Leveraging Family Data to Identify Genetic Variants for Sleep Apnea

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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.