Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype

Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype Heming Wang, Pavithra Nagarajan, Nuzulul Kurniansyah, Jiwon Lee, and 33 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5337531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Obstructive sleep apnea (OSA) is a multifactorial sleep disorder characterized by a strong genetic basis. Excessive daytime sleepiness (EDS) is a symptom that is reported by a subset of OSA patients, persisting even after treatment with continuous positive airway pressure (CPAP). It is recognized as a clinical subtype underlying OSA carrying alarming heightened cardiovascular risk. Thus, conceptualizing EDS as an exposure variable, we sought to investigate EDS’s influence on genetic variation linked to apnea-hypopnea index (AHI), a diagnostic measure of OSA severity. This study serves as the first large-scale genome-wide gene x environment interaction analysis for AHI, investigating the interplay between its genetic markers and EDS across and within specific sex. Our work pools together whole genome sequencing data from seven cohorts, enabling a diverse dataset (four population backgrounds) of over 11,500 samples. Among the total 16 discovered genetic targets with interaction evidence with EDS, eight are previously unreported for OSA, including CCDC3 , MARCHF1 , and MED31 identified in all sexes; TMEM26 , CPSF4L , and PI4K2B identified in males; and RAP1GAP and YY1 identified in females. We discuss connections to insulin resistance, thiamine deficiency, and resveratrol use that may be worthy of therapeutic consideration for excessively sleepy OSA patients. Biological sciences/Genetics/Genetic interaction Biological sciences/Genetics/Genome/Genetic variation Health sciences/Diseases/Neurological disorders/Sleep disorders Figures Figure 1 Figure 2 INTRODUCTION Obstructive sleep apnea (OSA) is characterized by recurrent upper airway collapse and obstruction, resulting in arousal and oxygen desaturation events that induce variable degrees of sympathetic activation, inflammation, endothelial dysfunction, and metabolic perturbations 1 . The clinical presentation of this disorder can vary and includes both disturbed sleep (insomnia) and excessive daytime sleepiness (EDS) 1 . A strong genetic basis has been established for OSA with heritability estimates between 69–83% in twin studies, 25–40% in family studies, and up to 21% from population-based genome-wide association studies (GWAS) 2 , 3 . Adequately characterizing the genetic architecture of OSA can provide insight into disease mechanisms and better therapeutic regimens, ultimately improving patient outcomes. In OSA, EDS is found in over 30% of patients 4 . Although often exhibiting improvement with OSA treatment, EDS persists in 9–22% of CPAP-adherent individuals 4 , 5 . Patients with EDS (excessive sleepy OSA subtype) are reported to experience higher risk of incident cardiovascular disease, potentially reflecting elevated inflammation and a more severe endophenotype (high airway collapsibility, low arousal threshold) 1 , 6 , 7 . EDS can reflect behavioral factors associated with OSA risk such as insufficient sleep duration, poor diet quality, and reduced physical activity 8 – 12 . As a marker of underlying physiological conditions and environmental exposures, EDS may moderate genetic effects that influence severity of OSA and its cardiovascular risk, with possible pathways linked to the microbiome, systemic inflammation, and adipose tissue function 1 , 13 . We thus sought to investigate whether there is evidence of interaction between EDS and genetic variant effects for apnea-hypopnea index (AHI). We conducted genome-wide interaction analyses with single common variant effects, and rare variant gene set-based effects, in over 11,500 individuals using National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine (TOPMed) data 14 . A dataset comprising of multiple race/ethnicities (African American/Black [AFR], Asian [ASN], Caucasian/White/European [EUR], Hispanic/Latino [HIS]) was consolidated for this analysis with stratification according to sex (males, females, all sex). Preliminary results have been previously reported in the form of an abstract 15 . RESULTS Common Variant Analysis Common variant analysis with whole genome sequencing (WGS) data in combined sex samples revealed two intronic variants with strong interaction with EDS - rs281851 (P GxE : 6.59e-09, P G,GxE : 2.2e-08) mapped to CCDC3 , and rs13118183 (P GxE : 2.92e-08, P G,GxE : 4.7e-08) mapped to MARCHF1 (Table 1 , Fig. 2 A). Variant to gene mapping by FUMA SNP2GENE (using position, expression quantitative trait loci, chromatin interaction methods) additionally identified UPF2, DHTKD1, SEC61A2, NUDT5, CDC123, CAMK1D, OPTN , and PHYH mapped to rs281851 and NAF1, NPY1R, NPY5R, TKTL2, FAM218A , and TRIM60 mapped to rs13118183 (Table 1 , Supplementary Table 2). Table 1 Genetic Variants Interacting with EDS Identified in Common Variant Discovery Analysis * These significant variants (RSID) passed genome-wide significance criteria (p < 5e-08) for the 1df GxE interaction test and 2df G,GxE joint test, and were identified in combined sex analysis. Main Effect Interaction Effect Joint Effect rsID * Chr:Position (b38) Effect Allele/ Alternative Allele Effect Allele Frequency Gene Locus N Beta (Standard Error) P-Value Beta (Standard Error) P-Value P-Value rs13118183 4:164200775 A/G 0.94 NAF1, NPY1R, NPY5R, TKTL2, MARCHF1, FAM218A, TRIM60 11614 -0.0094 (0.0025) 1.50e-04 0.034 (0.0062) 2.92e-08 4.70e-08 rs281851 10:12924495 A/C 0.59 UPF2, DHTKD1, SEC61A2, NUDT5, CDC123, CAMK1D, CCDC3, OPTN, PHYH 11615 -0.0010 (0.0011) 3.82e-01 0.0187 (0.0032) 6.59e-09 2.23e-08 Rare Variant Set-Based Analysis Rare variant set-based analysis with WGS data identified SCUBE2 in combined sex, and TMEM26 and CPSF4L in male sex (P G,GxE <3e-06, P GxE <P G ) (Table 2 ). Meta-analysis of WGS and imputed genotype data identified UBLCP1 and MED31 in combined sex; YY1, CPNE5, MYMX, ZNF773, YBEY , and RAP1GAP in female sex; and PI4K2B, IQCB1 , and CORO1A in male sex (Table 3 ) (P G,GxE <3e-06, P GxE <P G ). Among these, YY1 additionally showed significance in the GxE interaction effect (P GxE <3e-06) (Table 3 ). Table 2 Genes Interacting with EDS Identified in Rare Variant Set-Based Discovery Analysis Gene * Position Sex Dataset Number of Variants Main Effect P-Value Interaction Effect P-Value Joint Effect P-Value SCUBE2 11:9,019,476-9,138,114 Combined WGS 25 4.16e-02 2.02e-05 3.12e-06 Imputed 3 4.97e-01 7.91e-01 7.60e-01 TMEM26 † 10:61,406,642 − 61,453,381 Male WGS 1 9.63e-03 3.63e-05 2.54e-06 CPSF4L 17:73,248,449 − 73,262,352 Male WGS 5 9.19e-05 1.69e-05 2.39e-08 Imputed 1 5.71e-01 9.66e-01 8.80e-01 * These genes passed Bonferroni-corrected significance criteria (p P GxE ). † Variant sets mapped to TMEM26 were not available in imputed genotype data. Table 3 Genes Interacting with EDS Identified in Rare Variant Set-Based Meta-Analysis Gene * Position Sex Number of Variants Main Effect P-Value Interaction Effect P-Value Joint Effect P-Value UBLCP1 5:159,263,290 − 159,286,036 Combined 2 4.75e-04 2.29e-05 1.96e-07 MED31 17:6,643,311-6,651,634 Combined 3 1.51e-02 4.49e-06 3.22e-07 RAP1GAP 1:21,596,221 − 21,669,357 Female 2 1.73e-01 7.48e-06 2.37e-06 CPNE5 6:36,740,775 − 36,839,444 Female 9 3.30e-04 2.07e-04 1.08e-06 MYMX 6:44,216,926 − 44,218,234 Female 1 9.13e-04 4.15e-05 3.64e-07 YY1 14:100,238,298 − 100,282,788 Female 1 2.36e-01 2.13e-06 7.08e-07 ZNF773 19:57,499,915 − 57,518,404 Female 6 5.46e-04 2.51e-05 1.41e-07 YBEY 21:46,286,342 − 46,297,751 Female 3 8.62e-02 1.16e-05 2.35e-06 IQCB1 3:121,769,761 − 121,835,079 Male 9 7.69e-02 7.48e-06 2.60e-06 PI4K2B 4:25,160,663 − 25,279,204 Male 4 3.50e-02 3.54e-06 6.28e-07 CORO1A 16:30,182,827 − 30,189,076 Male 2 7.96e-03 1.02e-05 6.07e-07 * These genes passed Bonferroni-corrected significance criteria (P P GxE ). Validation of Results For WGS common variant results ( rs281851 , rs13118183 ) imputed genotype samples did not contain either variant. For rare variant WGS gene-set analysis results ( SCUBE2, TMEM26, CPSF4L ) imputed genotype samples did not replicate findings, showing different variant sets available for each gene compared to WGS data (Supplementary Table 3). Secondary Findings Genetic associations with AHI identified by the main genetic effect B G (not considering role of EDS interaction) in the WGS discovery dataset are reported in Supplementary Tables 4–6. Unreported Gene Targets for OSA The variants from common variant analysis and genes from rare variant set-based analysis were assessed with prior OSA trait-related GWAS and three catalogs: GWAS Catalog, PheWeb, Sleep Disorders Knowledge Portal 3 , 16 – 18 (Supplementary Table 7). SCUBE2, UBLCP1, CPNE5, MYMX, ZNF773, YBEY, IQCB1 , and CORO1A are identified in a prior gene-based analysis for sleep-disordered breathing traits with samples overlapping this GxE work 16 . SCUBE2 is additionally reported in the Sleep Disorders Knowledge Portal for sleep apnea syndrome (P: 3.7e-04; common variants). rs281851 (intronic variant of CCDC3 ), rs13118183 (intronic variant of MARCHF1 ), TMEM26, CPSF4L, MED31, YY1, RAP1GAP , and PI4K2B are previously unreported for OSA (Supplementary Table 7). Four of these have been reported for cardiometabolic traits including TMEM26 for PR interval, hypertension, systolic blood pressure, and diastolic blood pressure; MED31 for systolic blood pressure; RAP1GAP for abdominal aortic calcification levels; and YY1 for aortic atherosclerosis, and pulse pressure. Effects in EDS vs non-EDS groups We performed EDS-stratified analysis for the significant interaction loci ( rs281851 , rs13118183 ) to compare how the effect estimate (B G ) changes dependent on whether an individual experiences excessive daytime sleepiness. Figure 2 B and C and Supplementary Table 8 show that for individuals who have the allele A for rs281851 or allele A for rs13118183, the presence of EDS increases AHI. For rare variant set-based analysis differences in the variants mapped to each gene in each EDS group was observed (Supplementary Table 9). Bioinformatics Analysis MAGMA analysis run on FUMA’s SNP2GENE platform with WGS common variant GxE interaction summary statistics revealed tissue enrichment in breast mammary tissue and tibial nerve for females (Supplementary Table 10). MAGMA gene-based analysis identified NOP53, EYA2 , and ZNF563 in combined sex and WDR19 in females (Supplementary Table 11). Open Targets Platform mouse model data identified functional roles in immune system response ( MARCHF1, EYA2, RAP1GAP, CPNE5, CORO1A ), adipose tissue ( CCDC3, IQCB1 ), metabolism ( CCDC3, RAP1GAP, CPSF4L ), nervous system or behavior ( UBLCP1, MED31, EYA2, CPNE5, YY1, IQCB1, CORO1A, WDR19 ), respiratory system ( MYMX, YY1 ), cardiovascular system ( IQCB1 ), and craniofacial measures ( WDR19, MED31 ) (Supplementary Table 12). Open