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. Author manuscript; available in PMC: 2015 Apr 1.
Published in final edited form as: Hypertension. 2014 Jan 6;63(4):819–826. doi: 10.1161/HYPERTENSIONAHA.113.02077

Identifying common genetic variants in blood pressure due to polygenic pleiotropy with associated phenotypes

Ole A Andreassen 1,2,3,*, Linda K McEvoy 4,5, Wesley K Thompson 3, Yunpeng Wang 1,2,4,14, Sjur Reppe 7, Andrew J Schork 4,6, Verena Zuber 1,2; The International Consortium for Blood Pressure GWAS, The GEFOS Consortium, Elizabeth Barrett-Connor 8, Kaare Gautvik 7,9,10, Pål Aukrust 11, Tom H Karlsen 12,13, Srdjan Djurovic 1,2, Rahul S Desikan 4,5, Anders M Dale 3,4,5,6,14,*
PMCID: PMC3984909  NIHMSID: NIHMS557974  PMID: 24396023

Abstract

Blood pressure is a critical determinant of cardiovascular morbidity and mortality. It is affected by environmental factors, but has a strong heritable component. Despite recent large genome-wide association studies, few genetic risk factors for blood pressure have been identified. Epidemiological studies suggest associations between blood pressure and several diseases and traits, which may partly arise from a shared genetic basis (genetic pleiotropy). Using genome-wide association studies summary statistics and a genetic pleiotropy-informed conditional False Discovery Rate method, we systematically investigated genetic overlap between systolic blood pressure and 12 co-morbid traits and diseases. We found significant ‘enrichment’ of single nucleotide polymorphisms associated with systolic blood pressure as a function of their association with body mass index, low density lipoprotein, waist hip ratio, schizophrenia, bone mineral density, type 1 diabetes and celiac disease. In contrast, the magnitude of enrichment due to shared polygenic effects was smaller with the other phenotypes (triglycerides, high density lipoproteins, type 2 diabetes, rheumatoid arthritis, and height). Applying the conditional False Discovery Rate method to the enriched phenotypes, we identified 62 loci associated with systolic blood pressure (False Discovery Rate < 0.01), including 42 novel loci. The observed polygenic overlap between systolic blood pressure and several related disorders indicates that the epidemiological associations are not mediated solely via lifestyle factors, but also reflect an etiological relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to lipid biology and the immune system in systolic blood pressure.

Keywords: Genome-wide association study, genetic pleiotropy, systolic blood pressure, comorbid disorders

INTRODUCTION

High blood pressure affects over one billion individuals1, and even small increments increase morbidity and mortality. Though heritability estimates of systolic blood pressure (SBP) exceed 50%2,3, genes identified to date explain only a small proportion of heritability4. It has been argued that the genetic architecture of blood pressure regulation in the general population cannot be explained by commonly occurring genetic variation, suggesting that genome-wide association studies (GWAS) will continue to fail in hypertension5,6. However, recent results indicate that GWAS have the potential to explain a greater proportion of the heritability of most common complex phenotypes7,8. This polygenic architecture suggests that a large number of Single Nucleotide Polymorphisms (SNPs) will have associations too weak to be identified using traditionally employed analytic methods and limited sample sizes9. This has led to recent National Institute of Health and European Union calls for new cost-effective analytical methods to reliably identify a larger proportion of SNPs associated with complex diseases and traits using existing GWAS since recruitment and genotyping of new participants are expensive. One such approach relies on genetic pleiotropy10, i.e. the association of individual SNPs or genes with two or more phenotypes. Given the large number of traits in humans, and the relatively small number of genes, some genes are likely to affect multiple traits. Moreover, since there are often overlapping traits among behaviorally or clinically defined phenotypes, shared genetic influences between such phenotypes are likely.

