Genome‐wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores

Abstract Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) and horizontal pleiotropy analysis of S/DBP summary statistics from the UK Biobank (UKB)‐International Consortium for Blood Pressure (ICBP) (UKB‐ICBP) BP genome‐wide association study and construct a composite genetic risk score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11–3.26 fold) heritability for BP traits and increases (1.09‐ and 2.01‐fold) Nagelkerke's R 2 for hypertension and CVD. We replicated 118 novel BP horizontal pleiotropic variants including 18 novel BP loci using summary statistics from the Million Veteran Program (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after a meta‐analysis of the UKB‐ICBP and MVP summary statistics but without further independent replication. Our study provides further insight into BP regulation and provides a novel way to construct a GRS by including pleiotropic variants for other complex diseases.


Abstract. 1
Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for 2 cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) 3 and pleiotropy analysis of systolic and diastolic blood pressure (BP) summary statistics from the 4 UKB-ICBP BP genome-wide association study (GWAS) and construct a composite genetic risk 5 score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11-3.26 6 fold) heritability for BP traits and increases (1.09-and 2.01-fold) Nagelkerke's R 2 for 7 hypertension (HTN) and cardiovascular disease (CVD). We replicated 118 novel BP pleiotropic 8 variants including 18 novel BP loci using summary statistics from the Million Veteran Program 9 (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after meta-10 analysis of the UKB-ICBP and MVP summary statistics but without further independent 11 replication. Our study provides further insight into BP regulation and provides a novel way to 12 construct a GRS by including pleiotropic variants for other complex diseases. 13 Introduction 1 Poorly controlled blood pressure (BP) accounts for a large portion of the risk for 2 cardiovascular disease (CVD), stroke, and heart failure 1 . Understanding biological mechanisms 3 for BP regulation could thus potentially help improve BP control and lead to a reduction in the 4 burden of CVD. BP, characterized by systolic and diastolic blood pressure (SBP/DBP), are 5 long-standing risk predictors for CVD. To date, genome-wide association studies (GWAS) have 6 been performed on BP traits by focusing on main effects and in studies that included subjects of 7 diverse ancestry over 1,000 BP-associated loci have been identified 2-14 . Genome-wide search of 8 gene-environment interactions on BP traits have also been recently conducted, however, only a 9 few new gene-environment interactions have been identified, in part owing to low statistical 10 power 13; 14 . Although many GWAS variants are shared between SBP and DBP, both of which 11 are correlated, some seem associated only with SBP or DBP, suggesting evidence of trait-12 specific biological mechanisms. It has been reported that joint analysis of SBP and DBP leads to 13 the identification of BP variants missed by analyzing SBP or DBP separately 9 . However prior 14 studies have not addressed the mechanisms underlying the SBP-DBP relationship, reflecting 15 arterial stiffness or arterial compliance 15 . Dissecting the causal relationships of SBP and DBP 16 variants, in particular, whether they affect SBP and DBP through the same (mediation) or 17 different (pleiotropic) paths, and how many pleiotropic variants contribute jointly to these highly 18 correlated traits, is thus important for understanding the biology of BP regulation. 19 Genetic risk scores (GRS) are constructed as weighted linear combinations of individual 20 variant effects estimated from GWAS to predict individual-level risk of a common disease. An 21 overall GRS is the average of SBP-and DBP-specific GRS 3; 6 . However, published BP GRS's 22 have explained ~6% of the heritability of SBP and DBP, and have limited predictive power for 23 HTN and CVD. GWAS of gene-age interaction analysis have also identified genetic variants 1 with age-dependent effect sizes, including for BP 16;17 ,lipid levels 18 and BMI 19 . A recent study 2 based on a proportional hazards model reported age-varying risk profiles in nine diseases, 3 including HTN 20 . However, these studies were under powered because the interactive 4 contribution by variant and age is often weak. 5 In this study we address the mechanistic relationship between SBP and DBP by 6 performing a bidirectional Mendelian Randomization (MR) 21 and GWAS pleiotropy analysis 7 using summary statistics from >750,000 subjects of European ancestry from the UKB and ICBP 8 consortium 6 , followed by summary statistics of 318,891 multi-ethnic subjects from the Million 9 Veteran Program (MVP) 12 . We searched for novel BP variants with pleiotropic effects and 10 constructed a composite GRS using variants with and without pleiotropic effects, and studied the 11 age-varying effects of GRS for prediction of BP, HTN and CVD in European, African and Asian 12 descent individuals. 13 14