Targets Genetics revealed associations to medication use for lung disease (atrovent- YY1 , ventolin- WDR19 ), hypertension or cardiovascular disease (bendroflumethiazide- CORO1A , atenolol- TMEM26 , atenolol- MARCHF1 , bumetanide- MYMX , dipyridamole- TMEM26 ), and cholesterol (statin- NOP53 , statin- MYMX ) (Supplementary Data). STRINGdb identified enriched terms (FDR < 0.05) from the resultant protein-protein interaction network built by the rs281851 and rs13118183 gene loci (Supplementary Table 13). Enriched gene ontology (GO) Molecular Function terms related to the highly conserved pancreatic polypeptide hormone family NPY-PYY-PP ( NPY1R, NPY5R ) and thiamine pyrophosphate-transketolase activity ( DHTKD1, TKTL2 ) (Supplementary Table 13). DGIdb revealed the following drug-gene interactions: velneperit (prior Phase II clinical trial for obesity) for NPY5R and losartan (FDA-approved for hypertension) for CAMK1D (Supplementary Table 14). Qiagen’s Ingenuity Pathway Analysis software constructed a fully-connected interactome with the primary 16 genes from Tables 1 – 3 provided as input (Supplementary Fig. 1). The identified connections included neuroinflammation and nerve-function ( TRIM67, HTT ), inflammation ( TNF, INFG ), tumor suppressor ( TP53 ), DNA damage response ( RNF4, FANCD2 ), and transcription processing ( SIX1, MEPCE, FIP1L1 ). Canonical pathway enrichment analysis revealed over-representation of eight pathways (p < 0.05) (Supplementary Table 15). DISCUSSION This is the first large-scale GxE analysis for AHI, with the goal of uncovering gene targets for a deeper understanding of the pathophysiology of obstructive sleep apnea. This study examines the effect of EDS on AHI-associated genetic variants in order to elucidate any important biological targets or pathways for the excessively sleepy clinical OSA subtype. We identified significant interactions at 16 genes, of which the following are previously unreported for OSA pathophysiology: CCDC3, MARCHF1, TMEM26, CPSF4L, MED31, YY1, RAP1GAP , and PI4K2B . This study’s findings suggest the potential of thiamine and resveratrol supplementation for use in OSA patients experiencing excessive daytime sleepiness. Bioinformatics analysis identified cross-trait associations with cardiometabolic traits and medication use, suggesting pathways that may be pertinent to investigate for clinical utility in sleepy OSA patients, given their elevated cardiovascular risk. Thiamine (vitamin B1) metabolism is highlighted by genes mapped to the MARCHF1 and CCDC3 genetic loci from the enrichment of thiamine pyrophosphate and transketolase terms in STRINGdb. Thiamine deficiency can result from high calorie malnutrition, increased age, and gastrointestinal tract factors 19 . Thiamine deficiency is promoted by fluid loss or antacids use – which can occur in OSA patients due to nighttime sweating or treatment of comorbid gastroesophageal reflux disorder (GERD) 20 . Thiamine deficiency is also linked to long sleep duration (which is associated with EDS or high sleep propensity), connected to alterations in adenosine triphosphate production 21 , 22 . Thiamine-containing supplements demonstrate improvement in sleep disturbance and insomnia symptoms 22 . Thiamine is notably a potent inhibitor of human carbonic anhydrase II with activity comparable to acetazolamide – a medication shown to both lower blood pressure and vascular stiffness, as well as improve AHI and ventilatory instability, in central and obstructive sleep apnea 23 – 25 . NPY5R antagonism, resveratrol mechanism, and insulin sensitivity mechanisms may also be important to investigate. MARCHF1 , the primary mapped gene of the rs13118183 locus is a regulator of insulin sensitivity, controlling cell surface insulin receptor degradation as a ubiquitin ligase 26 . This is important as insulin resistance has been identified as an antecedent risk factor for OSA and associated with ventilatory control abnormalities, increased upper airway fat, and increased upper airway collapsibility (an endotype associated with both EDS and inflammation) 7 , 27 , 28 . MARCHF1 gene expression has been noted to decrease in response to resveratrol, a polyphenol supplement that has anti-inflammatory, antioxidant, and estrogen modulator effects 29 , 30 . In the context of OSA, resveratrol has been prior suggested for consideration of clinical use in treating both OSA and cancer 29 , 30 . Resveratrol is able to induce SIRT1 activity (downregulated in OSA), alter insulin sensitivity in visceral white adipose tissue that can occur from sleep fragmentation, and reduce myocardial injury that can occur from chronic intermittent hypoxia 31 – 33 . In vitro studies report resveratrol’s inhibitory activity against type II phosphatidylinositol 4-kinases, which supports the previously unreported gene for OSA this study identified for male sex in rare variant gene-set analysis - PI4K2B 34 . In addition to MARCHF1 , NPY1R and NPY5R genes mapped to the rs13118183 locus which revealed through PPI enriched terms a closely connected family: neuropeptide Y ( NPY ) - peptide YY ( PYY ) - and pancreatic polypeptide ( PP ). Notably NPY is a vasoconstrictive neuropeptide linked to sleep-wake behavior and may be connected to OSA through its roles in insulin resistance, inflammation, and vascular remodeling 35 , 36 . PYY is sensitive to sleep duration and energy intake and postulated to be involved in obesity development through circadian disruption 37 . Drug-gene interaction reported in DGIdb supports this as NPY5R is the pharmacological target of velneperit, an investigational obesity drug with anorectic effects. The strength of this study is in it being the first to assess on a genome-wide scale the interaction role of EDS using two approaches – common variant association analysis, and rare variant gene-based analysis. This work included multi-ethnic source data, sex-specific analyses, and results from multiple bioinformatics tools. One limitation of this work is utilization of race/ethnicity opposed to genetic ancestry for population group definitions. In addition polysomnography based on a single night may result in some misclassification and self-reported EDS can vary over time. Classification of EDS based on self-report data is subjective although the most widely utilized questionnaire for EDS that discriminates sleep disorders groups was used. Unfortunately we were unable to replicate the significant variant and genes identified in WGS discovery results in imputed data. Intra-variability in EDS prevalence was observed within specific ancestries (elevated in HIS, relatively stable in EUR). Thus future ancestry-specific WGS analyses with a large enough sample size for adequate statistical power to enable ancestry-specific analyses would be invaluable. It is difficult to disentangle the role of EDS with respect to OSA – as a symptom, or separate entity. The results here suggest potential gene targets of exploration for the excessively sleepy clinical subtype of OSA – but cannot definitively identify evidence of exact pathways without future validation. In conclusion, incorporating EDS interactions enabled discovery of genes for OSA that were not revealed by traditional GWAS. This GxE modeling approach assists with precision sleep medicine, as therapeutic designs could differ by exposures or disorder subtypes. Our findings suggest resveratrol and thiamine as supplements for the excessive sleepy subtype of OSA given their modulating pathways. This study’s identified gene targets may help address the complexity inherent to obstructive sleep apnea pathophysiology. METHODS Figure 1 displays the analysis design workflow with cohort description details available in the Supplementary Note. Each contributing study had its protocol approved by the respective Institutional Review Board and participants provided written informed consent. Data Preparation For the discovery analysis, whole-genome sequencing (WGS) TOPMed Freeze 8 data from 11,619 individuals (15% AFR, 2% ASN, 28% EUR, 55% HIS) from seven cohorts (Supplementary Table 1) was utilized. Apnea-hypopnea index (AHI) with > = 3% oxygen desaturation was retrieved from each cohort. Excessive daytime sleepiness (EDS) was modeled as a binary term from the Epworth Sleepiness Scale (> 10: 1, <=10: 0). Age, sex, and body mass index (BMI) were measures obtained at the time of sleep recording. Race/ethnicity measures were derived from dbGaP harmonized demographic data. Genotype data was restricted to minimum sequencing depth of 10, polymorphic PASS only variants, and missingness rate < = 5%. A full description of TOPMed whole-genome sequencing can be found at https://topmed.nhlbi.nih.gov/topmed-whole-genome-sequencing-methods-freeze-8 . For replication analysis, TOPMed-imputed genotype data was prepared using the TOPMed Imputation Server powered by Minimac4, with retention of variants with imputation quality > = 0.4. Gene-Environment Interaction Analysis Model Y ~ B 0 + B G G + B E E + B GxE GxE + B C C The gene x environment interaction (GxE) model in Eq. (1) denotes Y as the continuous outcome, apnea-hypopnea index (AHI). The interacting environment term (E) is excessive daytime sleepiness (EDS), defined by the Epworth Sleepiness Scale (> 10: 1, <=10: 0). C denotes random effects (PC-Relate estimated kinship matrix, household matrix for HCHS/SOL) and fixed effects (age, sex, BMI, age x EDS, sex x EDS, BMI x EDS, 10 PC-AiR PCs for ancestral group population structure, and race/ethnicity-specific cohort) 38 , 39 . With this model design, GENESIS (v2.22.2) was first used to retrieve residuals from the null model allowing for race/ethnicity- and cohort-specific variance, with fully-adjusted two-stage rank normalization of AHI with the norm=’ALL’ and rescale=’residSD’ parameters 40 . Common Variant Analysis For common variant WGS discovery analysis, GEM (v.1.5.2) software was used to conduct common variant (minor allele frequency [MAF] ≥0.1%) association analysis on the GENESIS output residuals with robust standard error estimation (--robust 1) and no outcome centering (--center 0) 41 . Summary statistics were retrieved from the following tests: 1 degree-of-freedom (df) genetic effect (B G ), 1df GxE interaction effect (B GxE ), and the 2df joint G,GxE effect (B G , B GxE ). Significant variants were those passing genome-wide significance level (p < 5e-8). Summary statistics were processed using EasyQC2 to remove variants with missing and out of range values 42 . Rare Variant Set-Based Analysis For rare variant set-based analysis, first variants with MAF < 1% were aggregated into genes based on GENCODEv28, restricting variants to those marked as high-confidence non-synonymous loss of function, damaging or deleterious missense (by SIFT4G, Polyphen2 HumDiv, Polyphen2 HumVar, or LRT), or in-frame insertion-deletion with positive FATHMM-XF coding score 43 – 46 . Following this, MAGEE (v1.2.0) was used to conduct