A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations | 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 A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations Heming Wang, Pavithra Nagarajan, Thomas Winkler, Amy Bentley, and 158 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4163414/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes’ functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep ( 12 ) and long sleep ( 10 ) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management. Biological sciences/Genetics Health sciences/Diseases Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Abnormal sleep duration is detrimental to cardiovascular health – increasing the risk of incident cardiovascular disease (CVD) and mortality – and inherently complex, with suspected heterogeneous effects according to sex and race/ethnicity( 1 , 2 ). Deviation from healthy sleep can impact diurnal rhythms, hormone levels (e.g. ghrelin, cortisol), autonomous nervous system balance, and even remodel vascular structure - resulting in adverse consequences, such as reduced nocturnal blood pressure (BP) dipping and sustained daytime hypertension( 1 , 3 ). Yet the mechanistic pathways underlying the biomolecular connection between short and long sleep with cardiovascular health remain unclear. Evidence implicates heightened sympathetic tone and metabolic dysfunction in the mechanism of short sleep, but there remains a gap in clarity with the added complexity of interwoven pathways like oxidative stress and endothelial dysfunction( 1 , 4 ). The role of long sleep is more elusive, with recent work highlighting the pertinence of inflammatory markers, underlying comorbidity burden (i.e. dyslipidemia, depression) and arterial stiffness metrics( 5 , 6 ). This incomplete understanding of the intersection between habitual sleep duration and cardiovascular health necessitates further investigation. Hypertension is a major risk factor for CVD, with blood pressure traits known to have a strong genetic background. Recent genome-wide association analyses (GWASs) have discovered more than 2,000 loci explaining ~ 40% of systolic or diastolic BP heritability among European descent individuals( 7 ). It is important to investigate the role of sleep health in such a polygenic landscape. This may both explain additional heritability of BP traits, as well as bring to the forefront novel genomic loci that inform perspective on sleep’s influence on biomolecular pathways underlying BP. Moreover, incorporating diverse population groups is essential – as this can reveal novel gene targets specific to particular subgroups or shared across – improving downstream therapeutic designs, and offering tangible insight to counter disparities in health. Our prior work in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Gene-Lifestyle Interactions Working Group highlighted novel non-overlapping gene-sleep interactions for BP, suggesting distinct roles of influence for short and long sleep duration( 8 ). Our current analysis advances the field by including a 12-fold larger sample size and additional sex-stratified analyses, yielding enhanced statistical power and granularity. MATERIALS AND METHODS This work was approved by the Institutional Review Board of Washington University in St. Louis and complies with all relevant ethical regulations. For each of the participating cohorts, the appropriate ethics review board approved the data collection and all participants provided informed consent. Data Harmonization Data from each cohort (Supplementary Tables S1-S2) were harmonized following this centralized protocol. Data were stratified by population group, based on self-reported ancestry and individual cohort definitions (AFR: African, EAS: East Asian, EUR: European, HIS: Hispanic/Latinos, SAS: South Asian), and sex (combined sex, female sex, male sex). Analyses considered 3 primary blood pressure (BP) traits as outcome variables (SBP: systolic, DBP: diastolic, PP: pulse pressure) and 2 dichotomous lifestyle exposures (LTST: long total sleep time, STST: short total sleep time). Genetic variants (G) were restricted to autosomal chromosomes 1–22 imputation quality≥0.3, and minor allele frequency≥0.1%. Age was restricted to ≥18 years, and reported total sleep time constrained within 3 and 14 hours. In scenarios of multiple visits, the single visit with largest sample size was utilized and in case-control study designs, cases and controls were required to be analyzed separately. For BP outcome measures, if multiple readings were taken in a single visit the mean was used. All BP values were winsorized at 6 standard deviations from the mean. BP values were adjusted for reported use of anti-hypertensive medications as follows: SBP (+ 15 mmHg) and DBP (+ 10 mmHg). PP was derived as SBP – DBP. In the case of studies with known between-sample relatedness, null model residuals (regressing BP traits on a kinship matrix/genetic covariance matrix) were denoted as the BP outcome. STST and LTST were derived from total sleep time (TST) by regressing TST on age, sex, age×sex and using the residuals’ 20th and 80th percentiles as cutoffs (STST = 1 if ≤ 20th percentile, LTST = 1 if ≥ 80th percentile, STST = 0 if > 20th percentile, LTST = 0 if < 80th percentile). Covariates included population-group specific principal components, cohort-specific confounders (study center), age, age 2 , sex, age×S/LTST, age 2 ×S/LTST, and sex×S/LTST. Samples with missing data were excluded. Genome-wide Gene-Sleep Interaction Analysis After data harmonization, each population-group specific cohort ran 2 regression models (M1 and M2) for 18 phenotype-exposure-sex combinations (3 phenotypes x 2 exposures x 3 sex groups: combined sex, female sex, male sex). Below E denotes the lifestyle exposure (STST or LTST), Y denotes the BP outcome (SBP, DBP, or PP), C 1 denotes the vector of covariates incorporating E (age, age 2 , S/LTST, age*S/LTST, age 2 *S/LTST, sex, sex*S/LTST), and C 2 denotes the subset without incorporating E (age, age 2 , sex). Female-specific and male-specific analyses were not adjusted for sex. Specialized software choice included LinGxEScanR v1.0 ( https://github.com/USCbiostats/LinGxEScanR ), GEM v1.4.1 ( https://github.com/large-scale-gxe-methods/GEM ), and/or MMAP (latest version available) ( https://github.com/MMAP/MMAP.github.io ) with robust standard errors (SEs) enforced( 9 ). One degree of freedom (df) tests for the marginal effect (β M2_G ), the main effect (β M1_G ), and the interaction effect (β M1_GxE ) were conducted; alongside the 2df joint test that simultaneously assesses the main effect and the interaction effect (β M1_G , β M1_GxE )( 10 ). Model 1 (Primary GxE Model of Interest) (1) M1 : Y = β M1_0 + β E E + β M1_G G + β M1_GxE E⋅G + β M1_C1 C 1 Model 2 (Marginal Effect Model for Comparison) (2) M2 : Y = β M2_0 + β M2_G G + β M2_C2 C 2 Summary statistics were centrally processed after individual studies submitted results. EasyQC2 software ( www.genepi-regensburg.de/easyqc2 ) was used to perform quality control (QC) on resultant data( 11 ). Data were filtered for degrees of freedom≥20 calculated as minor allele count * imputation quality (e.g. MACxR 2 provided by each cohort) within the unexposed, the exposed, and the total sample. Missing or invalid/out of range values for statistics and duplicated or monomorphic variants were discarded. hg19 genomic coordinates were lifted over to hg38 genomic coordinates. Allele frequency discrepancies relative to TOPMed-imputed 1000G reference panels (Trans-Omics for Precision Medicine imputed 1000Genomes) were assessed for each specific population group, along with genomic control (GC) lambda inflation. Meta-level quality control was conducted within groups based on population group, with evaluation of unwanted centering of the outcome variable, outlying cohorts highlighting unstable numerical computation, or alarming inflation. Meta-Analysis Meta-analysis was designed as the following paradigm. Cross-population meta-analysis (CPMA) was designed to be combine all population group results, with additional focused population-group specific and sex-specific analyses. This resulted in 18 total meta-analyses to be run: 6 population groups (CPMA, EUR, HIS, EAS, AFR, SAS) and 3 sex groups (combined sex, female sex, male sex). To accomplish this, METAL software was first used to run all meta-analyses within each specific population group for the marginal effect (β M2_G ), main effect (β M1_G ), interaction effect (β M1_GxE ), and joint effect (β M1_G , β M1_GxE ) with GC correction for inflation( 12 ). Inverse-variance weights were used and Manning et. al’s method for the 2df joint test ( 13 ). CPMA was subsequently executed on the resultant population-group specific METAL output results with GC correction. Genome-wide Significant Loci Identification EasyStrata2 software was used to prioritize top loci from significant results identified from the 1df interaction and 2df joint tests( 14 ). GC correction for population-group specific results was applied. Variants found within 1 Mb distance of the major histocompatibility complex (MHC) region were excluded. Either minimum sample size (N > 20000) or multiple cohorts (≥3) was required as necessary criteria for processing results from a specific sex-stratified, and/or population group-stratified meta-analysis. Significant variants were identified using the following threshold criteria: (i) i variants with significant interaction effect ( P M1_GxE <5e-9, FDR < 0.05); (ii) j variants with significant joint effect ( P M1_G,GxE <5e-9, FDR < 0.05) were filtered as top variants; and (iii) k top variants for the interaction effect were identified using a 2-step method – identifying first z variants by the marginal effect ( P M2_G <1e-5) and then filtering these by the interaction effect ( P M1_GxE <0.05/N G, FDR GxE <0.05) where N G is the number of independent tests calculated using principal components analysis on the z variants. This 2-step method was incorporated to increase power for detecting interactions( 15 ). This design was executed to maintain both stringent threshold criteria and incorporate false discovery correction implemented by the Benjamini-Hochberg method. All such i + j + k significant variants were narrowed down to loci based on 500 kilobase (kb) regions. Finally, within these regions independent lead variants were identified as the top significant variant within the locus, subsequently defining variants in LD as those with linkage disequilibrium (LD) r 2 threshold < 0.1 using TOPMed-imputed 1000G reference panels. If variants were missing in the LD panels, then the most significant variant within each 500kb region was retained for combined sex meta-analyses results. Prioritizing Novel Sleep Duration Interaction Loci Significant independent loci were subsequently filtered to prioritize gene-sleep duration interaction loci. From the 1df interaction test, X interaction loci were prioritized as those not found within 1Mb of previously identified gene-sleep duration loci for BP( 8 ). Loci were annotated as whether novel for BP genetic architecture, or not, by checking for overlap with 1Mb of previous GWAS variants (Supplementary Table S3 ). For the 2df test, first loci were filtered to those variants not found within 1Mb of previous GWAS identified variants for BP traits, and with insignificant marginal effect ( P M2_G >5e-09, FDR M2_G >0.05). From these variants, Y loci were prioritized as driven by interaction if they harbored a stronger interaction effect relative to the main effect ( P M1_GxE < P M1_G ), and Z loci deemed as supported (but not driven) by interaction if this was not true. Thus, collectively X + Y gene-sleep duration interaction loci were highlighted, alongside secondarily Z loci supported by interaction. Heterogeneity by Sex To test for interaction effects showing evidence of heterogeneity by sex (p < 0.05/ Q ), two-sample Z-tests assuming independence, were conducted for each of the top interaction loci and adjusted for multiple testing. Mapped Protein Coding Genes Gene mapping prioritized protein-coding genes for downstream interpretation. Variants directly overlapping protein-coding gene regions were top priority criteria for gene assignment. For intergenic variants nearest distance to transcription start site (TSS) or gene start/end site was queried from Open Target Genetics v22.10( 16 ) or MyGene.Info using Python package mygene v3.2.2 ( https://github.com/biothings/mygene.py ). Variant mapping annotations were additionally noted from Open Target Genetics, Functional Annotation of Variants – Online Resource v2.0 (FAVOR)( 17 ), HaploReg v4.2 ( https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php ), BRAVO variant browser ( https://bravo.sph.umich.edu/freeze8/hg38/ ), Functional Mapping and Annotation of Genome-wide Association Studies v1.5.6 (FUMA)( 18 ), and MyGene.Info. Variant Annotations FAVOR was queried to annotate deleteriousness or functionality scores( 17 ), and RegulomeDB v2.2 was used to extract aggregate regulatory function evidence scores, along with chromatin state, DNA accessibility, overlap with transcription factor (TF) binding sites or TF motifs, and expression quantitative trait loci (eQTL)( 19 ). FUMA’s SNP2GENE pipeline was used to annotate a comprehensive list of genes for each top locus, incorporating positional, chromatin interaction (FDR 0.1 within 500kb)( 18 ). At the variant level, PheWeb, Open Target Genetics, Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org/ ), and Oxford Brain Imaging Genetics Server (BIG40) were queried for significant trait associations (p < 5e-08) from past GWAS( 16 , 20 , 21 ). At the gene level, International Mouse Phenotyping Consortium release 19.1 (IMPC), Online Mendelian Inheritance in Man (OMIM; https://omim.org/ ), PheWeb, Phenotype-Genotype Integrator (PheGenI), and Open Target Genetics were queried for phenotypic annotations from mice knockout study results, involvement in Mendelian disorders, and significant trait associations (p < 5e-08) ( 16 , 20 , 22 , 23 ). All STST and LTST mapped protein-coding genes were then queried using FUMA’s GENE2FUNC pipeline to identify significant (adjusted p-value < 0.05) pathways and traits( 18 ). STRING v12.0 was additionally queried using medium confidence threshold (0.4) to note significantly (FDR < 0.05) enriched traits or pathways to compare and contrast LTST and STST loci ( 24 ). Druggability Analysis The Drug-Gene Interaction database (v4.2.0) was first utilized to identify druggability potential, with genes also annotated for implicated pathways and functions using the Kyoto Encyclopedia of Genes and Genomes database. Druggability target categories were annotated and all interacting drugs queried from reports across 43 databases (BaderLabGenes, CarisMolecularIntelligence, dGene, FoundationOneGenes, GO, HingoraniCasas, HopkinsGroom, HumanProteinAtlas, IDG, MskImpact, Oncomine, Pharos, RussLampel, Tempus, CGI, CIViC, COSMIC, CancerCommons, ChemblDrugs, ChemblInteractions, ClearityFoundationBiomarkers, ClearityFoundationClinicalTrial, DTC, DoCM, DrugBank, Ensembl, Entrez, FDA, GuideToPharmacology, JACX-CKB, MyCancerGenome, MyCancerGenomeClinicalTrial, NCI, OncoKB, PharmGKB, TALC, TEND, TTD, TdgClinicalTrial, Wikidata). Protein targets for available active ligands in ChEMBL were also noted. Gene targets were looked up in the druggable genome using the most recent druggable genome list established from the NIH Illuminating the Druggable Genome Project ( https://github.com/druggablegenome/IDGTargets ) available through the Pharos web platform. Lastly, FDA-approved drugs, late-stage clinical trials and disease indications were queried in the DrugBank, ChEMBL, ClinicalTrials.gov databases to provide results for the top MESH and DrugBank indications and clinical trials. RESULTS Discovery of Novel Gene-Sleep Duration Interactions From an initial source of 37 studies, 59 population-group specific cohorts (derived from self-reported ancestry) resulted in a pooled sample size of 811,405 individuals comprising of 5.9% AFR (12 cohorts), 6.0% EAS (5 cohorts), 83.4% EUR (34 cohorts), 3.7% HIS (7 cohorts), and 0.9% SAS (1 cohort) (Supplementary Tables S1-S2). The 1df test discovered seven loci and the 2-step method discovered one locus. The 2df joint test first identified 3629 significant loci, from which 18 were novel for BP (Supplementary Table S3 ) with insignificant marginal effect – revealing 14 loci driven by the interaction effect, and four not driven. Thus in total we discovered 22 gene-sleep duration interaction loci, and 4 secondary loci – of which 21 are novel for BP traits (Supplementary Table S4). Among the 22 prioritized interaction loci, four loci exhibited cross-population effects (Table 1 , Supplementary Figures S1 -S3) – one identified in combined sex, three in female sex-stratified analyses, and 18 identified specific to either one of the AFR, HIS, or EUR population groups (Table 2, Supplementary Figures S1 -S3). Specifically, AFR analysis revealed one gene-sleep duration interaction locus, HIS analysis revealed 11 gene-sleep duration interaction loci, and EUR analysis revealed six gene-sleep duration loci (Table 2). Three variants identified in combined sex meta-analyses showed evidence of heterogeneous effect by sex (Table 2). Table 1 Novel Gene-Sleep Duration Interaction Loci Identified in Cross-Population Meta-Analysis All results herein are from the M1 model. Supplementary Table S21 provides summary statistics according to each population group identified in cross-population results. For the sex column, C denotes combined sex, and F denotes female sex-stratified meta-analysis. For the alleles column, E denotes effect allele, and O denotes other allele used as reference. Bold denotes significance. Exposure Variant Nearest gene(s) Position (hg38) Alleles (E/O) AF N Sex Population Groups Trait B M1_G (se M1_G ) B GxE (se GxE ) P M1_G P GxE P G,GxE P sex_diff Loci Identified by 1df Interaction Test STST rs11314421 1 WBP1L 10:102808541 C/CG 0.551 279527 F AFR, EAS, EUR, HIS, SAS PP 0.12 (0.04) 0.40 (0.09) 1.60E-03 7.82E-06 3.54E-11 8.84E-05 LTST rs538479553 2 FAM98A 2:33903659 C/G 0.102 41814 F AFR, EAS, EUR, HIS SBP 1.12 (0.40) -5.09 (0.86) 5.27E-03 3.32E-09 2.35E-08 3.59E-06 Loci Identified by 2df Joint Test, Driven by Interaction Effect STST rs76458410 2 YWHAB 20:44851866 G/A 0.023 47678 C AFR,HIS PP 0.14 (0.34) -3.65 (0.73) 6.93E-01 6.38E-07 3.88E-09 6.87E-02 LTST rs1431999695 2 ALG10B 12:37297511 T/C 0.016 29473 F EUR, HIS PP -0.57 (0.44) -3.28 (0.78) 1.88E-01 2.46E-05 4.55E-09 4.07E-04 1 denotes the variant was identified using the two-step approach. 