Summary statistics of UK Biobank (UKB) and International Consortium for Blood 16
Pressure (ICBP). UKB and the ICBP consists of data on 458,577 UK and 299,024 European des 17 cent subjects. GWAS of SBP and DBP were conducted in UKB and ICBP separately and the 18 results were meta-analyzed. 3; 6 Our analysis was based on the summary results from the UKB 19 and ICBP GWAS that were calculated based on up to 757,601 participants and ~7.1 M 20 genotyped and imputed SNPs with MAF ≥ 1% for variants present in both the UKB data and 21 ICBP meta-analysis for SBP, DBP and pulse pressure (PP defined as SBP-DBP). 22 cases in subjects with European, African and Asian ancestries. The study was approved by the 1 Case Western Reserve University Institutional Review Board (STUDY20180592). 2 3 Mendelian Randomization analysis. We performed a bi-directional MR analysis of SBP and 4 DBP by applying the software IMRP 24 and MRmix 25 , as well as estimated the causal 5 contributions of BP on Coronary Artery Disease (CAD), myocardial infarction (MI) and Stroke. 6 Considering an exposure ( 1 ) and an outcome ( 2 ) the following association model as described 7 in Figure 1A was used: 8 where is a genetic instrumental variable (IV), 1 and 2 are the direct contributions of to 10 exposure and outcome, β is the causal effect of the exposure 1 to the outcome 2 , U represents 11 confounding factors and ε 1 and ε 2 are error terms, respectively. MR analysis estimates the causal 12 effect β through the genetic IV . A valid genetic IV satisfies 1 ≠ 0 and 2 = 0, representing 13 the genetic contribution to outcome through the mediation of the exposure. We termed these 14 variants ( 2 = 0) as mediation variants. We define a pleiotropic variant as one with 1 ≠ 0 and 15 2 ≠ 0, interpreted as the genetic contributions to exposure and outcome through two 16 independent paths (or a pleiotropic path) ( Figure 1A) (2) 1 where Γ and ̂ are the estimated effect sizes of a genetic IV on exposure and the outcome, 2 respectively, and is the causal estimate which is updated at each iterative step. We have 3 previously shown that tests the null hypothesis 2 = 0 24 . In MR analysis, a valid IV 4 satisfies 1 ≠ 0, therefore rejecting null hypothesis 2 = 0 and suggesting a pleiotropic effect. 5 IMRP takes advantage of MR-Egger, which is less biased, and IVW, which is more efficient. 6 IMRP can be applied to GWAS summary statistics of an exposure and an outcome obtained with 7 overlapping or unique samples. To ensure the causal estimate is robust, we also applied a 8 substantially different MR approach MRmix 25 , an estimating equation approach that assumes 9 Γ −̂ follows a normal mixture model. MRmix usually shows a good trade-off between bias 10 and variance even with more than 50% invalid IVs 25 . MRmix requires standardized summary 11 statistics and IMRP does not. For a continuous trait, the effect size is rescaled by √ , where z and 12 n correspond to the z-score for an IV and the sample size, respectively, with its standard error 1 √ . 13 For a binary trait, the effect size is rescaled by √ (1− ) , where beta and p correspond to the effect 14 size and minor allele frequency of an IV, respectively. This standardizing procedure has been 15 used in MRmix 25 . 16 17 GWAS of pleiotropy analysis for SBP and DBP. After performing a bi-directional MR 18 analysis of SBP and DBP and estimating the causal effects of SBP on DBP and DBP on SBP, we 19 extended the pleiotropy test to all 7.1M SNPs by fixing the causal effects estimated from 20 IMRP analysis in the two causal paths, using UKB-ICBP summary statistics (Figure 1B and C). 21 This is equivalent to performing GWAS for two new traits: BPpleio1 = DBP − × SBP and 22 BPpleio2 = SBP − ′ × DBP, where and ′ are the estimated causal effects of SBP on DBP and 1 DBP on SBP, respectively: 2 1(2) = γ 2 G + ε.
