Showing papers by "Shaun Purcell published in 2020"

Sex differences in obstructive sleep apnea phenotypes, the multi-ethnic study of atherosclerosis

Abstract: STUDY OBJECTIVES The bases for sex disparities in obstructive sleep apnea (OSA), is poorly understood. We quantified the influences of event definitions, sleep-state, and body position on apnea-hypopnea indices (AHIs) in men and women, and evaluated sex differences in pathophysiological endotypes. METHODS Polysomnography (PSG) data were analyzed from 2057 participants from the multi-ethnic study of atherosclerosis. Alternative AHIs were compared using various desaturation and arousal criteria. Endotypes (loop gain, airway collapsibility, arousal threshold) were derived using breath-by-breath analysis of PSG signals. Regression models estimated the extent to which endotypes explained sex differences in AHI. RESULTS The sample (mean 68.5 ± 9.2 years) included 54% women. OSA (AHI4P ≥15/h, defined by events with ≥4% desaturations) was found in 41.1% men and 21.8% women. Compared to AHI4P, male/female AHI ratios decreased by 5%-10% when using 3%-desaturation and/or arousal criteria; p < 0.05. REM-OSA (REM-AHI ≥15/h) was similar in men and women regardless of event desaturation criteria. REM-AHI4P ≥15/h was observed in 57% of men and women each. In NREM, AHI4P in men was 2.49 (CI95: 2.25, 2.76) of that in women. Women demonstrated lower loop gain, less airway collapsibility, and lower arousal threshold in NREM (ps < 0.0005). Endotypes explained 30% of the relative sex differences in NREM-AHI4P. CONCLUSIONS There are significant sex differences in NREM-AHI levels and in physiological endotypes. Physiological endotypes explained a significant portion of the relative sex differences in NREM-AHI. Definitions that use 4%-desaturation criteria under-estimate AHI in women. Combining NREM and REM events obscures OSA prevalence in REM in women.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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. Joint analysis of multiple traits can increase power and provide insights into shared genetic architecture. Here, Nguyen et al. develop multi-trait TADA (mTADA), an extension of TADA (transmission and de novo association test) that jointly analyses de novo mutations of traits for improved risk-gene identification power.

Statistical Power and the Classical Twin Design

Abstract: 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

A group refactorization procedure for sleep electroencephalography

Abstract: [WORKING DRAFT] In order to relate health and disease to brain state, patterns of activity in the brain must be phenotyped. In this regard, polysomnography datasets present both an opportunity and a challenge, as although sleep data are extensive and multidimensional, features of the sleep EEG are known to correlate with clinical outcomes. Machine learning methods for rank reduction are attractive means for bringing the phenotyping problem to a manageable size. The whole-night power spectrogram is nonnegative, and so applying nonnegative matrix factorization (NMF) to separate spectrograms into time and frequency factors is a natural choice for dimension reduction. However, NMF converges differently depending on initial conditions, and there is no guarantee that factors obtained from one individual will be comparable with those from another, hampering inter-individual analysis. We therefore reseed time-frequency NMF with group frequency factors obtained from the entire sample. This “refactorization” extends classical frequency bands to frequency factors. The group reseeding procedure coerces factors into equivalence classes, making them comparable across individuals. By comparing frequency factor properties, we illustrate age-related effects on the sleep EEG. The procedure can presumably be adapted to higher resolutions, e.g. to local field potential datasets, for characterizing individual time-frequency events.