Epidemiological studies have identified several major risk factors for cardiovascular disease (CVD)11,12 including hypertension, obesity, diabetes and dyslipidemia1,13-15. Several other traits and disorders have also been associated with blood pressure, including height2,3,16, osteoporosis4,17, schizophrenia5,6,18, diabetes7,8,19, and autoimmune disorders9,20. However, observational and clinical studies cannot fully elucidate the etiologic relationship between these phenotypes. Methods for assessing genetic pleiotropy offer great promise for delineating the basis of shared phenotypic correlations and for cost-effective identification of new loci10,21,22. This could be particularly meaningful for ‘essential hypertension’, where multiple pathogenic processes are likely involved11,12,23, and overlapping genetic associations with multiple phenotypes may be frequent. Here, we applied a recently developed genetic pleiotropy-informed analytical method for GWAS that captures more of the polygenic effects in complex disorders and traits (hereafter referred to as polygenic pleiotropy)22. We used this approach to leverage the power of multiple large independent GWAS for identifying SNPs exhibiting pleiotropy between SBP and 12 associated traits and disorders where recent GWAS results are available: bone mineral density (BMD)24, low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides (TG)25, type 2 diabetes (T2D)26, body mass index (BMI)27, waist to hip ratio (WHR)28, height (HT)29, schizophrenia (SCZ)30, type 1 diabetes (T1D)31, Rheumatoid Arthritis (RA)32 and Celiac Disease (CeD)33,34. By combining data from these different GWAS, we hypothesized that the genetic pleiotropy-informed approach can improve discovery of SBP genes, and inform the etiologic relationship between blood pressure and epidemiologically related phenotypes.

METHODS

Participant Samples

We obtained complete GWAS results in the form of summary statistics p-values from public access websites or through collaboration with investigators (Table 1). Details on the inclusion criteria and phenotype characteristics of the different GWAS are described in the original publications4,25-28. There was some overlap among several of the participants in the CVD risk factor GWAS and the SBP GWAS sample4. The relevant institutional review boards or ethics committees approved the research protocol of the individual GWAS and all participants gave written informed consent. All studies adhered to the principles of the Declaration of Helsinki.

Table 1.

The GWAS data used in the current study

Disease/Trait N # SNPs Reference
Systolic Blood Pressure (SBP) 203,056 2,382,073 Ehret GB, Munroe PB, Rice KM, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011;478:103-9.
Low Density Lipoprotein (LDL) 99,900 2,508,375 Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010;466:707-13.
High Density Lipoprotein (HDL) 96,598 2,508,370
Triglycerides (TG) 96,568 2,508,369
Height (HT) 183,727 2,398,527 Lango Allen et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010, 467: 832-8
Body Mass Index (BMI) 123,865 2,400,377 Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010;42:937-48.
Waist to hip ratio (WHR) 77,167 2,376,820 Heid IM, Jackson AU, Randall JC, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010;42:949-60.
Type 2 Diabetes (T2D) 22,044 2,426,886 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 2010;42:579-89.
Type 1 Diabetes (T1D) 16,559 841,622 Barrett, J.C. et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. NatGen 2009, 41, 703-7.
Rheumatoid arthritis (RA) 25,708 2,560,000 Stahl, E.A. et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet 2010 42, 508-14.
Bone Mineral Density (BMD) 32,961 2,500,000 Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 2012;44:491-501.
Celiac Disease (CeD) 15,283 528,969 Dubois, P.C. et al Multiple common variants for celiac disease influencing immune gene expression. Nat Genet 2010, 42, 295-302
Schizophrenia 21,856 1,171,056 Schizophrenia Psychiatric Genome-Wide Associatino Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 2011;43:969-76.
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For more details, see also http://www.genome.gov/gwastudies

Statistical Analyses

Genomic Control

We applied a control method using only intergenic SNPs to compute the inflation factor, λGC and divided all test statistics by λGC, as detailed in prior publications21,22.

