Common variants implicated by genome-wide association studies (GWAS) of complex diseases are known to be enriched for coding and regulatory variants. We applied methods to partition the heritability explained by genotyped SNPs (h 2 g ) across functional categories (while accounting for shared variance due to linkage disequilibrium) to genotype and imputed data for 11 common diseases. DNaseI Hypersensitivity Sites (DHS) from 218 cell-types, spanning 16% of the genome, explained an average of 79% of h 2 g (5.1× enrichment; P < 10 −20 ); further enrichment was observed at enhancer and cell-type specific DHS elements. The enrichments were much smaller in analyses that did not use imputed data or were restricted to GWASassociated SNPs. In contrast, coding variants, spanning 1% of the genome, explained only 8% of h 2 g (13.8× enrichment; P = 5 × 10 −4 ). We replicated these findings but found no significant contribution from rare coding variants in an independent schizophrenia cohort genotyped on GWAS and exome chips.Recent work by ENCODE and other projects has shown that specific classes of non-coding variants can have complex and diverse impacts on cell function and phenotype 1-7 . With many potentially informative functional categories and competing biological hypotheses, quantifying the contribution of variants in these categories to complex traits would inform trait biology and focus fine-mapping. The availability of significantly associated variants from hundreds of genome-wide association studies (GWAS) 8 has opened one avenue for quantifying enrichment. Indeed, 11% of GWAS hits lie in coding regions 8 and 57% of GWAS hits lie in broadly-defined DHS (spanning 42% of the genome) 5 , with additional GWAS hits tagging these regions. The full distribution of GWAS association statistics exhibits enriched P-values in coding and untranslated regions (UTR) 9 . Analysis of DHS sub-classes and other histone marks has revealed a complex pattern of cell-type specific relationships with known disease associations 4 . However, the question of how much each functional category contributes to disease heritability remains unanswered 10 .Here, we jointly estimate the heritability explained by all SNPs (h 2 g ) in different functional categories, generalizing recent work using variance-component methods [11][12][13][14][15][16][17] . In contrast to analyses of top GWAS hits, this approach leverages the entire polygenic architecture of each trait and can obtain accurate estimates even in the face of pervasive linkage disequilibrium (LD) across functional categories, as we show via extensive simulations. We apply this approach to functional categories in GWAS and exome chip data from > 50, 000 samples.1

Building on previous work linking changes in the electroencephalogram (EEG) spectral slope to arousal level, Lendner et al. (2021) reported that wake, non rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep exhibit progressively steeper 30-45 Hz slopes, interpreted in terms of increasing cortical inhibition. Here we sought to replicate Lendner et al.’s scalp EEG findings (based on 20 individuals) in a larger sample of 11,630 individuals from multiple cohorts in the National Sleep Research Resource (NSRR). In a final analytic sample of N = 10,255 distinct recordings, there was unambiguous statistical support for the hypothesis that, within individuals, the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic or electrocardiographic artifacts were likely to bias 30-45 Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Although conceptually distinct, spectral slope did not predict sleep state substantially better than other summaries of the high frequency EEG power spectrum (>20 Hz, in this context) including beta band power, however. Finally, to more fully describe sources of variation in the spectral slope and its relationship to other sleep parameters, we quantified state-dependent differences in the variances (both within and between individuals) of spectral slope, power and interhemispheric coherence, as well as their covariances. In contrast to the common conception of the REM EEG as relatively wake-like (i.e. ‘paradoxical’ sleep), REM and wake were the most divergent states for multiple metrics, with NREM exhibiting intermediate profiles. Under a simplified modelling framework, changes in spectral slope could not, by themselves, fully account for the observed differences between states, if assuming a strict power law model. Although the spectral slope is an appealing, theoretically inspired parameterization of the sleep EEG, here we underscore some practical considerations that should be borne in mind when applying it in diverse datasets. Future work will be needed to fully characterize state-dependent changes in the aperiodic portions of the EEG power spectra, which appear to be consistent with, albeit not fully explained by, changes in the spectral slope.

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy. meta-analyzing DNMs and rare case-control (CC) variants, an approach that has been particularly successful for autism spectrum disorders (ASD) 9,10 . For epilepsy (EPI), multiple associated genes have been identified through DN based studies 4,5,11 , and in recent years, a number of EPI significant genes have also been identified through CC studies 12,13 . We hypothesized that, as for ASD, additional significant EPI genes could be discovered through the integration of DN and CC data. EPI is a serious brain disorder which includes multiple subtypes. Studies of cases/controls and twins have shown that genetic components have played important roles in EPI [14][15][16] . Some of EPI's subtypes can be explained by single genes, but multiple subtypes might be caused by multiple genes 15 . It is still challenging to develop specific drugs for this disorder. There have been multiple antiepileptic drugs used for EPI treatments; however, 20-30% of EPI patients have not been successful in controlling their seizures by using current medications 17 . Identifying additional genes or gene sets might help better understand its etiology as well as better design drug targets for the disorder.Due to the high p...