Available: 12/09/19, Expires: 12/31/21
Sleep apnea (SA) and insomnia are the two most common sleep disorders. They are thought to be comprised of heterogeneous subtypes, which remain poorly defined. Both disorders may contribute differently to the risk of cardiopulmonary, metabolic and psychiatric diseases. They often present jointly in the same patient and contribute additional risks of developing comorbidities. Despite their high prevalence, treatments for SA and insomnia remain suboptimal. Identifying genes contributing to physiologically relevant pathways in sufficiently powered studies and identifying disease subtypes associated with increased risk of specific comorbidities will aid in the discovery of improved countermeasures, guiding patient stratification, and personalizing treatment for severely impacted patients.
Our team has longstanding experience with “big data.” We have led the largest genetic studies (GWAS) of SA with objective recordings to date, performed a GWAS of self-reported insomnia in the UK Biobank, identified the first genetic associations with SA, and are leading the NHLBI TOPMed Program Sleep Working Group and its whole-genome sequence (WGS) analyses of SA and insomnia. This work has laid the foundation for analyses that address the critical gaps of reliable phenotyping of sufficient participants to discover additional genetic associations and identifying clinically-diagnosed SA and insomnia subtypes that lead to increased risk of disease.
To fill these critical gaps, the goals of our study are to perform the largest genetic analysis of validated diagnosed SA and insomnia to date, characterize novel loci, and study associations with clinical diagnosis data to improve patient classification in the Partners Biobank. We will identify and characterize genetic associations with sleep apnea and insomnia, identify distinct SA and insomnia subgroups of patients with related comorbidity profiles, and quantify the additional burden of concurrent SA and insomnia.
These are computational analyses that will use Python and R programming, often on a Linux-based computer cluster. We anticipate multiple publication opportunities, which you could help lead. In return we ask that you substantially contribute to the computational infrastructure of these analyses (e.g. run the pipeline scripts, or ideally improve the existing scripts and automate more processes). Multiple additional opportunities exist for genomic and epidemiological studies with our group, including WGS analyses in the TOPMed consortium, and other analyses of biobank data. Most objective measures of sleep (e.g. sleep architecture) remain under-explored in genetic analyses.