Conditional Quantile-Quantile (Q-Q) plots for pleiotropic enrichment

Enrichment of statistical association relative to that expected under the global null hypothesis can be visualized through Q-Q plots of nominal p-values obtained from GWAS summary statistics. Genetic enrichment results in a leftward shift in the Q-Q curve, corresponding to a larger fraction of SNPs with nominal −log10 p-value greater than or equal to a given threshold. Conditional Q-Q plots are constructed by creating subsets of SNPs based on the significance of each SNP's association with a related phenotype, and computing Q-Q plots separately for each level of association (for further details, see references 21, 22). We constructed conditional Q-Q plots of empirical quantiles of nominal –log10(p) values for SNP association with SBP for all SNPs, and for subsets of SNPs determined by the nominal p-values of their association with each of the 12 related phenotypes (–log10(p) ≥ 0, –log10(p) ≥ 1, – log10(p) ≥2, and –log10(p) ≥3 corresponding to p ≤ 1, p ≤ 0.1, p ≤ 0.01, and p ≤ 0.001, respectively). The nominal p-values (–log10(p)) are plotted on the y-axis, and the empirical quantiles (–log10(q), where q=1-cdf(p)) are plotted on the x-axis. To assess polygenic effects, we focused the conditional Q-Q plots on SNPs with nominal –log10(p) < 7.3 (corresponding to p > 5×10−8).

Conditional False Discovery Rate (FDR)

Enrichment seen in the conditional Q-Q plots can be directly interpreted in terms of False Discovery Rate (FDR)21,22 (equivalent to 1 – True Discovery Rate (TDR)35). We applied a conditional FDR method22,36,37, and constructed TDR plots, as described earlier21,22, and detailed in Online Supplement (please see http://hyper.ahajournals.org).

Conditional statistics – test of association with Systolic Blood Pressure

To improve detection of SNPs associated with SBP, we conditioned SNPs based on p-values in the related phenotype21,22. We then assigned a conditional FDR value (denoted as FDRSBP | related-phenotype) for SBP to each SNP, for each related phenotype by interpolation, using a two-dimensional look-up table of conditional FDR values21,22 computed for each of the specific datasets used in the current study (Figure S3, please see http://hyper.ahajournals.org). All SNPs with FDRSBP | related-phenotype < 0.01 (−log10(FDRSBP | related-phenotype) > 2) in SBP given association with any of the 12 related phenotypes are listed in Table 1 after ‘pruning’ (i.e., removing all SNPs with r2 > 0.2 based on 1000 Genomes Project linkage disequilibrium (LD) structure). A significance threshold of FDR < 0.01 corresponds to 1 false positive per 100 reported associations.

Conditional FDR Manhattan plots

To illustrate the localization of the genetic markers associated with SBP given the related phenotype effect, we used a ‘Conditional FDR Manhattan plot’, plotting all SNPs within an LD block in relation to their chromosomal locations. The strongest signal in each LD block was identified by ranking all SNPs in increasing order, based on the conditional FDR value for SBP, and then removing SNPs in LD r2 > 0.2 with any higher ranked SNP. Thus, the selected locus was the most significantly associated with SBP in each LD block.

RESULTS

Pleiotropic enrichment – polygenic overlap

Conditional Q-Q plots for SBP conditioned on nominal p-values of association with LDL, BMI, BMD, T1D, SCZ, and CeD showed enrichment across different levels of significance (Figure 1A-F). For LDL, the proportion of SNPs in the −log10(pLDL) ≥ 3 category reaching a given significance level (e.g., −log10(pSBP) > 6) was roughly 100 times greater than for −log10(pLDL) ≥ 0 category (all SNPs), indicating a very high level of enrichment (Figure 1A). A similar level of enrichment was seen for BMI and SCZ (Figure 1B,C); CeD, T1D and BMD also showed a high level of enrichment (Figure 1D-F). Weaker pleiotropic enrichment was seen for WHR (Figure S1, please see http://hyper.ahajournals.org), with little or no evidence for enrichment in RA, HDL, TG, T2D, HT (Figure S1, please see http://hyper.ahajournals.org). We also illustrate the high level of polygenic pleiotropic enrichment in LDL, BMI, BMD, T1D, SCZ, and CeD using “Enrichment Plots” (Figure S2, please see http://hyper.ahajournals.org).

Figure 1. Q-Q plots of pleiotropic enrichment in SBP conditioned on associated phenotypes.

Figure 1

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Conditional Q-Q plot of nominal versus empirical −log10 p-values (corrected for inflation) in systolic blood pressure (SBP) below the standard GWAS threshold of p < 5×10−8 as a function of significance of association with A) Low density lipoprotein cholesterol (LDL), B) body mass index (BMI), C) bone mineral density (BMD), D) type 1 diabetes (T1D), E) schizophrenia (SCZ) and F) celiac disease (CeD) at the level of −log10(p) > 0, –log10(p) > 1, –log10(p) > 2, –log10(p) > 3 corresponding to p < 1, p < 0.1, p < 0.01, p < 0.001, respectively. Dotted lines indicate the null-hypothesis.