(3) 3 Equivalently, tests the null hypothesis 2 = 0. Unlike MR analysis where the IVs are 4 selected to be associated with exposure, we required the pleiotropy test for both BPpleio1 5 and BPpleio2 to be significant to declare a pleiotropic variant. We performed the same analysis for 6 replication using MVP summary statistics. Meta-analysis of UKB-ICBP and MVP was further 7 performed to increase statistical power and to identify additional variants. 8 We applied the LD score regression method 29 to test for genomic inflation in the GWAS 9 pleiotropy analysis. It is expected that BPpleio1(2) will have a large genomic control inflation 10 coefficient because of large sample sizes, genetic variants in high LD and a large number of BP 11 variants 6 . We examined the degree of inflation from the intercept of the LD score regression. 12 13 Novel locus definition. Novel loci were defined as genome wide significant pleiotropy variants 14 > 1Mb away from known BP variants as well as LD r 2 <0.1 with any known BP variants. Novel 15 signals at a known locus were genome wide significant pleiotropy variants within 1 Mb of 16 known BP variants as well as not being in LD with any known BP variants (r 2 < 0.1) at the locus. 17 The 1000G European ancestry data was used as the reference genetic data for LD calculation. 18 19 Functional annotations. We evaluated all sentinel SNPs at novel loci for evidence of mediation 20 of expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) in all 44 21 tissues using the Genotype-Tissue Expression (GTEx) database. Following the method in 22 Evangelou et al. 6 , a locus is annotated with a given eGene(sGene) only if the most significant 23

Results 1
We present a bi-directional MR analysis of SBP and DBP using 1,125 and 1,183 2 independent genome-wide significant variants for SBP and DBP (P< 5×10 -8 ) as genetic IVs 3 obtained from the UKB-ICBP GWAS 6 . We standardized SBP and DBP and obtained an identical 4 causal effect of SBP on DBP and DBP on SBP (0.864 ± 0.005 and 0.862 ± 0.005 by IMRP, 5 respectively, Supplementary Table 1), which is significantly larger than the observed trait 6 correlation 0.738 between SBP and DBP in UKB European subjects, with an estimated 74.8% of 7 variation is the shared causal contribution between SBP and DBP. The causal estimates by 8 MRmix were concordant (0.89 ± 0.012 and 0.90 ± 0.01, respectively, Supplementary Table 1). 9 Among the genetic IVs, 43% of the variants had pleiotropic effects on SBP and DBP. 10 We next extended the pleiotropic effect analysis to search for variants by performing two 11 GWAS of BPpleio1 and BPpleio2; the Manhattan and QQ plots are presented in Figure 2. The GC 12 lambda value was 1.533 and LDSR intercept was 1.057 (0.013), with inflation ratio 4.23%, 13 suggesting little inflation in the BPpleio1; a similar result was observed for BPpleio2. LD score 14 regression analysis 29 estimated 8.7% of the heritability arising from pleiotropic variants for 15 BPpleio1. We calculated genetic correlations of BPpleio1 and BPpleio2 with SBP, DBP and PP using 16 summary statistics (Supplementary Table 2). BPpleio1 and BPpleio2 are genetically highly 17 correlated (rg =-0.902±0.006), also highly correlated with PP (rg =-0.618±0.016 and 18 0.897±0.004) but less so with SBP or DBP. BPpleio1 is negatively correlated with SBP as is 19 BPpleio2 with DBP; in contrast, PP is positively correlated with both SBP and DBP. 20 We observed 906 independent variants (r 2 <0.1) reaching genome-wide significance in 21 either BPpleio1 or BPpleio2 (P<5×10 -8 ). To declare a variant as pleiotropic, we required one 22 pleiotropy test P-value 5×10 -8 and the other test P-value 0.05/906 by adjusting for multiple 23 comparisons. We observed 815 independent pleiotropy variants with 91 variants genome wide 1 significant also for either SBP or DBP. Among them, 234 (or 29%) were not detected by the 2 univariate GWAS analysis of SBP, DBP or PP in the original UK Biobank+ICBP consortium 6 3 (Supplementary Table 3). Among the 234 variants, 201 (or 86%) variants were not reported in 4 any previous BP GWAS. In the set of associations, 163 variants in 124 loci were within a 1Mb 5 region of previously reported known BP loci but were not in LD with known BP variants (r 2 6 <0.1); the remaining 38 variants were at least 1Mb away from the previous reported known BP 7 loci and resided at 35 loci; the corresponding locus zoom plots are presented in Supplementary 8  Table 4). 