A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations 

A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 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 Research Article A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations Songmi Lee, Clint L Miller, Amy R Bentley, Michael R Brown, Pavithra Nagarajan, and 146 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6025759/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample. Results Our study included 564,680 adults aged 18 years or older from 67 cohorts and 4 population backgrounds (African (5%), Asian (7%), European (85%), and Hispanic (3%)). We discovered seven novel gene-DEPR interaction loci for BP traits. These loci mapped to genes implicated in neurogenesis ( TGFA , CASP3 ), lipid metabolism ( ACSL1 ), neuronal apoptosis ( CASP3 ), and synaptic activity ( CNTN6 , DBI ). We also identified evidence for gene-DEPR interaction at nine known BP loci, further suggesting links between mood disturbance and BP regulation. Of the 16 identified loci, 11 loci were derived from African, Asian, or Hispanic populations. Post-GWAS analyses prioritized 36 genes, including genes involved in synaptic functions ( DOCK4 , MAGI2 ) and neuronal signaling ( CCK , UGDH , SLC01A2 ). Integrative druggability analyses identified 11 druggable candidate gene targets, including genes implicated in pathways linked to mood disorders as well as gene products targeted by known antihypertensive drugs. Conclusions Our findings emphasize the importance of considering gene-DEPR interactions on BP, particularly in non-European populations. Our prioritized genes and druggable targets highlight biological pathways connecting mood disorders and hypertension and suggest opportunities for BP drug repurposing and risk factor prevention, especially in individuals with DEPR. Figures Figure 1 Figure 2 Figure 3 Background Hypertension and high blood pressure (BP) are major risk factors for cardiovascular disease, stroke, chronic kidney disease, and vascular dementia, significantly contributing to global morbidity and mortality [ 1 ]. Despite the widespread availability of effective anti-hypertensive medications, the prevalence of hypertension has doubled worldwide over the past three decades and is projected to affect 1.6 billion individuals by 2025 [ 2 ]. Moreover, while the age-adjusted prevalence of hypertension has declined in some regions, global disparities in hypertension rates have widened [ 3 , 4 ]. Genetic and environmental factors can independently increase the risk of hypertension, but gene-environment interaction (GxE) may provide a more comprehensive understanding of the genetic contributions to the disease [ 5 – 7 ]. A recent genome-wide association study (GWAS) of BP identified a total of 2,103 independent genetic signals, which accounts for approximately 60% of the heritability of BP [ 8 ]. Consequently, a substantial portion of heritability remains unexplained. Incorporating GxE in genetic analyses of BP may yield additional information about its genetic architecture and provide new avenues to improve health by more precisely characterizing risk of high BP in the context of potentially modifiable environmental, lifestyle, and behavioral risk factors [ 9 ]. The influence of psychosocial factors on BP level is well known [ 10 – 12 ]. Psychosocial stress increases the incidence of hypertension, and is associated with poor hypertension control, unhealthy lifestyle behaviors, and non-compliance with treatment regimens [ 13 ]. The relationship between depressive symptoms and BP is complex. While some studies have shown an association of depressive symptoms with incidence of hypertension [ 14 ], others have reported an association of depressive symptoms with lower BP levels [ 15 ]. Whilst, a recent study provided evidence of depression as a causal risk factor of hypertension using Mendelian Randomization [ 16 ]. Our previous study examined the effect modification of genetic factors by dichotomous psychosocial factors on BP in up to 128,894 individuals [ 17 ]. This highlighted the significance of gene-psychosocial factors interactions in gene discovery for BP, especially among individuals of African ancestry. However, the statistical power and population diversity of the study were limited. To address these shortcomings, we increased the sample size up to five-fold by incorporating now available biobank data. In addition, we defined psychosocial exposures as both dichotomous and quantitative, potentially improving the statistical power to identify novel findings. We report genome-wide association meta-analyses of systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) in the context of depressive symptomatology (DEPR) in a sample of up to 564,680 participants from populations of African (AFR), Asian (ASN), European (EUR), and Hispanic (HIS) backgrounds. Results Overview A total of 564,680 individuals from four populations were included in the study, comprising 85% EUR, 7% ASN, 5% AFR, and 3% HIS. Descriptive statistics are provided in Supplemental Table 1 . Because the quantitative DEPR exposure was not available in some biobanks, sample sizes were larger for dichotomous DEPR (dDEPR) than quantitative DEPR (qDEPR). As shown in Fig. 1 , the dDEPR analyses included 563,538 individuals after excluding two studies where the number of individuals with DEPR (N exp ) was less than 10 ( Supplemental Table 2 ). Among individuals with dDEPR, 15% had DEPR on average. The qDEPR analyses consisted of 294,029 participants from EUR (80%), ASN (7%), AFR (7%), and HIS (6%) populations. dDEPR analyses We identified nine independent loci that showed evidence of association with BP traits modified by dDEPR in cross-population meta-analyses (CPMA) or population-specific meta-analyses (Table 1 ). Of these, three loci tagged by rs1664073690 (1q31.3), rs10178576 (2q13.3), and rs113521945 (4q35.1) were novel. The other six loci tagged by rs115760284 (3p22.1), rs147967138 (7q21.11), rs757194 (7q31.1), rs7979305 (12p12.1), rs75095906 (13q32.1), and rs9931605 (16q23.2) were previously reported for BP ( Supplemental Table 3 ). Eight of the nine loci were identified via the 1df interaction test (P.Int < 5 x 10 − 8 ) (Table 1 ). In the 2df joint test, a total of 904 loci were associated with at least one BP trait (350 loci were associated with SBP, 337 loci were associated with DBP, and 364 loci were associated with PP). Among them, one previously reported BP locus (rs757194 on 7q31.1) showed evidence of association with SBP through interaction with dDEPR using the specified criteria (P.Joint = 7.99 x 10 − 9 ; P.Int = 1.39 x 10 − 7 ). Table 1 Novel and known Loci associated with BP traits discovered through SNP × dDEPR interactions Locus CHR:position (hg38) Alleles (E/A) rsID Analysis group EAF MAF AFR/EUR/ASN/HIS Nearest gene Position Int Effect Int SE P Int P Joint P FDR a P Het b Sample size P.Sex .Het 1q31.3 1:194548555 A/G rs1664073690 *ⴕ CPMA-SBP 0.98 0/0.02/0/0.01 CDC73 intergenic 7.07 1.25 1.44 x 10 − 8 9.93 x 10 − 4 0.09 0.30 30577 NA 2q13.3 2:70509396 C/T rs10178576 * CPMA-PP 0.91 0.11/0/0/0.02 TGFA intronic 2.59 0.46 2.16 x 10 − 8 5.11 x 10 − 7 0.24 0.61 39482 0.56 3p22.1 3:42213248 G/T rs115760284 CPMA-SBP 0.01 0.01/0/0/0.003 TRAK1 Intronic -13.30 2.39 2.78 x 10 − 8 0.048 0.10 0.01 22241 NA 4q35.1 4:184777291 A/G rs113521945 * CPMA-DBP 0.91 0.02/0.09/0.05/0.09 ACSL1 intronic -0.57 0.10 2.72 x 10 − 8 5.74 x 10 − 7 0.26 0.71 488129 0.05 7q21.11 7:78342531 A/T rs147967138 ASN-PP 0.04 0/0/0.04/0 MAGI2 Intronic 5.12 0.93 3.34 x 10 − 8 2.89 x 10 − 7 0.14 0.62 26307 0.8 7q31.1 7:112203372 A/G rs757194 AFR-SBP 0.03 0.03/0/0/0.006 DOCK4 Intronic 13.62 2.58 1.39 x 10 − 7 7.99 x 10 − 9 0.05 0.73 11644 NA 12p12.1 12:21435910 C/T rs7979305 AFR-PP 0.95 0.05/0/0/0.007 PYROXD1 intergenic -8.63 1.56 3.09 x 10 − 8 9.96 x 10 − 8 0.18 0.26 13093 NA 13q32.1 13:96826633 A/G rs75095906 CPMA-SBP 0.15 0.03/0.15/0.12/0.1 HS6ST3 Intronic -0.76 0.14 4.29 x 10 − 8 2.45 x 10 − 5 0.13 0.79 518557 0.99 16q23.2 16:81545886 C/T rs9931605 CPMA-SBP 0.81 0.83/0.81/0.78/0.77 CMIP Intronic 0.68 0.12 1.36 x 10 − 8 1.23 x 10 − 5 0.09 0.86 543909 0.05 Allele E, effect allele; Allele A, non effect allele; EAF, effect allele frequency; MAF, minor allele frequency; AFR, African; EUR, European; ASN, Asian; HIS, Hispanic; Int Effect, interaction effects estimated in the 1df interaction test (Effect is in mmHg). Int SE, standard error of interaction effects estimated in the 1df interaction test; P Int, P value of interaction effects in the 1df interaction test; P Joint, P value of joint effects of SNP main effect and interaction effect in 2df joint test; P.Sex.Het, sex heterogeneity P value in two-sample Z tests * rs1664073690 , rs10178576 , rs113521945 : top SNPs at novel loci (at least 500 Kbp away from any previously reported BP locus) ⴕ rs1664073690: absent in the 1000G Phase3 reference panels a P.FDR: interaction FDR P value for 1df interaction test; joint FDR P value for 2df joint test b P.Het: heterogeneity P value across population groups in CPMA; heterogeneity P value across studies in ancestry-specific meta-analyses The three top single nucleotide polymorphisms (SNPs) at novel loci (1q31.3, 2q13.3, and 4q35.1) were identified in the CPMA and showed no evidence of heterogeneity across population groups (P.Het > 0.003) (Table 1 ). Two of them were common variants with minor allele frequency (MAF) greater than 0.05 in at least one population group while one (rs1664073690 on 1q31.3) had a low frequency (MAF = 0.02). This variant was present at low frequency in EUR and HIS but was absent in both ASN and AFR. rs10178576 (2q13.3) was common in AFR (MAF = 0.11) but was not observed in either ASN or EUR populations (Fig. 2 ). rs113521945 (4q35.1) was observed across all four population groups. While a significant interaction was observed only in EUR, the direction of the effect was consistent across all four groups (Fig. 2 ). Among the six top SNPs at known BP loci (3p22.1, 7q21.11, 7q31.1, 12p12.1, 13q32.1, and 16q23.2), four SNPs on 3p22.1, 7q21.11, 7q31.1, and 12p12.1 showed the most significant associations or were exclusively observed in non-EUR populations (Fig. 2 ). Notably, three of them (rs115760284 on 3p22.1, rs757194 on 7q31.1, and rs7979305 on 12p12.1) were absent in both EUR and ASN but were present at low frequency in AFR (0.01 ≤ MAF ≤ 0.05) and were rare in HIS (MAF 80%, P.Het < 0.01), with a greater effect size in AFR (Fig. 2 ). Moreover, a locus on 7q21.11 was detected solely in ASN population among 26,307 individuals, with no evidence of heterogeneity across ASN studies (P.Het > 0.003). Two loci tagged by rs75095906 (13q32.1) and rs9931605 (16q23.2) were identified in the CPMA analyses, with no evidence of heterogeneity by population group. Across all nine top SNPs identified in the dDEPR analyses, no evidence of sex heterogeneity was observed. However, some SNPs could not be evaluated due to a limited sample size in males passing QC. qDEPR analyses We identified seven independent loci that showed evidence of association with BP traits modified by qDEPR in CPMA or population-specific meta-analyses (Table 2). Four loci tagged by rs77572777 (2q14.2), rs148780833 (3p26.3), rs748650739 (3q13.11), and rs140618249 (17p13.3) were novel. The other three loci tagged by rs59284269 (3p25.3), rs145132348 (4p14), and rs114544309 (12q13.13) were previously reported for BP ( Supplemental Table 3 ). Five loci, including two novel, were identified using the 1df interaction test (P.Int < 5 x 10 − 8 ) (Table 2). In the 2df joint test, a total of 316 loci were associated with at least one BP trait (144 loci were associated with SBP, 160 loci were associated with DBP, and 157 loci were associated with PP). Among them, two novel loci tagged by rs77572777 (2q14.2) and rs748650739 (3q13.11) were associated with PP through interaction with qDEPR. Notably, two of the novel loci rs148780833 (3p26.3) and rs140618249 (17p13.3) identified in the 1df test (P.Int < 5 x 10 − 8 ) also showed evidence of an association with SBP through interaction with qDEPR using the 2df joint test (P.Joint < 5 x 10 − 8 ). Table 2 Novel and known Loci associated with BP traits discovered through SNP × qDEPR interactions Locus CHR:position (hg38) Alleles (E/A) rsID Analysis group EAF MAF AFR/EUR/ASN/HIS Nearest gene Position Int Effect Int SE P Int P Joint P FDR a P Het b Sample size P.Sex .Het 2q14.2 2:118537183 A/G rs77572777 * HIS-PP 0.99 0/0.02/0/0.01 RP11-19E11.1 intergenic 2.48 0.44 3.55 x 10 − 5 1.74 x 10 − 8 0.06 0.47 16077 0.60 3p26.3 3:1301059 C/T rs148780833 *ⴕ CPMA-SBP 0.01 0.01/0/0/0.002 CNTN6 intronic 5.94 1.04 9.91 x 10 − 9 2.85 x 10 − 9 0.11 0.70 27204 0.01 3p25.3 3:8726816 A/G rs59284269 CPMA-SBP 0.09 0.23/0.02/0/0.06 SSUH2 intronic 0.86 0.16 4.29 x 10 − 8 8.65 x 10 − 7 0.18 0.25 251948 0.73 3q13.11 3:104214171 C/CA rs748650739 *ⴕ HIS-PP 0.99 0.05/0/0/0.01 RP11-40M23.1 intergenic 2.25 0.48 3.76 x 10 − 5 4.66 x 10 − 8 0.08 0.94 16077 0.16 4p14 4:39689605 C/T rs145132348 AFR-DBP 0.02 0.03/0/0/0.006 UBE2K intergenic 2.92 0.51 1.19 x 10 − 8 6.33 x 10 − 8 0.19 0.82 17147 0.85 12q13.13 12:52010638 C/T rs114544309 CPMA-DBP 0.01 0.02/0/0/0.008 GRASP intronic 2.45 0.44 1.85 x 10 − 8 2.56 x 10 − 7 0.31 0.01 31068 0.37 17p13.3 17:3225579 C/T rs140618249 * CPMA-SBP 0.98 0.02/0/0/0.004 OR1A1 intergenic -4.10 0.74 3.18 x 10 − 8 3.77 x 10 − 8 0.18 0.20 28685 0.50 Allele E, effect allele; Allele A, non effect allele; EAF, effect allele frequency; MAF, minor allele frequency; AFR, African; EUR, European; ASN, Asian; HIS, Hispanic; Int Effect, interaction effects estimated in the 1df interaction test (Effect is in mmHg). Int SE, standard error of interaction effects estimated in the 1df interaction test; P Int, P value of interaction effects in the 1df interaction test; P Joint, P value of joint effects of SNP main effect and interaction effect in 2df joint test; P.Sex.Het, sex heterogeneity P value in two-sample Z tests * rs77572777, rs148780833, rs748650739, rs140618249: top SNPs at novel loci ⴕ rs148780833, rs748650739: absent in the 1000G Phase3 reference panels a P.FDR: Interaction FDR P value for 1df interaction test; Joint FDR P value for 2df joint test b P.Het: Heterogeneity P value across population groups in CPMA; Heterogeneity P value across studies in ancestry-specific meta-analyses The four top SNPs tagging the novel loci include rs77572777 (2q14.2) and rs748650739 (3q13.11) from the HIS-specific analyses, and rs148780833 (3p26.3) and rs140618249 (17p13.3) from the CPMA. None of these four SNPs showed evidence of heterogeneity across populations or studies (P.Het > 0.003) and all were of low frequency (MAF = 0.01–0.02). Except for rs77572777 on 2q14.2, the three other SNPs were polymorphic only in AFR and HIS populations. Among the three known loci (3p25.3, 4p14, and 12q13.13) identified in the qDEPR analyses, two loci on 4p14 and 12q13.13 were not observed in EUR population. Of these two, the 4p14 locus tagged by rs145132348 and identified in AFR-specific analyses showed no heterogeneity across AFR studies contributing to the meta-analyses in this population (Fig. 3). The other locus on 12q13.13 tagged by rs114544309 and identified in CPMA showed the most significant association in HIS, with some evidence of heterogeneity between AFR and HIS and a greater effect size in HIS (Fig. 3). rs59284269 (3p25.3) identified in CPMA showed no evidence of heterogeneity by population group. No evidence of sex heterogeneity was observed across all seven top SNPs identified in the qDEPR analyses. Comparison between dDEPR and qDEPR analyses Of the 16 identified loci (Tables 1 and 2 ), four top SNPs (rs77572777, rs148780833, rs748650739, and rs114544309) were identified exclusively in the qDEPR analyses ( Supplemental Figs. 1 and 2 ). This appears to largely reflect differences in included studies in the two types of analyses ( Supplemental Fig. 3 ). In the CPMA, approximately 15 million SNPs were included in the dDEPR