The genetics of obstructive sleep apnoea

Mukherjee, Sutapa; Saxena, Richa; Palmer, Lyle J.

doi:10.1111/resp.13212

Cited by 74 publications

(64 citation statements)

References 109 publications

(173 reference statements)

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“… Plasma biomarkers to diagnose and risk stratify OSA: Biomarkers are being used increasingly to diagnose various conditions, for example plasma exosomes and microRNAs are being applied to OSA such that a clinician may ultimately be able to diagnose a patient with OSA based on a blood test . Moreover, these approaches may provide some insight into why a given patient has OSA and what is the risk of a particular complication. Genome sequencing for individuals to predict risk of OSA and risk of OSA complications: Genetics studies are also providing insight into OSA risk and mechanisms . Big data approaches are now being applied to sleep medicine by using huge sample size to answer important questions .…”

Section: New Diagnosticsmentioning

confidence: 99%

Update in respiratory sleep disorders: Epilogue to a modern review series

2017

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Section: New Diagnosticsmentioning

confidence: 99%

Update in respiratory sleep disorders: Epilogue to a modern review series

2017

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“…This underscores gaps in our knowledge of the pathophysiological pathways linking OSA to other diseases. 19,21 Pathophysiological pathways linking OSA to other diseases and factors that influence individual differences in susceptibility are poorly understood. While there are several effective treatments for OSA, including continuous positive airway pressure (CPAP), there appears to be substantial variation in overall clinical response and attenuation of cardiometabolic consequences, suggesting heterogeneity in both the etiology of the disease and susceptibility to its physiological disturbances.…”

Section: Introductionmentioning

confidence: 99%

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Akle

Chun

Teodosiadis

et al. 2019

Preprint

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Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and invasiveness of the required phenotyping. This reduces statistical power to discover multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology, using obstructive sleep apnea (OSA) as an exemplar. OSA is a common disorder diagnosed via overnight physiological testing (polysomnography). Here, we leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS.We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example we find links between OSA, a measure of lung function (FEV 1 /FVC), and an eQTL of desmoplakin (DSP) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase (HK1) locus to hematocrit and other red blood cell related traits.Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

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“…OSA is a complex, phenotypically heterogeneous disorder driven by both obesity-related factors and other mechanisms 17–19 . A number of studies have observed that OSA has a heritable component, with a two-fold increased risk in first-degree relatives of individuals with OSA 20–26 .…”

Section: Introductionmentioning

confidence: 99%

“…Given the high heritability of obesity and the strong relationship with OSA, it is important to note that studies indicate OSA familial aggregation is not simply due to association with obesity 21; 24 . Genomic variation is also associated with measures of OSA severity, like the apnea-hypopnea index (AHI) 19 . Furthermore, a number of syndromes with well-defined genetic causes are associated with increased prevalence of OSA (e.g., Achondroplasia 35 , Down syndrome 36; 37 , Marfan’s syndrome 38 , Prader-Willi syndrome 39 ).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the substantial evidence implicating genetic factors on expression of OSA, no strong genetic candidates have been robustly established 19; 40 . This is likely due to underpowered genetic association studies 19; 40 , lack of independent replication 41; 42 , and the substantial heterogeneity in the expression of OSA limiting the ability to identify genomic variants affecting all patients similarly. With the expansion of biorepositories linked to electronic health records (EHR), large sample sizes and a variety of OSA-related information—as well as other clinical and diagnostic data—are available to evaluate candidate genes and variants previously implicated in OSA.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Genetic and Phenotypic Heterogeneity of Obstructive Sleep Apnea Using Electronic Health Records

Veatch

Bauer

Josyula

et al. 2019

Preprint

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38Obstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete 39 cessation of airflow during sleep and is linked to negative health outcomes. Understanding the 40 genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic 41 health records can be leveraged to both validate previously reported OSA-associated genomic 42 variation and detect novel relationships between these variants and comorbidities. We identified 43 candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing 44 research. Using datasets available at Geisinger (n=39,407) and Vanderbilt University Medical 45Center (n=24,084), we evaluated associations between 48 SNPs and OSA diagnosis, defined 46 using clinical codes. We also evaluated associations between these SNPs and OSA severity 47 measures obtained from sleep reports at Geisinger (n=6,571). Finally, we used a phenome-wide 48 approach to perform discovery and replication analyses testing associations between OSA 49 candidate SNPs and other clinical codes and laboratory values. Ten SNPs were associated with 50 OSA diagnosis in at least one dataset, and one additional SNP was associated following meta-51 analysis across all datasets. Three other SNPs were solely associated in subgroups defined by 52 established risk factors (i.e., age, sex, and BMI). Five OSA diagnosis-associated SNPs, and 16 53 additional SNPs, were associated with OSA severity measures. SNPs associated with OSA 54 diagnosis were also associated with codes reflecting cardiovascular disease, diabetes, celiac 55 disease, peripheral nerve disorders and genitourinary symptoms. Results highlight robust OSA-56 associated SNPs, and provide evidence of convergent mechanisms influencing risk for co-57 occurring conditions. This knowledge can lead to more personalized treatments for OSA and 58 related comorbidities. 59 60In the United States (US), 50-70 million individuals suffer from a disorder of sleep and 62 wakefulness 1 . Among the most common sleep disorders is obstructive sleep apnea (OSA). OSA 63 is defined by frequent episodes of reduced (hypopnea) or complete cessation (apnea) of airflow 64 that occur as a consequence of upper airway obstruction during sleep. Approximately 34% of 65 men and 17% of women aged 30-70 exhibit mild OSA, and 13% and 6%, respectively, suffer 66 from moderate to severe OSA 2 . Notably, prevalence rates substantially increase by the age of 67 50 years old 2 . When left untreated, OSA represents a significant public health burden carrying a 68 higher risk of serious comorbidities such as cardiovascular disease 3-11 , cancer incidence 12 and 69 mortality, all causes 13; 14 , cognitive impairment 15 , and rate of progression of 70 neurodegeneration 16 . As such, identifying more effective diagnosis and management of OSA 71 are important areas of research. 72OSA is a complex, phenotypically heterogeneous disorder driven by both obesity-related 73 factors and other mechanisms [17][18][19] . A number of studies have ...

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The genetics of obstructive sleep apnoea

Cited by 74 publications

References 109 publications

Update in respiratory sleep disorders: Epilogue to a modern review series

Update in respiratory sleep disorders: Epilogue to a modern review series

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Characterization of Genetic and Phenotypic Heterogeneity of Obstructive Sleep Apnea Using Electronic Health Records

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