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.

Public Health Relevance

Obstructive Sleep Apnea (OSA) is a common health condition, conferring a large health burden, especially in minority populations. Development of targeted treatment strategies has been limited by an incomplete understanding of its molecular basis of this condition. This project will identify genes that increase susceptibility to OSA in a multi-ethnic sample, thus potentially revolutionizing the scientific understanding of the molecular pathways leading to it and its co-morbidities, such as heart disease and diabetes.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZHL1-CSR-H (F2))
Program Officer
Gan, Weiniu
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brigham and Women's Hospital
United States
Zip Code
Liu, Zhonghua; Lin, Xihong (2017) Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics :
Purcell, S M; Manoach, D S; Demanuele, C et al. (2017) Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat Commun 8:15930
Javaheri, Sogol; Sharma, Ravi K; Bluemke, David A et al. (2017) Association between central sleep apnea and left ventricular structure: the Multi-Ethnic Study of Atherosclerosis. J Sleep Res 26:477-480
Li, Xiaoyin; Redline, Susan; Zhang, Xiang et al. (2017) Height associated variants demonstrate assortative mating in human populations. Sci Rep 7:15689
Chen, Han; Cade, Brian E; Gleason, Kevin J et al. (2017) Multi-ethnic Meta-analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea Related Quantitative Trait Locus in Men. Am J Respir Cell Mol Biol :
Barfield, Richard; Shen, Jincheng; Just, Allan C et al. (2017) Testing for the indirect effect under the null for genome-wide mediation analyses. Genet Epidemiol 41:824-833
Sun, Ryan; Carroll, Raymond J; Christiani, David C et al. (2017) Testing for gene-environment interaction under exposure misspecification. Biometrics :
He, Karen Y; Wang, Heming; Cade, Brian E et al. (2017) Rare variants in fox-1 homolog A (RBFOX1) are associated with lower blood pressure. PLoS Genet 13:e1006678
Cade, Brian E; Chen, Han; Stilp, Adrienne M et al. (2016) Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans. Am J Respir Crit Care Med 194:886-897
Sul, Jae Hoon; Cade, Brian E; Cho, Michael H et al. (2016) Increasing Generality and Power of Rare-Variant Tests by Utilizing Extended Pedigrees. Am J Hum Genet 99:846-859

Showing the most recent 10 out of 32 publications