Gene loci associated with SBP

The “conditional FDR” Manhattan plot in Figure 2 shows the 62 independent gene loci significantly associated with SBP based on conditional FDR < 0.01 obtained from associated phenotypes. The 30 complex loci and 32 single gene loci (after pruning) were located on 16 chromosomes (Table 2). Only 11 of these loci would have been discovered using standard statistical methods (Bonferroni correction; bold values in the “SBP p-value” column, Table 2). Using the FDR method, 25 loci were identified (bold values in the “SBP-FDR” column, Table 2). The remaining 37 loci would not have been identified in the current sample without using the pleiotropy-informed conditional FDR method. Of the 62 loci identified, 42 were novel; 20 were reported in the primary analysis of the current sample4. Many of these new loci are located in regions with borderline significant association with SBP in previous studies4. Of interest, several loci had multiple pleiotropic SNPs from several associated phenotypes, indicating overlapping genetic factors among these phenotypes.

Figure 2. ‘Conditional FDR Manhattan plot’.

Figure 2

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‘Conditional FDR Manhattan plot’ of conditional –log10 (FDR) values for Systolic Blood Pressure (SBP) alone (black) and SBP given the associated phenotypes low density lipoprotein cholesterol (LDL; SBP|LDL, red), body mass index (BMI; SBP|BMI, orange), bone mineral density (BMD; SBP|BMD, green), type 1 diabetes (T1D; SBP|T1D, purple), schizophrenia (SCZ; SBP|SCZ; turquois) and celiac disease (CeD; SBP|CeD, blue). SNPs with conditional –log10 FDR > 2 (i.e. FDR < 0.01) are shown with large points. A black circle around the large points indicates the most significant SNP in each LD block and this SNP was annotated with the closest gene which is listed above the symbols in each locus, except for the HLA region on chromosome 6, and in Table S2 (please see http://hyper.ahajournals.org). The figure shows the localization of 62 loci on 16 chromosomes (1-12, 15-17 and 20). Details for the loci with –log10 FDR > 2 (i.e. FDR < 0.01) are shown in Table 1.

Table 2.

Independent loci associated with SBP through Conditional FDR (<0.01) with associated phenotypes.