rare variant gene-based analyses enforcing the double Fisher’s method (tests==‘JD’) 47 . P-value summary statistics output for the main genetic effect (“MF” test), interaction effect (“IF” test), and the joint effect (“JD” test) were retrieved. Summary statistics were processed using EasyQC2 to remove variants with missing and out of range values and inflation corrected 42 . Variant and Gene Prioritization For common variant analysis, FUMA SNP2GENE (v.1.5.2) was used to filter significant (P < 5x10 -8 ) signals found on the same chromosome to independent loci defined by lead SNPs using distance criteria (500 kilobases) and linkage disequilibrium (r 2 < 0.1) from the 1000G Phase 3 ALL panel 48 . The Open Targets Genetics platform was then used to map each final lead variant to its primary mapped gene by prioritizing firstly direct gene overlap (e.g. intronic), followed by nearest transcription start site, or lastly highest V2G score 49 . For rare variant set-based analysis, significant genes were those with p < 3e-6, accounting for the total number of tested genes. Replication and Meta-Analysis After WGS discovery analysis, variants in common variant analysis and genes from rare variant set analysis were checked for replication (P < 0.05) by executing the same analyses in 8,904 separate TOPMed-imputed samples from 7 cohorts (Supplementary Table 1) comprising of 3 population groups (1% AFR, 52% EUR, 47% HIS). Common variant meta-analysis was conducted on the WGS and imputed summary statistics using METAL (v2010-02-08), utilizing SCHEME INTERACTION ( https://genome.sph.umich.edu/wiki/Meta_Analysis_of_SNPxEnvironment_Interaction ) for the 2df joint G,GxE test and SCHEME STDERR for the 1df tests. Rare variant set-based meta-analysis was conducted using MAGEE software 47 , 50 – 52 . Final Genes and Variants The above steps were repeated for pooled sex, female sex, and male sex analysis. The final prioritized genomic loci were (a) significant by the 1df GxE interaction test or (b) significant by the G,GxE joint test with stronger GxE interaction signal (P G > P GxE ). Bioinformatics Analysis Post-GxE analysis first included annotating whether final genes and variants for OSA have been previously reported, by querying PheWeb ( https://pheweb.org/UKB-TOPMed/ ), GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ), Sleep Disorders Knowledge Portal ( https://sleep.hugeamp.org ) and four large-scale prior genomic analyses 3 , 16 – 18 . Next, aforementioned FUMA SNP2GENE analysis output was processed to denote the extended gene loci for each lead variant identified in the common variant association analysis, and any enriched tissues defined by MAGMA. FUMA SNP2GENE specifically identifies extended gene loci for each lead variant identified in individual common variant association analysis, based on significant (FDR < 0.05) cis-eQTL associations up to 1Mb away and significant (false discovery rate (FDR) < 1e-6) chromatin interactions with genes 250bp upstream or 500bp downstream of the transcription start site (TSS). STRINGdb (v12.0) was used to investigate each of these corresponding gene loci by pathway/ontology enrichment analysis from the database’s identified protein-protein interactions. Third, the set of final primary genes from the rare variant and common variant analyses were queried in Open Target Genetics (v22.1) to identify any strong cross-trait associations (Locus-to-Gene score > = 0.7, p < 5e-08) and medication-related traits (p = 1.0); and analyzed by QIAGEN Ingenuity Pathway Analysis 48 , 49 , 53 – 57 . Declarations Acknowledgements: This work was supported by the National Institute of Health (NIH) grants R01HL153814 (to H.W.) and R35HL135818 (to S.R.). Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Whole-genome sequencing for “NHLBI TOPMed: The Cleveland Family Study (CFS)” (phs000954) was performed at the University of Washington Northwest Genomics Center (3R01HL098433-05S1). Whole genome sequencing for “NHLBI TOPMed - NHGRI CCDG: Atherosclerosis Risk in Communities (ARIC)” (phs001211) was performed at Baylor College of Medicine Human Genome Sequencing Center and Broad Institute of MIT and Harvard (3U54HG003273-12S2/HHSN268201500015C, 3R01HL092577-06S1). Whole-genome sequencing for “NHLBI TOPMed - NHGRI CCDG: Hispanic Community Health Study/Study of Latinos (HCHS/SOL)” (phs001395) was performed at Baylor College of Medicine Human Genome Sequencing Center (HHSN268201600033I). Whole-genome sequencing for “NHLBI TOPMed: Genomic Activities such as Whole Genome Sequencing and Related Phenotypes in the Framingham Heart Study” (phs000974) was performed at Broad Institute of MIT and Harvard (3U54HG003067-12S2). Whole-genome sequencing for “NHLBI TOPMed: NHLBI TOPMed: MESA” (phs001416) was performed at Broad Institute of MIT and Harvard (3U54HG003067-13S1). Whole-genome sequencing for “NHLBI TOPMed: Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study” (phs001368) was performed at Baylor College of Medicine Human Genome Sequencing Center (HHSN268201600033I, 3U54HG003273-12S2/HHSN268201500015C). Whole-genome sequencing for “NHLBI TOPMed: The Jackson Heart Study” (phs000964) was performed at University of Washington Northwest Genomics Center (HHSN268201100037C). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). Funding was also supported by R01HL087641 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The Genome Sequencing Program (GSP) was funded by the National Human Genome Research Institute (NHGRI), the National Heart, Lung, and Blood Institute (NHLBI), and the National Eye Institute (NEI). The GSP Coordinating Center (U24 HG008956) contributed to cross program scientific initiatives and provided logistical and general study coordination. The Centers for Common Disease Genomics (CCDG) program was supported by NHGRI and NHLBI, and whole genome sequencing was performed at the Baylor College of Medicine Human Genome Sequencing Center (UM1 HG008898). The authors thank the staff and participants of the ARIC study for their important contributions. The Cleveland Family Study has been supported in part by National Institutes of Health grants [R01-HL046380, KL2-RR024990, R35-HL135818, and R01-HL113338]. Cardiovascular Health Study was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006; and NHLBI grants U01HL080295, R01HL085251, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Framingham Heart Study (FHS) acknowledges the support of contracts NO1-HC-25195, HHSN268201500001I and 75N92019D00031 from the National Heart, Lung and Blood Institute and grant supplement R01 HL092577-06S1 for this research. We also acknowledge the dedication of the FHS study participants without whom this research would not be possible. Dr. Vasan is supported in part by the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine. The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (HHSN268201300001I / N01-HC-65233), University of Miami (HHSN268201300004I / N01-HC-65234), Albert Einstein College of Medicine (HHSN268201300002I / N01-HC-65235), University of Illinois at Chicago (HHSN268201300003I / N01- HC-65236 Northwestern Univ), and San Diego State University (HHSN268201300005I / N01-HC-65237). The following Institutes/Centers/Offices have contributed to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, NIH Institution-Office of Dietary Supplements. The Genetic Analysis Center at the University of Washington was supported by NHLBI and NIDCR contracts (HHSN268201300005C AM03 and MOD03). MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420. MESA Family is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071258, R01HL071259, and by the National Center for Research Resources, Grant UL1RR033176. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The Osteoporotic Fractures in Men (MrOS) Study is supported by NIH funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), NCATS, and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The NHLBI provides funding for the MrOS Sleep ancillary study "Outcomes of Sleep Disorders in Older Men" under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. The NIAMS provides funding for the MrOS ancillary study ‘Replication of candidate gene associations and bone strength phenotype in MrOS’ under the grant number R01 AR051124. The NIAMS provides funding for the MrOS ancillary study ‘GWAS in MrOS and SOF’ under the grant number RC2 AR058973. Funding for the Western Australian Sleep Health Study was obtained from the Sir Charles Gairdner and Hollywood Private Hospital Research Foundations, the Western Australian Sleep Disorders Research Institute, and the Centre for Genetic Epidemiology and Biostatistics at the University of Western Australia. Funding for the GWAS genotyping obtained from the Ontario Institute for Cancer Research and a McLaughlin Centre Accelerator Grant from the University of Toronto. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). 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Nucleic Acids Res 45:D985–D994 Kramer A, Green J, Pollard J Jr., Tugendreich S (2014) Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523 – 30 Additional Declarations Yes there is potential Competing Interest. Laura M Raffield is a consultant for the TOPMed Administrative Coordinating Center (through Westat). Supplementary Files SupplementaryFigures.pdf SupplementaryTables.pdf SupplementaryNote.pdf SupplementaryData.xlsx Dataset 1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Michigan","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Smith","suffix":""},{"id":374325483,"identity":"f396fb2d-2ac6-4a73-95d0-a3ce97100b92","order_by":32,"name":"Katie Stone","email":"","orcid":"","institution":"California Pacific Medical Center Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Katie","middleName":"","lastName":"Stone","suffix":""},{"id":374325484,"identity":"07dbb35d-22c6-4c58-ad42-42b21c70e12a","order_by":33,"name":"Xiaofeng Zhu","email":"","orcid":"https://orcid.org/0000-0003-0037-411X","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhu","suffix":""},{"id":374325485,"identity":"93b39c6c-583c-4ac2-b2cb-771933b53c19","order_by":34,"name":"Brian Cade","email":"","orcid":"https://orcid.org/0000-0003-1424-0673","institution":"Brigham and Women's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Cade","suffix":""},{"id":374325486,"identity":"3e7c4fc2-fb34-40df-8d46-5eaad8851299","order_by":35,"name":"Tamar Sofer","email":"","orcid":"https://orcid.org/0000-0001-8520-8860","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Tamar","middleName":"","lastName":"Sofer","suffix":""},{"id":374325487,"identity":"b6d71ad3-ec7a-4932-b459-ad3c842f47c5","order_by":36,"name":"Susan Redline","email":"","orcid":"https://orcid.org/0000-0002-6585-1610","institution":"Brigham and Women's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Redline","suffix":""}],"badges":[],"createdAt":"2024-10-26 12:45:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5337531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5337531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68384036,"identity":"7c215aef-a7b5-4452-9c56-f6a6b8d6ad59","added_by":"auto","created_at":"2024-11-06 17:09:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":740278,"visible":true,"origin":"","legend":"\u003cp\u003eOverall conceptual workflow of this study.