and 21 million SNPs in the qDEPR analyses. Notably, nearly 6 million SNPs were analyzed only in the qDEPR analyses, mainly because they were filtered out in the dDEPR analyses by the stringent study-level filters. Conversely, fewer than half a million SNPs were analyzed exclusively in dDEPR analyses, likely due to some large biobank samples where only dichotomous exposure was available while the quantitative exposure was not. The four SNPs identified only in qDEPR analyses were filtered out of the dDEPR analyses during study-level QC (rs148780833) or at the meta-analysis QC because they were present in only one study (rs77572777 and rs748650739), or in only one population (rs114544309) ( Supplemental Figs. 2 ). The remaining 12 loci were present in both dDEPR and qDEPR analyses. As illustrated in Supplemental Figs. 1 and 2 , there was a remarkable consistency between the two analyses even though magnitude of effects and statistical significance varied between them. Gene-based and pathway analyses Using 1df interaction test results, Multi-marker Analysis of GenoMic Annotation (MAGMA) and Versatile Gene-Based Association Study 2 (VEGAS2) ranked genes and pathways based on the combined association of SNPs within a gene with BPs. Both MAGMA and VEGAS2 gene-based tests identified a gene-wide significant association for GLTPD2 , with similar results observed for several other genes among the top 20 genes ( Supplemental Table 4 ). An additional gene, TMEM199 , was discovered by VEGAS2. These two genes were not identified at genome-wide significance in the GWAS. Pathway analyses suggest DEPR-specific genetic pathways that influence BP, including retinoid signaling, remodeling of acyl chains of phosphatidylethanolamine, nucleotide-binding oligomerization domain containing 2 (NOD2) protein signaling, and response to stress ( Supplemental Table 5 ). Functional annotation and gene prioritization Functional annotation was conducted for all SNPs in linkage disequilibrium (LD) (r 2 > 0.4) with the top SNPs tagging all identified novel and known BP loci. All the top SNPs were annotated as either intergenic or intronic variants, suggesting a potential role for regulatory mechanisms. Among 31 genes identified by FUMA, six genes were predicted to be highly intolerant to loss-of-function mutation based on probability of loss-of-function intolerance (pLI) score > 0.9, including CMIP , ZBTB47 , DOCK4 , UBE2K , PDS5A , and GRASP ( Supplemental Table 6 ). Multiple genes exhibited high CADD scores (> 12.37) among SNPs in LD, suggesting potential deleterious effects. Three additional genes were identified through associations with various quantitative trait loci (xQTL), which include CASP3 , DBI , and UGGT2 . Details on functional annotations are described in Supplemental Table 7 . A total of 36 genes were prioritized by functional annotations of both novel and known loci, as well as gene-based analyses. These prioritized genes showed enrichment of gene expression in the brain and whole blood ( Supplemental Fig. 4 ). Additionally, they demonstrated evidence of enrichment in two pathways involved in myogenesis and immune system in dendrite cells, as well as enrichment in four potential microRNA regulatory targets ( Supplemental Table 8 ). Druggability analyses We investigated the potential druggability of the identified 36 candidate gene product targets using an integrative approach as previously described [ 18 ]. We queried dDEPR and qDEPR exposure candidate gene targets using the Drug-Gene Interaction database (DGIdb), which identified 11 genes annotated as members of the druggable genome ( Supplemental Table 9 ). Several of these gene targets are implicated in metabolic pathways ( ACSL1 , DBI , UGDH , SLCO1A2 ), vascular wall signaling ( TGFA , CAV3 , SSUH2 , DOCK4 ), DNA damage response or apoptosis ( CASP3 , RFC1 , RECQL ), and neuroactive ligand-receptor interaction ( VIPR1 , CCK ). We identified 11 genes with FDA approved drug interactions that have been evaluated in late-stage clinical trials using DrugBank, ChEMBL, and ClinicalTrials.gov databases ( Supplemental Table 10 ). Two of these gene targets ( CASP3 and UGDH ) were identified as targets of aspirin, a well-established and safe drug used to treat pain, inflammation, and reduce cardiovascular events. UBE2K was identified as a target of the central nervous system stimulant, dextroamphetamine, used to treat attention-deficit disorder (ADHD) and narcolepsy, however its use has been federally controlled due to the high potential for abuse. CCK was also identified as a target of the vasodilator, diazoxide, which is used to manage hypoglycemia due to pancreatic cancer or other conditions. Several genes ( CCK , SLCO1A2 , UGGT2 ) were identified as targets of drugs (diazoxide, nadolol, hydrochlorothiazide) used to treat hypertension, suggesting opportunities for drug repositioning and risk factor prevention. Discussion In this large-scale genome-wide interaction study, we identified 16 genetic loci whose association with BP was modified by DEPR defined as a dichotomous or a quantitative exposure. These data provide support for molecular mechanisms connecting DEPR and BP and highlight several genes as possible druggable targets with clinical potential for BP regulation in individuals with DEPR. Nearly 70% of our findings were derived from non-EUR populations, likely due to differences in allele frequency across populations and/or to population differences in SNP x DEPR interaction effect sizes. Notably, several of the identified SNPs were monomorphic in EUR. Variations in MAF across population groups have been shown to contribute to differences in disease prevalence across populations [ 19 ]. The risk of hypertension varies considerably across populations, being more prevalent in AFR and HIS populations [ 20 , 21 ]. More than half of our findings come from AFR and/or HIS. AFR populations generally exhibit greater genetic diversity and more pronounced allele frequency differences compared to other populations [ 22 ]. Self-identified HIS populations in the US include admixed individuals with varying proportions of EUR, AFR, and Amerindian genetic backgrounds, adding further complexity. Interestingly, patterns of associations were similar in AFR and HIS populations at several loci near the genes TGFA , TRAK1 , CNTN6 , and OR1A1 . GWAS of BP have identified differences in BP loci by population groups, while partial generalization of BP loci between populations has also been reported [ 23 – 25 ]. Thus, there is a critical need for expanding genetic studies of BP in non-EUR populations. In our study, among nine known BP loci identified with evidence for gene-DPER interaction, six loci (3p22.1, 7q21.11, 7q31.1, 12p12.1, 4p14, and 12q13.13) were derived from non-EUR populations while they were previously discovered as BP loci in EUR population. This further underscores the importance of considering DEPR effect modification on BP for diverse populations. Multiple studies have shown mixed results regarding the association between depressive symptomatology and hypertension [ 14 , 26 , 27 ]. Despite this variability, depression has been consistently linked to an increased risk of cardiovascular morbidity and mortality [ 28 ]. Typically, depression arises in response to stressful events, and stress is a major risk factor for hypertension [ 29 ]. Both hypertension and depression show higher prevalence among individuals of non-EUR populations, highlighting significant racial and ethnic disparities [ 20 , 21 , 30 , 31 ]. Functional annotation of the novel loci revealed genes implicated in neurogenesis, lipid metabolism, neuronal apoptosis, and synaptic activity. A locus on chromosome 2 mapped to an intron of the TGFA gene, which encodes a ligand for the epidermal growth factor receptor and plays a crucial role in neural cell proliferation and differentiation [ 32 , 33 ]. Previous studies suggested TGFA ’s role in neurogenesis and angiogenesis in adult injured brain and the immune system [ 34 , 35 ]. Furthermore, genetic variants in TGFA have been associated with response to antidepressant treatment in GWAS [ 36 , 37 ]. ACSL1 encodes an isozyme of the long-chain fatty-acid-coenzyme A ligase family, which operates in lipid biosynthesis and fatty acid degradation. Animal models have demonstrated that ACSL1 modulates lipid metabolism, inflammation, and oxidative stress in kidney disease [ 38 , 39 ]. In fact, the kidney plays a critical role in BP regulation [ 40 ]. The ACSL1 locus was associated with DNA methylation levels (mQTL) of ACSL1 in blood. Functional annotations of this novel locus also highlight several additional genes, including CASP3 . CASP3 encodes a cysteine-aspartic acid protease (Caspase-3) that plays a critical role in neuronal apoptosis, neurogenesis, and synaptic activity [ 41 – 44 ]. Notably, the ACSL1 locus was associated with the splicing event of CASP3 in brain tissue. Interestingly, a recent study highlighted the role of Caspase-3 in pathogenesis of depressive disorders [ 45 ]. CNTN6 encodes Contactin-6, a neuronal cell adhesion molecule that facilitates neurite outgrowth and synaptogenesis [ 46 ]. Mutations in this gene increase the risk for autism spectrum disorders [ 47 ]. DBI encodes a diazepam binding inhibitor, which is regulated by hormones and acts as a neuropeptide in brain synapses [ 48 ]. Our results showed an intergenic variant (rs77572777 on 2q14.2) with an expression quantitative trait locus (eQTL) of DBI in brain tissue. A previous study reported that DBI expression in the brain decreased with long-term social isolation stress [ 49 ]. An increased level of the protein encoded by DBI has been suggested as a prognostic value in cardiovascular disease [ 50 ]. Several known loci for BP were identified through interactions with DEPR in our study and implicated several genes previously reported to be associated with mental disorders. These genes include DOCK4 , HS6ST3 , and MAGI2 . The DOCK4 locus was associated with SBP in the AFR population. DOCK4 is a member of the dedicator of cytokinesis family and is involved in cell migration [ 51 ]. Animal models have suggested a role of DOCK4 in excitatory synaptic transmission and social behavior [ 52 ]. Variants in DOCK4 have been associated with response to antidepressants, autism spectrum disorder, and schizophrenia [ 53 , 54 ]. A recent GWAS of stress-induced vasomotion identified an association with variants in DOCK4 , which were also linked to an increased risk of adverse cardiovascular events [ 55 ]. HS6ST3 encodes heparin sulfate sulfotransferases involved in proliferation, inflammation, and blood coagulation. Variants within or near this gene have been associated with schizophrenia, major depressive disorder, and coronary artery calcified atherosclerotic plaque [ 56 – 58 ]. MAGI2 encodes a synaptic scaffolding molecule and shows high expression in the brain and postsynaptic density area of spine [ 59 ]. In our data, the MAGI2 locus was observed only in ASN population, and variants in this gene have been associated with depressive symptoms in an East Asian cohort as well as in other population groups [ 60 – 62 ]. Our druggability analyses suggest potential opportunities for drug repurposing and risk factor prevention. The identified genes include CASP3 and UGDH as targets for aspirin and CCK , SLC01A2 , and UGGT2 for antihypertensive medications. UGDH encodes an integral Golgi membrane protein involved in signal transduction and cell migration. A previous study has shown its nominal association with brain electrical activity linked to psychiatric conditions including depression, and suggested that this association may be population-specific [ 63 ]. This is consistent with our finding that the associated SNP (rs145132348 on 4p14) was identified only in individuals of AFR and HIS populations. CCK encodes cholecystokinin (CCK), a digestive enzyme and a neuropeptide that regulates emotional states [ 64 , 65 ]. Patients with major depression showed increased CCK levels in cerebrospinal fluid [ 66 ]. CCK enzyme also plays role in BP regulation and predicts cardiovascular mortality in elder females [ 67 , 68 ]. SLCO1A2 (or OATP1A2 ) encodes a sodium-independent transporter that is crucial for transporting hormones across the blood-brain barrier into the central nervous system and has been suggested as a potential modulator of mood disorders [ 69 – 71 ]. UGGT2 encodes a soluble protein of the endoplasmic reticulum and has been associated with impulsive behaviors [ 72 , 73 ]. It is important to note that some of these drug-gene interactions may also reflect the medication use for individuals with chronic depression and warrant follow-up to determine their direct impact on hypertension and cardiovascular risk [ 26 ]. Findings from our prior study [ 17 ] were generally not replicated in this study, likely due to the use of a new modeling strategy that includes additional adjustment for potential confounders. One notable exception is the reported gene-DEPR interaction at the FSTL5 locus by 2df joint test, tagged by two SNPs (rs138187213 and rs5863461). In our dDEPR analyses, both SNPs showed associations in the 1df interaction test (rs138187213, P.Int = 8.48 x 10 − 4 ; rs5863461, P.Int = 2.89 x 10 − 4 ). Similar results were observed in the qDEPR analyses, with both SNPs showing evidence of interactions (rs138187213, P.Int = 8.19 x 10 − 5 ; rs5863461, P.Int = 1.06 x 10 − 4 ). Our study benefits from a large sample size with diverse population backgrounds, which allows for a comprehensive analysis of the interactions across different populations. Moreover, our methodological approach using two complementary definitions of DEPR sought to enhance novel discoveries. The dDEPR analyses, with a larger sample size, provided greater statistical power, while the qDEPR analyses were designed to capture subtle variations in exposure and potentially reveal associations that might have been missed in the dDEPR analyses. Notably, we observed a substantial number of SNPs analyzed in the qDEPR but not included in the dDEPR, likely due to stringent filters required for binary exposure analyses. The qDEPR analyses enabled us to identify additional loci at genome-wide level, possibly due to the assumption of linearity between the exposure and outcome being met for those specific loci. Furthermore, the consistency of associations across both analytical approaches reinforces robustness of our findings. Several limitations should be acknowledged. First, the sample size for non-EUR population groups was relatively small compared to the EUR population, which may have limited the discovery of population-specific findings. For this reason, we combined East ASN and South ASN populations into a single population group although that may introduce heterogeneity. While combining distinct populations can introduce complexity due to underlying genetic and cultural differences, this approach was chosen to increase statistical power. Second, DEPR was captured by several different validated instruments in the participating cohorts with different sensitivities and specificities to detect depressive symptoms, which may have introduced heterogeneity and measurement error, potentially reducing statistical power. Lastly, while extensive functional annotation and druggability analyses provide biological validation/support for our findings, replication in independent samples was not possible in this study since dividing cohorts into discovery and replication analyses encountered insufficient power. Because we made extensive efforts at recruiting most of the studies known to have DEPR data, identifying suitable independent cohorts with large sample size and DEPR data availability for replication remains a major challenge. This is a particular issue for interactions identified only in non-European population groups, often in relatively modest sample sizes. In conclusion, we identified multiple genetic loci associated with BP traits