14 We defined the variants with P-value of SBP < 5×10 -8 and P-value of BPpleio1 >0.05/906, 15 or P-value of DBP < 5×10 -8 and P-value of BPpleio2 >0.05/906 into mediation variants, which 16 resulted in 1,415 independent variants. We compared the SBP and DBP effect sizes for these 17 mediation variants and observed that the mediation variants have the same effect directions for 18 SBP and DBP ( Figure. 3). In comparison, 71% of pleiotropic variants show opposite effect 19 directions for SBP and DBP, indicating new discoveries. 20 Replication of novel signals in MVP. Since the BP summary statistics in MVP were obtained 21 from multiethnic populations of non-Hispanic whites, non-Hispanic blacks, Hispanics, non-22 Hispanic Asians and non-Hispanic Native Americans, we performed MR analysis to estimate 23 causal effect sizes between SBP and DBP in MVP, instead of using the causal effects estimated 1 from UKB-ICBP. With a fewer number of IVs in MVP, we obtained relatively smaller but 2 similar causal effect sizes (0.692 and 0.724) between SBP and DBP as compared to UKB-ICBP 3 results, possibly due to the multiethnic samples in MVP (Supplementary Table 1). We then 4 performed the pleiotropy test among the 201 novel variants (7 were not available in 5 MVP). We examined how many novel variants significantly associated with BPpleio1(2) (P<5×10 -6 8 ) in UKB-ICBP also showed replication with corresponding BPpleio1(2) at significance level 7 P<0.05/201. We were able to replicate 23 variants (Supplementary  Table 3) and all the 118 variants having the same effect direction between UKB-ICBP and 12 MVP: thus, our identified novel pleiotropic signals were replicable. 13 We further examined the BPpleio1(2) effect size consistency of the 815 independent 14 pleiotropic variants between UKB-ICBP and MVP. For comparison, we also examined the SBP 15 and DBP effect size consistency of the independent 1,415 mediation variants between UKB-16 ICBP and MVP. We observed that the BPpleio1(2) effect sizes have higher correlations for the 17 pleiotropic variants than the mediation variants ( Figure. 4, correlation 0.90 vs 0.74), even after 18 exclusion of 2 significant outliers (rs113081691 and rs17057329). We observed that 96% of 19 pleiotropic variants have consistent effect directions between UKB-ICBP and MVP. We then 20 performed inverse variance weighted meta-analysis to combine the summary statistics of 21 BPpleio1(2) for UKB-ICBP and MVP. The combined pleiotropy evidence was further strengthened 22 for the 118 novel variants (Table 1 and Supplementary 3). 23 1 Functional annotations. We performed expression quantitative trait locus (eQTL) analysis 2 using GTEx data. Among the 35 novel loci listed in Supplementary Table 3, we identified 26  3 with expression quantitative trait locus (eQTL) (Supplementary Table 5) and 11 with Splicing 4 Quantitative Trait Loci (sQTLs) (Supplementary Table 6). The eQTLs were most often 5 enriched in arterial tissues, followed by adipose, heart and nerve tibial tissues. SNP rs17713879 6 is an eQTL affecting expression of the SH3YL1 and ACP1 genes in 34 tissues and is also a sQTL 7 affecting splicing of these two genes in 50 tissues. SNP rs112500920 is an eQTL affecting 8 expression of several genes, including EFL1 and AB3B2, in multiple tissues, notably adipose and 9 arterial tissues. SNP rs12478520 is an eQTL affecting expression of multiple genes, including 10 C2orf72, HTR2B, ARMC9 and PSMD1. 11 In the UKB-ICBP data, we identified 815 independent pleiotropic variants. We also 12 observed 1,451 independent mediation variants,. We assessed tissue enrichment of BP loci using 13 DEPICT 30 at a false discovery rate (FDR) < 5% but separated 1,415 mediation from 815 14 pleiotropic variants in the analysis. DEPICT identified enrichment across 43 and 51 tissues and 15 cells using mediation and pleiotropic variants, respectively (Supplementary Table 7). The 16 enriched tissues are highly similar (correlation=0.78) but there are also notable differences 17 (Supplementary Figure 2a). Enrichment was greatest for arteries in the cardiovascular system 18 for both mediation and pleiotropic variants (P= 1.28 × 10 -3 and 2.19 × 10 -11 , respectively). In 19 general, enrichment observed for mediation variants were also observed for pleiotropic variants, 20 but not vice versa. For example, heart