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/0615a118c20bbf090d66b4f6.jpeg"},{"id":68384634,"identity":"244a4346-d9c0-40f5-90f5-ee5d93f44535","added_by":"auto","created_at":"2024-11-06 17:17:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":410567,"visible":true,"origin":"","legend":"\u003cp\u003eCommon variant x EDS interaction effect on AHI. A. Manhattan plot of 1df GxE interaction effect. Two common genetic variants are significant at the genome-wide level (p\u0026lt;5e-08) –rs13118183 (MARCHF1) and rs281851 (CCDC3) – for their interaction effect with EDS. B. Forest plot of rs13118183 (MARCHF1) association with AHI in EDS and non-EDS groups, stratified by sex. C. Forest plot of rs281851 (CCDC3) association with AHI in EDS and non-EDS groups, stratified by sex.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/aa0753c05645ccef57dd8aac.jpeg"},{"id":76483509,"identity":"20eb0b7a-709a-4543-af9f-b6ac2a13c5c6","added_by":"auto","created_at":"2025-02-17 15:09:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2267017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/15fa5aec-0673-4ac9-bb5d-2c0fe3f1d0fb.pdf"},{"id":68384038,"identity":"78981b8e-463d-40b3-a786-759446a084bd","added_by":"auto","created_at":"2024-11-06 17:09:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1144571,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/33231e09825be82aff1a58e9.pdf"},{"id":68384635,"identity":"45639a61-b7e3-48f0-b539-29dbf3bf3c43","added_by":"auto","created_at":"2024-11-06 17:17:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":263095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/036a99002837664b25ce747e.pdf"},{"id":68385788,"identity":"0e68d1a6-6c05-4923-9e58-16041c999040","added_by":"auto","created_at":"2024-11-06 17:33:50","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":151999,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryNote.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/b3f5049355d1a161c55a6ade.pdf"},{"id":68384040,"identity":"57cd19b3-184d-49b2-b4db-dc1e8938e8c5","added_by":"auto","created_at":"2024-11-06 17:09:50","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":657819,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"SupplementaryData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5337531/v1/f1b36629a41fc1c04da757c9.xlsx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nLaura M Raffield is a consultant for the TOPMed Administrative Coordinating Center (through Westat).","formattedTitle":"Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eObstructive sleep apnea (OSA) is characterized by recurrent upper airway collapse and obstruction, resulting in arousal and oxygen desaturation events that induce variable degrees of sympathetic activation, inflammation, endothelial dysfunction, and metabolic perturbations \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The clinical presentation of this disorder can vary and includes both disturbed sleep (insomnia) and excessive daytime sleepiness (EDS) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A strong genetic basis has been established for OSA with heritability estimates between 69\u0026ndash;83% in twin studies, 25\u0026ndash;40% in family studies, and up to 21% from population-based genome-wide association studies (GWAS) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Adequately characterizing the genetic architecture of OSA can provide insight into disease mechanisms and better therapeutic regimens, ultimately improving patient outcomes.\u003c/p\u003e \u003cp\u003eIn OSA, EDS is found in over 30% of patients \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Although often exhibiting improvement with OSA treatment, EDS persists in 9\u0026ndash;22% of CPAP-adherent individuals \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Patients with EDS (excessive sleepy OSA subtype) are reported to experience higher risk of incident cardiovascular disease, potentially reflecting elevated inflammation and a more severe endophenotype (high airway collapsibility, low arousal threshold) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. EDS can reflect behavioral factors associated with OSA risk such as insufficient sleep duration, poor diet quality, and reduced physical activity \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As a marker of underlying physiological conditions and environmental exposures, EDS may moderate genetic effects that influence severity of OSA and its cardiovascular risk, with possible pathways linked to the microbiome, systemic inflammation, and adipose tissue function \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe thus sought to investigate whether there is evidence of interaction between EDS and genetic variant effects for apnea-hypopnea index (AHI). We conducted genome-wide interaction analyses with single common variant effects, and rare variant gene set-based effects, in over 11,500 individuals using National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine (TOPMed) data \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A dataset comprising of multiple race/ethnicities (African American/Black [AFR], Asian [ASN], Caucasian/White/European [EUR], Hispanic/Latino [HIS]) was consolidated for this analysis with stratification according to sex (males, females, all sex). Preliminary results have been previously reported in the form of an abstract \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCommon Variant Analysis\u003c/h2\u003e \u003cp\u003eCommon variant analysis with whole genome sequencing (WGS) data in combined sex samples revealed two intronic variants with strong interaction with EDS - \u003cem\u003ers281851\u003c/em\u003e (P\u003csub\u003eGxE\u003c/sub\u003e: 6.59e-09, P\u003csub\u003eG,GxE\u003c/sub\u003e: 2.2e-08) mapped to \u003cem\u003eCCDC3\u003c/em\u003e, and \u003cem\u003ers13118183\u003c/em\u003e (P\u003csub\u003eGxE\u003c/sub\u003e: 2.92e-08, P\u003csub\u003eG,GxE\u003c/sub\u003e: 4.7e-08) mapped to \u003cem\u003eMARCHF1\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Variant to gene mapping by FUMA SNP2GENE (using position, expression quantitative trait loci, chromatin interaction methods) additionally identified \u003cem\u003eUPF2, DHTKD1, SEC61A2, NUDT5, CDC123, CAMK1D, OPTN\u003c/em\u003e, and \u003cem\u003ePHYH\u003c/em\u003e mapped to \u003cem\u003ers281851\u003c/em\u003e and \u003cem\u003eNAF1, NPY1R, NPY5R, TKTL2, FAM218A\u003c/em\u003e, and \u003cem\u003eTRIM60\u003c/em\u003e mapped to \u003cem\u003ers13118183\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenetic Variants Interacting with EDS Identified in Common Variant Discovery Analysis\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003eThese significant variants (RSID) passed genome-wide significance criteria (p\u0026thinsp;\u0026lt;\u0026thinsp;5e-08) for the 1df GxE interaction test and 2df G,GxE joint test, and were identified in combined sex analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMain Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eInteraction Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eJoint Effect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ersID\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr:Position\u003c/p\u003e \u003cp\u003e(b38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect Allele/\u003c/p\u003e \u003cp\u003eAlternative Allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect Allele Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGene Locus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBeta (Standard Error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBeta (Standard Error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers13118183\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:164200775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNAF1, NPY1R, NPY5R, TKTL2, MARCHF1, FAM218A, TRIM60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0094 (0.0025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.034 (0.0062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.92e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.70e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers281851\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10:12924495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eUPF2, DHTKD1, SEC61A2, NUDT5, CDC123, CAMK1D, CCDC3, OPTN, PHYH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0010 (0.0011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.82e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0187 (0.0032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.59e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.23e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRare Variant Set-Based Analysis\u003c/h3\u003e\n\u003cp\u003eRare variant set-based analysis with WGS data identified \u003cem\u003eSCUBE2\u003c/em\u003e in combined sex, and \u003cem\u003eTMEM26\u003c/em\u003e and \u003cem\u003eCPSF4L\u003c/em\u003e in male sex (P\u003csub\u003eG,GxE\u003c/sub\u003e\u0026lt;3e-06, P\u003csub\u003eGxE\u003c/sub\u003e\u0026lt;P\u003csub\u003eG\u003c/sub\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Meta-analysis of WGS and imputed genotype data identified \u003cem\u003eUBLCP1\u003c/em\u003e and \u003cem\u003eMED31\u003c/em\u003e in combined sex; \u003cem\u003eYY1, CPNE5, MYMX, ZNF773, YBEY\u003c/em\u003e, and \u003cem\u003eRAP1GAP\u003c/em\u003e in female sex; and \u003cem\u003ePI4K2B, IQCB1\u003c/em\u003e, and \u003cem\u003eCORO1A\u003c/em\u003e in male sex (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (P\u003csub\u003eG,GxE\u003c/sub\u003e\u0026lt;3e-06, P\u003csub\u003eGxE\u003c/sub\u003e\u0026lt;P\u003csub\u003eG\u003c/sub\u003e). Among these, \u003cem\u003eYY1\u003c/em\u003e additionally showed significance in the GxE interaction effect (P\u003csub\u003eGxE\u003c/sub\u003e\u0026lt;3e-06) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes Interacting with EDS Identified in Rare Variant Set-Based Discovery Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMain Effect P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInteraction Effect P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJoint Effect P-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCUBE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11:9,019,476-9,138,114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.16e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.02e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.12e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImputed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.97e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.91e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.60e-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTMEM26\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10:61,406,642\u0026thinsp;\u0026minus;\u0026thinsp;61,453,381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.63e-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.63e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.54e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCPSF4L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17:73,248,449\u0026thinsp;\u0026minus;\u0026thinsp;73,262,352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.19e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.39e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImputed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.71e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.66e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.80e-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e*\u003c/sup\u003eThese genes passed Bonferroni-corrected significance criteria (p\u0026thinsp;\u0026lt;\u0026thinsp;3e-06) for the joint G,GxE test with stronger interaction signal relative to the main genetic effect (P\u003csub\u003eG\u003c/sub\u003e \u0026gt; P\u003csub\u003eGxE\u003c/sub\u003e). \u003csup\u003e\u0026dagger;\u003c/sup\u003eVariant sets mapped to TMEM26 were not available in imputed genotype data.