0002 Fine Mapping Genome-Wide Association in Narcolepsy Defines Novel Disease Mechanisms

Abstract

Introduction: Sleep spindles are associated with various aspects of learning and memory and are potential biomarkers of neuropsychiatric disease. Although twin studies indicate that spindle activity is partially heritable, specific genes are yet to be identified. Here we detect and characterize spindle phenotypes in 11,630 individuals (aged 5 to 95), confirm their heritable basis and initiate genome-wide association analyses to map individual genes. Methods: We compiled whole-night polysomnography, demographic and medical data from the US National Sleep Research Resource (NSRR), applying automated artifact rejection and wavelet analyses to detect spindles from two central electrodes. Univariate heritabilities and genetic correlations were estimated using within-family intraclass correlations and variance components models for the genome-wide single nucleotide polymorphism (SNP) data. Results: Spindle and spectral phenotypes demonstrated high test-retest reliabilities (r>0.8), based on over 4,000 individuals with repeated polysomnograms. We identified and corrected potential confounders that might impact genetic studies, including body mass index (mediated by cardiac interference in the EEG) and age. In 730 individuals from the Cleveland Family Study, spindle density was highly heritable in both white (h 2 = 0.45, p = 8x10 -6 ) and black individuals (h 2 = 0.43, p = 3x10 -6 ), adjusting for age and sex. Spindle density in stage 3 sleep (N3) had a high genetic overlap (r G = 0.89, p = 2x10 -4 in whites, r G = 0.88, p = 1x10 -5 in blacks) with N2 spindles. In contrast, fast (15Hz) and slow (11Hz) spindles showed significant heritabilities but no genetic overlap (r G = -0.15 and 0.16, p = 0.31 and 0.18 for whites and blacks respectively), suggesting distinct genetic architectures. These results will help in the optimal selection of independent phenotypes for ongoing genome-wide association analyses, the results of which are expected early 2017. Conclusion: We observed evidence for robust genetic influences on spindle phenotypes, controlling for a range of demographic and clinical covariates. This work can inform future genetic studies that aim to understand better the genetic architecture of spindles and their relation to health and disease.