that were modified by DEPR. These data emphasize the importance of considering DEPR as an effect modifier in BP gene discovery, particularly in non-EUR populations. They also provide insights into the molecular basis of the relationships between DEPR and BP, and highlight the potential of applying such information to enhance more personalized approaches to hypertension management in individuals with DEPR. Methods Study design and participants All participating cohorts were part of Gene-Lifestyle Interactions Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium [ 74 ]. Except for the UK Biobank, the study included adult men and women aged 18 years or older from four population groups defined based on self-reported participant’s race and ethnicity: AFR (including self-reported Black), ASN (including East Asian and South Asian), EUR (including self-reported White), and HIS. The UK Biobank used the Pan-UKB data to define population groups based on shared genetic similarity and demographic history [ 75 ]. GWAS considering the interaction between gene and DEPR were conducted within each individual study by population group. Population-specific meta-analyses were then performed using summary statistics, followed by cross-population meta-analyses based on the population-specific results (Fig. 1 ). All participating studies obtained written informed consent from their participants and approval from the appropriate institutional review boards. Details about the participating studies are provided in the Supplemental Material . Blood pressure (BP) traits Three BP traits were considered as outcome variables: SBP, DBP, and PP. Pulse pressure was calculated as the difference between SBP and DBP. When multiple BP readings were taken during the same examination, the average of all SBP or DBP readings were used. For participants taking any anti-hypertensive medications, SBP and DBP values were adjusted by adding 15 mm Hg and 10 mm Hg, respectively, to the measured values [ 76 , 77 ]. Extreme values for each BP variable were winsorized if they were more than six standard deviations (SDs) above or below the mean. Depressive symptomatology (DEPR) exposures Each participating study collected information on DEPR using validated screening questionnaires, as detailed in Supplemental Table 11 . Measurements of DEPR and BP were taken during the same examination. We defined two variables as exposures: dDEPR and qDEPR. The dDEPR exposure was defined as a binary variable by dichotomizing DEPR measures using recommended standard cut off points specific to each screening instrument. Individuals with higher depressive symptom score were categorized as the exposed group and coded as E = 1. The specific cut off points used to define the dDEPR for each study are provided in Supplemental Table 11 . Descriptive statistics on depression score are provided in Supplemental Table 2. The qDEPR exposure was defined as a standardized residual after adjusting for age and sex effects within each cohort. For studies that included multiple population groups, the variable was computed separately for each population. First, DEPR scores were winsorized if a value was more than 6 SDs above or below the mean. The scores were then regressed on age, sex, and age × sex interaction in the sex-combined samples. The resulting age- and sex-adjusted residuals were standardized using the Z-score in the combined sample. Thus, in each study, the mean and SD of qDEPR were approximately 0 and 1, respectively, as shown in Supplemental Table 12 . For the sex-stratified analyses, the same qDEPR estimates from the sex-combined group were used. Genotype data Most of the participating studies performed genotyping using Illumina or Affymetrix. Imputations were primarily carried out using Trans-Omics for Precision Medicine (TOPMed) or Haplotype Reference Consortium (HRC) reference panels. Details on genotyping and imputation are presented in Supplemental Table 13 . Before analysis, genotype data for each cohort were restricted to SNPs mapping to autosomal chromosomes, with MAF ≥ 0.1% across all samples and an imputation quality ≥ 0.3. Indels (insertions and deletions) were also included. Individual study statistical analyses Each cohort performed analyses by population subgroup using two statistical models designed for different purposes. Model 1 was a joint effect model that accounts for the SNP main effect, DEPR effect, and the interaction effect between SNP and DEPR: $$\:E\left(BP\right)={\beta\:}_{0}+\:{\beta\:}_{SNP}SNP+{\beta\:}_{DEPR}DEPR+\:{\beta\:}_{SNP\times\:DEPR}SNP\times\:DEPR+{\beta\:}_{C}C$$ Where DEPR was either dDEPR or qDEPR, and C was a vector of covariates, including age, age 2 , sex, field centers (if relevant), and population-specific principal components, as well as any additional cohort-specific covariates, if applicable ( Supplemental Table 13 ). In model 1, additional DEPR × covariate interaction terms with age, age 2 , and sex were included in the model to minimize potential false positive findings that could result from confounding effects [ 78 ]. For the sex-stratified analyses, both sex and DEPR x sex were excluded from the model. A 1 degree of freedom (1df) interaction test was performed to evaluate SNP x DEPR interaction effect alone under the null hypothesis that β SNPxDEPR = 0. A 2df joint test was used to simultaneously assess the SNP main effect and SNP x DEPR interaction effects, under the null hypothesis that β SNP = β SNPxDEPR = 0 [ 79 ]. When both the SNP main effect and interaction effects exist, the 2df joint test typically provides more power than the 1df interaction test [ 79 ]. Model 2 was a SNP marginal effect model: $$\:E\left(BP\right)={\beta\:}_{0}+\:{\beta\:}_{SNP}SNP+{\beta\:}_{C}C$$ The SNP marginal P-value (P.Marginal) was used to identify SNPs with significant evidence of interaction effects by comparing P.Marginal to the 1df interaction P-value (P.Int) in Model 1. To ensure a fair comparison, we conducted a standard GWAS (Model 2) with the same covariates used in Model 1 other than the DEPR x covariate interaction terms. Analyses excluded subjects without genotype data or with missing data for the DEPR exposure or any covariates. Each study selected one of the specialized software tools to run analyses: GEM ( https://github.com/large-scale-gxe-methods/GEM ), LinGxEScanR ( https://github.com/USCbiostats/LinGxEScanR ), or MMAP ( https://github.com/MMAP/MMAP.github.io ), as described in Supplemental Table 13 . For the studies with related subjects, MMAP was used to account for familial relatedness using linear mixed models. Quality control of study-specific and meta-analyses results Quality control (QC) was performed for both study-specific and meta-analyses results using EasyQC2 software ( www.genepi-regensburg.de/easyqc2 ). For results submitted in build hg19, genomic coordinates were lifted over to build hg38. At the study-level, QC involved different SNP filters for the two exposures. For the dDEPR, SNPs were excluded if degree of freedom (DF) was less than 20 in the unexposed, exposed, or total samples. The DF was calculated as minor allele count * imputation quality score. For the qDEPR, SNPs were removed if the DF was less than 20 in the total samples. To identify systematic errors in data preparation, allele frequency (AF) discrepancy, outliers, and missing data were assessed visually through comparison of results to reference panels derived by imputation of population-specific 1000 Genomes phase 3 version 5 (p3v5) panels to the TOPMed reference panels using the TOPMed imputation server. Any resulting concerns were addressed through consultation with the contributing studies. Genomic control (GC) inflation factors were also estimated. Next, meta-level QC was performed within each population group (AFR: 18 cohorts; ASN: 8 cohorts, EUR: 36 cohorts, HIS: 5 cohorts) to assess improper transformation of BP variables, unstable numerical computation, and excessive inflation. Meta-analyses Meta-analyses were performed using an inverse-variance weighted fixed-effect model for the 1df interaction test and an inverse-covariance-matrix-weighted model for the 2df joint test [ 80 , 81 ]. Analyses were first conducted separately for each population group, and then the results were combined for CPMA. The primary focus was on analyses within the sex-combined group, considering three phenotypes and two exposures. For the identified loci in the sex-combined group analyses, we performed sex-stratified analyses to assess differences in GxE by sex. GC correction was applied to the population-specific meta-analyses and subsequently once more to the CPMA [ 80 ]. Quantile-quantile (QQ) plots and GC inflation factors are shown in Supplemental Figs. 5–14 . In the 2df joint test, there were mild to moderate inflations, mainly due to the significance at previously reported loci for BP. Identification of independent associated loci The EasyStrata2 software was used to prioritize the top loci among significant results identified in 1df interaction and 2df joint tests [ 82 ]. For the CPMA, SNPs had to be present in at least two population groups with a minimum sample size of 20,000 individuals. In the EUR-specific meta-analyses, SNPs were reported if they appeared in at least three studies and in at least 3,000 individuals. These criteria were relaxed for other population groups due to smaller sample size, as shown in Supplemental Table 14 . Only SNPs with MAF greater than 1% were reported for both population-specific and cross-population meta-analyses. SNPs located within 1 Mb of the major histocompatibility complex (MHC) region were excluded. We considered SNPs with significant evidence of DEPR interaction effects on BP as top SNPs based on the following criteria: (1) SNPs with significant 1df interaction effect (P.Int 10 − 6 ); (2) SNPs with significant 2df joint effects (P.Joint < 5 x 10 − 8 ), and P.Int < Bonferroni-corrected P adjusted for the number of 2df joint variants identified in the respective CPMA or population-specific subgroup (e.g, for CPMA: dDEPR: 0.05/904 = 5.53 x 10 − 5 ; qDEPR: 0.05/316 = 1.58 x 10 − 4 ), and P.Int < P.Marginal. False discovery rates (FDR) were also calculated using EastyStrata2. To identify independent loci among all significant variants, we grouped the significant variants within 500-kilobase regions and identified independent loci by LD R 2 < 0.1, using TOPMed-imputed 1000G reference panels. If variants within regions were missing in the LD panels, the most significant variant within each region was reported. The independent loci were considered novel if the SNPs are located ± 500 kb away from the known loci previously reported in BP GWAS ( Supplemental Table 15 ). For the identified independent loci, we additionally examined heterogeneity of the interaction effects by sex using the results from the sex-stratified analyses. Heterogeneity of SNP x DEPR effects between men and women was tested using two-sample Z tests [ 83 ]. The significance threshold for heterogeneity tests was defined at Bonferroni-corrected threshold based on the number of the identified independent loci. Gene-based analyses We performed gene-based tests on meta-analysis summary statistics for the 1df interaction results using MAGMA implemented in FUMA [ 84 ] and VEGAS2 [ 85 ]. Both tools computed gene-based p-values by considering variants within each gene. The MAGMA method utilized a multiple linear regression model [ 86 ], while VEGAS2 analyses were conducted using the ‘top10’ parameter, which selects the top 10% variants within a gene, taking into account the number of variants and LD. This approach allowed us to include SNPs with stronger signals and exclude those that might dilute the summary statistics [ 85 ]. For both MAGMA and VEGAS2, we used 1000 Genomes phase 3 reference panels specific to AFR, EAS (for ASN), EUR, AMR (for HIS) populations to compute LD for population-specific analyses. In MAGMA, the CPMA was conducted using the “all” 1000 Genomes phase 3 reference panel in the FUMA setting. For VEGAS2, we performed meta-analyses of population-specific gene-based results using Stouffer’s method, with p-values weighted by sample size. Gene-wide significance in MAGMA was defined as P < 2.61 x 10 − 6 , correcting for 19,122 protein-coding genes. VEGAS2 included 19,263 protein-coding genes, leading to a gene-wide significance threshold of P < 2.61 x 10 − 6 . Gene-set or Pathway-based analysis We conducted gene-set analysis using MAGMA in FUMA to identify associations between gene sets and biological pathways. The analyses were performed based on the gene-based results from MAGMA, with statistical significance threshold at P < 2.94 x 10 − 6 , correcting for 17,009 gene sets. As a sensitivity analysis, we performed pathway-based analysis using VEGAS2Pathway [ 87 ], based on population-specific gene-based association results generated with VEGAS2. The meta-analyses were conducted using Stouffer’s method. VEGAS2Pathway included 2,748 pathways, resulting in a significance threshold of empirical P < 1.82 x 10 − 5 . Functional Annotations All identified independent loci were assessed for potential functional annotations using multiple tools. First, we used the FUMA v1.5.2 to annotate functional information of the novel and known loci [ 84 ]. At the genomic region level, the FUMA SNP2GENE pipeline was used to prioritize genes based on the results of the top SNPs and SNPs in LD (r 2 > 0.4 within 250 kb) through three gene mapping approaches: positional mapping, GTEx v8 eQTL mapping, and 3D chromatin interaction mapping (FDR ≤ 1 x 10 − 6 , 250bp upstream and 500bp downstream of the transcription start site [TSS] by default settings). At the variant level, we used QTLbase [ 88 ] and Open Target Genetics [ 89 ] databases to explore xQTL that link our loci to tissue or cell type specific functions. The xQTL include gene expression (eQTL), DNA methylation (mQTL), histone modification (hQTL), splicing event (sQTL), protein expression (pQTL), alternative polyadenylation (apaQTL), and others. To investigate whether the identified loci were associated with other phenotypes, we utilized a phenome-wide association studies (PheWAS) tool implemented in Open target genetics and GWAS ATLAS [ 90 ]. Using all the prioritized genes, we performed FUMA GENE2FUNC analysis to test enrichment of the gene sets and provide expression of those prioritized genes (adjusted p-value < 0.05). Druggability analyses To assess the clinical potential of the candidate genes, we conducted integrative druggability analyses. We first used the Drug-Gene Interaction database (DGIdb; v4.2.0) to query high or medium priority and determine the potential druggability of the candidate gene targets. We annotated genes for implicated pathways and functions using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We annotated the druggability target categories and queried all interacting drugs reported in 44 databases (Ensembl, HGNC, NCBI, ChemIDplus, Drugs@FDA, HemOnc, NCIt, RxNorm, Wikidata, CancerCommons, CGI, ChEMBL, CIViC, ClearityFoundationBiomarkers, ClearityFoundationClinicalTrial, COSMIC, DoCM, DrugBank, DTC, FDA, GuidetoPharmacology, JAX-CKB, MyCancerGenome, MyCancerGenomeClinicalTrial, OncoKB, PharmGKB, TALC, TdgClinicalTrial, TEND, TTD, BaderLab, CarisMolecularIntelligence, dGene, FoundationOneGenes, GO, HingoraniCasas, HopkinsGroom, HumanProteinAtlas, IDG, MskImpact, Oncomine, Pharos, RussLampel, Tempus). We queried protein targets for available active ligands in ChEMBL. We queried gene targets 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. We also queried FDA-approved drugs, late-stage clinical trials and disease indications in the DrugBank, ChEMBL, ClinicalTrials.gov databases and provided results for the top MESH and DrugBank indications and clinical trials. Declarations Ethics approval and consent: All participating studies obtained written informed consent from their participants and ethics approval from the appropriate institutional review boards. Details about the participating studies are provided in the Supplemental Material . Consent for publication: Not applicable Availability of data and materials : Due to restrictions in the written informed consent and local regulations, individual genotype-level data from this project could not be shared. Summary statistics are available at the CHARGE (Cohorts for Heart and Ageing Research in Genomics Epidemiology) dbGaP summary site (phs000930 [https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000930.v1.p1]). Competing interest : C.L.M has received funding from AstraZeneca on an unrelated project. B.M.P serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. D.C. received consulting fees from Trimedics. H.J.G has received travel grants and speakers honoraria from Neuraxpharm, Servier, Indorsia and Janssen Cilag. The remaining authors declare no competing interests. Funding : 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, R01HL156991, and HL105756. PBM acknowledges support from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at Barts (NIHR202330). KEN and KLM were provided in part by R01HL142302, R01HL151152, R01DK122503, R01HD057194, R01HG010297, R01HL143885, R01HL163262. NF was supported by R01DK117445, R01MD012765, R01HL163972. Study-specific acknowledgements and funding sources are included in the Supplemental Material . Author’s contributions : SL, WJG, TWW, LJB., LdlF, DR, and MF contributed to conception and design of the study. HC, WJG, JO, TWW, and JLM were involved in the development of software. SL, CLM, and MF prepared the initial manuscript draft. All other co-authors contributed to data acquisition, analysis, and interpretation, as well as the critical revision of the manuscript. Acknowledgements : Not applicable References Lawes CM, Vander Hoorn S, Rodgers A, International Society of H: Global burden of blood-pressure-related disease, 2001. Lancet 2008, 371: 1513-1518. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, Chen J, He J: Global Disparities of Hypertension Prevalence and Control: A Systematic Analysis of Population-Based Studies From 90 Countries. Circulation 2016, 134: 441-450. 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MI","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"R","lastName":"Weir","suffix":""},{"id":415786058,"identity":"9fe2d5e7-fd69-4d69-8adb-7e1e847c1b7f","order_by":138,"name":"Xiaofeng Zhu","email":"","orcid":"","institution":"Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhu","suffix":""},{"id":415786059,"identity":"74d62050-5645-4243-93dd-cd8ceb7e5902","order_by":139,"name":"Nora Franceschini","email":"","orcid":"","institution":"Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC","correspondingAuthor":false,"prefix":"","firstName":"Nora","middleName":"","lastName":"Franceschini","suffix":""},{"id":415786060,"identity":"58985d20-3ba3-4624-a21a-eae476cf4632","order_by":140,"name":"Jeffrey R O’Connell","email":"","orcid":"","institution":"Division of Endocrinology, 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20:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6025759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6025759/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76487395,"identity":"2b816015-ee2c-4d89-8c77-eb183c4d0e8d","added_by":"auto","created_at":"2025-02-17 16:00:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated in BioRender. Lee, S. (2024) BioRender.com/a15i893\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. For each BP trait, association analyses were conducted accounting for SNP x depressive symptomatology (DEPR) interaction effects using two exposures: dichotomous DEPR (dDEPR) and quantitative (qDEPR). For each population group, study-specific results were combined to perform 1df interaction test and 2df joint test. Population-specific meta-analyses were carried out separately for each group: African (AFR), Asian (ASN), European (EUR), and Hispanic (HIS) and subsequently combined for cross-population meta-analyses. \u003cstrong\u003eB\u003c/strong\u003e. A total of 16 independent loci were identified through SNP x DEPR interaction effects, including seven novel and nine known loci for BP. \u003cstrong\u003eC\u003c/strong\u003e. Gene prioritization was performed using FUMA, gene-based analyses, and xQTL. Druggability analyses of 36 prioritized genes identified 11 druggable gene targets.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/d5662bfd4d44b03161b79f07.png"},{"id":76486387,"identity":"d62e85d2-5368-46a5-bdc2-5d0df0528f35","added_by":"auto","created_at":"2025-02-17 15:44:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":305375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of interaction effects at novel and known loci identified in the dDEPR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCPMA, cross-population meta-analyses; AFR, African; ASN, Asian, EUR, European; HIS, Hispanic; b, the interaction effects estimated in the 1df interaction test (Effect is in mmHg); SE, standard error of interaction effects estimated in the 1df interaction test; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eBlack squares and error bars represent the effect size and its 95% CI for each population in CPMA or for each study in population-specific meta-analyses. Red diamond represents the overall effect size calculated in the meta-analysis where the center indicates the point estimate and its edges represent 95% CI of the estimate.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/87cd5844a83aca8bdeb19a37.png"},{"id":76486624,"identity":"9cc8dcfb-1fba-4237-b136-1b8db6c1eb98","added_by":"auto","created_at":"2025-02-17 15:52:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of interaction effects at novel and known loci identified in the qDEPR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCPMA, cross-population meta-analyses; AFR, African; ASN, Asian, EUR, European; HIS, Hispanic; b, the interaction effects estimated in the 1df interaction test (Effect is in mmHg); SE, standard error of interaction effects estimated in the 1df interaction test; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eBlack squares and error bars represent the effect size and its 95% CI for each population in CPMA or for each study in population-specific meta-analyses. Red diamond represents the overall effect size calculated in the meta-analysis where the center indicates the point estimate and its edges represent 95% CI of the estimate.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/6c2c1bee4e6d795d02a1edb9.png"},{"id":101436907,"identity":"ca9ccbc0-32e1-4c8f-81f5-d41420403b5a","added_by":"auto","created_at":"2026-01-29 16:26:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7296994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/7b5efdc9-5a75-4dc5-a4ef-7209d7d4ca38.pdf"},{"id":76486385,"identity":"e15f5878-a0ae-4b81-89d2-d47f400da8a3","added_by":"auto","created_at":"2025-02-17 15:44:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":63680,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/cc89a52ff072f4ff905bd0f9.docx"},{"id":76486386,"identity":"6d0a1a42-0e18-4003-812d-45461713b869","added_by":"auto","created_at":"2025-02-17 15:44:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":920084,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/7ff384a508cd3ed29d284c65.xlsx"},{"id":76486635,"identity":"be3a0500-45db-4b0a-a002-0f0cd0278d39","added_by":"auto","created_at":"2025-02-17 15:52:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12456574,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6025759/v1/c6cc32c54a44b443a2b72f24.docx"}],"financialInterests":"Competing interest reported. C.L.M has received funding from AstraZeneca on an unrelated project. B.M.P serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson \u0026 Johnson. D.C. received consulting fees from Trimedics. H.J.G has received travel grants and speakers honoraria from Neuraxpharm, Servier, Indorsia and Janssen Cilag. The remaining authors declare no competing interests.","formattedTitle":"A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations ","fulltext":[{"header":"Background","content":"\u003cp\u003eHypertension and high blood pressure (BP) are major risk factors for cardiovascular disease, stroke, chronic kidney disease, and vascular dementia, significantly contributing to global morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the widespread availability of effective anti-hypertensive medications, the prevalence of hypertension has doubled worldwide over the past three decades and is projected to affect 1.6\u0026nbsp;billion individuals by 2025 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, while the age-adjusted prevalence of hypertension has declined in some regions, global disparities in hypertension rates have widened [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic and environmental factors can independently increase the risk of hypertension, but gene-environment interaction (GxE) may provide a more comprehensive understanding of the genetic contributions to the disease [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A recent genome-wide association study (GWAS) of BP identified a total of 2,103 independent genetic signals, which accounts for approximately 60% of the heritability of BP [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, a substantial portion of heritability remains unexplained. Incorporating GxE in genetic analyses of BP may yield additional information about its genetic architecture and provide new avenues to improve health by more precisely characterizing risk of high BP in the context of potentially modifiable environmental, lifestyle, and behavioral risk factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe influence of psychosocial factors on BP level is well known [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Psychosocial stress increases the incidence of hypertension, and is associated with poor hypertension control, unhealthy lifestyle behaviors, and non-compliance with treatment regimens [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The relationship between depressive symptoms and BP is complex. While some studies have shown an association of depressive symptoms with incidence of hypertension [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], others have reported an association of depressive symptoms with lower BP levels [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Whilst, a recent study provided evidence of depression as a causal risk factor of hypertension using Mendelian Randomization [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our previous study examined the effect modification of genetic factors by dichotomous psychosocial factors on BP in up to 128,894 individuals [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This highlighted the significance of gene-psychosocial factors interactions in gene discovery for BP, especially among individuals of African ancestry. However, the statistical power and population diversity of the study were limited. To address these shortcomings, we increased the sample size up to five-fold by incorporating now available biobank data. In addition, we defined psychosocial exposures as both dichotomous and quantitative, potentially improving the statistical power to identify novel findings. We report genome-wide association meta-analyses of systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) in the context of depressive symptomatology (DEPR) in a sample of up to 564,680 participants from populations of African (AFR), Asian (ASN), European (EUR), and Hispanic (HIS) backgrounds.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview\u003c/h2\u003e \u003cp\u003eA total of 564,680 individuals from four populations were included in the study, comprising 85% EUR, 7% ASN, 5% AFR, and 3% HIS. Descriptive statistics are provided in \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e. Because the quantitative DEPR exposure was not available in some biobanks, sample sizes were larger for dichotomous DEPR (dDEPR) than quantitative DEPR (qDEPR). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the dDEPR analyses included 563,538 individuals after excluding two studies where the number of individuals with DEPR (N\u003csub\u003eexp\u003c/sub\u003e) was less than 10 (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). Among individuals with dDEPR, 15% had DEPR on average. The qDEPR analyses consisted of 294,029 participants from EUR (80%), ASN (7%), AFR (7%), and HIS (6%) populations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003edDEPR analyses\u003c/h3\u003e\n\u003cp\u003eWe identified nine independent loci that showed evidence of association with BP traits modified by dDEPR in cross-population meta-analyses (CPMA) or population-specific meta-analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, three loci tagged by rs1664073690 (1q31.3), rs10178576 (2q13.3), and rs113521945 (4q35.1) were novel. The other six loci tagged by rs115760284 (3p22.1), rs147967138 (7q21.11), rs757194 (7q31.1), rs7979305 (12p12.1), rs75095906 (13q32.1), and rs9931605 (16q23.2) were previously reported for BP (\u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e). Eight of the nine loci were identified via the 1df interaction test (P.Int\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the 2df joint test, a total of 904 loci were associated with at least one BP trait (350 loci were associated with SBP, 337 loci were associated with DBP, and 364 loci were associated with PP). Among them, one previously reported BP locus (rs757194 on 7q31.1) showed evidence of association with SBP through interaction with dDEPR using the specified criteria (P.Joint\u0026thinsp;=\u0026thinsp;7.99 x 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e; P.Int\u0026thinsp;=\u0026thinsp;1.39 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e).\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 and known Loci associated with BP traits discovered through SNP \u0026times; dDEPR interactions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR:position\u003c/p\u003e \u003cp\u003e(hg38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003cp\u003e(E/A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ersID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAF AFR/EUR/ASN/HIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNearest\u003c/p\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInt\u003c/p\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eInt\u003c/p\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eInt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eFDR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eHet\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003esize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eP.Sex\u003c/p\u003e \u003cp\u003e.Het\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1q31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:194548555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ers1664073690\u003c/b\u003e\u003csup\u003e*ⴕ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPMA-SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0/0.02/0/0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCDC73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.44 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.93 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e30577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2q13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2:70509396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ers10178576\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPMA-PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11/0/0/0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTGFA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e2.16 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.11 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e39482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3p22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:42213248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers115760284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPMA-SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01/0/0/0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTRAK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e2.78 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e22241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4q35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:184777291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ers113521945\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPMA-DBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02/0.09/0.05/0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eACSL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e2.72 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.74 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e488129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7q21.