related tissues, aortic valves and atrial appendage were 21 enriched for pleiotropic variants (P< 1.38 × 10 -4 ) but not for mediation variants (Supplementary 22 Table 7). Pathway enrichments for mediation variants and pleiotropic variants were less well 23 correlated (correlation=0.51, Supplementary Figure 2b). Pleiotropic variants were enriched in 1 many molecular pathways that were missed by mediation variants, including response to hypoxia, 2 oxygen levels, basement membrane, and renal system development (P<8.66×10 -7 , 3 Supplementary Table 8). In contrast, negative regulation of transcription from RNA 4 polymerase II promoter, histone deacetylase binding, hormone receptor binding and Ras protein 5 signal transduction, among others, were only enriched by mediation variants (P<6.09×10 -7 , 6 Supplementary

Improved prediction of BP, hypertension and CVD by including pleiotropic variants. 22
Polygenic scores derived from multiple related traits can improve prediction of outcomes 35-38 . 23 Chasman et al. decomposed a GRS into nearly independent components relative to biological 1 mechanisms inferred from pleiotropic relationships 39 while Udler et al. factorized the genetic 2 association matrix according to different classes of genetic variants relative to traits 40 . However 3 these approaches do not use the discovered pleiotropic variants directly. Here we construct a 4 traditional BP GRS using all independent 1,615 BP variants from the UKB-ICBP, which we term 5 the core genetic risk score (cGRS) (see Material and Methods). We further constructed a pGRS 6 using the 906 variants associated with BPpleio1 or BPpleio2, which is a genetic risk score from 7 pleiotropic variants. We jointly modeled cGRS and pGRS adjusting for age, gender, BMI and 10 8 principal components, and observed that pGRS significantly predicted BP traits, as well as risk 9 of HTN and CVD, conditional on cGRS in all models (Table 2A and Figure 5) in the UKB 10 European ancestry subjects. The cGRS captured 5.91%, 6.09% and 2.23% SBP, DBP and PP 11 heritability excluding pGRS and 7.13%, 6.75% and 7.27% including pGRS, or a 1.11-to 3.26-12 fold increase. Similarly, Nagelkerke's R 2 for HTN and CVD was 4.71% and 0.47% excluding 13 pGRS and 5.14% and 0.53% including pGRS, representing a 1.09-and 1.14-fold increase. We 14 observed odds ratios (ORs) of 1.64 and 1.15 for individual cGRS and pGRS on the risk of HTN 15 (P < 1 × 10 -300 ), respectively. The observed ORs of individual cGRS and pGRS for CVD were 16 1.21 and 1.06 (P = 1.71 × 10 -231 and P = 2.74 × 10 -25 ), respectively, and increased to 1.66 and 17 1.19 (P = 9.83 × 10 -97 and P = 1.28 × 10 -13 ) when comparing the upper versus lower quantiles of 18 the cGRS and pGRS, respectively. However, the odds ratios were further increased to 6.78 and 19 2.44 (P < 1 × 10 -300 and P = 1.15 × 10 -39 ) for HTN and CAD, respectively, when comparing the 20 top decile and quintile with bottom decile and quintile of cGRS and pGRS ( Figure 5). We 21 observed a clear advantage of including pGRS over cGRS only (Figure 5). It has been 22 suggested that there are gene and age interactions that contributing to blood pressure and 23 hypertension 16; 17; 20 . However, the detected interactions are limited because of low statistical 1 power. We thus examined the interaction effects between age and cGRS and pGRS. After 2 including the interactions of age and cGRS and pGRS the main effects for cGRS and pGRS on 3 BP, HTN and CVD were unchanged. However, we observed a significant interaction effect of 4 age and cGRS for all BP traits and HTN but not CVD. The interaction of age and pGRS 5 significantly contributed to all BP traits, HTN and CVD (P value between 3.0×10 -2 and 7.74×10 -6 29 , Table 2A). 7 Extension to other ancestries. We examined associations with BP and CVD of the above 8 defined European cGRS and pGRS in unrelated African (N=7,904) and South Asian (N=8,509) 9 subjects in the UKB ( Table 2. B, C). Although sample sizes were much smaller than among 10 UKB European subjects, the cGRS is significantly associated with SBP, DBP, PP, HTN and 11 CVD in both the UKB African-and Asian ancestry subjects. In the UKB African ancestry 12 individuals, including the pGRS results in a 1.10-to 2.44-fold increase of SBP, DBP and PP 13 heritability. Nagelkerke's R 2 for HTN and CVD has 1.23-and 1.22-fold increases, respectively. 