Locus SNP Pos Gene chr SBP p-value SBP FDR Min cond FDR Associated Phenotype
1 rs2748975 1886519 KIAA1751 1 1.81E-06 0.01493 0.0095053 WHR
2 rs880315 10796866 CASZ1 1 1.44E-05 0.04983 0.0040514 CeD
3 rs17367504 11862778 MTHFR † 1 9.86E-11 0.00003 0.0000013 WHR
rs2050265 11879699 CLCN6 1 2.38E-10 0.00003 0.0000026 WHR
4 rs6676300 11925300 NPPB 1 1.47E-05 0.04983 0.0054695 CeD
5 rs783622 42366988 HIVEP3 1 1.04E-05 0.03839 0.0028136 LDL
6 rs12048528 113210534 CAPZA1 1 3.84E-06 0.02209 0.0014541 BMI
rs2932538 113216543 MOV10 † 1 1.78E-06 0.01493 0.0014684 BMI
7 rs4332966 43083831 HAAO 2 1.58E-05 0.04983 0.0025790 BMI
8 rs9309112 44169889 LRPPRC 2 1.56E-05 0.04983 0.0047478 LDL
9 rs12619842 164945044 FIGN 2 1.01E-05 0.03839 0.0089999 LDL
rs16849397 165108248 GRB14 2 4.76E-07 0.00665 0.0025354 WHR
10 rs2594992 11360997 ATG7 3 2.24E-06 0.01687 0.0076216 WHR
11 rs6806067 14948702 FGD5 3 2.23E-06 0.01493 0.0033240 BMI
12 rs6797587 48197614 CDC25A 3 1.32E-06 0.01180 0.0043919 BMI
13 rs223102 169100755 MECOM † 3 4.56E-08 0.00112 0.0006796 WHR
14 rs9290369 169324783 MECOM 3 8.04E-07 0.00909 0.0066551 WHR
15 rs10006384 38385187 FLJ13197 4 2.71E-06 0.01687 0.0054382 BMI
16 rs1458038 81164723 FGF5 † 4 1.08E-09 0.00004 0.0000228 WHR
17 rs13107325 103188709 SLC39A8 † 4 1.55E-07 0.00271 0.0000229 BMI
18 rs1173743 32775047 NPR3 5 4.78E-07 0.00665 0.0007773 BMI
rs1173771 32815028 C5orf23 † 5 8.44E-08 0.00162 0.0004338 WHR
19 rs458158 122482181 PRDM6 5 6.76E-06 0.02945 0.0071865 SCZ
20 rs11750782 122976743 CSNK1G3 5 6.75E-06 0.02945 0.0070289 BMD
21 rs11953630 157845402 EBF1 † 5 3.64E-07 0.00558 0.0029954 WHR
22 rs199205 7736417 BMP6 6 2.29E-06 0.01687 0.0076216 WHR
23 rs9467445 25234884 BC029534 6 2.20E-06 0.01493 0.0011956 T1D
24 rs11754013 25370200 LRRC16A 6 1.32E-05 0.04368 0.0076472 LDL
25 rs2736155 31605199 PRRC2A (BAT2) † 6 1.41E-06 0.01180 0.0002670 BMI
rs805303 31616366 BAG6(BAT3) † 6 8.17E-07 0.00909 0.0000941 SCZ
26 rs429150 32075563 TNXB 6 1.70E-05 0.04983 0.0090475 LDL
27 rs394199 33553580 GGNBP1 (AY383626) 6 3.96E-05 0.08570 0.0034152 T1D
28 rs581484 126665180 CENPW (C6orf173) 6 3.08E-06 0.01922 0.0089438 LDL
29 rs853964 127029267 AK127472 6 2.63E-06 0.01687 0.0076216 WHR
30 rs2969070 2512545 BC034268 7 2.64E-07 0.00386 0.0014814 T1D
31 rs3735533 27245893 HOTTIP (AK093987) 7 1.37E-05 0.04368 0.0056631 LDL
32 rs7777128 27337113 EVX1 7 6.04E-06 0.02945 0.0020776 LDL
33 rs7787898 106409897 AF086203 7 2.60E-06 0.01687 0.0062017 SCZ
34 rs3088186 10226355 MSRA 8 1.97E-05 0.05707 0.0019924 SCZ
35 rs4735337 95973465 NDUFA6 (C8orf38) 8 3.54E-05 0.07505 0.0028564 T1D
36 rs12006112 21042299 PTPLAD2 9 5.02E-05 0.09719 0.0058735 T1D
37 rs4978374 111646983 IKBKAP 9 9.87E-06 0.03839 0.0094345 BMD
38 rs12570727 18425519 CACNB2 † 10 4.07E-08 0.00093 0.0001882 SCZ
39 rs12258967 18727959 CACNB2 10 1.42E-07 0.00271 0.0015659 WHR