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7:78342531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers147967138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASN-PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0/0/0.04/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMAGI2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e3.34 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.89 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e26307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7q31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7:112203372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers757194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFR-SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03/0/0/0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eDOCK4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.39 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e7.99 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e11644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12p12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12:21435910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7979305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFR-PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05/0/0/0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePYROXD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-8.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e3.09 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.96 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13q32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13:96826633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers75095906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPMA-SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03/0.15/0.12/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eHS6ST3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e4.29 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.45 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e518557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16q23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16:81545886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers9931605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPMA-SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83/0.81/0.78/0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCMIP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIntronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.36 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.23 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e543909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c18\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"18\" nameend=\"c18\" namest=\"c1\"\u003e \u003cp\u003eAllele E, effect allele; Allele A, non effect allele; EAF, effect allele frequency; MAF, minor allele frequency; AFR, African; EUR, European; ASN, Asian; HIS, Hispanic; Int Effect, interaction effects estimated in the 1df interaction test (Effect is in mmHg).\u003c/p\u003e \u003cp\u003eInt SE, standard error of interaction effects estimated in the 1df interaction test; P Int, P value of interaction effects in the 1df interaction test; P Joint, P value of joint effects of SNP main effect and interaction effect in 2df joint test; P.Sex.Het,\u003c/p\u003e \u003cp\u003esex heterogeneity P value in two-sample Z tests\u003c/p\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cb\u003ers1664073690\u003c/b\u003e, \u003cb\u003ers10178576\u003c/b\u003e, \u003cb\u003ers113521945\u003c/b\u003e: top SNPs at novel loci (at least 500 Kbp away from any previously reported BP locus)\u003c/p\u003e \u003cp\u003e\u003csup\u003eⴕ\u003c/sup\u003ers1664073690: absent in the 1000G Phase3 reference panels\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eP.FDR: interaction FDR P value for 1df interaction test; joint FDR P value for 2df joint test\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eP.Het: heterogeneity P value across population groups in CPMA; heterogeneity P value across studies in ancestry-specific meta-analyses\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe three top single nucleotide polymorphisms (SNPs) at novel loci (1q31.3, 2q13.3, and 4q35.1) were identified in the CPMA and showed no evidence of heterogeneity across population groups (P.Het\u0026thinsp;\u0026gt;\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two of them were common variants with minor allele frequency (MAF) greater than 0.05 in at least one population group while one (rs1664073690 on 1q31.3) had a low frequency (MAF\u0026thinsp;=\u0026thinsp;0.02). This variant was present at low frequency in EUR and HIS but was absent in both ASN and AFR. rs10178576 (2q13.3) was common in AFR (MAF\u0026thinsp;=\u0026thinsp;0.11) but was not observed in either ASN or EUR populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). rs113521945 (4q35.1) was observed across all four population groups. While a significant interaction was observed only in EUR, the direction of the effect was consistent across all four groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the six top SNPs at known BP loci (3p22.1, 7q21.11, 7q31.1, 12p12.1, 13q32.1, and 16q23.2), four SNPs on 3p22.1, 7q21.11, 7q31.1, and 12p12.1 showed the most significant associations or were exclusively observed in non-EUR populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, three of them (rs115760284 on 3p22.1, rs757194 on 7q31.1, and rs7979305 on 12p12.1) were absent in both EUR and ASN but were present at low frequency in AFR (0.01\u0026thinsp;\u0026le;\u0026thinsp;MAF\u0026thinsp;\u0026le;\u0026thinsp;0.05) and were rare in HIS (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interestingly, rs115760284 (3p22.1) showed some heterogeneity between AFR and HIS (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;80%, P.Het\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with a greater effect size in AFR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, a locus on 7q21.11 was detected solely in ASN population among 26,307 individuals, with no evidence of heterogeneity across ASN studies (P.Het\u0026thinsp;\u0026gt;\u0026thinsp;0.003). Two loci tagged by rs75095906 (13q32.1) and rs9931605 (16q23.2) were identified in the CPMA analyses, with no evidence of heterogeneity by population group. Across all nine top SNPs identified in the dDEPR analyses, no evidence of sex heterogeneity was observed. However, some SNPs could not be evaluated due to a limited sample size in males passing QC.\u003c/p\u003e\n\u003ch3\u003eqDEPR analyses\u003c/h3\u003e\n\u003cp\u003eWe identified seven independent loci that showed evidence of association with BP traits modified by qDEPR in CPMA or population-specific meta-analyses (Table\u0026nbsp;2). Four loci tagged by rs77572777 (2q14.2), rs148780833 (3p26.3), rs748650739 (3q13.11), and rs140618249 (17p13.3) were novel. The other three loci tagged by rs59284269 (3p25.3), rs145132348 (4p14), and rs114544309 (12q13.13) were previously reported for BP (\u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e). Five loci, including two novel, were identified using the 1df interaction test (P.Int \u0026lt; 5 x 10\u003csup\u003e− 8\u003c/sup\u003e) (Table\u0026nbsp;2). In the 2df joint test, a total of 316 loci were associated with at least one BP trait (144 loci were associated with SBP, 160 loci were associated with DBP, and 157 loci were associated with PP). Among them, two novel loci tagged by rs77572777 (2q14.2) and rs748650739 (3q13.11) were associated with PP through interaction with qDEPR. Notably, two of the novel loci rs148780833 (3p26.3) and rs140618249 (17p13.3) identified in the 1df test (P.Int \u0026lt; 5 x 10\u003csup\u003e− 8\u003c/sup\u003e) also showed evidence of an association with SBP through interaction with qDEPR using the 2df joint test (P.Joint \u0026lt; 5 x 10\u003csup\u003e− 8\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eTable 2 Novel and known Loci associated with BP traits discovered through SNP × qDEPR interactions\u003c/p\u003e\n \u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLocus\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCHR:position\u003c/p\u003e\n \u003cp\u003e(hg38)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAlleles\u003c/p\u003e\n \u003cp\u003e(E/A)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ersID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAnalysis\u003c/p\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eEAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eMAF AFR/EUR/ASN/HIS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eNearest\u003c/p\u003e\n \u003cp\u003egene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eInt\u003c/p\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eInt\u003c/p\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003eInt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003eJoint\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003eFDR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003eHet\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003eP.Sex\u003c/p\u003e\n \u003cp\u003e.Het\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2q14.2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2:118537183\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers77572777\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHIS-PP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0/0.02/0/0.01\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eRP11-19E11.1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e3.55 x 10\u003csup\u003e− 5\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e1.74 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e16077\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3p26.3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3:1301059\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers148780833\u003csup\u003e*ⴕ\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCPMA-SBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.01/0/0/0.002\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eCNTN6\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e9.91 x 10\u003csup\u003e− 9\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e2.85 x 10\u003csup\u003e− 9\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e27204\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3p25.3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3:8726816\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers59284269\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCPMA-SBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.23/0.02/0/0.06\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eSSUH2\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e4.29 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e8.65 x 10\u003csup\u003e− 7\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e251948\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3q13.11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3:104214171\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eC/CA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers748650739\u003csup\u003e*ⴕ\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHIS-PP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.05/0/0/0.01\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eRP11-40M23.1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e3.76 x 10\u003csup\u003e− 5\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e4.66 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e16077\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4p14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4:39689605\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers145132348\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAFR-DBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.03/0/0/0.006\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eUBE2K\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e1.19 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e6.33 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e17147\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e12q13.13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12:52010638\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers114544309\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCPMA-DBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.02/0/0/0.008\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eGRASP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e1.85 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e2.56 x 10\u003csup\u003e− 7\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e31068\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e17p13.3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e17:3225579\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ers140618249\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCPMA-SBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.02/0/0/0.004\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cem\u003eOR1A1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e-4.10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e3.18 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e3.77 x 10\u003csup\u003e− 8\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c16\"\u003e\n \u003cp\u003e28685\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c17\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAllele E, effect allele; Allele A, non effect allele; EAF, effect allele frequency; MAF, minor allele frequency; AFR, African; EUR, European; ASN, Asian; HIS, Hispanic; Int Effect, interaction effects estimated in the 1df interaction test (Effect is in mmHg).\u003c/p\u003e\n \u003cp\u003eInt SE, standard error of interaction effects estimated in the 1df interaction test; P Int, P value of interaction effects in the 1df interaction test; P Joint, P value of joint effects of SNP main effect and interaction effect in 2df joint test; P.Sex.Het,\u003c/p\u003e\n \u003cp\u003esex heterogeneity P value in two-sample Z tests\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e rs77572777, rs148780833, rs748650739, rs140618249: top SNPs at novel loci\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eⴕ\u003c/sup\u003e rs148780833, rs748650739: absent in the 1000G Phase3 reference panels\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eP.FDR: Interaction FDR P value for 1df interaction test; Joint FDR P value for 2df joint test\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eP.Het: Heterogeneity P value across population groups in CPMA; Heterogeneity P value across studies in ancestry-specific meta-analyses\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe four top SNPs tagging the novel loci include rs77572777 (2q14.2) and rs748650739 (3q13.11) from the HIS-specific analyses, and rs148780833 (3p26.3) and rs140618249 (17p13.3) from the CPMA. None of these four SNPs showed evidence of heterogeneity across populations or studies (P.Het \u0026gt; 0.003) and all were of low frequency (MAF = 0.01–0.02). Except for rs77572777 on 2q14.2, the three other SNPs were polymorphic only in AFR and HIS populations.