Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project.

BACKGROUND: Objective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project. METHODS: The research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain. CONCLUSION: Pilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

Objectively regular sleep patterns and mortality in a prospective cohort: The Multi-Ethnic Study of Atherosclerosis.

Irregular sleep and non-optimal sleep duration separately have been shown to be associated with increased disease and mortality risk. We used data from the prospective cohort Multi-Ethnic Study of Atherosclerosis sleep study (2010-2013) to investigate: do aging adults whose sleep is objectively high in regularity in timing and duration, and of sufficient duration tend to have increased survival compared with those whose sleep is lower in regularity and duration, in a diverse US sample? At baseline, sleep was measured by 7-day wrist actigraphy, concurrent with at-home polysomnography and questionnaires. Objective metrics of sleep regularity and duration from actigraphy were used for statistical clustering using sparse k-means clustering. Two sleep patterns were identified: "regular-optimal" (average duration: 7.0 ± 1.0 hr obtained regularly) and "irregular-insufficient" (duration: 5.8 ± 1.4 hr obtained with twice the irregularity). Using proportional hazard models with multivariate adjustment, we estimated all-cause mortality hazard ratios. Among 1759 participants followed for a median of 7.0 years (Q1-Q3, 6.4-7.4 years), 176 deaths were recorded. The "regular-optimal" group had a 39% lower mortality hazard than did the "irregular-insufficient" sleep group (hazard ratio [95% confidence interval]: 0.61 [0.45, 0.83]) after adjusting for socio-demographics, lifestyle, medical comorbidities and sleep disorders. In conclusion, a "regular-optimal" sleep pattern was significantly associated with a lower hazard of all-cause mortality. The regular-optimal phenotype maps behaviourally to regular bed and wake times, suggesting sleep benefits of adherence to recommended healthy sleep practices, with further potential benefits for longevity.

A Potential Source of Bias in Group-Level EEG Microstate Analysis.

Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Between and within individuals, microstates can differ both in terms of characteristic topographies and temporal dynamics, which leads to analytic challenges as the measurement of microstate dynamics is dependent on assumptions about their topographies. Here we focus on the analysis of group differences, using simulations seeded on real data from healthy control subjects to compare approaches that derive separate sets of maps within subgroups versus a single set of maps applied uniformly to the entire dataset. In the absence of true group differences in either microstate maps or temporal metrics, we found that using separate subgroup maps resulted in substantially inflated type I error rates. On the other hand, when groups truly differed in their microstate maps, analyses based on a single set of maps confounded topographic effects with differences in other derived metrics. We propose an approach to alleviate both classes of bias, based on a paired analysis of all subgroup maps. We illustrate the qualitative and quantitative impact of these issues in real data by comparing waking versus non-rapid eye movement sleep microstates. Overall, our results suggest that even subtle chance differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

Sleep Architecture, Obstructive Sleep Apnea, and Cognitive Function in Adults.

Importance: Good sleep is essential for health, yet associations between sleep and dementia risk remain incompletely understood. The Sleep and Dementia Consortium was established to study associations between polysomnography (PSG)-derived sleep and the risk of dementia and related cognitive and brain magnetic resonance imaging endophenotypes. Objective: To investigate association of sleep architecture and obstructive sleep apnea (OSA) with cognitive function in the Sleep and Dementia Consortium. Design, Setting, and Participants: The Sleep and Dementia Consortium curated data from 5 population-based cohorts across the US with methodologically consistent, overnight, home-based type II PSG and neuropsychological assessments over 5 years of follow-up: the Atherosclerosis Risk in Communities study, Cardiovascular Health Study, Framingham Heart Study (FHS), Osteoporotic Fractures in Men Study, and Study of Osteoporotic Fractures. Sleep metrics were harmonized centrally and then distributed to participating cohorts for cohort-specific analysis using linear regression; study-level estimates were pooled in random effects meta-analyses. Results were adjusted for demographic variables, the time between PSG and neuropsychological assessment (0-5 years), body mass index, antidepressant use, and sedative use. There were 5946 participants included in the pooled analyses without stroke or dementia. Data were analyzed from March 2020 to June 2023. Exposures: Measures of sleep architecture and OSA derived from in-home PSG. Main Outcomes and Measures: The main outcomes were global cognitive composite z scores derived from principal component analysis, with cognitive domains investigated as secondary outcomes. Higher scores indicated better performance. Results: Across cohorts, 5946 adults (1875 females [31.5%]; mean age range, 58-89 years) were included. The median (IQR) wake after sleep onset time ranged from 44 (27-73) to 101 (66-147) minutes, and the prevalence of moderate to severe OSA ranged from 16.9% to 28.9%. Across cohorts, higher sleep maintenance efficiency (pooled ß per 1% increase, 0.08; 95% CI, 0.03 to 0.14; P < .01) and lower wake after sleep onset (pooled ß per 1-min increase, -0.07; 95% CI, -0.13 to -0.01 per 1-min increase; P = .02) were associated with better global cognition. Mild to severe OSA (apnea-hypopnea index [AHI] ≥5) was associated with poorer global cognition (pooled ß, -0.06; 95% CI, -0.11 to -0.01; P = .01) vs AHI less than 5; comparable results were found for moderate to severe OSA (pooled ß, -0.06; 95% CI, -0.11 to -0.01; P = .02) vs AHI less than 5. Differences in sleep stages were not associated with cognition. Conclusions and Relevance: This study found that better sleep consolidation and the absence of OSA were associated with better global cognition over 5 years of follow-up. These findings suggest that the role of interventions to improve sleep for maintaining cognitive function requires investigation.