40 rs4590817 63467553 C10orf107 † 10 3.40E-08 0.00077 0.0001588 WHR
41 rs12247028 75410052 SYNPO2L 10 1.59E-06 0.0132B 0.0067916 WHR
42 rs932764 95895940 PLCE1 † 10 1.47E-07 0.00271 0.0001182 LDL
43 rs10786156 96014622 PLCE1 10 2.51E-06 0.01687 0.0020927 BMI
44 rs10883766 104464763 ARL3 10 1.91E-05 0.05707 0.0071447 CeD
rs284844 126665180 WBP1L (C10orf26) 10 5.48E-09 0.00015 0.0000039 BMI
rs1926032 127029267 CNNM2 10 2.77E-10 0.00003 0.0000001 BMI
rs11191548 2512545 NT5C2 † 10 2.43E-10 0.00003 0.0000001 SCZ
45 rs7129220 27245893 EF537580 † 11 6.92E-08 0.00135 0.0006154 SCZ
46 rs1580005 27337113 EF537580 11 2.80E-06 0.01687 0.0057696 LDL
47 rs381815 106409897 PLEKHA7 † 11 1.25E-09 0.00005 0.0000205 BMI
48 rs642803 10226355 OVOL1 11 1.14E-05 0.04368 0.0065527 LDL
49 rs633185 95973465 FLJ32810 † 11 2.98E-08 0.00077 0.0004474 WHR
50 rs11105328 21042299 POC1B (WDR51B) 12 5.35E-10 0.00003 0.0000080 SCZ
rs2681472 111646983 ATP2B1 † 12 5.14E-13 0.00003 0.0000062 SCZ
51 rs7297186 1425519 CUX2 12 1.88E-06 0.01493 0.0005328 CeD
rs3742004 18727959 FAM109A 12 6.39E-07 0.00783 0.0003417 WHR
rs653178 63467553 ATXN2 12 4.58E-10 0.00003 0.0000002 BMI
rs1005902 75410052 HECTD4 (C12orf51) 12 2.62E-06 0.01687 0.0005845 LDL
rs12580178 95895940 RPH3A 12 4.21E-06 0.02209 0.0007345 LDL
52 rs7299238 96014622 CABP1 12 6.25E-05 0.10892 0.0053975 LDL
53 rs11070252 104464763 GOLGA8T (AK310526) 15 3.86E-06 0.02209 0.0078255 CeD
54 rs1378942 75077367 CSK † 15 1.63E-10 0.00003 0.0000002 CeD
55 rs8032315 91418297 FURIN 15 1.83E-07 0.00323 0.0000828 SCZ
rs2521501 91437388 FES † 15 7.16E-08 0.00162 0.0011762 WHR
56 rs11643718 56933519 SLC12A3 16 3.30E-05 0.07505 0.0037698 T1D
57 rs4793172 43131480 DCAKD 17 7.05E-07 0.00783 0.0040625 SCZ
rs2239923 43176804 NMT1 17 3.97E-07 0.00558 0.0008079 BMD
rs12946454 43208121 PLCD3 17 5.17E-08 0.00112 0.0000647 BMD
58 rs11012 PLEKHM1 17 4.12E-05 0.08570 0.0034152 T1D
59 rs17608766 GOSR2 † 17 4.59E-07 0.00665 0.0005684 BMI
60 rs6055905 PLCB1 20 3.04E-05 0.07505 0.0064506 LDL
61 rs6072403 CHD6 20 5.59E-06 0.02552 0.0058812 LDL
62 rs6015450 ZNF831 † 20 5.63E-08 0.00135 0.0006154 SCZ
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Independent complex or single gene loci (r2 < 0.2) of SNP(s) with a conditional FDR (condFDR) < 0.01 in Systolic Blood Pressure (SBP) given the significance level in the associated phenotype. We defined the most significant SBP SNP in each LD block based on the minimum condFDR (min condFDR) for each associated phenotype. The most significant SNPs in each gene of the LD block are listed along with the associated phenotype that provided the signal. Low density lipoprotein (LDL) cholesterol, body mass index (BMI), waist hip ratio (WHR), bone mineral density (BMD), type 1 diabetes (T1D), celiac disease (CeD), schizophrenia (SCZ), chromosome location (Chr). SBP FDR values < 0.01 and p-values < 5 × 10−8 are in bold.