14 Similar increments in the UKB Asian cohort is also observed (Table 2. C). Significant 15 interactions of age and pGRS were again observed for BP traits and CVD in UKB Asians (Table  16 2. C). 17 Comparison between BPpleio1(BPpleio2) and PP. We noted PP is genetically positively correlated 18 with both SBP and DBP but BPpleio1 or BPpleio2 is negatively correlated with SBP and DBP 19 (Supplementary Table 2). Among the 815 pleiotropic variants, 274 were genome wide 20 significant with either SBP or DBP and 467 with PP. We then calculated the GRS of PP (GRSPP) 21 in UKB using the independent variants associated with PP by the effect sizes estimated in the 22 UBK-ICBP data. We observed that the GRSPP is positively correlated with both the GRSs of 23 SBP and DBP. In comparison, the pGRS is again oppositely correlated with the GRSs of SBP 1 and DBP (Supplementary Table 2). We next compared BP variances explained by the GRSPP 2 and pGRS in UKB. In general, GRSPP and pGRS can account for similar amounts of BP, HTN 3 and CVD variation (Supplementary Table 9). However, when we included cGRS, pGRS and 4 GRSPP in a regression model, we observed that all three genetic risk scores significantly 5 predicted variation in BP, HTN and CVD in UKB European subjects (Supplementary Table 9 (Table 3). For SBP and DBP, IVs were the genetic 11 variants genome wide significantly associated with SBP and DBP but with no pleiotropic 12 evidence. For BPpleio1 and BPpleio2, IVs were the genetic variants associated with BPpleio1 and 13 BPpleio2. For PP, we selected all variants independently associated with PP as the IVs. The effect 14 size and standard error of an IV for BPpleio1 or BPpleio2 were the corresponding numerator and 15 denominator of the test statistic in equation (2). As expected, SBP, DBP and PP causally 16 contributed to CAD, MI and stroke. The new trait -BPpleio -also causally contributed to CAD, 17 MI and stroke, suggesting a primary causal pleiotropy pathway that is not associated with BP 18 mediation pathway directly contributing to the outcomes ( Table 3). The estimated ORs ranged 19 from 1.66 to 1.85 per SD unit increase in BP on the three outcomes using mediation variants and 20 ranged from 1.13 to 1.48 using pleiotropic variants. Our analysis identified 1 to 22% IVs 21 demonstrating pleiotropic effects for BP and the three clinical outcomes ( Table 3). 22 Meta-analysis of UKB-ICBP and MVP. We performed the inverse variance weighted meta-1 analysis of pleiotropy test to combine UKB-ICBP and MVP. We observed an additional 219 2 novel signals with either BPpleio1 or BPpleio2 at a significance level 5×10 -8 , including an additional 3 40 novel loci (Supplementary Table 10). 4 5 Discussion 6 Our analysis of GWAS summary statistics from over 1 million subjects have here revealed 7 important aspects of the genetic architecture of the two principle and highly correlated BP traits, 8 SBP and DBP. The bi-directional MR analysis of SBP and DBP demonstrated that 1 SD unit 9 increase of SBP leads to 0.86 SD unit increase of DBP, and vice versa, indicating that SBP and 10 DBP share 74.8% of its variation. We assume these arise from common genetic factors and 11 common biological mechanisms between SBP and DBP. This shared causal contribution is 12 substantially higher than the 55.6% estimated by phenotype correlation analysis of SBP and DBP 13 in UKB European ancestry subjects. We then identified the genetic variants that impact SBP and 14 DBP through two different paths: 1) mediation path (either from SBP to DBP or vice versa) and 15 departure from the mediation path (pleiotropic path, Figure 1). We defined the variants 16 contributing to BP traits through the pleiotropic path as pleiotropic variants, which have different 17 biological process from the BP variants through the mediation path. We observed that most of 18 the BP variants identified through SBP or DBP univariate associations were mediation variants 19 and would be expected to be discovered in either the SBP or DBP GWAS. By examining the 20 variants departing from the mediation paths, we identified 815 independent variants 21 demonstrating pleiotropy evidence in the original UK Biobank + ICBP consortium data 6 , of 22 which 201 were undetected by univariate GWAS of SBP, DBP or PP in literature. Replication 23 analysis in the MVP confirmed 118 of the 201 novel variants, including the 18 novel loci (Table  1   1 and Supplementary