Multiethnic Meta-Analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea–related Quantitative Trait Locus in Men

Abstract

Obstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea-hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genome-wide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7 × 10) but not in women (P = 0.77). The association with NREM AHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki-Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.

Whole-Genome Association Analyses of Sleep-disordered Breathing Phenotypes in the NHLBI TOPMed Program

“…Treatment options, however, are limited by a lack of knowledge of molecular pathways, including those that may be "druggable". Recent analyses of SDB traits have focused on common variants and identified several preliminary genome-level significant associations using GWAS, admixture mapping, and linkage approaches [ [11][12][13][14][15] ], but did not address gene-based or rare variant effects. Ten studies and over 21,000 individuals of multiple ancestries with WGS data at unprecedented resolution from the NHLBI TOPMed program combined with densely imputed data from other sources contributed to these results.…”

“…Therefore, there is a critical need to identify molecular pathways that could provide specific therapeutic targets. The need for overnight studies to phenotype SDB traits has limited the available sample size for genetic analyses, and only several common-frequency genome-wide analysis studies have been reported [11][12][13][14][15]. Increased statistical power may increase the genetic resolution of regions that may not be adequately tagged by current genotyping arrays due to population differences and/or reduced linkage disequilibrium with biologically relevant regions [ 16 ].…”

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

“…In another study we identified a locus in 17p11 with a male specific effect on AHI. 23 Furthermore, in an admixture mapping study in Hispanic/Latino Americans, we identified a locus on 2q37 associated with AHI and one in a locus on 18q21 associated with AHI and SpO 2 < 90%. 24 The low number of genetic associations reported to date only explains a small fraction of OSA trait heritability.…”

“…The full description of phenotyping protocols is present in the original study which first reported their genetic analysis in the context of OSA. [22][23][24] We rank-normalized all OSA traits, separately in each cohort, in order to obtain normally distributed phenotypes.…”

Genome-wide association analysis of excessive daytime sleepiness identifies 42 loci that suggest phenotypic subgroups

“…Additional novel loci were identified within or near genes with known actions on sleep-wake control regulation or that are associated with sleep disorders (e.g. PLCL1 31 , GABRA2 32 , BTBD9 33 , HTR7 34 , RAI1 35 ) , metabolic traits (e.g. GCKR 36 , SLC39A8 37 ) and psychiatric traits (e.g.…”