†

Same locus identified in previous SBP genome-wide association studies. The most significant phenotype associations per gene are shown. All genes are shown in Table S1. All data were first corrected for genomic inflation. Gene titles and gene ontology functional terms are displayed in Table S2 (please see http://hyper.ahajournals.org). Ingenuity pathway analysis was used to generate a network displaying direct interactions among proteins encoded by these SBP-related genes (shown in Figure S3, please see http://hyper.ahajournals.org). The molecules associated with some of the top functional clusters of these genes are show in Tables S3 and S4 (please see http://hyper.ahajournals.org).

Follow-up Ingenuity Pathways Analysis (IPA) are presented in Table S3 and S4 (please see http://hyper.ahajournals.org) identifying the traits in the categories “Cardiovascular disease” or “Cardiovascular System Development and Function”, respectively, that may be affected by the gene heterogeneities in the vicinity of the indicated SBP associated genes. Figure S4, made by the network function in IPA, demonstrates that a large proportion of SBP associated genes are functionally related (please see http://hyper.ahajournals.org).

DISCUSSION

Our findings demonstrate polygenic pleiotropy between SBP and BMI, T1D, SCZ, CeD and BMD, with strongest pleiotropy between SBP and LDL. Combining GWAS data from multiple different phenotypes, we identify 62 SBP susceptibility loci, including 42 novel loci.

In the original SBP GWAS sample, 29 loci were identified4. By combining the original SBP sample with GWAS of epidemiologically related phenotypes, we found significant pleiotropic signals in 62 loci. Thus, even though the original SBP GWAS was quite large4, the increased power provided by additional GWAS of associated phenotypes together with the FDR method more than doubled gene discovery. These findings underline the cost-effectiveness of the current statistical methods, and strongly suggest that SBP is a highly polygenic trait, in line with recent findings38.

Our findings also provide novel insights into the relationship between SBP and other major CVD risk factors, which frequently co-occur. The combination of dyslipidemia (primarily increased TG levels and decreased HDL levels), T2D, and high blood pressure forms the metabolic syndrome12-16. These results demonstrate an interesting genetic dissociation among cardiovascular risk factors. We found that LDL, a classic CVD risk factor, showed strongest pleiotropy with SBP while factors associated with the metabolic syndrome (TG, HDL, and T2D) showed little genetic pleiotropy with SBP. Further research is needed to determine whether there is strong genetic pleiotropy among the metabolic risk factors that would provide a genetic basis for the metabolic syndrome. The strong pleiotropy between LDL and SBP suggests that many genes related to lipid biology are pleiotropic with SBP and suggests common mechanisms related to atherosclerosis. This is further supported by the individual loci identified, of which the majority was based on conditional FDR with LDL, BMI or WHR. Several of the genes in LD with these new SBP-associated loci are involved in lipid metabolism and regulation. Lipid metabolism regulation may also underlie the observed pleiotropy between SBP and BMD, as suggested by gene expression in bone tissue39. However, age-related mechanisms may also underlie the overlap seen between SBP and BMD40.

Pleiotropy is defined as a single gene or variant being associated with more than one distinct phenotype41. Rather than representing genetic pleiotropy, it is also possible that some of the loci identified in the current study may underlie common aspects of the SBP and CVD phenotypes. Moreover, the shared genetic loci may also represent mediated pleiotropy. For example, for LDL and SBP the overlap may be due to the fact that lipid deposition leads to stiff arteries and thus higher blood pressure.

Another novel finding is the overlap between SBP and immune-related disorders, including CeD and T1D. Based on conditional analysis of these two phenotypes, 24 loci were identified. These phenotypes also showed strong polygenic pleiotropy, with clear enrichment in the Q-Q plots. While previous studies have suggested a link between T2D and SBP, the present study found an overlap between T1D, but not T2D, and SBP, suggesting immune-mediated rather than metabolic links between diabetes and SBP. The immune-related mechanisms that are involved in SBP seem to be quite specific as we found little enrichment with RA, a prototypical auto-immune disorder. Moreover, while we found no or weak association with other inflammatory bowel disorders (data not shown), CeD a T-cell mediated disease42, showed much stronger enrichment. SCZ also showed strong enrichment with 12 independent SBP loci identified based on enrichment from the SCZ GWAS. In a previous study21 we successfully used the polygenic pleiotropy approach to increase gene discovery in SCZ by enriching on CVD risk factors, identifying a shared genetic basis for the increased CVD mortality and higher incidence of hypertension in SCZ patients18. Our findings of several shared loci between SBP and SCZ point to common underlying mechanisms that warrant further experimental investigation.