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes Interacting with EDS Identified in Rare Variant Set-Based Meta-Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Effect P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInteraction Effect P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJoint Effect P-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUBLCP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5:159,263,290\u0026thinsp;\u0026minus;\u0026thinsp;159,286,036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.75e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.29e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.96e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMED31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17:6,643,311-6,651,634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.49e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.22e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRAP1GAP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:21,596,221\u0026thinsp;\u0026minus;\u0026thinsp;21,669,357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.48e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.37e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCPNE5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:36,740,775\u0026thinsp;\u0026minus;\u0026thinsp;36,839,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.30e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.07e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMYMX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:44,216,926\u0026thinsp;\u0026minus;\u0026thinsp;44,218,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.13e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.15e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.64e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYY1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14:100,238,298\u0026thinsp;\u0026minus;\u0026thinsp;100,282,788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.36e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.13e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.08e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZNF773\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19:57,499,915\u0026thinsp;\u0026minus;\u0026thinsp;57,518,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.46e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.51e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYBEY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21:46,286,342\u0026thinsp;\u0026minus;\u0026thinsp;46,297,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.62e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.35e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIQCB1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:121,769,761\u0026thinsp;\u0026minus;\u0026thinsp;121,835,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.69e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.48e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.60e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI4K2B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:25,160,663\u0026thinsp;\u0026minus;\u0026thinsp;25,279,204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.28e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCORO1A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16:30,182,827\u0026thinsp;\u0026minus;\u0026thinsp;30,189,076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.96e-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.07e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003eThese genes passed Bonferroni-corrected significance criteria (P\u0026thinsp;\u0026lt;\u0026thinsp;3e-06) for the GxE interaction test or for the joint G,GxE test with stronger interaction signal relative to the main genetic effect (P\u003csub\u003eG\u003c/sub\u003e \u0026gt; P\u003csub\u003eGxE\u003c/sub\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eValidation of Results\u003c/h3\u003e\n\u003cp\u003eFor WGS common variant results (\u003cem\u003ers281851\u003c/em\u003e, \u003cem\u003ers13118183\u003c/em\u003e) imputed genotype samples did not contain either variant. For rare variant WGS gene-set analysis results (\u003cem\u003eSCUBE2, TMEM26, CPSF4L\u003c/em\u003e) imputed genotype samples did not replicate findings, showing different variant sets available for each gene compared to WGS data (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\n\u003ch3\u003eSecondary Findings\u003c/h3\u003e\n\u003cp\u003eGenetic associations with AHI identified by the main genetic effect B\u003csub\u003eG\u003c/sub\u003e (not considering role of EDS interaction) in the WGS discovery dataset are reported in Supplementary Tables\u0026nbsp;4\u0026ndash;6.\u003c/p\u003e\n\u003ch3\u003eUnreported Gene Targets for OSA\u003c/h3\u003e\n\u003cp\u003eThe variants from common variant analysis and genes from rare variant set-based analysis were assessed with prior OSA trait-related GWAS and three catalogs: GWAS Catalog, PheWeb, Sleep Disorders Knowledge Portal \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;7). \u003cem\u003eSCUBE2, UBLCP1, CPNE5, MYMX, ZNF773, YBEY, IQCB1\u003c/em\u003e, and \u003cem\u003eCORO1A\u003c/em\u003e are identified in a prior gene-based analysis for sleep-disordered breathing traits with samples overlapping this GxE work\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSCUBE2\u003c/em\u003e is additionally reported in the Sleep Disorders Knowledge Portal for sleep apnea syndrome (P: 3.7e-04; common variants). \u003cem\u003ers281851\u003c/em\u003e (intronic variant of \u003cem\u003eCCDC3\u003c/em\u003e), \u003cem\u003ers13118183\u003c/em\u003e (intronic variant of \u003cem\u003eMARCHF1\u003c/em\u003e), \u003cem\u003eTMEM26, CPSF4L, MED31, YY1, RAP1GAP\u003c/em\u003e, and \u003cem\u003ePI4K2B\u003c/em\u003e are previously unreported for OSA (Supplementary Table\u0026nbsp;7). Four of these have been reported for cardiometabolic traits including \u003cem\u003eTMEM26\u003c/em\u003e for PR interval, hypertension, systolic blood pressure, and diastolic blood pressure; \u003cem\u003eMED31\u003c/em\u003e for systolic blood pressure; \u003cem\u003eRAP1GAP\u003c/em\u003e for abdominal aortic calcification levels; and \u003cem\u003eYY1\u003c/em\u003e for aortic atherosclerosis, and pulse pressure.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEffects in EDS vs non-EDS groups\u003c/h2\u003e \u003cp\u003eWe performed EDS-stratified analysis for the significant interaction loci (\u003cem\u003ers281851\u003c/em\u003e, \u003cem\u003ers13118183\u003c/em\u003e) to compare how the effect estimate (B\u003csub\u003eG\u003c/sub\u003e) changes dependent on whether an individual experiences excessive daytime sleepiness. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and C and Supplementary Table\u0026nbsp;8 show that for individuals who have the allele A for rs281851 or allele A for rs13118183, the presence of EDS increases AHI. For rare variant set-based analysis differences in the variants mapped to each gene in each EDS group was observed (Supplementary Table\u0026nbsp;9).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eMAGMA analysis run on FUMA\u0026rsquo;s SNP2GENE platform with WGS common variant GxE interaction summary statistics revealed tissue enrichment in breast mammary tissue and tibial nerve for females (Supplementary Table\u0026nbsp;10). MAGMA gene-based analysis identified \u003cem\u003eNOP53, EYA2\u003c/em\u003e, and \u003cem\u003eZNF563\u003c/em\u003e in combined sex and \u003cem\u003eWDR19\u003c/em\u003e in females (Supplementary Table\u0026nbsp;11).\u003c/p\u003e \u003cp\u003eOpen Targets Platform mouse model data identified functional roles in immune system response (\u003cem\u003eMARCHF1, EYA2, RAP1GAP, CPNE5, CORO1A\u003c/em\u003e), adipose tissue (\u003cem\u003eCCDC3, IQCB1\u003c/em\u003e), metabolism (\u003cem\u003eCCDC3, RAP1GAP, CPSF4L\u003c/em\u003e), nervous system or behavior (\u003cem\u003eUBLCP1, MED31, EYA2, CPNE5, YY1, IQCB1, CORO1A, WDR19\u003c/em\u003e), respiratory system (\u003cem\u003eMYMX, YY1\u003c/em\u003e), cardiovascular system (\u003cem\u003eIQCB1\u003c/em\u003e), and craniofacial measures (\u003cem\u003eWDR19, MED31\u003c/em\u003e) (Supplementary Table\u0026nbsp;12). Open Targets Genetics revealed associations to medication use for lung disease (atrovent-\u003cem\u003eYY1\u003c/em\u003e, ventolin-\u003cem\u003eWDR19\u003c/em\u003e), hypertension or cardiovascular disease (bendroflumethiazide-\u003cem\u003eCORO1A\u003c/em\u003e, atenolol-\u003cem\u003eTMEM26\u003c/em\u003e, atenolol-\u003cem\u003eMARCHF1\u003c/em\u003e, bumetanide-\u003cem\u003eMYMX\u003c/em\u003e, dipyridamole-\u003cem\u003eTMEM26\u003c/em\u003e), and cholesterol (statin-\u003cem\u003eNOP53\u003c/em\u003e, statin-\u003cem\u003eMYMX\u003c/em\u003e) (Supplementary Data).\u003c/p\u003e \u003cp\u003eSTRINGdb identified enriched terms (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the resultant protein-protein interaction network built by the \u003cem\u003ers281851\u003c/em\u003e and \u003cem\u003ers13118183\u003c/em\u003e gene loci (Supplementary Table\u0026nbsp;13). Enriched gene ontology (GO) Molecular Function terms related to the highly conserved pancreatic polypeptide hormone family \u003cem\u003eNPY-PYY-PP\u003c/em\u003e (\u003cem\u003eNPY1R, NPY5R\u003c/em\u003e) and thiamine pyrophosphate-transketolase activity (\u003cem\u003eDHTKD1, TKTL2\u003c/em\u003e) (Supplementary Table\u0026nbsp;13).