Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Abstract

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and invasiveness of the required phenotyping. This reduces statistical power to discover multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology, using obstructive sleep apnea (OSA) as an exemplar. OSA is a common disorder diagnosed via overnight physiological testing (polysomnography). Here, we leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example we find links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of desmoplakin (DSP) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.