2 denotes variants novel for BP. Table 2. Novel Gene-Sleep Duration Interaction Loci Identified Specific to Certain Population Groups. All results herein are from the M1 model. Supplementary Table S22 provides summary statistics according to each population group-specific cohort identified in these population-group specific results. For the sex column, C denotes combined sex meta-analysis, and M denotes male sex-stratified meta-analysis. For the alleles column, E denotes effect allele, and O denotes other allele used as reference. Bold denotes significance. Exposure Variant Nearest gene(s) Position (hg38) Alleles (E/O) Sex AF N Population Groups Trait B M1_G (se M1_G ) B GxE (se GxE ) P M1_G P GxE P G,GxE P sex_diff 2 Loci Identified by 1df Interaction Test STST rs114831731 AHCYL1 1:109970740 A/T C 0.009 23897 HIS PP 0.85 (0.64) -6.27 (1.04) 1.87E-01 1.57E-09 7.34E-11 5.67E-04 rs144229676 ZNF521 18:25303461 A/C C 0.019 27119 HIS DBP 2.32 (0.70) -7.23 (1.18) 8.70E-04 8.10E-10 1.80E-09 5.00e-01 rs1035064 ZNF682 19:19997730 T/C M 0.019 392939 EUR SBP -0.27 (0.18) 2.10 (0.36) 1.62e-01 4.82E-09 5.76E-08 1.52E-04 rs112958007 1 PAK5 20:9805888 C/T C 0.005 23897 HIS PP 2.09 (0.97) -10.83 (1.78) 3.03E-02 1.28E-09 1.23E-08 rs752086677 1 KRTAP13-2 21:30362234 C/G C 0.002 30009 EUR DBP 2.12 (0.87) -8.71 (1.23) 1.47E-02 1.46E-12 5.68E-13 LTST rs372262693 TG, SLA 8:133069499 T/C C 0.023 23897 HIS PP 0.62 (0.41) -4.91 (0.82) 1.34E-01 2.33E-09 7.11E-09 2.43E-01 Loci Identified by 2df Joint Test, Driven by Interaction Effect STST rs143863772 1 MROH7 1:54707218 T/G C 0.006 23902 HIS SBP -1.56 (1.06) -6.99 (1.74) 1.47E-01 5.79E-05 1.84E-10 rs141117715 1 KCNJ3 2:155446948 C/T C 0.021 23897 HIS PP 0.51 (0.44) -4.29 (0.76) 2.53E-01 1.74E-08 2.45E-09 7.86E-02 rs17011282 1 ZNF385D 3:22386254 C/G C 0.006 23897 HIS PP -0.01 (0.79) -6.00 (1.20) 9.93E-01 5.86E-07 4.13E-10 1.97E-01 rs764985249 1 EFNA5 5:105713137 T/C C 0.002 26230 EUR PP 0.97 (1.48) -11.78 (2.00) 5.09E-01 4.06E-09 6.45E-12 rs542745170 1 WWOX 16:78323227 A/C C 0.009 23897 HIS PP -0.36 (0.72) -4.96 (1.19) 6.20E-01 3.14E-05 5.00E-09 2.05E-03 LTST rs533724062 1 BRINP3 1:190792851 TA/T C 0.011 34442 AFR PP 1.13 (0.60) -4.40 (1.18) 6.03E-02 1.92E-04 3.69E-09 3.56E-04 rs138288695 1 CRBN 3:3295965 G/A C 0.006 23897 HIS DBP 0.57 (0.68) -5.50 (1.08) 4.07E-01 3.46E-07 1.31E-09 rs142966182 1 ALCAM 3:104791665 T/C C 0.006 26230 EUR DBP -0.20 (0.62) -5.37 (1.01) 7.50E-01 1.01E-07 1.21E-09 rs540041583 1 PAM 5:103002447 A/G C 0.003 26230 EUR PP -1.54 (1.20) -6.50 (1.66) 1.99E-01 8.93E-05 6.93E-10 rs113952142 1 SDK1 7:3917121 A/C C 0.005 23902 HIS SBP -0.28 (1.20) -9.07 (1.90) 8.18E-01 1.81E-06 5.49E-10 rs111392401 1 JMJD1C 10:65029197 T/G C 0.007 23897 HIS DBP 0.49 (0.63) -5.71 (1.00) 4.32E-01 1.27E-08 2.28E-10 rs772862932 1 ATP8A2 13:25395875 T/C C 0.002 30009 EUR DBP -0.11 (0.70) -4.85 (1.00) 8.70E-01 1.31E-06 1.28E-09 1 denotes variants novel for BP. 2 Empty cell in P sex_diff indicates the variant, after quality control, was not found in both sex-stratified meta-analyses. Table 3 Novel BP Loci Identified by the 2df Joint Test, Not Driven by the Interaction Effect Exposure Variant Nearest gene(s) Position (hg38) Alleles (E/O) Sex AF N Population Groups Trait B M1_G (se M1_G ) B GxE (se GxE ) P M1_G P GxE P G,GxE P sex_diff 2 Loci Identified by 1df Interaction Test STST rs114831731 AHCYL1 1:109970740 A/T C 0.009 23897 HIS PP 0.85 (0.64) -6.27 (1.04) 1.87E-01 1.57E-09 7.34E-11 5.67E-04 rs144229676 ZNF521 18:25303461 A/C C 0.019 27119 HIS DBP 2.32 (0.70) -7.23 (1.18) 8.70E-04 8.10E-10 1.80E-09 5.00e-01 rs1035064 ZNF682 19:19997730 T/C M 0.019 392939 EUR SBP -0.27 (0.18) 2.10 (0.36) 1.62e-01 4.82E-09 5.76E-08 1.52E-04 rs112958007 1 PAK5 20:9805888 C/T C 0.005 23897 HIS PP 2.09 (0.97) -10.83 (1.78) 3.03E-02 1.28E-09 1.23E-08 rs752086677 1 KRTAP13-2 21:30362234 C/G C 0.002 30009 EUR DBP 2.12 (0.87) -8.71 (1.23) 1.47E-02 1.46E-12 5.68E-13 LTST rs372262693 TG, SLA 8:133069499 T/C C 0.023 23897 HIS PP 0.62 (0.41) -4.91 (0.82) 1.34E-01 2.33E-09 7.11E-09 2.43E-01 Loci Identified by 2df Joint Test, Driven by Interaction Effect STST rs143863772 1 MROH7 1:54707218 T/G C 0.006 23902 HIS SBP -1.56 (1.06) -6.99 (1.74) 1.47E-01 5.79E-05 1.84E-10 rs141117715 1 KCNJ3 2:155446948 C/T C 0.021 23897 HIS PP 0.51 (0.44) -4.29 (0.76) 2.53E-01 1.74E-08 2.45E-09 7.86E-02 rs17011282 1 ZNF385D 3:22386254 C/G C 0.006 23897 HIS PP -0.01 (0.79) -6.00 (1.20) 9.93E-01 5.86E-07 4.13E-10 1.97E-01 rs764985249 1 EFNA5 5:105713137 T/C C 0.002 26230 EUR PP 0.97 (1.48) -11.78 (2.00) 5.09E-01 4.06E-09 6.45E-12 rs542745170 1 WWOX 16:78323227 A/C C 0.009 23897 HIS PP -0.36 (0.72) -4.96 (1.19) 6.20E-01 3.14E-05 5.00E-09 2.05E-03 LTST rs533724062 1 BRINP3 1:190792851 TA/T C 0.011 34442 AFR PP 1.13 (0.60) -4.40 (1.18) 6.03E-02 1.92E-04 3.69E-09 3.56E-04 rs138288695 1 CRBN 3:3295965 G/A C 0.006 23897 HIS DBP 0.57 (0.68) -5.50 (1.08) 4.07E-01 3.46E-07 1.31E-09 rs142966182 1 ALCAM 3:104791665 T/C C 0.006 26230 EUR DBP -0.20 (0.62) -5.37 (1.01) 7.50E-01 1.01E-07 1.21E-09 rs540041583 1 PAM 5:103002447 A/G C 0.003 26230 EUR PP -1.54 (1.20) -6.50 (1.66) 1.99E-01 8.93E-05 6.93E-10 rs113952142 1 SDK1 7:3917121 A/C C 0.005 23902 HIS SBP -0.28 (1.20) -9.07 (1.90) 8.18E-01 1.81E-06 5.49E-10 rs111392401 1 JMJD1C 10:65029197 T/G C 0.007 23897 HIS DBP 0.49 (0.63) -5.71 (1.00) 4.32E-01 1.27E-08 2.28E-10 rs772862932 1 ATP8A2 13:25395875 T/C C 0.002 30009 EUR DBP -0.11 (0.70) -4.85 (1.00) 8.70E-01 1.31E-06 1.28E-09 All results herein are from the M1 model. Supplementary Tables S21-S22 provides summary statistics according to each population group-specific cohort for rs59680540 and rs150586434, and according to each population group for rs34761985 and rs13032423 below. For the sex column, C denotes combined sex meta-analysis. For the alleles column, E denotes effect allele, and O denotes other allele used as reference. Bold denotes significance. 1 denotes variants novel for BP. 2 Empty cell in P sex_diff indicates the variant, after quality control, was not found in both sex-stratified meta-analyses. Prior Reported Gene-Sleep Interactions In our current cross-population meta-analysis, rs10406644 showed interaction evidence with STST ( P G×E =2.0×10 − 4 for PP), and rs7955964 with LTST ( P G×E =6.6×10 − 3 for SBP, P G×E =1.7×10 − 2 for DBP) with direction of association agreeing with prior work( 8 ), and non-significance ( P GxE >0.05) for the opposite sleep duration exposure (Supplementary Table S5). Functional Potential of Variants Four variants (rs11483173, rs372262693, rs113952142, rs11314421) were marked by high transcription activity chromatin states and eight variants (rs114831731, rs372262693, rs143863772, rs533724062, rs11314421, rs1035064, rs538479553, rs13032423) were marked by accessible chromatin in heart tissue or blood (Supplementary Table S6). Six variants (rs114831731, rs542745170, rs533724062, rs11314421, rs1035064, rs538479553) reflected marked regulatory potential with RegulomeDB scores ≥2c (Supplementary Table S6). Mapped Protein-Coding Genes All 26 variants identified were either intronic or intergenic, and mapped to a primary set of 27 protein coding genes (Supplementary Table S7). Extended gene mapping revealed 292 genes highlighted for the 12 STST interaction loci, 67 genes for the 10 LTST interaction loci, and 35 genes for the four joint 2df loci not driven by interaction (Supplementary Tables S7-S9). Expression Quantitative Trait Loci (eQTL) Tissue-specific (GTEXv8) eQTL associations were observed at rs11314421, rs1035064, and rs34761985 in tissues of the heart, vasculature, or blood (Supplementary Table S8)( 19 ). WBP1L -rs11314421 and ZNF682 -rs1035064 gene mappings were corroborated by eQTL evidence identified in venous blood or the tibial artery. Beyond these primary mapped genes, MFSD13A , BORCS7 , CALHM2 , RPARP-AS1 , AS3MT , and SFXN2 expression were mapped to rs11314421 by eQTL evidence in the ascending aorta, coronary artery, tibial artery, left ventricle myocardium, right atrium auricular region, venous blood, or lymphoblast. Similarly, ZNF56 , ZNF253 , ZNF93 , ZNF90 , and ZNF486 were mapped by coronary artery or venous blood eQTL evidence to rs1035064. UXS1 was mapped by rs34761985 eQTL data identified in the right atrium auricular region. Variant-Level Cross-Trait Associations Several variants show associations ( P < 5×10 − 8 ) with other traits (Supplementary Table S10). Querying Open Target Genetics ( https://genetics.opentargets.org ) identified rs11314421 ( WBP1L) to be associated with hypertension and testosterone levels, and rs13032423 (VRK2) with sleep duration and feeling miserable (Supplementary Table S11). Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org/ ) further identified rs13032423 (VRK2) to be associated with BMI and sleep duration (Supplementary Figure S4). Querying brain imaging phenotypes through the Oxford Brain Imaging Genetics Server (BIG40) revealed rs13032423’s ( VRK2) connection to brain functional connectivity by its association with rfMRI connectivity (ICA100 edge 965) (Supplementary Table S12, Supplementary Figure S5)( 21 ). Gene Functional Implications Mice knockout evidence highlighted genes important for heart morphology ( BRINP3, CRBN, ALG10B, PRMT6 ), and cardiac rhythm ( TG, WBP1L ) (Supplementary Table S13). Open Target Genetics, PheWeb, and PheGenI revealed genes implicated in genetic associations (p < 5×10 − 8 ), with OMIM ( https://omim.org/ ) identifying any linked Mendelian disorders (Supplementary Tables S14-S17)( 16 , 20 , 23 ). Specifically, eight genes harbored links to the cardiovascular domain through association with traits identifying by genetic studies: WBP1L , EFNA5, ZNF521 , WWOX , ZNF385D , FAM98A , PAM , and JMJD1C . ALG10B was identified to be implicated in the Mendelian disorder long QT syndrome. In the realm of sleep and circadian health, reported genetic associations corroborated the relevance of EFNA5 , ZNF52 1, WWOX, ALG10B , PAM and SDK1 with insomnia, daytime napping, or chronotype traits. Genetic associations to other pertinent domains including kidney function, neurological health, liver function, thyroid function, metabolism, lifestyle choice, and inflammation, were also noted (Supplementary Table S10). Gene Set Enrichment Analysis We performed gene set enrichment analyses on the aforementioned extended gene sets in the FUMA GENE2FUNC platform and STRING database (Supplementary Tables S18-S19)( 18 , 24 ). STST-mapped genes highlighted pathways in antioxidant defense and neuron excitation, along with phenotypic connection to lipid levels, neurological health, cardiovascular health, metabolism and immune defense. LTST-mapped genes implicated traits involving inflammation, neurological health, and metabolism. A clearly distinctive pattern differentiating short and long sleep duration interaction loci was thus not observed. Druggability We investigated druggability of the primary mapped genes using an integrative approach to highlight drug repurposing potential (Supplementary Table S20)( 25 ). Identified gene candidates revealed connections to serotonergic response ( HTR1F, KCNJ3 ), proteasome-mediated ubiquitination ( CRBN ), thyroid hormone synthesis ( TG ), and axon guidance ( PAK5, ALCAM ) pathways. Of these, KCNJ3, CRBN, HTR1F, TG , PAK5 , and ALCAM harbored links to reported drug interactions and active ligand interactions in the ChEMBL database. The following genes displayed evidence of pharmacological targeting: KCNJ3 (by small molecule inhibitors Atomoxetine and Dronedarone); CRBN ( by thalidomide analogs Pomalidomide and Lenalidomide); HTR1F (by selective serotonin receptor agonists like Lasmiditan); and ALCAM (by chemotherapy agent Fluorouracil). DISCUSSION In this large-scale effort investigating the biomolecular mechanisms underpinning the intersecting roles of sleep health and blood pressure traits, we conducted genome-wide gene-by-sleep duration (short and long sleep) interaction analyses in 811,405 individuals of diverse population backgrounds (AFR, EAS, EUR, HIS, SAS) for systolic blood pressure, diastolic blood pressure, and pulse pressure. We report novel discovery of 22 gene-sleep duration interaction loci for BP traits – 12 for short sleep, and 10 for long sleep. Several of the identified variants are rare with allele frequency < = 1%, with four variants identified in sex-stratified meta-analyses, and 18 variants specific to either the AFR( 1 ), EUR( 6 ), or HIS ( 11 ) population groups. In line with our previous research, the identified genomic loci exhibiting interactions with short and long sleep are non-overlapping (with non-significance in the opposing sleep duration exposure), suggesting distinct mechanisms influencing cardiovascular health. Nonetheless, we did not observe a clear differentiating pattern in the biological pathways implicated when comparing short sleep and long sleep. The functional annotation investigations of our prioritized genes point towards cardiovascular and neurological connections, along with revealing links to circadian rhythm, thyroid function, bone health, and hematopoiesis mechanisms. Our findings highlight potential pharmacological candidates and suggest pertinent pathways to consider when designing holistic therapeutic regimens for improving blood pressure control. Firstly, at a broad level, several identified genes are tied to neurological mechanisms. KCNJ3 encodes Kir3.1 – the alpha subunit for the I KACh potassium channel – and is interestingly implicated in bradyarrhythmia by its missense variant inducing a gain of function of I KACh , as activation of this channel is tied to the negative chronotropic effect on heart rate exerted by the parasympathetic nervous system( 29 ). CRBN is linked to cognitive function ( 30 ), SDK1 promotes synaptic connectivity ( 31 ), ZNF521 regulates neuron cell fate ( 32 ), and ATP8A2 is involved in both neuron vesicle transport and cardiac conduction ( 33 ). Further, KRTAP13-2 , WWOX, EFNA5 , and ALCAM are linked to nervous system development with additional roles for WWOX in myelination ( 34 ) and EFNA5 in vascular sympathetic innervation ( 35 ). These functional connections may suggest a potential nervous system-heart connection that could be influenced by sleep or circadian disturbances. In fact neurological pathway