\u003c/p\u003e\n\u003cp\u003eAmong the three known loci (3p25.3, 4p14, and 12q13.13) identified in the qDEPR analyses, two loci on 4p14 and 12q13.13 were not observed in EUR population. Of these two, the 4p14 locus tagged by rs145132348 and identified in AFR-specific analyses showed no heterogeneity across AFR studies contributing to the meta-analyses in this population (Fig.\u0026nbsp;3). The other locus on 12q13.13 tagged by rs114544309 and identified in CPMA showed the most significant association in HIS, with some evidence of heterogeneity between AFR and HIS and a greater effect size in HIS (Fig.\u0026nbsp;3). rs59284269 (3p25.3) identified in CPMA showed no evidence of heterogeneity by population group. No evidence of sex heterogeneity was observed across all seven top SNPs identified in the qDEPR analyses.\u003c/p\u003e\n\u003ch3\u003eComparison between dDEPR and qDEPR analyses\u003c/h3\u003e\n\u003cp\u003eOf the 16 identified loci (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), four top SNPs (rs77572777, rs148780833, rs748650739, and rs114544309) were identified exclusively in the qDEPR analyses (\u003cb\u003eSupplemental Figs.\u0026nbsp;1 and 2\u003c/b\u003e). This appears to largely reflect differences in included studies in the two types of analyses (\u003cb\u003eSupplemental Fig.\u0026nbsp;3\u003c/b\u003e). In the CPMA, approximately 15\u0026nbsp;million SNPs were included in the dDEPR and 21\u0026nbsp;million SNPs in the qDEPR analyses. Notably, nearly 6\u0026nbsp;million SNPs were analyzed only in the qDEPR analyses, mainly because they were filtered out in the dDEPR analyses by the stringent study-level filters. Conversely, fewer than half a million SNPs were analyzed exclusively in dDEPR analyses, likely due to some large biobank samples where only dichotomous exposure was available while the quantitative exposure was not. The four SNPs identified only in qDEPR analyses were filtered out of the dDEPR analyses during study-level QC (rs148780833) or at the meta-analysis QC because they were present in only one study (rs77572777 and rs748650739), or in only one population (rs114544309) (\u003cb\u003eSupplemental Figs.\u0026nbsp;2\u003c/b\u003e). The remaining 12 loci were present in both dDEPR and qDEPR analyses. As illustrated in \u003cb\u003eSupplemental Figs.\u0026nbsp;1 and 2\u003c/b\u003e, there was a remarkable consistency between the two analyses even though magnitude of effects and statistical significance varied between them.\u003c/p\u003e\n\u003ch3\u003eGene-based and pathway analyses\u003c/h3\u003e\n\u003cp\u003eUsing 1df interaction test results, Multi-marker Analysis of GenoMic Annotation (MAGMA) and Versatile Gene-Based Association Study 2 (VEGAS2) ranked genes and pathways based on the combined association of SNPs within a gene with BPs. Both MAGMA and VEGAS2 gene-based tests identified a gene-wide significant association for \u003cem\u003eGLTPD2\u003c/em\u003e, with similar results observed for several other genes among the top 20 genes (\u003cb\u003eSupplemental Table\u0026nbsp;4\u003c/b\u003e). An additional gene, \u003cem\u003eTMEM199\u003c/em\u003e, was discovered by VEGAS2. These two genes were not identified at genome-wide significance in the GWAS. Pathway analyses suggest DEPR-specific genetic pathways that influence BP, including retinoid signaling, remodeling of acyl chains of phosphatidylethanolamine, nucleotide-binding oligomerization domain containing 2 (NOD2) protein signaling, and response to stress (\u003cb\u003eSupplemental Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and gene prioritization\u003c/h2\u003e \u003cp\u003eFunctional annotation was conducted for all SNPs in linkage disequilibrium (LD) (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.4) with the top SNPs tagging all identified novel and known BP loci. All the top SNPs were annotated as either intergenic or intronic variants, suggesting a potential role for regulatory mechanisms. Among 31 genes identified by FUMA, six genes were predicted to be highly intolerant to loss-of-function mutation based on probability of loss-of-function intolerance (pLI) score\u0026thinsp;\u0026gt;\u0026thinsp;0.9, including \u003cem\u003eCMIP\u003c/em\u003e, \u003cem\u003eZBTB47\u003c/em\u003e, \u003cem\u003eDOCK4\u003c/em\u003e, \u003cem\u003eUBE2K\u003c/em\u003e, \u003cem\u003ePDS5A\u003c/em\u003e, and \u003cem\u003eGRASP\u003c/em\u003e (\u003cb\u003eSupplemental Table\u0026nbsp;6\u003c/b\u003e). Multiple genes exhibited high CADD scores (\u0026gt;\u0026thinsp;12.37) among SNPs in LD, suggesting potential deleterious effects. Three additional genes were identified through associations with various quantitative trait loci (xQTL), which include \u003cem\u003eCASP3\u003c/em\u003e, \u003cem\u003eDBI\u003c/em\u003e, and \u003cem\u003eUGGT2\u003c/em\u003e. Details on functional annotations are described in \u003cb\u003eSupplemental Table\u0026nbsp;7\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eA total of 36 genes were prioritized by functional annotations of both novel and known loci, as well as gene-based analyses. These prioritized genes showed enrichment of gene expression in the brain and whole blood (\u003cb\u003eSupplemental Fig.\u0026nbsp;4\u003c/b\u003e). Additionally, they demonstrated evidence of enrichment in two pathways involved in myogenesis and immune system in dendrite cells, as well as enrichment in four potential microRNA regulatory targets (\u003cb\u003eSupplemental Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDruggability analyses\u003c/h3\u003e\n\u003cp\u003eWe investigated the potential druggability of the identified 36 candidate gene product targets using an integrative approach as previously described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We queried dDEPR and qDEPR exposure candidate gene targets using the Drug-Gene Interaction database (DGIdb), which identified 11 genes annotated as members of the druggable genome (\u003cb\u003eSupplemental Table\u0026nbsp;9\u003c/b\u003e). Several of these gene targets are implicated in metabolic pathways (\u003cem\u003eACSL1\u003c/em\u003e, \u003cem\u003eDBI\u003c/em\u003e, \u003cem\u003eUGDH\u003c/em\u003e, \u003cem\u003eSLCO1A2\u003c/em\u003e), vascular wall signaling (\u003cem\u003eTGFA\u003c/em\u003e, \u003cem\u003eCAV3\u003c/em\u003e, \u003cem\u003eSSUH2\u003c/em\u003e, \u003cem\u003eDOCK4\u003c/em\u003e), DNA damage response or apoptosis (\u003cem\u003eCASP3\u003c/em\u003e, \u003cem\u003eRFC1\u003c/em\u003e, \u003cem\u003eRECQL\u003c/em\u003e), and neuroactive ligand-receptor interaction (\u003cem\u003eVIPR1\u003c/em\u003e, \u003cem\u003eCCK\u003c/em\u003e). We identified 11 genes with FDA approved drug interactions that have been evaluated in late-stage clinical trials using DrugBank, ChEMBL, and ClinicalTrials.gov databases (\u003cb\u003eSupplemental Table\u0026nbsp;10\u003c/b\u003e). Two of these gene targets (\u003cem\u003eCASP3\u003c/em\u003e and \u003cem\u003eUGDH\u003c/em\u003e) were identified as targets of aspirin, a well-established and safe drug used to treat pain, inflammation, and reduce cardiovascular events. \u003cem\u003eUBE2K\u003c/em\u003e was identified as a target of the central nervous system stimulant, dextroamphetamine, used to treat attention-deficit disorder (ADHD) and narcolepsy, however its use has been federally controlled due to the high potential for abuse. \u003cem\u003eCCK\u003c/em\u003e was also identified as a target of the vasodilator, diazoxide, which is used to manage hypoglycemia due to pancreatic cancer or other conditions. Several genes (\u003cem\u003eCCK\u003c/em\u003e, \u003cem\u003eSLCO1A2\u003c/em\u003e, \u003cem\u003eUGGT2\u003c/em\u003e) were identified as targets of drugs (diazoxide, nadolol, hydrochlorothiazide) used to treat hypertension, suggesting opportunities for drug repositioning and risk factor prevention.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale genome-wide interaction study, we identified 16 genetic loci whose association with BP was modified by DEPR defined as a dichotomous or a quantitative exposure. These data provide support for molecular mechanisms connecting DEPR and BP and highlight several genes as possible druggable targets with clinical potential for BP regulation in individuals with DEPR.\u003c/p\u003e \u003cp\u003eNearly 70% of our findings were derived from non-EUR populations, likely due to differences in allele frequency across populations and/or to population differences in SNP x DEPR interaction effect sizes. Notably, several of the identified SNPs were monomorphic in EUR. Variations in MAF across population groups have been shown to contribute to differences in disease prevalence across populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The risk of hypertension varies considerably across populations, being more prevalent in AFR and HIS populations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. More than half of our findings come from AFR and/or HIS. AFR populations generally exhibit greater genetic diversity and more pronounced allele frequency differences compared to other populations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Self-identified HIS populations in the US include admixed individuals with varying proportions of EUR, AFR, and Amerindian genetic backgrounds, adding further complexity. Interestingly, patterns of associations were similar in AFR and HIS populations at several loci near the genes \u003cem\u003eTGFA\u003c/em\u003e, \u003cem\u003eTRAK1\u003c/em\u003e, \u003cem\u003eCNTN6\u003c/em\u003e, and \u003cem\u003eOR1A1\u003c/em\u003e. GWAS of BP have identified differences in BP loci by population groups, while partial generalization of BP loci between populations has also been reported [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, there is a critical need for expanding genetic studies of BP in non-EUR populations. In our study, among nine known BP loci identified with evidence for gene-DPER interaction, six loci (3p22.1, 7q21.11, 7q31.1, 12p12.1, 4p14, and 12q13.13) were derived from non-EUR populations while they were previously discovered as BP loci in EUR population. This further underscores the importance of considering DEPR effect modification on BP for diverse populations. Multiple studies have shown mixed results regarding the association between depressive symptomatology and hypertension [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite this variability, depression has been consistently linked to an increased risk of cardiovascular morbidity and mortality [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Typically, depression arises in response to stressful events, and stress is a major risk factor for hypertension [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Both hypertension and depression show higher prevalence among individuals of non-EUR populations, highlighting significant racial and ethnic disparities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFunctional annotation of the novel loci revealed genes implicated in neurogenesis, lipid metabolism, neuronal apoptosis, and synaptic activity. A locus on chromosome 2 mapped to an intron of the \u003cem\u003eTGFA\u003c/em\u003e gene, which encodes a ligand for the epidermal growth factor receptor and plays a crucial role in neural cell proliferation and differentiation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous studies suggested \u003cem\u003eTGFA\u003c/em\u003e’s role in neurogenesis and angiogenesis in adult injured brain and the immune system [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, genetic variants in \u003cem\u003eTGFA\u003c/em\u003e have been associated with response to antidepressant treatment in GWAS [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cem\u003eACSL1\u003c/em\u003e encodes an isozyme of the long-chain fatty-acid-coenzyme A ligase family, which operates in lipid biosynthesis and fatty acid degradation. Animal models have demonstrated that \u003cem\u003eACSL1\u003c/em\u003e modulates lipid metabolism, inflammation, and oxidative stress in kidney disease [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In fact, the kidney plays a critical role in BP regulation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The \u003cem\u003eACSL1\u003c/em\u003e locus was associated with DNA methylation levels (mQTL) of \u003cem\u003eACSL1\u003c/em\u003e in blood. Functional annotations of this novel locus also highlight several additional genes, including \u003cem\u003eCASP3\u003c/em\u003e. \u003cem\u003eCASP3\u003c/em\u003e encodes a cysteine-aspartic acid protease (Caspase-3) that plays a critical role in neuronal apoptosis, neurogenesis, and synaptic activity [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Notably, the \u003cem\u003eACSL1\u003c/em\u003e locus was associated with the splicing event of \u003cem\u003eCASP3\u003c/em\u003e in brain tissue. Interestingly, a recent study highlighted the role of Caspase-3 in pathogenesis of depressive disorders [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. \u003cem\u003eCNTN6\u003c/em\u003e encodes Contactin-6, a neuronal cell adhesion molecule that facilitates neurite outgrowth and synaptogenesis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Mutations in this gene increase the risk for autism spectrum disorders [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. \u003cem\u003eDBI\u003c/em\u003e encodes a diazepam binding inhibitor, which is regulated by hormones and acts as a neuropeptide in brain synapses [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Our results showed an intergenic variant (rs77572777 on 2q14.2) with an expression quantitative trait locus (eQTL) of \u003cem\u003eDBI\u003c/em\u003e in brain tissue. A previous study reported that \u003cem\u003eDBI\u003c/em\u003e expression in the brain decreased with long-term social isolation stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. An increased level of the protein encoded by \u003cem\u003eDBI\u003c/em\u003e has been suggested as a prognostic value in cardiovascular disease [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral known loci for BP were identified through interactions with DEPR in our study and implicated several genes previously reported to be associated with mental disorders. These genes include \u003cem\u003eDOCK4\u003c/em\u003e, \u003cem\u003eHS6ST3\u003c/em\u003e, and \u003cem\u003eMAGI2\u003c/em\u003e. The \u003cem\u003eDOCK4\u003c/em\u003e locus was associated with SBP in the AFR population. \u003cem\u003eDOCK4\u003c/em\u003e is a member of the dedicator of cytokinesis family and is involved in cell migration [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Animal models have suggested a role of \u003cem\u003eDOCK4\u003c/em\u003e in excitatory synaptic transmission and social behavior [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Variants in \u003cem\u003eDOCK4\u003c/em\u003e have been associated with response to antidepressants, autism spectrum disorder, and schizophrenia [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. A recent GWAS of stress-induced vasomotion identified an association with variants in \u003cem\u003eDOCK4\u003c/em\u003e, which were also linked to an increased risk of adverse cardiovascular events [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cem\u003eHS6ST3\u003c/em\u003e encodes heparin sulfate sulfotransferases involved in proliferation, inflammation, and blood coagulation. Variants within or near this gene have been associated with schizophrenia, major depressive disorder, and coronary artery calcified