Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank

Campos, Adrián I.; García-Marín, Luis M.; Byrne, Enda M.; Martin, Nicholas G.; Cuéllar-Partida, Gabriel; Rentería, Miguel E.

doi:10.1038/s41467-020-14625-1

Cited by 101 publications

(111 citation statements)

References 80 publications

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“…17 Due to the importance of sleep in human health, there is growing interest in understanding the determinants of sleep-related traits and their relationships with other health conditions. Previous studies have reported associations between tobacco smoking, alcohol consumption, and body mass index with an increased risk of snoring; 15 insomnia and an increased risk of depression, diabetes, and cardiovascular disease; 16 longer sleep duration with an increased risk of breast cancer in women 19 and shorter sleep duration with a higher risk for myocardial infarction. 20 Nonetheless, most of these studies lack the design principles to perform causal inferences.…”

Section: Introductionmentioning

confidence: 99%

“…10,11 More recently, genome-wide association studies (GWAS) have achieved considerable progress in the characterisation of the genetic architecture of sleep-related traits. [12][13][14][15] GWAS have identified 351 genomic risk loci associated with chronotype, 13 41 genomic loci associated with snoring, 15 and 202 genomic loci associated with insomnia. 16 Furthermore, significant genetic overlap with other conditions has been uncovered.…”

Section: Introductionmentioning

confidence: 99%

“…16 While snoring was genetically correlated with sleep apnoea, excessive daytime sleepiness, body mass index (BMI), daytime dozing, schizophrenia, anorexia nervosa and neuroticism score, among others. 15 A genetic correlation between two polygenic traits may be due to horizontal pleiotropic effects, and therefore is not necessarily indicative of a causal relationship. 17 Horizontal pleiotropy poses challenges to statistical methods designed to infer causal relationships between environmental exposures and health outcomes using genetic data, such as Mendelian randomisation (MR).…”

Section: Introductionmentioning

confidence: 99%

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Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

García-Marín

Campos

Martin

et al. 2020

Preprint

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Study Objective. Sleep is essential for both physical and mental health.There is an increasing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data. Methods. We used genetic data and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 different phenotypes. Results. We identify 84 significant causal relationships. Among other findings, poor health of musculoskeletal and connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues increases insomnia, potentially through an increased risk of respiratory disease. Conclusion. Overall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

García-Marín

Campos

Martin

et al. 2020

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“…The genome-wide significant findings from these studies and the corresponding associations our study are found in Supplementary Table 1. Largerscale GWAS studies have been performed on OSA-related phenotypes such as snoring 16 . However, knowledge about OSA predisposing genetic loci is thus far limited 17 .…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health

Strausz

Ruotsalainen

Ollila

et al. 2020

Preprint

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There is currently only limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). The aim of our study is to identify genetic loci associated with OSA risk and to test if OSA and its comorbidities share a common genetic background. We conducted the first large-scale genome-wide association study of OSA using FinnGen Study (217,955 individuals) with 16,761 OSA patients identified using nationwide health registries. We estimated 8.3% [0.06-0.11] heritability and identified five loci associated with OSA (P < 5.0x10^-8): rs4837016 near GTPase activating protein and VPS9 domains 1 (GAPVD1), rs10928560 near C-X-C motif chemokine receptor 4 (CXCR4), rs185932673 near Calcium/calmodulin-dependent protein kinase ID (CAMK1D) and rs9937053 near Fat mass and obesity-associated protein (FTO) - a variant previously associated with body mass index (BMI). In a BMI-adjusted analysis, an association was observed for rs10507084 near Rhabdomyosarcoma 2 associated transcript (RMST)/NEDD1 gamma-tubulin ring complex targeting factor (NEDD1). We found genetic correlations between OSA and BMI (rg=0.72 [0.62-0.83]) and with comorbidities including hypertension, type 2 diabetes (T2D), coronary heart disease (CHD), stroke, depression, hypothyroidism, asthma and inflammatory rheumatic diseases (IRD) (rg > 0.30). Polygenic risk score (PRS) for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile and Mendelian randomization supported a causal relationship between BMI and OSA. Our findings support the causal link between obesity and OSA and joint genetic basis between OSA and comorbidities.

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“…The copyright holder for this this version posted September 30, 2020. ; https://doi.org/10.1101/2020.09.29.20199893 doi: medRxiv preprint for snoring we previously published (26) . FinnGen GWAS results for sleep apnoea and snoring were obtained from the open access FinnGen resource ( http://r3.finngen.fi/ ).…”

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confidence: 99%

Discovery of genomic loci associated with sleep apnoea risk through multi-trait GWAS analysis with snoring

Campos

Ingold

Huang

et al. 2020

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Rationale: Sleep apnoea is a complex disorder characterised by periods of halted breathing during sleep. Despite its association with serious health conditions such as cardiovascular disease, the aetiology of sleep apnoea remains understudied, and previous genetic studies have failed to identify replicable genetic risk factors. Objective: To advance our understanding of factors that increase susceptibility to sleep apnoea by identifying novel genetic associations. Methods: We conducted a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts, and a previously published GWAS of apnoea-hypopnea index (N Total =510,484). Further, we used multi-trait analysis of GWAS (MTAG) to boost statistical power, leveraging the high genetic correlations between apnoea, snoring and body mass index. Replication was performed in an independent sample from 23andMe, Inc (N Total =1,477,352; N cases =175,522). Results: Our results revealed 39 independent genomic loci robustly associated with sleep apnoea risk, and significant genetic correlations with multisite chronic pain, sleep disorders, diabetes, high blood pressure, osteoarthritis, asthma and BMI-related traits. We also derived polygenic risk scores for sleep apnoea in a leave-one-out independent cohort and predicted probable sleep apnoea in participants (OR=1.15 to 1.22; variance explained = 0.4 to 0.9%). Conclusions: We report novel genetic markers robustly associated with sleep apnoea risk and substantial molecular overlap with other complex traits, thus advancing our understanding of the underlying biological mechanisms of susceptibility to sleep apnoea.

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Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank

Cited by 101 publications

References 80 publications

Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health

Discovery of genomic loci associated with sleep apnoea risk through multi-trait GWAS analysis with snoring

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