connections to circadian rhythm reveal themselves through two enzymes - PAK5 and PAM . Given that circadian rhythm and clock gene expression is intimately connected to blood pressure patterns, of note is PAK5 – a serine/threonine kinase protective of adult neurons from injury and ischemic stress( 36 ). PAK5 has both been shown to be targeted by clock gene-regulated miRNAs in the liver and identified to strongly bind to 14-3-3 proteins – a protein family connected to light-sensitive melatonin diurnal patterns and plausibly influential for sleep behavior( 37 , 38 ). This strong binding affinity to 14-3-3 proteins suggests an interesting connection, as YWHAB (one of this study’s primary genes mapped to a STST interaction locus), is part of this protein family. Another enzyme informing the neurological-sleep axis is PAM , encoding a copper-dependent enzyme important for synthesizing amidated neuropeptides like NPY – which regulates sleep through noradrenergic signals( 39 , 40 ). Further, TG and JMJD1C , both encoding proteins intrinsically tied to thyroid hormone function (thyroglobulin and thyroid receptor-interacting protein 8 respectively) – present suggestive ties to the intersection between thyroid function and circadian rhythms. TG mRNA and protein expression levels have shown to increase in response to melatonin, along with its genetic variants associated with autoimmune thyroid diseases( 41 , 42 ). Gene silencing of JMJD1C ’s paralog has shown arrhythmicity and prolonged sleep in Drosophila( 43 ). Given that circadian clock and thyroid function are increasingly suggested to be interconnected, and sleep deprivation can disrupt temporal hormone profiles (e.g. increased morning plasma thyroid-stimulating hormone (TSH) levels), it may be valuable to investigate further the overlapping pathways between thyroid function, healthy sleep duration, and cardiovascular morbidity( 44 ). Beyond thyroidal pathways, hematopoiesis presents a possible comprehensive perspective on the interconnectedness between sleep health and nervous system response. WBP1L , one of the primary genes identified (mapped to a STST interaction locus identified in female-specific CPMA) has suggestive connection to regulating the CXCL12 - CXCR4 signaling pathway by its inhibitory role on CXCR4 , the receptor for ligand CXCL12 ( 45 ). This pathway is both influential for inflammation and hematopoietic state, reflects circadian control, and directly implicates the sympathetic nervous system response – pertinent as stressors are suspected to induce a more exacerbated response in females( 46 , 47 ). If stress factors (e.g. sleep loss) induce noradrenaline, this can downregulate CXCL12 , with resultant increased cell proliferation of pro-inflammatory cells from the bone marrow, incurring vascular damage( 46 ). For instance, fragmented sleep has shown to promote myelopoiesis and lower hypocretin release by the hypothalamus, in turn accelerating atherosclerosis progression( 48 ). Thus perhaps WBP1L can offer insight into the intersections between sympathetic activation, neurological control, and unhealthy sleep impacting cardiovascular health, especially in women. On a similar note of addressing sex-specificity, of relevance is FAM98A , a gene identified in female-specific CPMA for interaction with long sleep. FAM98A , harboring multiple arginine demethylation sites, is a substrate of PRMT1 - an enzyme which catalyzes the synthesis of asymmetric dimethylarginine (ADMA), a molecule associated with cardiovascular harm as it induces endothelial dysfunction( 49 ). Thus seeking to lower harmful ADMA levels to counter harmful effects of sleep loss may be relevant in preservation of vascular integrity( 50 ). FAM98A , encoding a microtubule-associated protein, is also functionally linked to osteoclast formation, which is key to bone resorption and involved in postmenopausal osteoporosis etiology( 51 ). Given that osteoporosis and CVD share pathology, the FAM98A locus may shed light on the importance of considering holistic treatment for hypertensive women approaching or after menopause – an example being Felodipine, an antihypertensive found to additionally discourage osteoclast differentiation (52, 53) . Apart from FAM98A , specific genes highlight pathway connections to offer possible avenues for enhancing treatment efficacy for hypertension. Addressing the role of inflammation, SLA may lend promise as an immunosuppressant, with cytoplasm-specific delivery of specific domains of SLA shown to inhibit the T cell receptor functional cascade( 54 ). ALG10B closely interacts with KCNH2 to protect it from inhibition by pharmaceuticals and thus prevent acquired long QT syndrome - interesting, as past work has identified KCNH2 genetic variation to associate with efficacy of specific antihypertensive drugs( 55 , 56 ). PAK5 is the effector protein of CDC42 , vital for endothelial integrity and involved in the mechanism of Nebivolol, a third generation beta-blocker( 57 , 58 ). CRBN , due to its intrinsic role in ubiquitination, is recruited as an E3 ligase ligand in protease-targeted chimeras (PROTACs), which hold promise in cardiovascular therapeutics – an example being P22A shown to reduce collateral damage of HMGCR upregulation caused by statins( 59 ). These findings point to the need for future preclinical and clinical studies to confirm the hypothesized mechanisms and test promising interventions. Our druggability analysis specified genes acting as existing pharmacological targets of FDA-approved drugs, offering perspective for drug repurposing. HTR1F and KCNJ3 are linked to the serotonergic pathway and are targets of approved ADHD and antiarrhythmic drugs Atomoxetine and Dronedarone, respectively. This is potentially relevant given that serotonin may impact blood pressure regulation, and serotonin receptor desensitization is implicated in chronic sleep restriction( 60 , 61 ). HTR1F encodes for 5-HT 1F , shown to function in smooth muscle and trigeminal nerves, with its selective agonists (i.e. Lasmiditan) offering greater efficacy for migraine treatment without the collateral harm of vasoconstrictive effects induced by non-selective triptans( 62 ). Noticeably all 22 gene-sleep duration interaction loci we identified were specific to a particular population group, a subset of population groups, or a particular sex. This may be due to substantial heterogeneity in BP architecture and sleep lifestyle as a result of cultural differences, uniquely varying stressors due to socioeconomics, and genetic risk that are both shaped by and influence lifestyle choices. For example, admixed African and Hispanic populations are more likely to have poorly controlled hypertension and circadian abnormalities in BP regulation, as well as higher prevalence of both short and long sleep duration relative to individuals of European ancestry( 63 , 64 ). Females generally sleep longer, have higher prevalence of insomnia, and experience an increased proinflammatory response to sleep deprivation compared to males( 65 ). Such differential risk profiles are likely attributed to a myriad of social or environmental variables along with genetic and epigenetic susceptibility( 8 ). Therefore, it is likely that the same duration of self-reported sleep has different etiologies and physiological effects across sex and population background. Future research incorporating extensive phenotyping may help clarify whether gender-specific or population-specific findings are explained by differences in sleep-related or other lifestyle behaviors, mechanisms underlying response to sleep disturbance, or are spurious. This study has several strengths including its large-scale nature made possible by inclusion of several international biobanks and cohort studies, rigorous data harmonization and quality control protocols, and robust statistical analysis pipelines. Our findings are reinforced by multiple lines of evidence from bioinformatics analysis. Focused druggability analysis and interpretation of drug-gene interactions offer promising insight in drug repurposing and candidate targets for future pursuits. Limitations of this study include the risk of unidentified misclassification of self-reported sleep duration (opposed to objective measurements from actigraphy or polysomnography) due to recall bias, sleep misperception, or other psychosocial factors. Sleep health is complex, with key dimensions beyond duration (e.g., timing, quality, satisfaction, and regularity)( 66 ). Abnormality in these other sleep dimensions were not tested here due to lack of readily available data. Adding to the complexity, sleep duration itself reflects heterogeneous health effects influenced by genetic determinants. For example, genetic variation conducive to naturally short sleepers may even lend neuroprotection against harmful brain pathology( 67 ). In addition, there may be residual confounding bias due to unadjusted comorbidities or environmental factors. Lastly, despite notable diversity of our sample, our data was dominated by individuals of European ancestry. It is striking that several of our loci are HIS-specific – which may be resultant of complex admixture present in this population group. Although we were able to delve into sex-specific interpretations for FAM98A, and WBP1L – future investigation is desired to understand the reasons behind heterogeneous effects by sex. Enrichment of sample sizes in minority populations is critical for future investigations. In conclusion this study advances our understanding of the interaction between sleep duration extremes and genetic risk factors shaping the genetic landscape of blood pressure. Our novel discovery of 22 gene-sleep duration interaction loci both accentuates the relevance of proper sleep duration in cardiovascular health and the need to be conscious of heterogeneity present in specific sex or population groups, providing valuable perspective for therapeutic intervention strategies to address cardiovascular disease burden. Declarations CONFLICT OF INTEREST C.L.M. has received funding from AstraZeneca not related to the current study. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. D.C. receives consultancy fees from Roche Diagnostics and Trimedics and speaker fees from Servier. D.A.L. has received support from Medtronic LTD and Roche Diagnostics for biomarker research not related to the current study. The remaining authors declare no competing interests. Author Contributions: P.N. and H.W. conducted centralized project data analyses, data consolidation, meta-analyses, quality control, bioinformatics analysis, and contextual interpretation. P.N., H.W., A.R.B., A.T.K., C.L.M. K.S., T.W.W., P.B.M., R.N., D.C.R., S.R., and D.v.H. were part of the writing group and participated in project workflow design, interpretation of results, and drafting the manuscript. L.d.l.F., A.D., C.G., and D.C.R. participated in centralized study coordination. A.T.K., J.L.M., J.R.O., T.W.W., H.A., J.B.Meigs, X.Z., Han Chen, J.G., A.K.M., C.L.M., P.B.M., and P.A.P. acted as collaborators facilitating project design, specific code scripts, or specialized analyses. All other co-authors participated in final result interpretation, cohort-level study concept and design, cohort-level phenotype data acquisition and/or quality control, cohort-level genotype data acquisition and/or quality control, and/or cohort-level data-analysis and interpretation. All authors approved the final version of the paper that was submitted to the journal. ACKNOWLEDGMENTS This project was largely supported by two grants from the U.S. National Heart, Lung, and Blood Institute (NHLBI), the National Institutes of Health, R01HL118305 and R01HL156991. Study-specific acknowledgements are included in the Supplementary Note. The research on the Million Veteran Program, was supported by award, CX001897 (PI A.M. Hung) from BLR&D, VA Office of R&D. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. 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Additional Declarations CONFLICT OF INTEREST C.L.M. has received funding from AstraZeneca not related to the current study. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. D.C. receives consultancy fees from Roche Diagnostics and Trimedics and speaker fees from Servier. D.A.L. has received support from Medtronic LTD and Roche Diagnostics for biomarker research not related to the current study. The remaining authors declare no competing interests. Supplementary Files STTables.pdf SupplementaryFiguresGLISleepBP.docx SupplementaryNoteGLISleepBP.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 19 Sep, 2024 Review # 1 received at journal 09 Jul, 2024 Reviewer # 2 agreed at journal 26 Jun, 2024 Reviewer # 1 agreed at journal 21 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 28 Mar, 2024 Submission checks completed at journal 28 Mar, 2024 First submitted to journal 28 Mar, 2024 Unknown event 26 Mar, 2024 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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Institute","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Holliday","suffix":""},{"id":316141953,"identity":"1a69aac9-ebe8-4265-9621-a5e2336ca2ad","order_by":133,"name":"Zoltan Kutalik","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zoltan","middleName":"","lastName":"Kutalik","suffix":""},{"id":316141954,"identity":"284a8dc7-5461-40b3-a053-d188659ff76f","order_by":134,"name":"Timo Lakka","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Timo","middleName":"","lastName":"Lakka","suffix":""},{"id":316141955,"identity":"243371f9-1857-4b34-984f-e9003c41f92f","order_by":135,"name":"Deborah Lawlor","email":"","orcid":"https://orcid.org/0000-0002-6793-2262","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Lawlor","suffix":""},{"id":316141956,"identity":"3d41cf1a-cba8-4a16-8971-26b112e6746a","order_by":136,"name":"Seunggeun Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Seunggeun","middleName":"","lastName":"Lee","suffix":""},{"id":316141957,"identity":"953d9989-9806-4803-a51b-a7a0511aa74b","order_by":137,"name":"Terho Lehtimäki","email":"","orcid":"https://orcid.org/0000-0002-2555-4427","institution":"Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University","correspondingAuthor":false,"prefix":"","firstName":"Terho","middleName":"","lastName":"Lehtimäki","suffix":""},{"id":316141958,"identity":"379ef871-3267-415d-b210-30fe1d7ad5ca","order_by":138,"name":"Changwei Li","email":"","orcid":"https://orcid.org/0000-0002-9203-304X","institution":"Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA","correspondingAuthor":false,"prefix":"","firstName":"Changwei","middleName":"","lastName":"Li","suffix":""},{"id":316141959,"identity":"039f396c-9eed-49a7-967d-8684a95fecf8","order_by":139,"name":"Ching-Ti Liu","email":"","orcid":"https://orcid.org/0000-0002-0703-0742","institution":"Boston University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Ching-Ti","middleName":"","lastName":"Liu","suffix":""},{"id":316141960,"identity":"f4a9f0df-1722-4b21-86a9-1473aaf98977","order_by":140,"name":"Reedik Mägi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Reedik","middleName":"","lastName":"Mägi","suffix":""},{"id":316141961,"identity":"d72e8146-d184-4cfd-b9e1-57c0c1ce6928","order_by":141,"name":"Fumihiko Matsuda","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fumihiko","middleName":"","lastName":"Matsuda","suffix":""},{"id":316141962,"identity":"33cb27cd-2913-4d6d-b917-daaa343957ec","order_by":142,"name":"Alanna Morrison","email":"","orcid":"https://orcid.org/0000-0001-6381-4296","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Alanna","middleName":"","lastName":"Morrison","suffix":""},{"id":316141963,"identity":"52de2b4c-613f-4704-9d67-c78256b27072","order_by":143,"name":"Brenda W.J.H. Penninx","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Brenda","middleName":"W.J.H.","lastName":"Penninx","suffix":""},{"id":316141964,"identity":"f0e0613b-1114-456e-bc1a-1d2b90f39bc9","order_by":144,"name":"Patricia Peyser","email":"","orcid":"https://orcid.org/0000-0002-9717-8459","institution":"Department of Epidemiology, School of Public Health, University of Michigan","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Peyser","suffix":""},{"id":316141965,"identity":"9d787bd8-6294-4aae-90e1-6634245a4c2c","order_by":145,"name":"Jerome Rotter","email":"","orcid":"https://orcid.org/0000-0001-7191-1723","institution":"The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jerome","middleName":"","lastName":"Rotter","suffix":""},{"id":316141966,"identity":"7d15586b-6a25-4aa3-ac47-eef5d529d4e9","order_by":146,"name":"Harold Snieder","email":"","orcid":"https://orcid.org/0000-0003-1949-2298","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Harold","middleName":"","lastName":"Snieder","suffix":""},{"id":316141967,"identity":"b8b8413e-bb75-4265-b19d-8c3124e5f78c","order_by":147,"name":"T Spector","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"T","middleName":"","lastName":"Spector","suffix":""},{"id":316141968,"identity":"251f1610-8191-400e-8304-53edf8edc184","order_by":148,"name":"Lynne Wagenknecht","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lynne","middleName":"","lastName":"Wagenknecht","suffix":""},{"id":316141969,"identity":"92993028-d8b2-48a1-b27a-46345d080449","order_by":149,"name":"Nicholas Wareham","email":"","orcid":"https://orcid.org/0000-0003-1422-2993","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Wareham","suffix":""},{"id":316141970,"identity":"9e11fe5a-272f-496b-ab75-f571aa61ee5d","order_by":150,"name":"Alan Zonderman","email":"","orcid":"https://orcid.org/0000-0002-6523-4778","institution":"National Institute on Aging, NIA/NIH/IRP","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"","lastName":"Zonderman","suffix":""},{"id":316141971,"identity":"b2b5390c-5338-4f97-bc52-dcccf8053194","order_by":151,"name":"Kari North","email":"","orcid":"https://orcid.org/0000-0002-8903-0366","institution":"UNC Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Kari","middleName":"","lastName":"North","suffix":""},{"id":316141972,"identity":"20d15b8b-b809-4917-a2c9-fef1f288e8f3","order_by":152,"name":"Myriam Fornage","email":"","orcid":"https://orcid.org/0000-0003-0677-8158","institution":"1. Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center 2. Human Genetics Center, Department of Epidemiology, School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Myriam","middleName":"","lastName":"Fornage","suffix":""},{"id":316141973,"identity":"8169cd28-8d12-448a-91d1-fbd21e06b9ed","order_by":153,"name":"Adriana Hung","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Hung","suffix":""},{"id":316141974,"identity":"3d197ac1-e940-46c5-b9b7-7dae6bae8921","order_by":154,"name":"Alisa Manning","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alisa","middleName":"","lastName":"Manning","suffix":""},{"id":316141975,"identity":"c30b7617-63db-412b-981c-c5cfa2d4241c","order_by":155,"name":"W. Gauderman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"W.","middleName":"","lastName":"Gauderman","suffix":""},{"id":316141976,"identity":"c804773f-d9cc-4e47-b944-565d92baea5c","order_by":156,"name":"Han Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Chen","suffix":""},{"id":316141977,"identity":"54d3c9f9-2f37-4773-9b04-78c15046f1ce","order_by":157,"name":"Patricia Munroe","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Munroe","suffix":""},{"id":316141978,"identity":"b7e99ea1-5ea9-481c-9d74-b6dcf538f21f","order_by":158,"name":"Dabeeru Rao","email":"","orcid":"","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"Dabeeru","middleName":"","lastName":"Rao","suffix":""},{"id":316141979,"identity":"959bb4ee-f004-4549-8fb4-351f1d0e92b4","order_by":159,"name":"Diana van Heemst","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"van","lastName":"Heemst","suffix":""},{"id":316141980,"identity":"4855fdbd-c4b7-43b9-b42f-979dff7ed06f","order_by":160,"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":""},{"id":316141981,"identity":"e7a17c22-0070-4a0c-851e-b4ae2bb927f2","order_by":161,"name":"Raymond Noordam","email":"","orcid":"","institution":"Leiden University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Raymond","middleName":"","lastName":"Noordam","suffix":""}],"badges":[],"createdAt":"2024-03-25 12:50:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4163414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4163414/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60529491,"identity":"45c08ec5-669f-4996-96de-65000bce0fcf","added_by":"auto","created_at":"2024-07-17 20:00:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":765059,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis Workflow\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/b89ef56cd577ac3ff8bf413f.jpg"},{"id":60530767,"identity":"0d2e574c-2c41-4da2-baf9-b2e833156160","added_by":"auto","created_at":"2024-07-17 20:08:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":649652,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plots of Gene-Sleep Duration Loci Identified by the 1df Interaction Test in Female-Specific Cross-Population Meta-Analyses.\u003c/p\u003e\n\u003cp\u003eThese are the prioritized 1df Interaction Test results from female-specific cross-population meta-analyses. Contributing female-specific population groups’ (if this variant is found in the particular population group, after quality control) summary statistics are depicted here that were pooled.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/b9468eb5f3a445342dd065b6.jpg"},{"id":60529494,"identity":"28966ec1-3678-47d2-b804-588920a20140","added_by":"auto","created_at":"2024-07-17 20:00:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":812122,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plots of Gene-Sleep Duration Loci Identified by the 1df Interaction Test in Combined Sex HIS-Specific Meta-Analyses\u003c/p\u003e\n\u003cp\u003eThese are the prioritized 1df Interaction Test results from HIS-specific meta-analyses. Other population group data (if this variant is found in the particular population group, after quality control) are shown here to emphasize that these gene-sleep duration interaction loci were significant only in the HIS population group.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/737e791c44e6bc03fa47786f.jpg"},{"id":60530977,"identity":"e9387943-1e9e-4385-98bb-e92906a14f2d","added_by":"auto","created_at":"2024-07-17 20:16:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3819110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/5c7c0677-dff8-45b4-9ce8-aad7b40fa1bf.pdf"},{"id":60529495,"identity":"49398006-d3d2-4814-85b4-db25aed77296","added_by":"auto","created_at":"2024-07-17 20:00:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3092047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"STTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/5f0a4f949d48fc69993ef457.pdf"},{"id":60529497,"identity":"38b52889-a728-424a-8052-7290ae646e7e","added_by":"auto","created_at":"2024-07-17 20:00:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":96869047,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresGLISleepBP.docx","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/de2a3a0f7d009a59717310be.docx"},{"id":60529493,"identity":"c286de34-362d-455a-9882-ea47a669f351","added_by":"auto","created_at":"2024-07-17 20:00:02","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":79673,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryNoteGLISleepBP.docx","url":"https://assets-eu.researchsquare.com/files/rs-4163414/v1/e7623edc7a51caa2498dfe50.docx"}],"financialInterests":"\nCONFLICT OF INTEREST\r\nC.L.M. has received funding from AstraZeneca not related to the current study. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson \u0026 Johnson. D.C. receives consultancy fees from Roche Diagnostics and Trimedics and speaker fees from Servier. D.A.L. has received support from Medtronic LTD and Roche Diagnostics for biomarker research not related to the current study. The remaining authors declare no competing interests.","formattedTitle":"A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAbnormal sleep duration is detrimental to cardiovascular health \u0026ndash; increasing the risk of incident cardiovascular disease (CVD) and mortality \u0026ndash; and inherently complex, with suspected heterogeneous effects according to sex and race/ethnicity(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Deviation from healthy sleep can impact diurnal rhythms, hormone levels (e.g. ghrelin, cortisol), autonomous nervous system balance, and even remodel vascular structure - resulting in adverse consequences, such as reduced nocturnal blood pressure (BP) dipping and sustained daytime hypertension(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eYet the mechanistic pathways underlying the biomolecular connection between short and long sleep with cardiovascular health remain unclear. Evidence implicates heightened sympathetic tone and metabolic dysfunction in the mechanism of short sleep, but there remains a gap in clarity with the added complexity of interwoven pathways like oxidative stress and endothelial dysfunction(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The role of long sleep is more elusive, with recent work highlighting the pertinence of inflammatory markers, underlying comorbidity burden (i.e. dyslipidemia, depression) and arterial stiffness metrics(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This incomplete understanding of the intersection between habitual sleep duration and cardiovascular health necessitates further investigation.\u003c/p\u003e \u003cp\u003eHypertension is a major risk factor for CVD, with blood pressure traits known to have a strong genetic background. Recent genome-wide association analyses (GWASs) have discovered more than 2,000 loci explaining\u0026thinsp;~\u0026thinsp;40% of systolic or diastolic BP heritability among European descent individuals(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It is important to investigate the role of sleep health in such a polygenic landscape. This may both explain additional heritability of BP traits, as well as bring to the forefront novel genomic loci that inform perspective on sleep\u0026rsquo;s influence on biomolecular pathways underlying BP. Moreover, incorporating diverse population groups is essential \u0026ndash; as this can reveal novel gene targets specific to particular subgroups or shared across \u0026ndash; improving downstream therapeutic designs, and offering tangible insight to counter disparities in health. Our prior work in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Gene-Lifestyle Interactions Working Group highlighted novel non-overlapping gene-sleep interactions for BP, suggesting distinct roles of influence for short and long sleep duration(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Our current analysis advances the field by including a 12-fold larger sample size and additional sex-stratified analyses, yielding enhanced statistical power and granularity.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis work was approved by the Institutional Review Board of Washington University in St. Louis and complies with all relevant ethical regulations. For each of the participating cohorts, the appropriate ethics review board approved the data collection and all participants provided informed consent.\u003c/p\u003e \u003cp\u003eData Harmonization\u003c/p\u003e \u003cp\u003eData from each cohort (Supplementary Tables S1-S2) were harmonized following this centralized protocol. Data were stratified by population group, based on self-reported ancestry and individual cohort definitions (AFR: African, EAS: East Asian, EUR: European, HIS: Hispanic/Latinos, SAS: South Asian), and sex (combined sex, female sex, male sex). Analyses considered 3 primary blood pressure (BP) traits as outcome variables (SBP: systolic, DBP: diastolic, PP: pulse pressure) and 2 dichotomous lifestyle exposures (LTST: long total sleep time, STST: short total sleep time). Genetic variants (G) were restricted to autosomal chromosomes 1\u0026ndash;22 imputation quality\u0026ge;0.3, and minor allele frequency\u0026ge;0.1%. Age was restricted to \u0026ge;18 years, and reported total sleep time constrained within 3 and 14 hours. In scenarios of multiple visits, the single visit with largest sample size was utilized and in case-control study designs, cases and controls were required to be analyzed separately. For BP outcome measures, if multiple readings were taken in a single visit the mean was used. All BP values were winsorized at 6 standard deviations from the mean. BP values were adjusted for reported use of anti-hypertensive medications as follows: SBP (+\u0026thinsp;15 mmHg) and DBP (+\u0026thinsp;10 mmHg). PP was derived as SBP \u0026ndash; DBP. In the case of studies with known between-sample relatedness, null model residuals (regressing BP traits on a kinship matrix/genetic covariance matrix) were denoted as the BP outcome. STST and LTST were derived from total sleep time (TST) by regressing TST on age, sex, age\u0026times;sex and using the residuals\u0026rsquo; 20th and 80th percentiles as cutoffs (STST\u0026thinsp;=\u0026thinsp;1 if\u0026thinsp;\u0026le;\u0026thinsp;20th percentile, LTST\u0026thinsp;=\u0026thinsp;1 if \u0026ge; 80th percentile, STST\u0026thinsp;=\u0026thinsp;0 if\u0026thinsp;\u0026gt;\u0026thinsp;20th percentile, LTST\u0026thinsp;=\u0026thinsp;0 if\u0026thinsp;\u0026lt;\u0026thinsp;80th percentile). Covariates included population-group specific principal components, cohort-specific confounders (study center), age, age\u003csup\u003e2\u003c/sup\u003e, sex, age\u0026times;S/LTST, age\u003csup\u003e2\u003c/sup\u003e\u0026times;S/LTST, and sex\u0026times;S/LTST. Samples with missing data were excluded.\u003c/p\u003e \u003cp\u003eGenome-wide Gene-Sleep Interaction Analysis\u003c/p\u003e \u003cp\u003eAfter data harmonization, each population-group specific cohort ran 2 regression models (M1 and M2) for 18 phenotype-exposure-sex combinations (3 phenotypes x 2 exposures x 3 sex groups: combined sex, female sex, male sex). Below E denotes the lifestyle exposure (STST or LTST), Y denotes the BP outcome (SBP, DBP, or PP), C\u003csub\u003e1\u003c/sub\u003e denotes the vector of covariates incorporating E (age, age\u003csup\u003e2\u003c/sup\u003e, S/LTST, age*S/LTST, age\u003csup\u003e2\u003c/sup\u003e*S/LTST, sex, sex*S/LTST), and C\u003csub\u003e2\u003c/sub\u003e denotes the subset without incorporating E (age, age\u003csup\u003e2\u003c/sup\u003e, sex). Female-specific and male-specific analyses were not adjusted for sex. Specialized software choice included LinGxEScanR v1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/USCbiostats/LinGxEScanR\u003c/span\u003e\u003cspan address=\"https://github.com/USCbiostats/LinGxEScanR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GEM v1.4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/large-scale-gxe-methods/GEM\u003c/span\u003e\u003cspan address=\"https://github.com/large-scale-gxe-methods/GEM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and/or MMAP (latest version available) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MMAP/MMAP.github.io\u003c/span\u003e\u003cspan address=\"https://github.com/MMAP/MMAP.github.io\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with robust standard errors (SEs) enforced(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). One degree of freedom (df) tests for the marginal effect (β\u003csub\u003eM2_G\u003c/sub\u003e), the main effect (β\u003csub\u003eM1_G\u003c/sub\u003e), and the interaction effect (β\u003csub\u003eM1_GxE\u003c/sub\u003e) were conducted; alongside the 2df joint test that simultaneously assesses the main effect and the interaction effect (β\u003csub\u003eM1_G\u003c/sub\u003e, β\u003csub\u003eM1_GxE\u003c/sub\u003e)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModel 1 (Primary GxE Model of Interest)\u003c/p\u003e \u003cp\u003e (1)\u003cb\u003eM1\u003c/b\u003e: Y\u0026thinsp;=\u0026thinsp;β\u003csub\u003eM1_0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003eE\u003c/sub\u003eE\u0026thinsp;+\u0026thinsp;β\u003csub\u003eM1_G\u003c/sub\u003eG\u0026thinsp;+\u0026thinsp;β\u003csub\u003eM1_GxE\u003c/sub\u003e E\u0026sdot;G\u0026thinsp;+\u0026thinsp;β\u003csub\u003eM1_C1\u003c/sub\u003eC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eModel 2 (Marginal Effect Model for Comparison)\u003c/p\u003e \u003cp\u003e(2) \u003cb\u003eM2\u003c/b\u003e: Y\u0026thinsp;=\u0026thinsp;β\u003csub\u003eM2_0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003eM2_G\u003c/sub\u003eG\u0026thinsp;+\u0026thinsp;β\u003csub\u003eM2_C2\u003c/sub\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eSummary statistics were centrally processed after individual studies submitted results. EasyQC2 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.genepi-regensburg.de/easyqc2\u003c/span\u003e\u003cspan address=\"http://www.genepi-regensburg.de/easyqc2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform quality control (QC) on resultant data(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Data were filtered for degrees of freedom\u0026ge;20 calculated as minor allele count * imputation quality (e.g. MACxR\u003csup\u003e2\u003c/sup\u003e provided by each cohort) within the unexposed, the exposed, and the total sample. Missing or invalid/out of range values for statistics and duplicated or monomorphic variants were discarded. hg19 genomic coordinates were lifted over to hg38 genomic coordinates. Allele frequency discrepancies relative to TOPMed-imputed 1000G reference panels (Trans-Omics for Precision Medicine imputed 1000Genomes) were assessed for each specific population group, along with genomic control (GC) lambda inflation. Meta-level quality control was conducted within groups based on population group, with evaluation of unwanted centering of the outcome variable, outlying cohorts highlighting unstable numerical computation, or alarming inflation.