\u003c/p\u003e \u003cp\u003eDGIdb revealed the following drug-gene interactions: velneperit (prior Phase II clinical trial for obesity) for \u003cem\u003eNPY5R\u003c/em\u003e and losartan (FDA-approved for hypertension) for \u003cem\u003eCAMK1D\u003c/em\u003e (Supplementary Table\u0026nbsp;14).\u003c/p\u003e \u003cp\u003eQiagen\u0026rsquo;s Ingenuity Pathway Analysis software constructed a fully-connected interactome with the primary 16 genes from Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provided as input (Supplementary Fig.\u0026nbsp;1). The identified connections included neuroinflammation and nerve-function (\u003cem\u003eTRIM67, HTT\u003c/em\u003e), inflammation (\u003cem\u003eTNF, INFG\u003c/em\u003e), tumor suppressor (\u003cem\u003eTP53\u003c/em\u003e), DNA damage response (\u003cem\u003eRNF4, FANCD2\u003c/em\u003e), and transcription processing (\u003cem\u003eSIX1, MEPCE, FIP1L1\u003c/em\u003e). Canonical pathway enrichment analysis revealed over-representation of eight pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table\u0026nbsp;15).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the first large-scale GxE analysis for AHI, with the goal of uncovering gene targets for a deeper understanding of the pathophysiology of obstructive sleep apnea. This study examines the effect of EDS on AHI-associated genetic variants in order to elucidate any important biological targets or pathways for the excessively sleepy clinical OSA subtype. We identified significant interactions at 16 genes, of which the following are previously unreported for OSA pathophysiology: \u003cem\u003eCCDC3, MARCHF1, TMEM26, CPSF4L, MED31, YY1, RAP1GAP\u003c/em\u003e, and \u003cem\u003ePI4K2B\u003c/em\u003e. This study\u0026rsquo;s findings suggest the potential of thiamine and resveratrol supplementation for use in OSA patients experiencing excessive daytime sleepiness. Bioinformatics analysis identified cross-trait associations with cardiometabolic traits and medication use, suggesting pathways that may be pertinent to investigate for clinical utility in sleepy OSA patients, given their elevated cardiovascular risk.\u003c/p\u003e \u003cp\u003eThiamine (vitamin B1) metabolism is highlighted by genes mapped to the \u003cem\u003eMARCHF1\u003c/em\u003e and \u003cem\u003eCCDC3\u003c/em\u003e genetic loci from the enrichment of thiamine pyrophosphate and transketolase terms in STRINGdb. Thiamine deficiency can result from high calorie malnutrition, increased age, and gastrointestinal tract factors \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Thiamine deficiency is promoted by fluid loss or antacids use \u0026ndash; which can occur in OSA patients due to nighttime sweating or treatment of comorbid gastroesophageal reflux disorder (GERD) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Thiamine deficiency is also linked to long sleep duration (which is associated with EDS or high sleep propensity), connected to alterations in adenosine triphosphate production\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Thiamine-containing supplements demonstrate improvement in sleep disturbance and insomnia symptoms \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Thiamine is notably a potent inhibitor of human carbonic anhydrase II with activity comparable to acetazolamide \u0026ndash; a medication shown to both lower blood pressure and vascular stiffness, as well as improve AHI and ventilatory instability, in central and obstructive sleep apnea \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eNPY5R\u003c/em\u003e antagonism, resveratrol mechanism, and insulin sensitivity mechanisms may also be important to investigate. \u003cem\u003eMARCHF1\u003c/em\u003e, the primary mapped gene of the \u003cem\u003ers13118183\u003c/em\u003e locus is a regulator of insulin sensitivity, controlling cell surface insulin receptor degradation as a ubiquitin ligase \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This is important as insulin resistance has been identified as an antecedent risk factor for OSA and associated with ventilatory control abnormalities, increased upper airway fat, and increased upper airway collapsibility (an endotype associated with both EDS and inflammation) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMARCHF1\u003c/em\u003e gene expression has been noted to decrease in response to resveratrol, a polyphenol supplement that has anti-inflammatory, antioxidant, and estrogen modulator effects \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In the context of OSA, resveratrol has been prior suggested for consideration of clinical use in treating both OSA and cancer \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Resveratrol is able to induce \u003cem\u003eSIRT1\u003c/em\u003e activity (downregulated in OSA), alter insulin sensitivity in visceral white adipose tissue that can occur from sleep fragmentation, and reduce myocardial injury that can occur from chronic intermittent hypoxia \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In vitro studies report resveratrol\u0026rsquo;s inhibitory activity against type II phosphatidylinositol 4-kinases, which supports the previously unreported gene for OSA this study identified for male sex in rare variant gene-set analysis - \u003cem\u003ePI4K2B\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In addition to \u003cem\u003eMARCHF1\u003c/em\u003e, \u003cem\u003eNPY1R\u003c/em\u003e and \u003cem\u003eNPY5R\u003c/em\u003e genes mapped to the \u003cem\u003ers13118183\u003c/em\u003e locus which revealed through PPI enriched terms a closely connected family: neuropeptide Y (\u003cem\u003eNPY\u003c/em\u003e) - peptide YY (\u003cem\u003ePYY\u003c/em\u003e) - and pancreatic polypeptide (\u003cem\u003ePP\u003c/em\u003e). Notably \u003cem\u003eNPY\u003c/em\u003e is a vasoconstrictive neuropeptide linked to sleep-wake behavior and may be connected to OSA through its roles in insulin resistance, inflammation, and vascular remodeling \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePYY\u003c/em\u003e is sensitive to sleep duration and energy intake and postulated to be involved in obesity development through circadian disruption \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Drug-gene interaction reported in DGIdb supports this as \u003cem\u003eNPY5R\u003c/em\u003e is the pharmacological target of velneperit, an investigational obesity drug with anorectic effects.\u003c/p\u003e \u003cp\u003eThe strength of this study is in it being the first to assess on a genome-wide scale the interaction role of EDS using two approaches \u0026ndash; common variant association analysis, and rare variant gene-based analysis. This work included multi-ethnic source data, sex-specific analyses, and results from multiple bioinformatics tools. One limitation of this work is utilization of race/ethnicity opposed to genetic ancestry for population group definitions. In addition polysomnography based on a single night may result in some misclassification and self-reported EDS can vary over time. Classification of EDS based on self-report data is subjective although the most widely utilized questionnaire for EDS that discriminates sleep disorders groups was used. Unfortunately we were unable to replicate the significant variant and genes identified in WGS discovery results in imputed data. Intra-variability in EDS prevalence was observed within specific ancestries (elevated in HIS, relatively stable in EUR). Thus future ancestry-specific WGS analyses with a large enough sample size for adequate statistical power to enable ancestry-specific analyses would be invaluable. It is difficult to disentangle the role of EDS with respect to OSA \u0026ndash; as a symptom, or separate entity. The results here suggest potential gene targets of exploration for the excessively sleepy clinical subtype of OSA \u0026ndash; but cannot definitively identify evidence of exact pathways without future validation.\u003c/p\u003e \u003cp\u003eIn conclusion, incorporating EDS interactions enabled discovery of genes for OSA that were not revealed by traditional GWAS. This GxE modeling approach assists with precision sleep medicine, as therapeutic designs could differ by exposures or disorder subtypes. Our findings suggest resveratrol and thiamine as supplements for the excessive sleepy subtype of OSA given their modulating pathways. This study\u0026rsquo;s identified gene targets may help address the complexity inherent to obstructive sleep apnea pathophysiology.\u003c/p\u003e "},{"header":"METHODS","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the analysis design workflow with cohort description details available in the Supplementary Note. Each contributing study had its protocol approved by the respective Institutional Review Board and participants provided written informed consent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Preparation\u003c/h2\u003e \u003cp\u003eFor the discovery analysis, whole-genome sequencing (WGS) TOPMed Freeze 8 data from 11,619 individuals (15% AFR, 2% ASN, 28% EUR, 55% HIS) from seven cohorts (Supplementary Table\u0026nbsp;1) was utilized. Apnea-hypopnea index (AHI) with \u0026gt;\u0026thinsp;=\u0026thinsp;3% oxygen desaturation was retrieved from each cohort. Excessive daytime sleepiness (EDS) was modeled as a binary term from the Epworth Sleepiness Scale (\u0026gt;\u0026thinsp;10: 1, \u0026lt;=10: 0). Age, sex, and body mass index (BMI) were measures obtained at the time of sleep recording. Race/ethnicity measures were derived from dbGaP harmonized demographic data. Genotype data was restricted to minimum sequencing depth of 10, polymorphic PASS only variants, and missingness rate\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;5%. A full description of TOPMed whole-genome sequencing can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://topmed.nhlbi.nih.gov/topmed-whole-genome-sequencing-methods-freeze-8\u003c/span\u003e\u003cspan address=\"https://topmed.nhlbi.nih.gov/topmed-whole-genome-sequencing-methods-freeze-8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. For replication analysis, TOPMed-imputed genotype data was prepared using the TOPMed Imputation Server powered by Minimac4, with retention of variants with imputation quality\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene-Environment Interaction Analysis Model\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eY\u0026thinsp;~\u0026thinsp;B\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;B\u003csub\u003eG\u003c/sub\u003eG + B\u003csub\u003eE\u003c/sub\u003eE + B\u003csub\u003eGxE\u003c/sub\u003eGxE + B\u003csub\u003eC\u003c/sub\u003eC\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe gene x environment interaction (GxE) model in Eq.