atherosclerotic plaque [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e–\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. \u003cem\u003eMAGI2\u003c/em\u003e encodes a synaptic scaffolding molecule and shows high expression in the brain and postsynaptic density area of spine [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In our data, the \u003cem\u003eMAGI2\u003c/em\u003e locus was observed only in ASN population, and variants in this gene have been associated with depressive symptoms in an East Asian cohort as well as in other population groups [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e–\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur druggability analyses suggest potential opportunities for drug repurposing and risk factor prevention. The identified genes include \u003cem\u003eCASP3\u003c/em\u003e and \u003cem\u003eUGDH\u003c/em\u003e as targets for aspirin and \u003cem\u003eCCK\u003c/em\u003e, \u003cem\u003eSLC01A2\u003c/em\u003e, and \u003cem\u003eUGGT2\u003c/em\u003e for antihypertensive medications. \u003cem\u003eUGDH\u003c/em\u003e encodes an integral Golgi membrane protein involved in signal transduction and cell migration. A previous study has shown its nominal association with brain electrical activity linked to psychiatric conditions including depression, and suggested that this association may be population-specific [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This is consistent with our finding that the associated SNP (rs145132348 on 4p14) was identified only in individuals of AFR and HIS populations. \u003cem\u003eCCK\u003c/em\u003e encodes cholecystokinin (CCK), a digestive enzyme and a neuropeptide that regulates emotional states [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Patients with major depression showed increased CCK levels in cerebrospinal fluid [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. CCK enzyme also plays role in BP regulation and predicts cardiovascular mortality in elder females [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. \u003cem\u003eSLCO1A2\u003c/em\u003e (or \u003cem\u003eOATP1A2\u003c/em\u003e) encodes a sodium-independent transporter that is crucial for transporting hormones across the blood-brain barrier into the central nervous system and has been suggested as a potential modulator of mood disorders [\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e–\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. \u003cem\u003eUGGT2\u003c/em\u003e encodes a soluble protein of the endoplasmic reticulum and has been associated with impulsive behaviors [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. It is important to note that some of these drug-gene interactions may also reflect the medication use for individuals with chronic depression and warrant follow-up to determine their direct impact on hypertension and cardiovascular risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFindings from our prior study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] were generally not replicated in this study, likely due to the use of a new modeling strategy that includes additional adjustment for potential confounders. One notable exception is the reported gene-DEPR interaction at the \u003cem\u003eFSTL5\u003c/em\u003e locus by 2df joint test, tagged by two SNPs (rs138187213 and rs5863461). In our dDEPR analyses, both SNPs showed associations in the 1df interaction test (rs138187213, P.Int = 8.48 x 10\u003csup\u003e− 4\u003c/sup\u003e; rs5863461, P.Int = 2.89 x 10\u003csup\u003e− 4\u003c/sup\u003e). Similar results were observed in the qDEPR analyses, with both SNPs showing evidence of interactions (rs138187213, P.Int = 8.19 x 10\u003csup\u003e− 5\u003c/sup\u003e; rs5863461, P.Int = 1.06 x 10\u003csup\u003e− 4\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eOur study benefits from a large sample size with diverse population backgrounds, which allows for a comprehensive analysis of the interactions across different populations. Moreover, our methodological approach using two complementary definitions of DEPR sought to enhance novel discoveries. The dDEPR analyses, with a larger sample size, provided greater statistical power, while the qDEPR analyses were designed to capture subtle variations in exposure and potentially reveal associations that might have been missed in the dDEPR analyses. Notably, we observed a substantial number of SNPs analyzed in the qDEPR but not included in the dDEPR, likely due to stringent filters required for binary exposure analyses. The qDEPR analyses enabled us to identify additional loci at genome-wide level, possibly due to the assumption of linearity between the exposure and outcome being met for those specific loci. Furthermore, the consistency of associations across both analytical approaches reinforces robustness of our findings.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the sample size for non-EUR population groups was relatively small compared to the EUR population, which may have limited the discovery of population-specific findings. For this reason, we combined East ASN and South ASN populations into a single population group although that may introduce heterogeneity. While combining distinct populations can introduce complexity due to underlying genetic and cultural differences, this approach was chosen to increase statistical power. Second, DEPR was captured by several different validated instruments in the participating cohorts with different sensitivities and specificities to detect depressive symptoms, which may have introduced heterogeneity and measurement error, potentially reducing statistical power. Lastly, while extensive functional annotation and druggability analyses provide biological validation/support for our findings, replication in independent samples was not possible in this study since dividing cohorts into discovery and replication analyses encountered insufficient power. Because we made extensive efforts at recruiting most of the studies known to have DEPR data, identifying suitable independent cohorts with large sample size and DEPR data availability for replication remains a major challenge. This is a particular issue for interactions identified only in non-European population groups, often in relatively modest sample sizes.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified multiple genetic loci associated with BP traits that were modified by DEPR. These data emphasize the importance of considering DEPR as an effect modifier in BP gene discovery, particularly in non-EUR populations. They also provide insights into the molecular basis of the relationships between DEPR and BP, and highlight the potential of applying such information to enhance more personalized approaches to hypertension management in individuals with DEPR.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design and participants\u003c/h2\u003e\n\u003cp\u003eAll participating cohorts were part of Gene-Lifestyle Interactions Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Except for the UK Biobank, the study included adult men and women aged 18 years or older from four population groups defined based on self-reported participant\u0026rsquo;s race and ethnicity: AFR (including self-reported Black), ASN (including East Asian and South Asian), EUR (including self-reported White), and HIS. The UK Biobank used the Pan-UKB data to define population groups based on shared genetic similarity and demographic history [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]. GWAS considering the interaction between gene and DEPR were conducted within each individual study by population group. Population-specific meta-analyses were then performed using summary statistics, followed by cross-population meta-analyses based on the population-specific results (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). All participating studies obtained written informed consent from their participants and approval from the appropriate institutional review boards. Details about the participating studies are provided in the \u003cstrong\u003eSupplemental Material\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eBlood pressure (BP) traits\u003c/h2\u003e\n\u003cp\u003eThree BP traits were considered as outcome variables: SBP, DBP, and PP. Pulse pressure was calculated as the difference between SBP and DBP. When multiple BP readings were taken during the same examination, the average of all SBP or DBP readings were used. For participants taking any anti-hypertensive medications, SBP and DBP values were adjusted by adding 15 mm Hg and 10 mm Hg, respectively, to the measured values [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]. Extreme values for each BP variable were winsorized if they were more than six standard deviations (SDs) above or below the mean.\u003c/p\u003e\n\u003ch2\u003eDepressive symptomatology (DEPR) exposures\u003c/h2\u003e\n\u003cp\u003eEach participating study collected information on DEPR using validated screening questionnaires, as detailed in \u003cstrong\u003eSupplemental Table\u0026nbsp;11\u003c/strong\u003e. Measurements of DEPR and BP were taken during the same examination. We defined two variables as exposures: dDEPR and qDEPR.\u003c/p\u003e\n\u003cp\u003eThe dDEPR exposure was defined as a binary variable by dichotomizing DEPR measures using recommended standard cut off points specific to each screening instrument. Individuals with higher depressive symptom score were categorized as the exposed group and coded as E\u0026thinsp;=\u0026thinsp;1. The specific cut off points used to define the dDEPR for each study are provided in \u003cstrong\u003eSupplemental Table\u0026nbsp;11\u003c/strong\u003e. Descriptive statistics on depression score are provided in \u003cstrong\u003eSupplemental Table\u0026nbsp;2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe qDEPR exposure was defined as a standardized residual after adjusting for age and sex effects within each cohort. For studies that included multiple population groups, the variable was computed separately for each population. First, DEPR scores were winsorized if a value was more than 6 SDs above or below the mean. The scores were then regressed on age, sex, and age \u0026times; sex interaction in the sex-combined samples. The resulting age- and sex-adjusted residuals were standardized using the Z-score in the combined sample. Thus, in each study, the mean and SD of qDEPR were approximately 0 and 1, respectively, as shown in \u003cstrong\u003eSupplemental Table\u0026nbsp;12\u003c/strong\u003e. For the sex-stratified analyses, the same qDEPR estimates from the sex-combined group were used.\u003c/p\u003e\n\u003ch2\u003eGenotype data\u003c/h2\u003e\n\u003cp\u003eMost of the participating studies performed genotyping using Illumina or Affymetrix. Imputations were primarily carried out using Trans-Omics for Precision Medicine (TOPMed) or Haplotype Reference Consortium (HRC) reference panels. Details on genotyping and imputation are presented in \u003cstrong\u003eSupplemental Table\u0026nbsp;13\u003c/strong\u003e. Before analysis, genotype data for each cohort were restricted to SNPs mapping to autosomal chromosomes, with MAF\u0026thinsp;\u0026ge;\u0026thinsp;0.1% across all samples and an imputation quality\u0026thinsp;\u0026ge;\u0026thinsp;0.3. Indels (insertions and deletions) were also included.\u003c/p\u003e\n\u003ch2\u003eIndividual study statistical analyses\u003c/h2\u003e\n\u003cp\u003eEach cohort performed analyses by population subgroup using two statistical models designed for different purposes. Model 1 was a joint effect model that accounts for the SNP main effect, DEPR effect, and the interaction effect between SNP and DEPR:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:E\\left(BP\\right)={\\beta\\:}_{0}+\\:{\\beta\\:}_{SNP}SNP+{\\beta\\:}_{DEPR}DEPR+\\:{\\beta\\:}_{SNP\\times\\:DEPR}SNP\\times\\:DEPR+{\\beta\\:}_{C}C$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere DEPR was either dDEPR or qDEPR, and \u003cstrong\u003eC\u003c/strong\u003e was a vector of covariates, including age, age\u003csup\u003e2\u003c/sup\u003e, sex, field centers (if relevant), and population-specific principal components, as well as any additional cohort-specific covariates, if applicable (\u003cstrong\u003eSupplemental Table\u0026nbsp;13\u003c/strong\u003e). In model 1, additional DEPR \u0026times; covariate interaction terms with age, age\u003csup\u003e2\u003c/sup\u003e, and sex were included in the model to minimize potential false positive findings that could result from confounding effects [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. For the sex-stratified analyses, both sex and DEPR x sex were excluded from the model. A 1 degree of freedom (1df) interaction test was performed to evaluate SNP x DEPR interaction effect alone under the null hypothesis that \u0026beta;\u003csub\u003eSNPxDEPR\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0. A 2df joint test was used to simultaneously assess the SNP main effect and SNP x DEPR interaction effects, under the null hypothesis that \u0026beta;\u003csub\u003eSNP\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026beta;\u003csub\u003eSNPxDEPR\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0 [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]. When both the SNP main effect and interaction effects exist, the 2df joint test typically provides more power than the 1df interaction test [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eModel 2 was a SNP marginal effect model:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:E\\left(BP\\right)={\\beta\\:}_{0}+\\:{\\beta\\:}_{SNP}SNP+{\\beta\\:}_{C}C$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe SNP marginal P-value (P.Marginal) was used to identify SNPs with significant evidence of interaction effects by comparing P.Marginal to the 1df interaction P-value (P.Int) in Model 1. To ensure a fair comparison, we conducted a standard GWAS (Model 2) with the same covariates used in Model 1 other than the DEPR x covariate interaction terms.\u003c/p\u003e\n\u003cp\u003eAnalyses excluded subjects without genotype data or with missing data for the DEPR exposure or any covariates. Each study selected one of the specialized software tools to run analyses: GEM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/large-scale-gxe-methods/GEM\u003c/span\u003e\u003c/span\u003e), LinGxEScanR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/USCbiostats/LinGxEScanR\u003c/span\u003e\u003c/span\u003e), or MMAP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MMAP/MMAP.github.io\u003c/span\u003e\u003c/span\u003e), as described in \u003cstrong\u003eSupplemental Table\u0026nbsp;13\u003c/strong\u003e. For the studies with related subjects, MMAP was used to account for familial relatedness using linear mixed models.\u003c/p\u003e\n\u003ch2\u003eQuality control of study-specific and meta-analyses results\u003c/h2\u003e\n\u003cp\u003eQuality control (QC) was performed for both study-specific and meta-analyses results using EasyQC2 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.genepi-regensburg.de/easyqc2\u003c/span\u003e\u003c/span\u003e). For results submitted in build hg19, genomic coordinates were lifted over to build hg38. At the study-level, QC involved different SNP filters for the two exposures. For the dDEPR, SNPs were excluded if degree of freedom (DF) was less than 20 in the unexposed, exposed, or total samples. The DF was calculated as minor allele count * imputation quality score. For the qDEPR, SNPs were removed if the DF was less than 20 in the total samples. To identify systematic errors in data preparation, allele frequency (AF) discrepancy, outliers, and missing data were assessed visually through comparison of results to reference panels derived by imputation of population-specific 1000 Genomes phase 3 version 5 (p3v5) panels to the TOPMed reference panels using the TOPMed imputation server. Any resulting concerns were addressed through consultation with the contributing studies. Genomic control (GC) inflation factors were also estimated. Next, meta-level QC was performed within each population group (AFR: 18 cohorts; ASN: 8 cohorts, EUR: 36 cohorts, HIS: 5 cohorts) to assess improper transformation of BP variables, unstable numerical computation, and excessive inflation.