\u003c/p\u003e \u003cp\u003eMeta-Analysis\u003c/p\u003e \u003cp\u003eMeta-analysis was designed as the following paradigm. Cross-population meta-analysis (CPMA) was designed to be combine all population group results, with additional focused population-group specific and sex-specific analyses. This resulted in 18 total meta-analyses to be run: 6 population groups (CPMA, EUR, HIS, EAS, AFR, SAS) and 3 sex groups (combined sex, female sex, male sex). To accomplish this, METAL software was first used to run all meta-analyses within each specific population group for the marginal effect (β\u003csub\u003eM2_G\u003c/sub\u003e), main effect (β\u003csub\u003eM1_G\u003c/sub\u003e), interaction effect (β\u003csub\u003eM1_GxE\u003c/sub\u003e), and joint effect (β\u003csub\u003eM1_G\u003c/sub\u003e, β\u003csub\u003eM1_GxE\u003c/sub\u003e) with GC correction for inflation(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Inverse-variance weights were used and Manning et. al\u0026rsquo;s method for the 2df joint test (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). CPMA was subsequently executed on the resultant population-group specific METAL output results with GC correction.\u003c/p\u003e \u003cp\u003eGenome-wide Significant Loci Identification\u003c/p\u003e \u003cp\u003eEasyStrata2 software was used to prioritize top loci from significant results identified from the 1df interaction and 2df joint tests(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). GC correction for population-group specific results was applied. Variants found within 1 Mb distance of the major histocompatibility complex (MHC) region were excluded. Either minimum sample size (N\u0026thinsp;\u0026gt;\u0026thinsp;20000) or multiple cohorts (\u0026ge;3) was required as necessary criteria for processing results from a specific sex-stratified, and/or population group-stratified meta-analysis.\u003c/p\u003e \u003cp\u003eSignificant variants were identified using the following threshold criteria: (i) \u003cem\u003ei\u003c/em\u003e variants with significant interaction effect (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eM1_GxE\u003c/sub\u003e\u0026lt;5e-9, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05); (ii) \u003cem\u003ej\u003c/em\u003e variants with significant joint effect (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eM1_G,GxE\u003c/sub\u003e\u0026lt;5e-9, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were filtered as top variants; and (iii) \u003cem\u003ek\u003c/em\u003e top variants for the interaction effect were identified using a 2-step method \u0026ndash; identifying first \u003cem\u003ez\u003c/em\u003e variants by the marginal effect (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eM2_G\u003c/sub\u003e\u0026lt;1e-5) and then filtering these by the interaction effect (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eM1_GxE\u003c/sub\u003e\u0026lt;0.05/N\u003csub\u003eG,\u003c/sub\u003e FDR\u003csub\u003eGxE\u003c/sub\u003e\u0026lt;0.05) where N\u003csub\u003eG\u003c/sub\u003e is the number of independent tests calculated using principal components analysis on the \u003cem\u003ez\u003c/em\u003e variants. This 2-step method was incorporated to increase power for detecting interactions(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This design was executed to maintain both stringent threshold criteria and incorporate false discovery correction implemented by the Benjamini-Hochberg method.\u003c/p\u003e \u003cp\u003eAll such \u003cem\u003ei\u0026thinsp;+\u0026thinsp;j\u0026thinsp;+\u0026thinsp;k\u003c/em\u003e significant variants were narrowed down to loci based on 500 kilobase (kb) regions. Finally, within these regions independent lead variants were identified as the top significant variant within the locus, subsequently defining variants in LD as those with linkage disequilibrium (LD) r\u003csup\u003e2\u003c/sup\u003e threshold\u0026thinsp;\u0026lt;\u0026thinsp;0.1 using TOPMed-imputed 1000G reference panels. If variants were missing in the LD panels, then the most significant variant within each 500kb region was retained for combined sex meta-analyses results.\u003c/p\u003e \u003cp\u003ePrioritizing Novel Sleep Duration Interaction Loci\u003c/p\u003e \u003cp\u003eSignificant independent loci were subsequently filtered to prioritize gene-sleep duration interaction loci. From the 1df interaction test, \u003cem\u003eX\u003c/em\u003e interaction loci were prioritized as those not found within 1Mb of previously identified gene-sleep duration loci for BP(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Loci were annotated as whether novel for BP genetic architecture, or not, by checking for overlap with 1Mb of previous GWAS variants (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the 2df test, first loci were filtered to those variants not found within 1Mb of previous GWAS identified variants for BP traits, and with insignificant marginal effect (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eM2_G\u003c/sub\u003e \u0026gt;5e-09, FDR\u003csub\u003eM2_G\u003c/sub\u003e \u0026gt;0.05). From these variants, \u003cem\u003eY\u003c/em\u003e loci were prioritized as driven by interaction if they harbored a stronger interaction effect relative to the main effect (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eM1_GxE \u0026lt;\u003c/sub\u003e \u003cem\u003eP\u003c/em\u003e\u003csub\u003eM1_G\u003c/sub\u003e), and \u003cem\u003eZ\u003c/em\u003e loci deemed as supported (but not driven) by interaction if this was not true.\u003c/p\u003e \u003cp\u003eThus, collectively \u003cem\u003eX\u0026thinsp;+\u0026thinsp;Y\u003c/em\u003e gene-sleep duration interaction loci were highlighted, alongside secondarily \u003cem\u003eZ\u003c/em\u003e loci supported by interaction.\u003c/p\u003e \u003cp\u003eHeterogeneity by Sex\u003c/p\u003e \u003cp\u003eTo test for interaction effects showing evidence of heterogeneity by sex (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05/\u003cem\u003eQ\u003c/em\u003e), two-sample Z-tests assuming independence, were conducted for each of the top interaction loci and adjusted for multiple testing.\u003c/p\u003e \u003cp\u003eMapped Protein Coding Genes\u003c/p\u003e \u003cp\u003eGene mapping prioritized protein-coding genes for downstream interpretation. Variants directly overlapping protein-coding gene regions were top priority criteria for gene assignment. For intergenic variants nearest distance to transcription start site (TSS) or gene start/end site was queried from Open Target Genetics v22.10(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) or MyGene.Info using Python package \u003cem\u003emygene\u003c/em\u003e v3.2.2 \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/biothings/mygene.py\u003c/span\u003e\u003cspan address=\"https://github.com/biothings/mygene.py\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Variant mapping annotations were additionally noted from Open Target Genetics, Functional Annotation of Variants \u0026ndash; Online Resource v2.0 (FAVOR)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), HaploReg v4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.broadinstitute.org/mammals/haploreg/haploreg.php\u003c/span\u003e\u003cspan address=\"https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), BRAVO variant browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bravo.sph.umich.edu/freeze8/hg38/\u003c/span\u003e\u003cspan address=\"https://bravo.sph.umich.edu/freeze8/hg38/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Functional Mapping and Annotation of Genome-wide Association Studies v1.5.6 (FUMA)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and MyGene.Info.\u003c/p\u003e \u003cp\u003eVariant Annotations\u003c/p\u003e \u003cp\u003eFAVOR was queried to annotate deleteriousness or functionality scores(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and RegulomeDB v2.2 was used to extract aggregate regulatory function evidence scores, along with chromatin state, DNA accessibility, overlap with transcription factor (TF) binding sites or TF motifs, and expression quantitative trait loci (eQTL)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). FUMA\u0026rsquo;s SNP2GENE pipeline was used to annotate a comprehensive list of genes for each top locus, incorporating positional, chromatin interaction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;1e-6, 250bp upstream \u0026minus;\u0026thinsp;500 bp downstream of TSS), and GTEXv8 eQTL evidence (agreeing with RegulomeDB) with the top variant or its variants in LD (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.1 within 500kb)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the variant level, PheWeb, Open Target Genetics, Common Metabolic Diseases Knowledge Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hugeamp.org/\u003c/span\u003e\u003cspan address=\"https://hugeamp.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Oxford Brain Imaging Genetics Server (BIG40) were queried for significant trait associations (p\u0026thinsp;\u0026lt;\u0026thinsp;5e-08) from past GWAS(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). At the gene level, International Mouse Phenotyping Consortium release 19.1 (IMPC), Online Mendelian Inheritance in Man (OMIM; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), PheWeb, Phenotype-Genotype Integrator (PheGenI), and Open Target Genetics were queried for phenotypic annotations from mice knockout study results, involvement in Mendelian disorders, and significant trait associations (p\u0026thinsp;\u0026lt;\u0026thinsp;5e-08) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). All STST and LTST mapped protein-coding genes were then queried using FUMA\u0026rsquo;s GENE2FUNC pipeline to identify significant (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) pathways and traits(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). STRING v12.0 was additionally queried using medium confidence threshold (0.4) to note significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) enriched traits or pathways to compare and contrast LTST and STST loci (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDruggability Analysis\u003c/p\u003e \u003cp\u003eThe Drug-Gene Interaction database (v4.2.0) was first utilized to identify druggability potential, with genes also annotated for implicated pathways and functions using the Kyoto Encyclopedia of Genes and Genomes database. Druggability target categories were annotated and all interacting drugs queried from reports across 43 databases (BaderLabGenes, CarisMolecularIntelligence, dGene, FoundationOneGenes, GO, HingoraniCasas, HopkinsGroom, HumanProteinAtlas, IDG, MskImpact, Oncomine, Pharos, RussLampel, Tempus, CGI, CIViC, COSMIC, CancerCommons, ChemblDrugs, ChemblInteractions, ClearityFoundationBiomarkers, ClearityFoundationClinicalTrial, DTC, DoCM, DrugBank, Ensembl, Entrez, FDA, GuideToPharmacology, JACX-CKB, MyCancerGenome, MyCancerGenomeClinicalTrial, NCI, OncoKB, PharmGKB, TALC, TEND, TTD, TdgClinicalTrial, Wikidata). Protein targets for available active ligands in ChEMBL were also noted. Gene targets were looked up in the druggable genome using the most recent druggable genome list established from the NIH Illuminating the Druggable Genome Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/druggablegenome/IDGTargets\u003c/span\u003e\u003cspan address=\"https://github.com/druggablegenome/IDGTargets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) available through the Pharos web platform. Lastly, FDA-approved drugs, late-stage clinical trials and disease indications were queried in the DrugBank, ChEMBL, ClinicalTrials.gov databases to provide results for the top MESH and DrugBank indications and clinical trials.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDiscovery of Novel Gene-Sleep Duration Interactions\u003c/h2\u003e \u003cp\u003eFrom an initial source of 37 studies, 59 population-group specific cohorts (derived from self-reported ancestry) resulted in a pooled sample size of 811,405 individuals comprising of 5.9% AFR (12 cohorts), 6.0% EAS (5 cohorts), 83.4% EUR (34 cohorts), 3.7% HIS (7 cohorts), and 0.9% SAS (1 cohort) (Supplementary Tables S1-S2). The 1df test discovered seven loci and the 2-step method discovered one locus. The 2df joint test first identified 3629 significant loci, from which 18 were novel for BP (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e) with insignificant marginal effect \u0026ndash; revealing 14 loci driven by the interaction effect, and four not driven. Thus in total we discovered 22 gene-sleep duration interaction loci, and 4 secondary loci \u0026ndash; of which 21 are novel for BP traits (Supplementary Table S4). Among the 22 prioritized interaction loci, four loci exhibited cross-population effects (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3) \u0026ndash; one identified in combined sex, three in female sex-stratified analyses, and 18 identified specific to either one of the AFR, HIS, or EUR population groups (Table\u0026nbsp;2, Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3). Specifically, AFR analysis revealed one gene-sleep duration interaction locus, HIS analysis revealed 11 gene-sleep duration interaction loci, and EUR analysis revealed six gene-sleep duration loci (Table\u0026nbsp;2). Three variants identified in combined sex meta-analyses showed evidence of heterogeneous effect by sex (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\u003eNovel Gene-Sleep Duration Interaction Loci Identified in Cross-Population Meta-Analysis All results herein are from the M1 model. Supplementary Table S21 provides summary statistics according to each population group identified in cross-population results. For the sex column, C denotes combined sex, and F denotes female sex-stratified meta-analysis. For the alleles column, E denotes effect allele, and O denotes other allele used as reference. Bold denotes significance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNearest gene(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003cp\u003e(hg38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlleles (E/O)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePopulation Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eB\u003csub\u003eM1_G\u003c/sub\u003e (se\u003csub\u003eM1_G\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eB\u003csub\u003eGxE\u003c/sub\u003e (se\u003csub\u003eGxE\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eP\u003csub\u003eM1_G\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eP\u003csub\u003eGxE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eP\u003csub\u003eG,GxE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eP\u003csub\u003esex_diff\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e \u003cp\u003eLoci Identified by 1df Interaction Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11314421\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWBP1L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:102808541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e279527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAFR, EAS, EUR, HIS, SAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.12 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.40 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.60E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e7.82E-06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.54E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e8.84E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers538479553\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFAM98A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2:33903659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAFR, EAS, EUR, HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.12 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-5.09 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.27E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e3.32E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.35E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e3.59E-06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"16\" nameend=\"c17\" namest=\"c2\"\u003e \u003cp\u003eLoci Identified by 2df Joint Test, Driven by Interaction Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers76458410\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYWHAB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20:44851866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAFR,HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.14 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.65 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.93E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6.38E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e3.88E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e6.87E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1431999695\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eALG10B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:37297511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEUR, HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.57 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.28 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.88E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.46E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e4.55E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e4.07E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003e1 denotes the variant was identified using the two-step approach.