Due to the overlap in some of the GWAS samples examined, we cannot exclude contribution from environmental or behavioral factors, or other non-genetic correlations. Still, our genetic pleiotropy results strongly imply the existence of shared pathophysiological processes across SBP and associated phenotypes because we controlled for pleiotropic inflation using genomic control correction of each primary single phenotype GWAS. Moreover, the overlapping loci are located on 16 chromosomes suggesting that the findings are not due to common genetic variation in potentially overlapping control groups. Further, the GWAS of blood lipids used the same sample to discover new genes for three different phenotypes25. Since we do not have access to additional samples or individual sub-studies, we cannot provide evidence of replication, which is a limitation of the current study. However, we have previously shown that the genetic findings obtained using the conditional FDR approach employed here replicate at the same or higher rate than findings obtained with traditional GWAS methods. Importantly, we have also demonstrated that these FDR-based methods increase sensitivity for a given specificity thus improving statistical power for SNP detection22. Due to the overlap in some of the GWAS samples examined, we cannot exclude contribution from environmental or behavioral factors, or other non-genetic correlations.

Another limitation of the current study is our inability to relate the genetic findings to clinical outcomes, such as stroke and congestive heart failure, as we do not have access to clinical outcome measures. However the current findings suggest that leveraging more powerful statistical techniques, building on Empirical Bayesian mixture models, may be a fruitful approach to better select plausible candidate SNPs for improved polygenic risk scores43 that may lead to personalized medicine approaches and potentially individual prediction of disease risk. We are currently working towards developing prediction and stratification algorithms that incorporate multiple small effects to increase prediction and classification power.

In conclusion, we found substantial genetic overlap between SBP and several related conditions, including BMI, WHR, T1D, SCZ, CeD, BMD and in particular LDL. This suggests an etiological relationship between these phenotypes that could include lipid disturbances and certain immunological pathways.

PERSPECTIVES

The current results demonstrate the feasibility of using a genetic epidemiology framework that leverages overlap in genetic signal from independent GWAS of associated phenotypes, both for cost-effective gene discovery and for elucidating the shared genetic basis between related phenotypes. This approach identified 42 novel gene loci in SBP, arguing that GWAS have the potential to uncover more of the genetic basis of hypertension when new statistical methods are used. The observed polygenic overlap between SBP and several comorbid disorders indicates that the epidemiological associations are not mediated solely via lifestyle factors, but also reflect an etiological relation. Our findings also shed new light on the pathogenic mechanisms in SBP that warrants further investigation. The novel genetic loci identified here implicate genetic mechanisms related to lipid biology and the immune system in SBP. These findings may have implications for early diagnosis, prevention strategies and therapeutic regimens for hypertension.

Supplementary Material

Online Material

Novelty and Significance.

  1. What Is New?
    • - We used new statistical methods to improve gene discovery
    • - We identified 42 novel gene loci associated with blood pressure
    • - We demonstrated shared genes between blood pressure and several associated diseases/traits
  2. What Is Relevant?
    • - The new gene loci may inform the underlying genetic mechanisms of hypertension
    • - The genetic overlap with immune-mediated diseases and blood lipids suggests common mechanisms with hypertension
    • - The findings may have implications for early diagnosis, prevention strategies and therapeutic regimens in hypertension

  3. Summary

We identified 42 new gene loci for blood pressure, and found genetic overlap between blood pressure and several associated diseases and traits, particularly immune-mediated diseases and blood lipids. This suggests an etiological relationship between hypertension and lipid disturbances and immunological abnormalities.

Acknowledgements

The authors would like to thank the DIAGRAM Consortium, The Psychiatric Genomics Consortium (PGC) Schizophrenia Working Group, the Celiac Disease GWAS Consortium, The Type 1 Diabetes Consortium for the summary statistics GWAS data.

Sources of Funding

OAA was supported by the Research Council of Norway, the South East Norway Health Authority and the Kristian Gerhard Jebsen Foundation. RSD was supported by a grant from the National Institutes of Health (T32 EB005970). AMD was supported by NIH grants R01AG031224, R01EB000790, and RC2DA29475. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

Authors’ contributions

Andreassen and Dale designed study; Djurovic, Schork, Reppe collected data, Schork prepared data; Dale performed the statistical analysis with assistance from Thompson, Wang, Zuber, Reppe; Andreassen and McEvoy drafted manuscript; Gautvik, Karlsen, Aukrust, Barrett-Connor, Desikan, McEvoy, Dale, Andreassen interpreted data; all authors critically revised manuscript and approved final version.

Disclosures

None reported.

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