Table 3). Pleiotropic variants often demonstrated an effect size opposite 2 in direction for SBP and DBP and yet contributed 8.71% heritability of the newly defined BP 3 trait (Figure 3). The effect sizes of pleiotropic variants also demonstrated a higher correlation 4 than that of the mediation variants between UKB-ICBP and MVP, suggesting that the pleiotropic 5 variants may be more transferable across ethnic populations. BPpleio1 and BPpleio2 were highly 6 correlated with PP (ρ ≥0.62) in UKB whites, taken as an indicator of arterial stiffness and 7 considered as an independent risk factor for CVD 44 . However, BPpleio1 and BPpleio2 were less 8 correlated than PP with either SBP or DBP, which is consistent with pGRS being less correlated 9 than GRSPP with either SBP or DBP-defined GRS (Supplementary Table 2). Thus, BPpleio1 or 10 BPpleio2 represent a different risk factor of CVD from PP. This is consistent with the finding that 11 GRSs of SBP, DBP, PP and BPpleio all contribute to risk of CAD, Stroke and MI in the MR 12 analysis (Table 3). Thus, our results clearly suggest that a substantial fraction of BP variants 13 affect both SBP and DBP through pleiotropic effects. By combining UKB-ICBP and MVP, we 14 identified an additional novel 219 variants with pleiotropic evidence, including 40 novel loci, 15 although independent replication of these latter results are warranted (Supplementary Table  16 10). Thus, pleiotropic variant searches in existing datasets can identify many new BP genes. 17 In addition to the traditional BP GRS 3; 6 , which we termed the core genetic risk score 18 cGRS, the pleiotropic genetic risk score pGRS independently predicted BP, HTN and CVD 19 outcomes (  UK Biobank European-, African-and Asian-ancestry subjects, the prediction of HTN and CVD 23 risk was significantly improved by including pGRS (Figure 5 C and D). The cGRS and pGRS 1 defined in European participants both consistently and significantly predicted BP, HTN and 2 CVD in UK Biobank Africans and Asians, suggesting that pGRS is able to improve prediction 3 accuracy across populations. Recent studies have suggested that cGRS models alone have 4 modest improvement of predictive accuracy for CAD [45][46][47] . The principle outcome of this set of 5 analyses, therefore, demonstrates that adding pGRS significantly improves the prediction model 6 over cGRS alone. This approach of constructing polygenic risk scores is conceptually different 7 from existing approaches using multiple related traits 35-38 and can be generalized to other 8 diseases by incorporating multiple disease-related traits through pleiotropy analysis. 9 In our analysis, the UK Biobank Europeans was a part of data for identifying BP variants 10 and pleiotropic variants. We then constructed the cGRS and pGRS in UK Biobank data by using 11 the estimated effect sizes of the independent genome wide significant variants from UKB-ICBP 12 summary statistics as the weights. This procedure was used in Evangelou et al. 6 and was not 13 involved in a model selection. However, there is a potential winner's curse effect as suggested by 14 Evangelou et al. When we applied the cGRS and pGRS to the UK Biobank Africans and Asians, 15 we again observed the improved R 2 (Supplementary Table 9), suggesting that adding cGRS 16 improves the prediction power. 17 Our analysis avoided an examination of the interaction of individual variants and age 18 because of insufficient power. We were able to observe interaction effects of both cGRS and 19 pGRS with age in UKB Europeans for SBP, DBP, PP and HTN although the interaction for CVD 20 was only significant for pGRS ( Table 2). Age-pGRS interactions were also replicated in Asians 21 despite a substantially smaller sample size. We observed that the interaction contribution to 22 phenotype variation was consistently small (0.1% to 0.3% BP heritability in both UKB 23 Europeans and Asians). The negative interaction contributions of both cGRS and pGRS to DBP 1 may partially explain the decline of DBP after 60 years older 48 . In comparison, the interaction of 2 age and cGRS was positive for SBP, suggesting genetic effects on SBP increases in older 3 individuals. As noted, the cGRS interaction effects in UKB Europeans could not be observed in 4 UKB Africans or Asians, likely as a result of the latter's smaller sample size. In comparison, the 5 age-modulated interaction of pGRS was observed in both UKB European and Asian subjects, 6 indicating stronger pGRS interactions than for the GRS. In our functional annotation analysis, 7 we observed a wider range of BP related tissues and biological pathways for the BP pleiotropic 8 variants than mediation variants, which implies that pleiotropic variants are influenced by a 9 wider range of environmental factors and therefore continue to make genetic contributions over 10 the life span. 11 Our analysis approach bears some similarity with a recent developed GWAS-by-12 subtraction 49 . However, there are significant differences. 