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003e2 denotes variants novel for BP.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Novel Gene-Sleep Duration Interaction Loci Identified Specific to Certain Population Groups.\u003c/p\u003e\n\u003cp\u003eAll results herein are from the M1 model. Supplementary Table S22 provides summary statistics according to each population group-specific cohort identified in these population-group specific results. For the sex column, C denotes combined sex meta-analysis, and M denotes male sex-stratified meta-analysis. For the alleles column, E denotes effect allele, and O denotes other allele used as reference. Bold denotes significance.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"117%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.434782608695652%\" colspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003eNearest gene(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003cp\u003e(hg38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eAlleles (E/O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003ePopulation Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003eB\u003csub\u003eM1_G\u0026nbsp;\u003c/sub\u003e(se\u003csub\u003eM1_G\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003eB\u003csub\u003eGxE\u003c/sub\u003e (se\u003csub\u003eGxE\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eP\u003csub\u003eM1_G\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003csub\u003eGxE\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eP\u003csub\u003eG,GxE\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003csub\u003esex_diff\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.0204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"92.85714285714286%\" colspan=\"17\" valign=\"top\"\u003e\n \u003cp\u003eLoci Identified by 1df Interaction Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.434782608695652%\" colspan=\"2\"\u003e\n \u003cp\u003eSTST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.695652173913043%\"\u003e\n \u003cp\u003ers114831731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e\u003cem\u003eAHCYL1\u003c/em\u003e\u003c/p\u003e\n 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width=\"5.434782608695652%\"\u003e\n \u003cp\u003eA/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e27119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e2.32 (0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e-7.23 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e8.70E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n 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width=\"5.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e23897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e2.09 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e-10.83 (1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e3.03E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.28E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\" colspan=\"2\"\u003e\n \u003cp\u003e1.23E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003eSTST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers143863772\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003eMROH7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e1:54707218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eT/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e23902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e34442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eAFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e1.13 (0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-4.40 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e6.03E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e1.92E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.69E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.56E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers138288695\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003eCRBN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e3:3295965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e23897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.57 (0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-5.50 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e4.07E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e3.46E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.31E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers142966182\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003eALCAM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e3:104791665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e26230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eEUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-0.20 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-5.37 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e7.50E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e1.01E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers540041583\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003ePAM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e5:103002447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e26230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eEUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-1.54 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-6.50 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e1.99E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e8.93E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.93E-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers113952142\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003eSDK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e7:3917121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eA/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e23902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-0.28 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-9.07 (1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e8.18E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e1.81E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.49E-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers111392401\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003eJMJD1C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e10:65029197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eT/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e23897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.49 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-5.71 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e4.32E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e1.27E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.28E-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.791208791208792%\"\u003e\n \u003cp\u003ers772862932\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u003cem\u003eATP8A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e13:25395875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2967032967032965%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" valign=\"top\"\u003e\n \u003cp\u003e30009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eEUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.395604395604396%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-0.11 (0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e-4.85 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e8.70E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\"\u003e\n \u003cp\u003e1.31E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.4945054945054945%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.28E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1 denotes variants novel for BP.\u003c/p\u003e\n\u003cp\u003e2 Empty cell in\u0026nbsp;P\u003csub\u003esex_diff\u003c/sub\u003e indicates the variant, after quality control, was not found in both sex-stratified meta-analyses.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNovel BP Loci Identified by the 2df Joint Test, Not Driven by the Interaction Effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNearest gene(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003cp\u003e(hg38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlleles (E/O)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePopulation Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eB\u003csub\u003eM1_G\u003c/sub\u003e (se\u003csub\u003eM1_G\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eB\u003csub\u003eGxE\u003c/sub\u003e (se\u003csub\u003eGxE\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eP\u003csub\u003eM1_G\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eP\u003csub\u003eGxE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eP\u003csub\u003eG,GxE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eP\u003csub\u003esex_diff\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"17\" nameend=\"c18\" namest=\"c2\"\u003e \u003cp\u003eLoci Identified by 1df Interaction Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers114831731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAHCYL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:109970740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.85 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-6.27 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.87E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.57E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e7.34E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e\u003cb\u003e5.67E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers144229676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZNF521\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18:25303461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.32 (0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-7.23 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.70E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e8.10E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.80E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e5.00e-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1035064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZNF682\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19:19997730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e392939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.27 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.10 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.62e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e4.82E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e5.76E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e\u003cb\u003e1.52E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers112958007\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePAK5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20:9805888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.09 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-10.83 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.03E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.28E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.23E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers752086677\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKRTAP13-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21:30362234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.12 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-8.71 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.47E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.46E-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e5.68E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers372262693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTG, SLA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8:133069499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.62 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.91 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.34E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e2.33E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e7.11E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e2.43E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"18\" nameend=\"c19\" namest=\"c2\"\u003e \u003cp\u003eLoci Identified by 2df Joint Test, Driven by Interaction Effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers143863772\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMROH7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:54707218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.56 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-6.99 (1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.47E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.79E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e1.84E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers141117715\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKCNJ3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2:155446948\u003c/p\u003e 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colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e2.45E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e7.86E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17011282\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZNF385D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3:22386254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.97 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-11.78 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.09E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.06E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e6.45E-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers542745170\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWWOX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16:78323227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.36 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.96 (1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.20E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.14E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e5.00E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e\u003cb\u003e2.05E-03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLTST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers533724062\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBRINP3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:190792851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTA/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.13 