\u003c/p\u003e\n\u003ch2\u003eMeta-analyses\u003c/h2\u003e\n\u003cp\u003eMeta-analyses were performed using an inverse-variance weighted fixed-effect model for the 1df interaction test and an inverse-covariance-matrix-weighted model for the 2df joint test [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. Analyses were first conducted separately for each population group, and then the results were combined for CPMA. The primary focus was on analyses within the sex-combined group, considering three phenotypes and two exposures. For the identified loci in the sex-combined group analyses, we performed sex-stratified analyses to assess differences in GxE by sex. GC correction was applied to the population-specific meta-analyses and subsequently once more to the CPMA [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]. Quantile-quantile (QQ) plots and GC inflation factors are shown in \u003cstrong\u003eSupplemental Figs.\u0026nbsp;5\u0026ndash;14\u003c/strong\u003e. In the 2df joint test, there were mild to moderate inflations, mainly due to the significance at previously reported loci for BP.\u003c/p\u003e\n\u003ch2\u003eIdentification of independent associated loci\u003c/h2\u003e\n\u003cp\u003eThe EasyStrata2 software was used to prioritize the top loci among significant results identified in 1df interaction and 2df joint tests [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]. For the CPMA, SNPs had to be present in at least two population groups with a minimum sample size of 20,000 individuals. In the EUR-specific meta-analyses, SNPs were reported if they appeared in at least three studies and in at least 3,000 individuals. These criteria were relaxed for other population groups due to smaller sample size, as shown in \u003cstrong\u003eSupplemental Table\u0026nbsp;14\u003c/strong\u003e. Only SNPs with MAF greater than 1% were reported for both population-specific and cross-population meta-analyses. SNPs located within 1 Mb of the major histocompatibility complex (MHC) region were excluded.\u003c/p\u003e\n\u003cp\u003eWe considered SNPs with significant evidence of DEPR interaction effects on BP as top SNPs based on the following criteria: (1) SNPs with significant 1df interaction effect (P.Int\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). In population-specific analyses, SNPs were also required to show no evidence of heterogeneity (P.Het\u0026thinsp;\u0026gt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e); (2) SNPs with significant 2df joint effects (P.Joint\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), and P.Int\u0026thinsp;\u0026lt;\u0026thinsp;Bonferroni-corrected P adjusted for the number of 2df joint variants identified in the respective CPMA or population-specific subgroup (e.g, for CPMA: dDEPR: 0.05/904\u0026thinsp;=\u0026thinsp;5.53 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; qDEPR: 0.05/316\u0026thinsp;=\u0026thinsp;1.58 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and P.Int\u0026thinsp;\u0026lt;\u0026thinsp;P.Marginal. False discovery rates (FDR) were also calculated using EastyStrata2.\u003c/p\u003e\n\u003cp\u003eTo identify independent loci among all significant variants, we grouped the significant variants within 500-kilobase regions and identified independent loci by LD R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, using TOPMed-imputed 1000G reference panels. If variants within regions were missing in the LD panels, the most significant variant within each region was reported. The independent loci were considered novel if the SNPs are located\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb away from the known loci previously reported in BP GWAS (\u003cstrong\u003eSupplemental Table\u0026nbsp;15\u003c/strong\u003e). For the identified independent loci, we additionally examined heterogeneity of the interaction effects by sex using the results from the sex-stratified analyses. Heterogeneity of SNP x DEPR effects between men and women was tested using two-sample Z tests [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]. The significance threshold for heterogeneity tests was defined at Bonferroni-corrected threshold based on the number of the identified independent loci.\u003c/p\u003e\n\u003ch2\u003eGene-based analyses\u003c/h2\u003e\n\u003cp\u003eWe performed gene-based tests on meta-analysis summary statistics for the 1df interaction results using MAGMA implemented in FUMA [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e] and VEGAS2 [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]. Both tools computed gene-based p-values by considering variants within each gene. The MAGMA method utilized a multiple linear regression model [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e], while VEGAS2 analyses were conducted using the \u0026lsquo;top10\u0026rsquo; parameter, which selects the top 10% variants within a gene, taking into account the number of variants and LD. This approach allowed us to include SNPs with stronger signals and exclude those that might dilute the summary statistics [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]. For both MAGMA and VEGAS2, we used 1000 Genomes phase 3 reference panels specific to AFR, EAS (for ASN), EUR, AMR (for HIS) populations to compute LD for population-specific analyses. In MAGMA, the CPMA was conducted using the \u0026ldquo;all\u0026rdquo; 1000 Genomes phase 3 reference panel in the FUMA setting. For VEGAS2, we performed meta-analyses of population-specific gene-based results using Stouffer\u0026rsquo;s method, with p-values weighted by sample size. Gene-wide significance in MAGMA was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;2.61 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, correcting for 19,122 protein-coding genes. VEGAS2 included 19,263 protein-coding genes, leading to a gene-wide significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;2.61 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eGene-set or Pathway-based analysis\u003c/h2\u003e\n\u003cp\u003eWe conducted gene-set analysis using MAGMA in FUMA to identify associations between gene sets and biological pathways. The analyses were performed based on the gene-based results from MAGMA, with statistical significance threshold at P\u0026thinsp;\u0026lt;\u0026thinsp;2.94 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, correcting for 17,009 gene sets. As a sensitivity analysis, we performed pathway-based analysis using VEGAS2Pathway [\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e], based on population-specific gene-based association results generated with VEGAS2. The meta-analyses were conducted using Stouffer\u0026rsquo;s method. VEGAS2Pathway included 2,748 pathways, resulting in a significance threshold of empirical P\u0026thinsp;\u0026lt;\u0026thinsp;1.82 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eFunctional Annotations\u003c/h2\u003e\n\u003cp\u003eAll identified independent loci were assessed for potential functional annotations using multiple tools. First, we used the FUMA v1.5.2 to annotate functional information of the novel and known loci [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]. At the genomic region level, the FUMA SNP2GENE pipeline was used to prioritize genes based on the results of the top SNPs and SNPs in LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.4 within 250 kb) through three gene mapping approaches: positional mapping, GTEx v8 eQTL mapping, and 3D chromatin interaction mapping (FDR\u0026thinsp;\u0026le;\u0026thinsp;1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, 250bp upstream and 500bp downstream of the transcription start site [TSS] by default settings). At the variant level, we used QTLbase [\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e] and Open Target Genetics [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e] databases to explore xQTL that link our loci to tissue or cell type specific functions. The xQTL include gene expression (eQTL), DNA methylation (mQTL), histone modification (hQTL), splicing event (sQTL), protein expression (pQTL), alternative polyadenylation (apaQTL), and others. To investigate whether the identified loci were associated with other phenotypes, we utilized a phenome-wide association studies (PheWAS) tool implemented in Open target genetics and GWAS ATLAS [\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e]. Using all the prioritized genes, we performed FUMA GENE2FUNC analysis to test enrichment of the gene sets and provide expression of those prioritized genes (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch2\u003eDruggability analyses\u003c/h2\u003e\n\u003cp\u003eTo assess the clinical potential of the candidate genes, we conducted integrative druggability analyses. We first used the Drug-Gene Interaction database (DGIdb; v4.2.0) to query high or medium priority and determine the potential druggability of the candidate gene targets. We annotated genes for implicated pathways and functions using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We annotated the druggability target categories and queried all interacting drugs reported in 44 databases (Ensembl, HGNC, NCBI, ChemIDplus, Drugs@FDA, HemOnc, NCIt, RxNorm, Wikidata, CancerCommons, CGI, ChEMBL, CIViC, ClearityFoundationBiomarkers, ClearityFoundationClinicalTrial, COSMIC, DoCM, DrugBank, DTC, FDA, GuidetoPharmacology, JAX-CKB, MyCancerGenome, MyCancerGenomeClinicalTrial, OncoKB, PharmGKB, TALC, TdgClinicalTrial, TEND, TTD, BaderLab, CarisMolecularIntelligence, dGene, FoundationOneGenes, GO, HingoraniCasas, HopkinsGroom, HumanProteinAtlas, IDG, MskImpact, Oncomine, Pharos, RussLampel, Tempus). We queried protein targets for available active ligands in ChEMBL. We queried gene targets 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\u003c/span\u003e) available through the Pharos web platform. We also queried FDA-approved drugs, late-stage clinical trials and disease indications in the DrugBank, ChEMBL, ClinicalTrials.gov databases and provided results for the top MESH and DrugBank indications and clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent:\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll participating studies obtained written informed consent from their participants and ethics approval from the appropriate institutional review boards. Details about the participating studies are provided in the \u003cstrong\u003eSupplemental Material\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003eDue to restrictions in the written informed consent and local regulations, individual genotype-level data from this project could not be shared. Summary statistics are available at the CHARGE (Cohorts for Heart and Ageing Research in Genomics Epidemiology) dbGaP summary site (phs000930 [https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000930.v1.p1]).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interest\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003eC.L.M has received funding from AstraZeneca on an unrelated project. B.M.P serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson \u0026amp; Johnson. D.C. received consulting fees from Trimedics. H.J.G has received travel grants and speakers honoraria from Neuraxpharm, Servier, Indorsia and Janssen Cilag. The remaining authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e:\u003c/p\u003e\n\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, R01HL156991, and HL105756.\u0026nbsp;PBM acknowledges support from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at Barts (NIHR202330). KEN and KLM were provided in part by R01HL142302, R01HL151152, R01DK122503, R01HD057194, R01HG010297, R01HL143885, R01HL163262. NF was supported by R01DK117445, R01MD012765, R01HL163972.\u0026nbsp;Study-specific acknowledgements and funding sources are included in the \u003cstrong\u003eSupplemental Material\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthor\u0026rsquo;s contributions\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003eSL, WJG, TWW, LJB., LdlF, DR, and MF contributed to conception and design of the study. HC, WJG, JO, TWW, and JLM were involved in the development of software. SL, CLM, and MF prepared the initial manuscript draft. All other co-authors contributed to data acquisition, analysis, and interpretation, as well as the critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e:\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLawes CM, Vander Hoorn S, Rodgers A, International Society of H: \u003cstrong\u003eGlobal burden of blood-pressure-related disease, 2001.\u003c/strong\u003e \u003cem\u003eLancet \u003c/em\u003e2008, \u003cstrong\u003e371:\u003c/strong\u003e1513-1518.\u003c/li\u003e\n\u003cli\u003eMills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, Chen J, He J: \u003cstrong\u003eGlobal Disparities of Hypertension Prevalence and Control: A Systematic Analysis of Population-Based Studies From 90 Countries.\u003c/strong\u003e \u003cem\u003eCirculation \u003c/em\u003e2016, \u003cstrong\u003e134:\u003c/strong\u003e441-450.\u003c/li\u003e\n\u003cli\u003eJaeger BC, Chen L, 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\u003cstrong\u003e51:\u003c/strong\u003e1339-1348.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6025759/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6025759/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study included 564,680 adults aged 18 years or older from 67 cohorts and 4 population backgrounds (African (5%), Asian (7%), European (85%), and Hispanic (3%)). We discovered seven novel gene-DEPR interaction loci for BP traits. These loci mapped to genes implicated in neurogenesis (\u003cem\u003eTGFA\u003c/em\u003e, \u003cem\u003eCASP3\u003c/em\u003e), lipid metabolism (\u003cem\u003eACSL1\u003c/em\u003e), neuronal apoptosis (\u003cem\u003eCASP3\u003c/em\u003e), and synaptic activity (\u003cem\u003eCNTN6\u003c/em\u003e, \u003cem\u003eDBI\u003c/em\u003e). We also identified evidence for gene-DEPR interaction at nine known BP loci, further suggesting links between mood disturbance and BP regulation. Of the 16 identified loci, 11 loci were derived from African, Asian, or Hispanic populations. Post-GWAS analyses prioritized 36 genes, including genes involved in synaptic functions (\u003cem\u003eDOCK4\u003c/em\u003e, \u003cem\u003eMAGI2\u003c/em\u003e) and neuronal signaling (\u003cem\u003eCCK\u003c/em\u003e, \u003cem\u003eUGDH\u003c/em\u003e, \u003cem\u003eSLC01A2\u003c/em\u003e). Integrative druggability analyses identified 11 druggable candidate gene targets, including genes implicated in pathways linked to mood disorders as well as gene products targeted by known antihypertensive drugs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings emphasize the importance of considering gene-DEPR interactions on BP, particularly in non-European populations. Our prioritized genes and druggable targets highlight biological pathways connecting mood disorders and hypertension and suggest opportunities for BP drug repurposing and risk factor prevention, especially in individuals with DEPR.\u003c/p\u003e","manuscriptTitle":"A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 15:44:08","doi":"10.21203/rs.3.rs-6025759/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"404be227-4392-47dc-a923-30dbb2f0dbbf","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T16:26:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 15:44:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6025759","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6025759","identity":"rs-6025759","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}