1) The GWAS-by-subtraction is based 13 on a genomic structure equation model (genomic-SEM) and our method is based on MR. 2) 14 GWAS-by-subtraction assumes that all genetic effects on one trait affect the other. In contrast, 15 our method does not make this assumption. Instead, our method estimates the causal effects in 16 both directions. 3) The GWAS-by-subtraction tests the null hypothesis Γ − γσ g /h E 2 =0, where Γ 17 and γ are the estimated effect sizes of a SNP on two traits (trait 1 and 2), σ g is the genetic 18 covariance between the two traits and h E 2 is the heritability of trait 2. In contrast, our method tests 19 the null hypothesis Γ − βγ =0 and γ − β′Γ =0 where β and β′ are the two causal effects 20 estimated from MR. Noted that σ g /h E 2 is not the same as causal effect β when pleiotropic 21 variants exist. The GWAS-by-subtraction includes the pleiotropic variants in estimating genetic 22 covariance and our method does not. Therefore, our method is less affected by pleiotropic 1 variants. However, further research is warranted in understanding these two methods better. 2 Our analysis was performed on two highly correlated BP traits, which led to define 3 mediation and pleiotropy variants. We were able to identify a "core" mediation pathway shared 4 by both SBP and DBP, and a pleiotropy pathway that has different effects to SBP and DBP. 5 Our method is therefore useful in analyzing different symptoms of a disease for understanding 6 the biological mechanism of the disease. Furthermore, our approach can also be applied to less 7 genetically correlated phenotypes to identify pleiotropic variants. In this case, we will less likely 8 to identify "core" phenotypes as we observed in BP traits. 9 Our study also supports an omnigenic model for complex traits 50-52 . In fact, it could be 10 inferred that pleiotropic variants act on multiple peripheral genes to impact the expression of 11 core genes. As a result, pleiotropic variants have weak effects on a phenotype and are more 12 difficult to detect in a traditional BP GWAS that focuses on single trait analysis, as observed 13 here. In comparison, mediation variants may be more likely to occur in core genes. We 14 acknowledge that the data presented here can only provide suggestive rather than conclusive 15 evidence for that hypothesis. 16 In conclusion, our new findings here include identifying 815 independent BP pleiotropic 17 variants -201 of which were not previously identified in BP GWAS in the UKB-ICBP study; of 18 these, 118 were confirmed including 18 novel BP loci. In addition, 219 novel BP signals and 40 19 novel loci were identified by combining UKB-ICBP and MVP. Our way to construct polygenic 20 risk score represents a substantial advance in understanding the genetic architecture of the highly 21 correlated SBP and DBP. 22                                   33 Sex, age, BMI, 10 PCs and geographic regions were adjusted. cGRS and pGRS were jointly modeled in the regression analyses. a The effect represents regression coefficient for SBP, DBP and PP and represents odd ratio for HTN and CVD. b The effect for CVD represents the odd ratio when comparing top quantile with bottom quantile of GRS and PGRS. c The heritability for HTN and CVD represents the Nagelkerke's R 2 . .095 a # of IVs: number of genetic instrumental variables that are genome wide significantly associated with exposure. For SBP and DBP, IVs were the genetic variants associated with SBP and DBP but with no pleiotropic evidence of SBP and DBP, respectively. For BPpleio1 and BPpleio2, IVs were the genetic variants associated with BPpleio1 and BPpleio2, respectively. b causal effect represents odd ratio c # of PVs: number of pleiotropic variants detected by IMRP among the IVs. CAD: coronary artery disease MI: myocardial infraction