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.40 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.03E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.92E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e3.69E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e\u003cb\u003e3.56E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers138288695\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCRBN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3:3295965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e 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align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers142966182\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eALCAM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3:104791665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.20 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-5.37 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.50E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.01E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e1.21E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers540041583\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePAM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5:103002447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.54 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-6.50 (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.99E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e8.93E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e6.93E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers113952142\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSDK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7:3917121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.28 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-9.07 (1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.18E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.81E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e5.49E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers111392401\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eJMJD1C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10:65029197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.49 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-5.71 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.32E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.27E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e2.28E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers772862932\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eATP8A2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13:25395875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.11 (0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4.85 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.70E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.31E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003e1.28E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003eAll results herein are from the M1 model. Supplementary Tables S21-S22 provides summary statistics according to each population group-specific cohort for rs59680540 and rs150586434, and according to each population group for rs34761985 and rs13032423 below. For the sex column, C denotes combined sex meta-analysis. For the alleles column, E denotes effect allele, and O denotes other allele used as reference. Bold denotes significance.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003e1 denotes variants novel for BP.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003e2 Empty cell in P\u003csub\u003esex_diff\u003c/sub\u003e indicates the variant, after quality control, was not found in both sex-stratified meta-analyses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePrior Reported Gene-Sleep Interactions\u003c/h2\u003e \u003cp\u003eIn our current cross-population meta-analysis, rs10406644 showed interaction evidence with STST (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eG\u0026times;E\u003c/sub\u003e=2.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for PP), and rs7955964 with LTST (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eG\u0026times;E\u003c/sub\u003e=6.6\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for SBP, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eG\u0026times;E\u003c/sub\u003e=1.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e for DBP) with direction of association agreeing with prior work(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and non-significance (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eGxE\u003c/sub\u003e\u0026gt;0.05) for the opposite sleep duration exposure (Supplementary Table S5).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional Potential of Variants\u003c/h3\u003e\n\u003cp\u003eFour variants (rs11483173, rs372262693, rs113952142, rs11314421) were marked by high transcription activity chromatin states and eight variants (rs114831731, rs372262693, rs143863772, rs533724062, rs11314421, rs1035064, rs538479553, rs13032423) were marked by accessible chromatin in heart tissue or blood (Supplementary Table S6). Six variants (rs114831731, rs542745170, rs533724062, rs11314421, rs1035064, rs538479553) reflected marked regulatory potential with RegulomeDB scores \u0026ge;2c (Supplementary Table S6).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMapped Protein-Coding Genes\u003c/h2\u003e \u003cp\u003eAll 26 variants identified were either intronic or intergenic, and mapped to a primary set of 27 protein coding genes (Supplementary Table S7). Extended gene mapping revealed 292 genes highlighted for the 12 STST interaction loci, 67 genes for the 10 LTST interaction loci, and 35 genes for the four joint 2df loci not driven by interaction (Supplementary Tables S7-S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExpression Quantitative Trait Loci (eQTL)\u003c/h2\u003e \u003cp\u003eTissue-specific (GTEXv8) eQTL associations were observed at rs11314421, rs1035064, and rs34761985 in tissues of the heart, vasculature, or blood (Supplementary Table S8)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). \u003cem\u003eWBP1L\u003c/em\u003e-rs11314421 and \u003cem\u003eZNF682\u003c/em\u003e-rs1035064 gene mappings were corroborated by eQTL evidence identified in venous blood or the tibial artery. Beyond these primary mapped genes, \u003cem\u003eMFSD13A\u003c/em\u003e, \u003cem\u003eBORCS7\u003c/em\u003e, \u003cem\u003eCALHM2\u003c/em\u003e, \u003cem\u003eRPARP-AS1\u003c/em\u003e, \u003cem\u003eAS3MT\u003c/em\u003e, and \u003cem\u003eSFXN2\u003c/em\u003e expression were mapped to rs11314421 by eQTL evidence in the ascending aorta, coronary artery, tibial artery, left ventricle myocardium, right atrium auricular region, venous blood, or lymphoblast. Similarly, \u003cem\u003eZNF56\u003c/em\u003e, \u003cem\u003eZNF253\u003c/em\u003e, \u003cem\u003eZNF93\u003c/em\u003e, \u003cem\u003eZNF90\u003c/em\u003e, and \u003cem\u003eZNF486\u003c/em\u003e were mapped by coronary artery or venous blood eQTL evidence to rs1035064. \u003cem\u003eUXS1\u003c/em\u003e was mapped by rs34761985 eQTL data identified in the right atrium auricular region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eVariant-Level Cross-Trait Associations\u003c/h2\u003e \u003cp\u003eSeveral variants show associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) with other traits (Supplementary Table S10). Querying Open Target Genetics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genetics.opentargets.org\u003c/span\u003e\u003cspan address=\"https://genetics.opentargets.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) identified rs11314421 (\u003cem\u003eWBP1L)\u003c/em\u003e to be associated with hypertension and testosterone levels, and rs13032423 \u003cem\u003e(VRK2)\u003c/em\u003e with sleep duration and feeling miserable (Supplementary Table S11). Common Metabolic Diseases Knowledge Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hugeamp.org/\u003c/span\u003e\u003cspan address=\"https://hugeamp.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) further identified rs13032423 \u003cem\u003e(VRK2)\u003c/em\u003e to be associated with BMI and sleep duration (Supplementary Figure S4). Querying brain imaging phenotypes through the Oxford Brain Imaging Genetics Server (BIG40) revealed rs13032423\u0026rsquo;s (\u003cem\u003eVRK2)\u003c/em\u003e connection to brain functional connectivity by its association with rfMRI connectivity (ICA100 edge 965) (Supplementary Table S12, Supplementary Figure S5)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGene Functional Implications\u003c/h2\u003e \u003cp\u003eMice knockout evidence highlighted genes important for heart morphology (\u003cem\u003eBRINP3, CRBN, ALG10B, PRMT6\u003c/em\u003e), and cardiac rhythm (\u003cem\u003eTG, WBP1L\u003c/em\u003e) (Supplementary Table S13). Open Target Genetics, PheWeb, and PheGenI revealed genes implicated in genetic associations (p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), with OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) identifying any linked Mendelian disorders (Supplementary Tables S14-S17)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Specifically, eight genes harbored links to the cardiovascular domain through association with traits identifying by genetic studies: \u003cem\u003eWBP1L\u003c/em\u003e, \u003cem\u003eEFNA5, ZNF521\u003c/em\u003e, \u003cem\u003eWWOX\u003c/em\u003e, \u003cem\u003eZNF385D\u003c/em\u003e, \u003cem\u003eFAM98A\u003c/em\u003e, \u003cem\u003ePAM\u003c/em\u003e, and \u003cem\u003eJMJD1C\u003c/em\u003e. \u003cem\u003eALG10B\u003c/em\u003e was identified to be implicated in the Mendelian disorder long QT syndrome. In the realm of sleep and circadian health, reported genetic associations corroborated the relevance of \u003cem\u003eEFNA5\u003c/em\u003e, \u003cem\u003eZNF52\u003c/em\u003e1, \u003cem\u003eWWOX, ALG10B\u003c/em\u003e, \u003cem\u003ePAM\u003c/em\u003e and \u003cem\u003eSDK1\u003c/em\u003e with insomnia, daytime napping, or chronotype traits. Genetic associations to other pertinent domains including kidney function, neurological health, liver function, thyroid function, metabolism, lifestyle choice, and inflammation, were also noted (Supplementary Table S10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis\u003c/h2\u003e \u003cp\u003eWe performed gene set enrichment analyses on the aforementioned extended gene sets in the FUMA GENE2FUNC platform and STRING database (Supplementary Tables S18-S19)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). STST-mapped genes highlighted pathways in antioxidant defense and neuron excitation, along with phenotypic connection to lipid levels, neurological health, cardiovascular health, metabolism and immune defense. LTST-mapped genes implicated traits involving inflammation, neurological health, and metabolism. A clearly distinctive pattern differentiating short and long sleep duration interaction loci was thus not observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDruggability\u003c/h2\u003e \u003cp\u003eWe investigated druggability of the primary mapped genes using an integrative approach to highlight drug repurposing potential (Supplementary Table S20)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Identified gene candidates revealed connections to serotonergic response (\u003cem\u003eHTR1F, KCNJ3\u003c/em\u003e), proteasome-mediated ubiquitination (\u003cem\u003eCRBN\u003c/em\u003e), thyroid hormone synthesis (\u003cem\u003eTG\u003c/em\u003e), and axon guidance (\u003cem\u003ePAK5, ALCAM\u003c/em\u003e) pathways. Of these, \u003cem\u003eKCNJ3, CRBN, HTR1F, TG\u003c/em\u003e, \u003cem\u003ePAK5\u003c/em\u003e, and \u003cem\u003eALCAM\u003c/em\u003e harbored links to reported drug interactions and active ligand interactions in the ChEMBL database. The following genes displayed evidence of pharmacological targeting: \u003cem\u003eKCNJ3\u003c/em\u003e (by small molecule inhibitors Atomoxetine and Dronedarone); \u003cem\u003eCRBN (\u003c/em\u003eby thalidomide analogs Pomalidomide and Lenalidomide); \u003cem\u003eHTR1F\u003c/em\u003e (by selective serotonin receptor agonists like Lasmiditan); and \u003cem\u003eALCAM\u003c/em\u003e (by chemotherapy agent Fluorouracil).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this large-scale effort investigating the biomolecular mechanisms underpinning the intersecting roles of sleep health and blood pressure traits, we conducted genome-wide gene-by-sleep duration (short and long sleep) interaction analyses in 811,405 individuals of diverse population backgrounds (AFR, EAS, EUR, HIS, SAS) for systolic blood pressure, diastolic blood pressure, and pulse pressure. We report novel discovery of 22 gene-sleep duration interaction loci for BP traits \u0026ndash; 12 for short sleep, and 10 for long sleep. Several of the identified variants are rare with allele frequency\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;1%, with four variants identified in sex-stratified meta-analyses, and 18 variants specific to either the AFR(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), EUR(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), or HIS (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) population groups. In line with our previous research, the identified genomic loci exhibiting interactions with short and long sleep are non-overlapping (with non-significance in the opposing sleep duration exposure), suggesting distinct mechanisms influencing cardiovascular health. Nonetheless, we did not observe a clear differentiating pattern in the biological pathways implicated when comparing short sleep and long sleep.\u003c/p\u003e \u003cp\u003eThe functional annotation investigations of our prioritized genes point towards cardiovascular and neurological connections, along with revealing links to circadian rhythm, thyroid function, bone health, and hematopoiesis mechanisms. Our findings highlight potential pharmacological candidates and suggest pertinent pathways to consider when designing holistic therapeutic regimens for improving blood pressure control.\u003c/p\u003e \u003cp\u003eFirstly, at a broad level, several identified genes are tied to neurological mechanisms. \u003cem\u003eKCNJ3\u003c/em\u003e encodes Kir3.1 \u0026ndash; the alpha subunit for the I\u003csub\u003eKACh\u003c/sub\u003e potassium channel \u0026ndash; and is interestingly implicated in bradyarrhythmia by its missense variant inducing a gain of function of I\u003csub\u003eKACh\u003c/sub\u003e, as activation of this channel is tied to the negative chronotropic effect on heart rate exerted by the parasympathetic nervous system(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). \u003cem\u003eCRBN\u003c/em\u003e is linked to cognitive function (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), \u003cem\u003eSDK1\u003c/em\u003e promotes synaptic connectivity (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), \u003cem\u003eZNF521\u003c/em\u003e regulates neuron cell fate (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and \u003cem\u003eATP8A2\u003c/em\u003e is involved in both neuron vesicle transport and cardiac conduction (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Further, \u003cem\u003eKRTAP13-2\u003c/em\u003e, \u003cem\u003eWWOX, EFNA5\u003c/em\u003e, and \u003cem\u003eALCAM\u003c/em\u003e are linked to nervous system development with additional roles for \u003cem\u003eWWOX\u003c/em\u003e in myelination (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and \u003cem\u003eEFNA5\u003c/em\u003e in vascular sympathetic innervation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These functional connections may suggest a potential nervous system-heart connection that could be influenced by sleep or circadian disturbances.