analysis to improve the chance of true discovery. Weighted methods and bioinformatics approaches (using gene networks) will be used to increase analysis power for detecting rare variants. Analyses will control for covariates including population stratification using principal components, local ancestry, and family relatedness. Multiple comparison adjustments will be carried out to control for overall type I error. Additionally, we will develop novel statistical approaches to meet the challenges of this study as well as other studies supported by this RFA, including methods for analyzing family and unrelated samples when multiple rare and common variants contribute to phenotypic variation. RFA HL-07-012 provides a critical opportunity to leverage the family data from the CFS, newly available data from large cohorts, statistical advances and advanced sequencing technology to together fill a major need to discover and replicate functionally important variants for OSA that may serve as targets for novel therapies for this serious health condition.