\u0026nbsp;(1) denotes Y as the continuous outcome, apnea-hypopnea index (AHI). The interacting environment term (E) is excessive daytime sleepiness (EDS), defined by the Epworth Sleepiness Scale (\u0026gt;\u0026thinsp;10: 1, \u0026lt;=10: 0). C denotes random effects (PC-Relate estimated kinship matrix, household matrix for HCHS/SOL) and fixed effects (age, sex, BMI, age x EDS, sex x EDS, BMI x EDS, 10 PC-AiR PCs for ancestral group population structure, and race/ethnicity-specific cohort) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. With this model design, GENESIS (v2.22.2) was first used to retrieve residuals from the null model allowing for race/ethnicity- and cohort-specific variance, with fully-adjusted two-stage rank normalization of AHI with the norm=\u0026rsquo;ALL\u0026rsquo; and rescale=\u0026rsquo;residSD\u0026rsquo; parameters \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCommon Variant Analysis\u003c/h2\u003e \u003cp\u003eFor common variant WGS discovery analysis, GEM (v.1.5.2) software was used to conduct common variant (minor allele frequency [MAF] \u0026ge;0.1%) association analysis on the GENESIS output residuals with robust standard error estimation (--robust 1) and no outcome centering (--center 0) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Summary statistics were retrieved from the following tests: 1 degree-of-freedom (df) genetic effect (B\u003csub\u003eG\u003c/sub\u003e), 1df GxE interaction effect (B\u003csub\u003eGxE\u003c/sub\u003e), and the 2df joint G,GxE effect (B\u003csub\u003eG\u003c/sub\u003e, B\u003csub\u003eGxE\u003c/sub\u003e). Significant variants were those passing genome-wide significance level (p\u0026thinsp;\u0026lt;\u0026thinsp;5e-8). Summary statistics were processed using EasyQC2 to remove variants with missing and out of range values\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRare Variant Set-Based Analysis\u003c/h2\u003e \u003cp\u003eFor rare variant set-based analysis, first variants with MAF\u0026thinsp;\u0026lt;\u0026thinsp;1% were aggregated into genes based on GENCODEv28, restricting variants to those marked as high-confidence non-synonymous loss of function, damaging or deleterious missense (by SIFT4G, Polyphen2 HumDiv, Polyphen2 HumVar, or LRT), or in-frame insertion-deletion with positive FATHMM-XF coding score \u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Following this, MAGEE (v1.2.0) was used to conduct rare variant gene-based analyses enforcing the double Fisher\u0026rsquo;s method (tests==\u0026lsquo;JD\u0026rsquo;)\u003csup\u003e47\u003c/sup\u003e. P-value summary statistics output for the main genetic effect (\u0026ldquo;MF\u0026rdquo; test), interaction effect (\u0026ldquo;IF\u0026rdquo; test), and the joint effect (\u0026ldquo;JD\u0026rdquo; test) were retrieved. Summary statistics were processed using EasyQC2 to remove variants with missing and out of range values and inflation corrected \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eVariant and Gene Prioritization\u003c/h2\u003e \u003cp\u003eFor common variant analysis, FUMA SNP2GENE (v.1.5.2) was used to filter significant (P\u0026thinsp;\u0026lt;\u0026thinsp;5x10\u003csup\u003e-8\u003c/sup\u003e) signals found on the same chromosome to independent loci defined by lead SNPs using distance criteria (500 kilobases) and linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) from the 1000G Phase 3 \u003cem\u003eALL\u003c/em\u003e panel \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The Open Targets Genetics platform was then used to map each final lead variant to its primary mapped gene by prioritizing firstly direct gene overlap (e.g. intronic), followed by nearest transcription start site, or lastly highest V2G score\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. For rare variant set-based analysis, significant genes were those with p\u0026thinsp;\u0026lt;\u0026thinsp;3e-6, accounting for the total number of tested genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eReplication and Meta-Analysis\u003c/h2\u003e \u003cp\u003eAfter WGS discovery analysis, variants in common variant analysis and genes from rare variant set analysis were checked for replication (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by executing the same analyses in 8,904 separate TOPMed-imputed samples from 7 cohorts (Supplementary Table\u0026nbsp;1) comprising of 3 population groups (1% AFR, 52% EUR, 47% HIS). Common variant meta-analysis was conducted on the WGS and imputed summary statistics using METAL (v2010-02-08), utilizing SCHEME INTERACTION (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.sph.umich.edu/wiki/Meta_Analysis_of_SNPxEnvironment_Interaction\u003c/span\u003e\u003cspan address=\"https://genome.sph.umich.edu/wiki/Meta_Analysis_of_SNPxEnvironment_Interaction\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the 2df joint G,GxE test and SCHEME STDERR for the 1df tests. Rare variant set-based meta-analysis was conducted using MAGEE software \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFinal Genes and Variants\u003c/h2\u003e \u003cp\u003eThe above steps were repeated for pooled sex, female sex, and male sex analysis. The final prioritized genomic loci were (a) significant by the 1df GxE interaction test or (b) significant by the G,GxE joint test with stronger GxE interaction signal (P\u003csub\u003eG\u003c/sub\u003e \u0026gt; P\u003csub\u003eGxE\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics Analysis\u003c/h2\u003e \u003cp\u003ePost-GxE analysis first included annotating whether final genes and variants for OSA have been previously reported, by querying PheWeb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pheweb.org/UKB-TOPMed/\u003c/span\u003e\u003cspan address=\"https://pheweb.org/UKB-TOPMed/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Sleep Disorders Knowledge Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sleep.hugeamp.org\u003c/span\u003e\u003cspan address=\"https://sleep.hugeamp.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and four large-scale prior genomic analyses \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Next, aforementioned FUMA SNP2GENE analysis output was processed to denote the extended gene loci for each lead variant identified in the common variant association analysis, and any enriched tissues defined by MAGMA. FUMA SNP2GENE specifically identifies extended gene loci for each lead variant identified in individual common variant association analysis, based on significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) cis-eQTL associations up to 1Mb away and significant (false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;1e-6) chromatin interactions with genes 250bp upstream or 500bp downstream of the transcription start site (TSS). STRINGdb (v12.0) was used to investigate each of these corresponding gene loci by pathway/ontology enrichment analysis from the database\u0026rsquo;s identified protein-protein interactions. Third, the set of final primary genes from the rare variant and common variant analyses were queried in Open Target Genetics (v22.1) to identify any strong cross-trait associations (Locus-to-Gene score\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;5e-08) and medication-related traits (p\u0026thinsp;\u0026lt;\u0026thinsp;5e-08) for therapeutic context; queried in Open Targets Platform (v.24.03) to understand mouse model functional effect; queried in DGIdb (v.5.0) for noting druggable gene targets (interaction score\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1.0); and analyzed by QIAGEN Ingenuity Pathway Analysis \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Institute of Health (NIH) grants R01HL153814 (to H.W.) and R35HL135818 (to S.R.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMolecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Whole-genome sequencing for \u0026ldquo;NHLBI TOPMed: The Cleveland Family Study (CFS)\u0026rdquo; (phs000954) was performed at the University of Washington Northwest Genomics Center (3R01HL098433-05S1). Whole genome sequencing for \u0026ldquo;NHLBI TOPMed - NHGRI CCDG: Atherosclerosis Risk in Communities (ARIC)\u0026rdquo; (phs001211) was performed at Baylor College of Medicine Human Genome Sequencing Center and Broad Institute of MIT and Harvard (3U54HG003273-12S2/HHSN268201500015C, 3R01HL092577-06S1). Whole-genome sequencing for \u0026ldquo;NHLBI TOPMed - NHGRI CCDG: Hispanic Community Health Study/Study of Latinos (HCHS/SOL)\u0026rdquo; (phs001395) was performed at Baylor College of Medicine Human Genome Sequencing Center (HHSN268201600033I). Whole-genome sequencing for \u0026ldquo;NHLBI TOPMed: Genomic Activities such as Whole Genome Sequencing and Related Phenotypes in the Framingham Heart Study\u0026rdquo; (phs000974) was performed at Broad Institute of MIT and Harvard (3U54HG003067-12S2). Whole-genome sequencing for \u0026ldquo;NHLBI TOPMed: NHLBI TOPMed: MESA\u0026rdquo; (phs001416) was performed at Broad Institute of MIT and Harvard (3U54HG003067-13S1). Whole-genome sequencing for \u0026ldquo;NHLBI TOPMed: Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study\u0026rdquo; (phs001368) was performed at Baylor College of Medicine Human Genome Sequencing Center (HHSN268201600033I, 3U54HG003273-12S2/HHSN268201500015C). Whole-genome sequencing for \u0026ldquo;NHLBI TOPMed: The Jackson Heart Study\u0026rdquo; (phs000964) was performed at University of Washington Northwest Genomics Center (HHSN268201100037C). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I).\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Atherosclerosis Risk in Communities\u0026nbsp;study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). Funding was also supported by R01HL087641 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The Genome Sequencing Program (GSP) was funded by the National Human Genome Research Institute (NHGRI), the National Heart, Lung, and Blood Institute (NHLBI), and the National Eye Institute (NEI). The GSP Coordinating Center (U24 HG008956) contributed to cross program scientific initiatives and provided logistical and general study coordination. The Centers for Common Disease Genomics (CCDG) program was supported by NHGRI and NHLBI, and whole genome sequencing was performed at the Baylor College of Medicine Human Genome Sequencing Center (UM1 HG008898). The authors thank the staff and participants of the ARIC study for their important contributions.\u003c/p\u003e\n\u003cp\u003eThe Cleveland Family Study has been supported in part by National Institutes of Health grants [R01-HL046380, KL2-RR024990, R35-HL135818, and R01-HL113338].\u003c/p\u003e\n\u003cp\u003eCardiovascular Health Study was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006; and NHLBI grants U01HL080295, R01HL085251, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003eThe Framingham Heart Study (FHS) acknowledges the support of contracts NO1-HC-25195, HHSN268201500001I and 75N92019D00031 from the National Heart, Lung and Blood Institute and grant supplement R01 HL092577-06S1 for this research. We also acknowledge the dedication of the FHS study participants without whom this research would not be possible. Dr. Vasan is supported in part by the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine.\u003c/p\u003e\n\u003cp\u003eThe Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (HHSN268201300001I / N01-HC-65233), University of Miami (HHSN268201300004I / N01-HC-65234), Albert Einstein College of Medicine (HHSN268201300002I / N01-HC-65235), University of Illinois at Chicago (HHSN268201300003I / N01- HC-65236 Northwestern Univ), and San Diego State University (HHSN268201300005I / N01-HC-65237). The following Institutes/Centers/Offices have contributed to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, NIH Institution-Office of Dietary Supplements. The Genetic Analysis Center at the University of Washington was supported by NHLBI and NIDCR contracts (HHSN268201300005C AM03 and MOD03).\u003c/p\u003e\n\u003cp\u003eMESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420. MESA Family is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071258, R01HL071259, and by the National Center for Research Resources, Grant UL1RR033176. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center.\u003c/p\u003e\n\u003cp\u003eThe Osteoporotic Fractures in Men (MrOS) Study is supported by NIH funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), NCATS, and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The NHLBI provides funding for the MrOS Sleep ancillary study \u0026quot;Outcomes of Sleep Disorders in Older Men\u0026quot; under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. The NIAMS provides funding for the MrOS ancillary study \u0026lsquo;Replication of candidate gene associations and bone strength phenotype in MrOS\u0026rsquo; under the grant number R01 AR051124. The NIAMS provides funding for the MrOS ancillary study \u0026lsquo;GWAS in MrOS and SOF\u0026rsquo; under the grant number RC2 AR058973.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding for the Western Australian Sleep Health Study was obtained from the Sir Charles Gairdner and Hollywood Private Hospital Research Foundations, the Western Australian Sleep Disorders Research Institute, and the Centre for Genetic Epidemiology and Biostatistics at the University of Western Australia. Funding for the GWAS genotyping obtained from the Ontario Institute for Cancer Research and a McLaughlin Centre Accelerator Grant from the University of Toronto.\u003c/p\u003e\n\u003cp\u003eThe Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD).\u0026nbsp;The authors also wish to thank the staffs and participants of the JHS. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePN, SR, and HW designed this study.\u003c/p\u003e\n\u003cp\u003ePN, NK, JL, SG, YX, YZ, BS, TF, BEC, TS, HW participated in data analysis.\u003c/p\u003e\n\u003cp\u003eAll authors participated in data acquisition including cohort data preparation and harmonization and/or interpretation of discussion of results.\u003c/p\u003e\n\u003cp\u003ePN, BC, TS, SR, and HW drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the paper that was submitted to the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLMR is a consultant for the TOPMed Administrative Coordinating Center (through Westat).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRedline S, Azarbarzin A, Peker Y (2023) Obstructive sleep apnoea heterogeneity and cardiovascular disease. Nat Rev Cardiol 20:560\u0026ndash;573\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzily M et al (2019) Genetic influences on the onset of obstructive sleep apnoea and daytime sleepiness: a twin study. Respir Res 20:125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSofer T et al (2023) Genome-wide association study of obstructive sleep apnoea in the Million Veteran Program uncovers genetic heterogeneity by sex. EBioMedicine 90:104536\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenberg R, Schweitzer PK, Steier J, Pepin JL (2021) Residual excessive daytime sleepiness in patients treated for obstructive sleep apnea: guidance for assessment, diagnosis, and management. Postgrad Med 133:772\u0026ndash;783\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLal C, Weaver TE, Bae CJ, Strohl KP (2021) Excessive Daytime Sleepiness in Obstructive Sleep Apnea. Mechanisms and Clinical Management. Ann Am Thorac Soc 18:757\u0026ndash;768\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinchuk A, Yaggi HK (2020) Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine. Chest 157:403\u0026ndash;420\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang T et al (2021) C-reactive Protein and Risk of OSA in Four US Cohorts. Chest 159:2439\u0026ndash;2448\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y et al (2022) Physical activity, sedentary behaviour and incidence of obstructive sleep apnoea in three prospective US cohorts. Eur Respir J 59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Tabung FK, Stampfer MJ, Redline S, Huang T (2022) Overall diet quality and proinflammatory diet in relation to risk of obstructive sleep apnea in 3 prospective US cohorts. 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J Enzyme Inhib Med Chem 28:316\u0026ndash;319\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEskandari D, Zou D, Grote L, Hoff E, Hedner J (2018) Acetazolamide Reduces Blood Pressure and Sleep-Disordered Breathing in Patients With Hypertension and Obstructive Sleep Apnea: A Randomized Controlled Trial. J Clin Sleep Med 14:309\u0026ndash;317\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmickl CN et al (2020) Acetazolamide for OSA and Central Sleep Apnea: A Comprehensive Systematic Review and Meta-Analysis. Chest 158:2632\u0026ndash;2645\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagarajan A et al (2016) MARCH1 regulates insulin sensitivity by controlling cell surface insulin receptor levels. Nat Commun 7:12639\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang T et al (2022) Insulin Resistance, Hyperglycemia, and Risk of Developing Obstructive Sleep Apnea in Men and Women in the United States. Ann Am Thorac Soc 19:1740\u0026ndash;1749\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlanos OL et al (2016) Pharyngeal collapsibility during sleep is elevated in insulin-resistant females with morbid obesity. Eur Respir J 47:1718\u0026ndash;1726\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai H et al (2020) Resveratrol inhibits the malignant progression of hepatocellular carcinoma via MARCH1-induced regulation of PTEN/AKT signaling. Aging 12:11717\u0026ndash;11731\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorcacchia AS, Moreira GA, Andersen ML, Tufik S (2022) The use of resveratrol in the treatment of obstructive sleep apnea and cancer: a commentary on common targets. J Clin Sleep Med 18:333\u0026ndash;334\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarreras A et al (2015) Effect of resveratrol on visceral white adipose tissue inflammation and insulin sensitivity in a mouse model of sleep apnea. Int J Obes (Lond) 39:418\u0026ndash;423\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun ZM et al (2020) Resveratrol protects against CIH-induced myocardial injury by targeting Nrf2 and blocking NLRP3 inflammasome activation. Life Sci 245:117362\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalicki M, Karuga FF, Szmyd B, Sochal M, Gabryelska A (2022) Obstructive Sleep Apnea, Circadian Clock Disruption, and Metabolic Consequences. Metabolites 13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrivastava R et al (2005) Resveratrol inhibits type II phosphatidylinositol 4-kinase: a key component in pathways of phosphoinositide turn over. Biochem Pharmacol 70:1048\u0026ndash;1055\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi MM, Zheng YL, Wang WD, Lin S, Lin HL, Neuropeptide Y (2021) An Update on the Mechanism Underlying Chronic Intermittent Hypoxia-Induced Endothelial Dysfunction. 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Nucleic Acids Res 45:D985\u0026ndash;D994\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKramer A, Green J, Pollard J Jr., Tugendreich S (2014) Causal analysis approaches in Ingenuity Pathway Analysis. \u003cem\u003eBioinformatics\u003c/em\u003e 30, 523\u0026thinsp;\u0026ndash;\u0026thinsp;30\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5337531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5337531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObstructive sleep apnea (OSA) is a multifactorial sleep disorder characterized by a strong genetic basis. Excessive daytime sleepiness (EDS) is a symptom that is reported by a subset of OSA patients, persisting even after treatment with continuous positive airway pressure (CPAP). It is recognized as a clinical subtype underlying OSA carrying alarming heightened cardiovascular risk. Thus, conceptualizing EDS as an exposure variable, we sought to investigate EDS\u0026rsquo;s influence on genetic variation linked to apnea-hypopnea index (AHI), a diagnostic measure of OSA severity. This study serves as the first large-scale genome-wide gene x environment interaction analysis for AHI, investigating the interplay between its genetic markers and EDS across and within specific sex. Our work pools together whole genome sequencing data from seven cohorts, enabling a diverse dataset (four population backgrounds) of over 11,500 samples. Among the total 16 discovered genetic targets with interaction evidence with EDS, eight are previously unreported for OSA, including \u003cem\u003eCCDC3\u003c/em\u003e, \u003cem\u003eMARCHF1\u003c/em\u003e, and \u003cem\u003eMED31\u003c/em\u003e identified in all sexes; \u003cem\u003eTMEM26\u003c/em\u003e, \u003cem\u003eCPSF4L\u003c/em\u003e, and \u003cem\u003ePI4K2B\u003c/em\u003e identified in males; and \u003cem\u003eRAP1GAP\u003c/em\u003e and \u003cem\u003eYY1\u003c/em\u003e identified in females. We discuss connections to insulin resistance, thiamine deficiency, and resveratrol use that may be worthy of therapeutic consideration for excessively sleepy OSA patients.\u003c/p\u003e","manuscriptTitle":"Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 17:09:45","doi":"10.21203/rs.3.rs-5337531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4cfffed-6371-49c4-92ad-571891dc557d","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39841003,"name":"Biological sciences/Genetics/Genetic interaction"},{"id":39841004,"name":"Biological sciences/Genetics/Genome/Genetic variation"},{"id":39841005,"name":"Health sciences/Diseases/Neurological disorders/Sleep disorders"}],"tags":[],"updatedAt":"2025-02-17T15:01:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 17:09:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5337531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5337531","identity":"rs-5337531","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}