\u003c/p\u003e \u003cp\u003eIn fact neurological pathway connections to circadian rhythm reveal themselves through two enzymes - \u003cem\u003ePAK5\u003c/em\u003e and \u003cem\u003ePAM\u003c/em\u003e. Given that circadian rhythm and clock gene expression is intimately connected to blood pressure patterns, of note is \u003cem\u003ePAK5\u003c/em\u003e \u0026ndash; a serine/threonine kinase protective of adult neurons from injury and ischemic stress(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). \u003cem\u003ePAK5\u003c/em\u003e has both been shown to be targeted by clock gene-regulated miRNAs in the liver and identified to strongly bind to 14-3-3 proteins \u0026ndash; a protein family connected to light-sensitive melatonin diurnal patterns and plausibly influential for sleep behavior(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This strong binding affinity to 14-3-3 proteins suggests an interesting connection, as \u003cem\u003eYWHAB\u003c/em\u003e (one of this study\u0026rsquo;s primary genes mapped to a STST interaction locus), is part of this protein family. Another enzyme informing the neurological-sleep axis is \u003cem\u003ePAM\u003c/em\u003e, encoding a copper-dependent enzyme important for synthesizing amidated neuropeptides like NPY \u0026ndash; which regulates sleep through noradrenergic signals(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, \u003cem\u003eTG\u003c/em\u003e and \u003cem\u003eJMJD1C\u003c/em\u003e, both encoding proteins intrinsically tied to thyroid hormone function (thyroglobulin and thyroid receptor-interacting protein 8 respectively) \u0026ndash; present suggestive ties to the intersection between thyroid function and circadian rhythms. \u003cem\u003eTG\u003c/em\u003e mRNA and protein expression levels have shown to increase in response to melatonin, along with its genetic variants associated with autoimmune thyroid diseases(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Gene silencing of \u003cem\u003eJMJD1C\u003c/em\u003e\u0026rsquo;s paralog has shown arrhythmicity and prolonged sleep in Drosophila(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Given that circadian clock and thyroid function are increasingly suggested to be interconnected, and sleep deprivation can disrupt temporal hormone profiles (e.g. increased morning plasma thyroid-stimulating hormone (TSH) levels), it may be valuable to investigate further the overlapping pathways between thyroid function, healthy sleep duration, and cardiovascular morbidity(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond thyroidal pathways, hematopoiesis presents a possible comprehensive perspective on the interconnectedness between sleep health and nervous system response. \u003cem\u003eWBP1L\u003c/em\u003e, one of the primary genes identified (mapped to a STST interaction locus identified in female-specific CPMA) has suggestive connection to regulating the \u003cem\u003eCXCL12\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e signaling pathway by its inhibitory role on \u003cem\u003eCXCR4\u003c/em\u003e, the receptor for ligand \u003cem\u003eCXCL12\u003c/em\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This pathway is both influential for inflammation and hematopoietic state, reflects circadian control, and directly implicates the sympathetic nervous system response \u0026ndash; pertinent as stressors are suspected to induce a more exacerbated response in females(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). If stress factors (e.g. sleep loss) induce noradrenaline, this can downregulate \u003cem\u003eCXCL12\u003c/em\u003e, with resultant increased cell proliferation of pro-inflammatory cells from the bone marrow, incurring vascular damage(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). For instance, fragmented sleep has shown to promote myelopoiesis and lower hypocretin release by the hypothalamus, in turn accelerating atherosclerosis progression(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Thus perhaps WBP1L can offer insight into the intersections between sympathetic activation, neurological control, and unhealthy sleep impacting cardiovascular health, especially in women.\u003c/p\u003e \u003cp\u003eOn a similar note of addressing sex-specificity, of relevance is \u003cem\u003eFAM98A\u003c/em\u003e, a gene identified in female-specific CPMA for interaction with long sleep. \u003cem\u003eFAM98A\u003c/em\u003e, harboring multiple arginine demethylation sites, is a substrate of \u003cem\u003ePRMT1\u003c/em\u003e - an enzyme which catalyzes the synthesis of asymmetric dimethylarginine (ADMA), a molecule associated with cardiovascular harm as it induces endothelial dysfunction(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Thus seeking to lower harmful ADMA levels to counter harmful effects of sleep loss may be relevant in preservation of vascular integrity(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). \u003cem\u003eFAM98A\u003c/em\u003e, encoding a microtubule-associated protein, is also functionally linked to osteoclast formation, which is key to bone resorption and involved in postmenopausal osteoporosis etiology(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Given that osteoporosis and CVD share pathology, the \u003cem\u003eFAM98A\u003c/em\u003e locus may shed light on the importance of considering holistic treatment for hypertensive women approaching or after menopause \u0026ndash; an example being Felodipine, an antihypertensive found to additionally discourage osteoclast differentiation\u003csup\u003e(52, 53)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eApart from \u003cem\u003eFAM98A\u003c/em\u003e, specific genes highlight pathway connections to offer possible avenues for enhancing treatment efficacy for hypertension. Addressing the role of inflammation, \u003cem\u003eSLA\u003c/em\u003e may lend promise as an immunosuppressant, with cytoplasm-specific delivery of specific domains of \u003cem\u003eSLA\u003c/em\u003e shown to inhibit the T cell receptor functional cascade(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). \u003cem\u003eALG10B\u003c/em\u003e closely interacts with \u003cem\u003eKCNH2\u003c/em\u003e to protect it from inhibition by pharmaceuticals and thus prevent acquired long QT syndrome - interesting, as past work has identified \u003cem\u003eKCNH2\u003c/em\u003e genetic variation to associate with efficacy of specific antihypertensive drugs(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). \u003cem\u003ePAK5\u003c/em\u003e is the effector protein of \u003cem\u003eCDC42\u003c/em\u003e, vital for endothelial integrity and involved in the mechanism of Nebivolol, a third generation beta-blocker(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). \u003cem\u003eCRBN\u003c/em\u003e, due to its intrinsic role in ubiquitination, is recruited as an E3 ligase ligand in protease-targeted chimeras (PROTACs), which hold promise in cardiovascular therapeutics \u0026ndash; an example being P22A shown to reduce collateral damage of HMGCR upregulation caused by statins(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). These findings point to the need for future preclinical and clinical studies to confirm the hypothesized mechanisms and test promising interventions.\u003c/p\u003e \u003cp\u003eOur druggability analysis specified genes acting as existing pharmacological targets of FDA-approved drugs, offering perspective for drug repurposing. \u003cem\u003eHTR1F\u003c/em\u003e and \u003cem\u003eKCNJ3\u003c/em\u003e are linked to the serotonergic pathway and are targets of approved ADHD and antiarrhythmic drugs Atomoxetine and Dronedarone, respectively. This is potentially relevant given that serotonin may impact blood pressure regulation, and serotonin receptor desensitization is implicated in chronic sleep restriction(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). \u003cem\u003eHTR1F\u003c/em\u003e encodes for 5-HT\u003csub\u003e1F\u003c/sub\u003e, shown to function in smooth muscle and trigeminal nerves, with its selective agonists (i.e. Lasmiditan) offering greater efficacy for migraine treatment without the collateral harm of vasoconstrictive effects induced by non-selective triptans(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNoticeably all 22 gene-sleep duration interaction loci we identified were specific to a particular population group, a subset of population groups, or a particular sex. This may be due to substantial heterogeneity in BP architecture and sleep lifestyle as a result of cultural differences, uniquely varying stressors due to socioeconomics, and genetic risk that are both shaped by and influence lifestyle choices. For example, admixed African and Hispanic populations are more likely to have poorly controlled hypertension and circadian abnormalities in BP regulation, as well as higher prevalence of both short and long sleep duration relative to individuals of European ancestry(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Females generally sleep longer, have higher prevalence of insomnia, and experience an increased proinflammatory response to sleep deprivation compared to males(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Such differential risk profiles are likely attributed to a myriad of social or environmental variables along with genetic and epigenetic susceptibility(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, it is likely that the same duration of self-reported sleep has different etiologies and physiological effects across sex and population background. Future research incorporating extensive phenotyping may help clarify whether gender-specific or population-specific findings are explained by differences in sleep-related or other lifestyle behaviors, mechanisms underlying response to sleep disturbance, or are spurious.\u003c/p\u003e \u003cp\u003eThis study has several strengths including its large-scale nature made possible by inclusion of several international biobanks and cohort studies, rigorous data harmonization and quality control protocols, and robust statistical analysis pipelines. Our findings are reinforced by multiple lines of evidence from bioinformatics analysis. Focused druggability analysis and interpretation of drug-gene interactions offer promising insight in drug repurposing and candidate targets for future pursuits.\u003c/p\u003e \u003cp\u003eLimitations of this study include the risk of unidentified misclassification of self-reported sleep duration (opposed to objective measurements from actigraphy or polysomnography) due to recall bias, sleep misperception, or other psychosocial factors. Sleep health is complex, with key dimensions beyond duration (e.g., timing, quality, satisfaction, and regularity)(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Abnormality in these other sleep dimensions were not tested here due to lack of readily available data. Adding to the complexity, sleep duration itself reflects heterogeneous health effects influenced by genetic determinants. For example, genetic variation conducive to naturally short sleepers may even lend neuroprotection against harmful brain pathology(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). In addition, there may be residual confounding bias due to unadjusted comorbidities or environmental factors. Lastly, despite notable diversity of our sample, our data was dominated by individuals of European ancestry. It is striking that several of our loci are HIS-specific \u0026ndash; which may be resultant of complex admixture present in this population group. Although we were able to delve into sex-specific interpretations for FAM98A, and WBP1L \u0026ndash; future investigation is desired to understand the reasons behind heterogeneous effects by sex. Enrichment of sample sizes in minority populations is critical for future investigations.\u003c/p\u003e \u003cp\u003eIn conclusion this study advances our understanding of the interaction between sleep duration extremes and genetic risk factors shaping the genetic landscape of blood pressure. Our novel discovery of 22 gene-sleep duration interaction loci both accentuates the relevance of proper sleep duration in cardiovascular health and the need to be conscious of heterogeneity present in specific sex or population groups, providing valuable perspective for therapeutic intervention strategies to address cardiovascular disease burden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e \u003cp\u003eC.L.M. has received funding from AstraZeneca not related to the current study. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson \u0026amp; Johnson. D.C. receives consultancy fees from Roche Diagnostics and Trimedics and speaker fees from Servier. D.A.L. has received support from Medtronic LTD and Roche Diagnostics for biomarker research not related to the current study. The remaining authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eP.N. and H.W. conducted centralized project data analyses, data consolidation, meta-analyses, quality control, bioinformatics analysis, and contextual interpretation. P.N., H.W., A.R.B., A.T.K., C.L.M. K.S., T.W.W., P.B.M., R.N., D.C.R., S.R., and D.v.H. were part of the writing group and participated in project workflow design, interpretation of results, and drafting the manuscript. L.d.l.F., A.D., C.G., and D.C.R. participated in centralized study coordination. A.T.K., J.L.M., J.R.O., T.W.W., H.A., J.B.Meigs, X.Z., Han Chen, J.G., A.K.M., C.L.M., P.B.M., and P.A.P. acted as collaborators facilitating project design, specific code scripts, or specialized analyses. All other co-authors participated in final result interpretation, cohort-level study concept and design, cohort-level phenotype data acquisition and/or quality control, cohort-level genotype data acquisition and/or quality control, and/or cohort-level data-analysis and interpretation. All authors approved the final version of the paper that was submitted to the journal.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eThis project was largely supported by two grants from the U.S. National Heart, Lung, and Blood Institute (NHLBI), the National Institutes of Health, R01HL118305 and R01HL156991. Study-specific acknowledgements are included in the Supplementary Note. The research on the Million Veteran Program, was supported by award, CX001897 (PI A.M. Hung) from BLR\u0026amp;D, VA Office of R\u0026amp;D. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. Acknowledgment of the Million Veteran Program leadership and staff contributions can be found in the Supplementary Note under MVP core acknowledgement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMakarem N, Shechter A, Carnethon MR, Mullington JM, Hall MH, Abdalla M. Sleep Duration and Blood Pressure: Recent Advances and Future Directions. Curr Hypertens Rep. 2019;21(5):33.\u003c/li\u003e\n\u003cli\u003eKanki M, Nath AP, Xiang R, Yiallourou S, Fuller PJ, Cole TJ, et al. Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants. Nat Commun. 2023;14(1):7096.\u003c/li\u003e\n\u003cli\u003eKario K. Sleep and nocturnal hypertension: Genes, environment, and individual profiles. 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Familial natural short sleep mutations reduce Alzheimer pathology in mice. iScience. 2022;25(4):103964.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4163414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4163414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes\u0026rsquo; functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and long sleep (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.\u003c/p\u003e","manuscriptTitle":"A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 19:59:57","doi":"10.21203/rs.3.rs-4163414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-09-19T14:30:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-09T22:01:25+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-26T14:00:15+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-21T16:10:31+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-06-19T01:15:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-28T15:52:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-28T15:51:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2024-03-28T14:36:44+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-03-26T13:55:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e4cfffed-6371-49c4-92ad-571891dc557d","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":33431806,"name":"Biological sciences/Genetics"},{"id":33431807,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-03-11T13:35:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-17 19:59:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4163414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4163414","identity":"rs-4163414","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}