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

A spectrum of altered non-rapid eye movement sleep in schizophrenia.

Background: Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. Results: We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. Conclusion: We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.

Sources of Variation in the Spectral Slope of the Sleep EEG.

The 1/f spectral slope of the electroencephalogram (EEG) estimated in the γ frequency range has been proposed as an arousal marker that differentiates wake, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Here, we sought to replicate and extend these findings in a large sample, providing a comprehensive characterization of how slope changes with age, sex, and its test-retest reliability as well as potential confounds that could affect the slope estimation. We used 10,255 whole-night polysomnograms (PSGs) from the National Sleep Research Resource (NSRR). All preprocessing steps were performed using an open-source Luna package and the spectral slope was estimated by fitting log-log linear regression models on the absolute power from 30 to 45 Hz separately for wake, NREM, and REM stages. We confirmed that 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- to 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. Our findings support that spectral slope can be a valuable arousal marker for both clinical and research endeavors but also underscore the importance of considering interindividual variation and multiple methodological aspects related to its estimation.

Non-rapid eye movement sleep and wake neurophysiology in schizophrenia.

Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key non-rapid eye movement (NREM) sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.

Impact of chronic sleep restriction on sleep continuity, sleep structure, and neurobehavioral performance.

Chronic sleep restriction (CSR) has been associated with adverse effects including cognitive impairment and increased risk of diabetes and cardiovascular disease. Yet, sleep restriction therapy is an essential component of most behavioral treatments for insomnia. Moreover, little is known about the impact of CSR on sleep continuity and structure in healthy people whose need for sleep is satiated. We investigated the impact of CSR on sleep continuity and structure in nine healthy participants. They had 4 nights of sleep extension, 2 nights of post-extension sleep, 21 nights of CSR (5/5.6-hour time-in-bed), and 9 nights of recovery sleep. Compared to postextension sleep, during CSR sleep duration was reduced by 95.4 ±â€…21.2 min per night, Slow-Wave Activity was significantly increased, and sleep was more consolidated. During recovery, sleep duration was increased by 103.3 ±â€…23.8 min compared to CSR, and the CSR-induced increase in Slow-Wave Activity persisted, particularly after the 5-hour exposure. Yet, we found that sustained vigilant attention was not fully recovered even after nine nights of recovery sleep. Our results suggest that CSR improves traditional metrics of sleep quality and may have a persistent impact on sleep depth, which is consistent with the reported benefits on sleep continuity and structure of sleep restriction therapy. However, these improvements in traditional metrics of sleep quality were associated with deterioration rather than improvement in neurobehavioral performance, demonstrating that sleep duration should be included in assessments of sleep quality. These results have implications for the long-term use of sleep restriction in the behavioral treatment of insomnia. Clinical Trial Registration: Impact of Chronic Circadian Disruption vs. Chronic Sleep Restriction on Metabolism (https://clinicaltrials.gov/ct2/show/; #NCT02171273).

Upregulated heme biosynthesis increases obstructive sleep apnea severity: a pathway-based Mendelian randomization study.

Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Iron and heme metabolism, implicated in ventilatory control and OSA comorbidities, was associated with OSA phenotypes in recent admixture mapping and gene enrichment analyses. However, its causal contribution was unclear. In this study, we performed pathway-level transcriptional Mendelian randomization (MR) analysis to investigate the causal relationships between iron and heme related pathways and OSA. In primary analysis, we examined the expression level of four iron/heme Reactome pathways as exposures and four OSA traits as outcomes using cross-tissue cis-eQTLs from the Genotype-Tissue Expression portal and published genome-wide summary statistics of OSA. We identify a significant putative causal association between up-regulated heme biosynthesis pathway with higher sleep time percentage of hypoxemia (p = 6.14 × 10-3). This association is supported by consistency of point estimates in one-sample MR in the Multi-Ethnic Study of Atherosclerosis using high coverage DNA and RNA sequencing data generated by the Trans-Omics for Precision Medicine project. Secondary analysis for 37 additional iron/heme Gene Ontology pathways did not reveal any significant causal associations. This study suggests a causal association between increased heme biosynthesis and OSA severity.

Whole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program.

BACKGROUND: Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing. METHODS: The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap. RESULTS: We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10-8) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways. CONCLUSIONS: We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response.

Macro and micro sleep architecture and cognitive performance in older adults.

We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.

Statistical Power and the Classical Twin Design.

Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.

mTADA is a framework for identifying risk genes from de novo mutations in multiple traits.

Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. Studies of neuropsychiatric disorders and congenital heart disease (CHD) which use de novo mutations (DNMs) from parent-offspring trios have reported multiple putatively causal genes. However, a joint analysis method designed to integrate DNMs from multiple studies has yet to be implemented. We here introduce multiple-trait TADA (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. We first demonstrate that mTADA is able to leverage genetic overlaps to increase the statistical power of risk-gene identification. We then apply mTADA to large datasets of >13,000 trios for five neuropsychiatric disorders and CHD. We report additional risk genes for schizophrenia, epileptic encephalopathies and CHD. We outline some shared and specific biological information of intellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.

Effects of a patient-derived de novo coding alteration of CACNA1I in mice connect a schizophrenia risk gene with sleep spindle deficits.

CACNA1I, a schizophrenia risk gene, encodes a subtype of voltage-gated T-type calcium channel CaV3.3. We previously reported that a patient-derived missense de novo mutation (R1346H) of CACNA1I impaired CaV3.3 channel function. Here, we generated CaV3.3-RH knock-in animals, along with mice lacking CaV3.3, to investigate the biological impact of R1346H (RH) variation. We found that RH mutation altered cellular excitability in the thalamic reticular nucleus (TRN), where CaV3.3 is abundantly expressed. Moreover, RH mutation produced marked deficits in sleep spindle occurrence and morphology throughout non-rapid eye movement (NREM) sleep, while CaV3.3 haploinsufficiency gave rise to largely normal spindles. Therefore, mice harboring the RH mutation provide a patient derived genetic model not only to dissect the spindle biology but also to evaluate the effects of pharmacological reagents in normalizing sleep spindle deficits. Importantly, our analyses highlighted the significance of characterizing individual spindles and strengthen the inferences we can make across species over sleep spindles. In conclusion, this study established a translational link between a genetic allele and spindle deficits during NREM observed in schizophrenia patients, representing a key step toward testing the hypothesis that normalizing spindles may be beneficial for schizophrenia patients.

Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry.

Schizophrenia is a common, chronic and debilitating neuropsychiatric syndrome affecting tens of millions of individuals worldwide. While rare genetic variants play a role in the etiology of schizophrenia, most of the currently explained liability is within common variation, suggesting that variation predating the human diaspora out of Africa harbors a large fraction of the common variant attributable heritability. However, common variant association studies in schizophrenia have concentrated mainly on cohorts of European descent. We describe genome-wide association studies of 6152 cases and 3918 controls of admixed African ancestry, and of 1234 cases and 3090 controls of Latino ancestry, representing the largest such study in these populations to date. Combining results from the samples with African ancestry with summary statistics from the Psychiatric Genomics Consortium (PGC) study of schizophrenia yielded seven newly genome-wide significant loci, and we identified an additional eight loci by incorporating the results from samples with Latino ancestry. Leveraging population differences in patterns of linkage disequilibrium, we achieve improved fine-mapping resolution at 22 previously reported and 4 newly significant loci. Polygenic risk score profiling revealed improved prediction based on trans-ancestry meta-analysis results for admixed African (Nagelkerke's R2 = 0.032; liability R2 = 0.017; P < 10-52), Latino (Nagelkerke's R2 = 0.089; liability R2 = 0.021; P < 10-58), and European individuals (Nagelkerke's R2 = 0.089; liability R2 = 0.037; P < 10-113), further highlighting the advantages of incorporating data from diverse human populations.