Leveraging Correlated Traits to Identify Genetic Associations with Sleep Disordered Breathing

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PROJECT SUMMARY/ABSTRACT Sleep Disordered Breathing (SDB) is a complex disorder that is common in the population and is associated with significant adverse health outcomes. Despite considerable heritability, elucidation of the genetic basis of this disorder has been limited by relatively small samples - due to under diagnosis and under reporting of SDB, and paucity of overnight measurements of SDB traits - and potential heterogeneity. Given that SDB is physiologically and metabolically related to other traits, there is an opportunity to increase power in the relatively-small genetic association studies of SDB traits by leveraging large association studies in correlated traits. Using correlated traits will also be useful for identifying SDB mechanisms captured by these traits, as a first step in explaining, at the genetic level, the heterogeneity of SDB. Therefore, we propose to utilize genetic associations for traits that correlate with SDB to dissect genetically-determined mechanisms of SDB, that are attributable to different molecular/physiological pathways, and are captured by different traits. We will take two approaches. First, we will study the genetic correlations between multiple cardiopulmonary and metabolic traits and SDB traits in the largest cohort with overnight measurements of SDB traits, the Hispanic Community Health Study/Study of Latinos. Based on these correlations, we will identify genetic loci associated with SDB from those that were previously implicated with the correlated traits. Second, we will study and implement approaches based on Polygenic Risk Scores (PRSs) in multiple, diverse, NHLBI cohorts. We will use previously known associations to construct PRSs for the detected correlated traits, and study their association with SDB traits. We will implement causal analyses using recently proposed methodologies of Mendelian Randomization in the presence of pleiotropy to study whether traits such as BMI, blood pressure, dyslipidemia, and insulin resistance are causally associated with SDB. These analyses will reveal specific genetically-determined mechanisms of SDB, setting the foundation for the ultimate goal of identifying subtypes of the disorder and consequently developing personalized therapies