SNP Interactions in the Genetic Architecture of Blood Pressure Jacob John Basson Washington University in St

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Summer 9-3-2013 SNP Interactions in the Genetic Architecture of Blood Pressure Jacob John Basson Washington University in St. Louis

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This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact [email protected]. WASHINGTON UNIVERSITY IN ST. LOUIS

Division of Biological and Biomedical Sciences

Human and Statistical Genetics

Dissertation Examination Committee:

DC Rao, Chair Jim Cheverud Lisa de las Fuentes Mike Province Nancy Saccone Alan Templeton

SNP Interactions in the Genetic Architecture of Blood Pressure

Jacob John Basson

A dissertation presented to the

Graduate School of Arts and Sciences

of Washington University in

partial fulfillment of the

requirements for the degree

of Doctor of Philosophy

August 2013

Table of Contents

List of Figures………………………………………………………………………….……………iv

List of Tables…………………………………………………………………………….…………..v

Acknowledgments……………………………………………………………………….………….vi

Abstract……………………………………………………………………………………………..vii

Chapter 1: Between Candidate Genes and Whole Genomes: Time for New Approaches in Blood

Pressure Genetics?

Introduction……………………………………………………………………..……………1

Candidate Gene Studies…………………………………………………………...…………2

Genome Wide Studies……………………………………………………………..………10

Discussion…………………………………………………………………………………..21

Chapter 2: Genome-Wide Association with BP in FHS

Introduction…………………………………………………………………..……………..25

Framingham Heart Study………………………………………………………………...... 25

Methods………………………………………………………………………….………….29

Results…………………………………………………………………………….…...……30

Discussion………………………………………………………………………………..…31

Chapter 3: SNP-SNP Interactions with BP in FHS

Introduction……………………………………………………………………..…………..32

Methods……………………………………………………………………….…………….36

Results………………………………………………………………………….…………...40

Discussion…………………………………………………………………………………..40

Chapter 4: SNP-Environment Interactions with BP in FHS

Introduction………………………………………………………………………..………..45

Methods………………………………………………………………………….………….46

Results…………………………………………………………………………….………...48

Discussion ………………………………………………………………………………48

Chapter 5: Multi-Marker Analysis of SNP Interactions with BP in FHS

Introduction…………………………………………………………………….….………..51

Methods……………………………………………………………………….……………52

Results……………………………………………………………………….……………...53

Discussion…………………………………………………………………………………..53

Chapter 6: Conclusions

Discussion…………………………………………………………………………………..56

Figures……………………………………………………………………….……………...61

Tables……………………………………………………………………….………………99

References………………………………………………………………….……………...131

iii

List of Figures Figure 1: Distribution of SBP, DBP, BMI, and Age in FHS visit 7……………….……….………61

Figure 2: SBP and DBP GWAS Manhattan plots………………………………………………….63

Figure 3: Pathway-only Manhattan plot for SNP main effects………………………...…………...64

Figure 4: Mean SBP by genotype for selected SNP-SNP interactions……………….…….………66

Figure 5: Mean DBP by genotype for selected SNP-SNP interactions………………………..…...73

Figure 6: Pathway-only Manhattan plot for SNP-BMI interactions………………………………..74

Figure 7: Pathway –only Manhattan plot for SNP-Sex interactions………….…..………………...76

Figure 8: SNP-BMI (2 df) interactions vs. SNP main effects…………………….………………...77

Figure 9: SNP-Sex (2 df) interactions vs. SNP main effects……………………………………….81

Figure 10: Pathway-only Manhattan plot for SNP-Hypertension interactions and stratified effects……………………………………………………………………………………………….85

Figure 11: QQ plot for SNP-Hypertension interactions…………………………….……………...88

Figure 12: QQ plot for SNP-Hypertension stratified effects………………………………..……...92

Figure 13: Mean SBP for top SNP-Hypertension interaction…………………………………...….96

List of Tables Table 1: Mendelian hypertension genes……………….…………………………...………………97

Table 2: Selected BP candidate genes…………………………………………………...…………99

Table 3: FHS visit 7 sample summary…………………………………………………………….101

Table 4: FHS visit 7 normality check……………………………………………...……………...102

Table 5: Pathway genes…………………………………………………………..……………….102

Table 6: Top SNP-SNP interactions by p-value…………………………………………………..104

Table 7: Top SNP-SNP interactions by q-value…………………………………………………..107

Table 8: Top SNP-BMI and SNP-Sex interactions by p-value……………..……………………..112

Table 9: Multi-marker model summary………………………………….………………………..112

Table 10: Multi-marker results……………………………………………………………………113

Acknowledgements

This research was supported by Grants T32HL083822,

R21HL095054, R01HL111239, and R01HL107552 from the National Heart, Lung, and Blood

Institute. The content is solely the responsibility of the authors and does not

necessarily represent the official views of the National Heart, Lung,

and Blood Institute or the National Institutes of Health.

Mentors: DC Rao, Lisa de las Fuentes

Committee: Jim Cheverud, Mike Province, Nancy Saccone, Alan Templeton

Rao Lab: Yun Ju Sung, Jeannette Simino, Karen Schwander, Rezart Kume, Treva Rice

HSG Directors: John Rice, Allison Goate, and Anne Bowcock

Mom, Dad, Duncan, Alex

ABSTRACT OF THE DISSERTATION

SNP Interactions in the Genetic Architecture of Blood Pressure

Jacob John Basson

Doctor of Philosophy in Human and Statistical Genetics

Washington University in St. Louis, 2013

Professor DC Rao, Co-Chair and Assistant Professor Lisa de las Fuentes, Co-Chair

Evidence suggests that there is a substantial genetic component to the regulation of blood pressure

(BP) but most BP variance remains unattributed to specific genetic variants. Candidate gene studies show linkage to rare Mendelian disorders but inconsistent association with BP. Massive consortia

of genome-wide association studies have identified several dozen loci explaining less than 3% of

BP variance. Many explanations have been offered for these observations, of which the current

study addresses two: the focus on SNP main effects to the exclusion of interactions, and the low

statistical power resulting from the strong multiple-testing burden necessarily imposed when analyzing millions of variants. This study examined the role of interactions among a limited set of variants (818 SNPs in 70 genes) drawn from two broad signaling pathways: renal ion balance and inflammation. Exhaustive SNP-SNP interaction testing was performed, as was analysis of SNP-sex,

SNP-BMI, and SNP-hypertension interactions. In hypertensive subjects only, rs12821401 in

ADIPOR2 was significantly associated with SBP (p=6.1e-5). A set of three SNP-SNP interactions among 6 inflammation genes (including ADIPOR2) showed significant evidence of containing one

or more true association signals (FDR=4.2%), though no single interaction reached experiment-

wise significance. These results provide evidence of a role for SNP-SNP and other SNP

vii

interactions and implicate an inflammation gene, ADIPOR2, not previously associated with

hypertension

viii

Ch1: Between candidate genes and whole genomes: time for new approaches in blood

pressure genetics?

I. Introduction

The US Department of Health and Human Services estimates that almost one third of US adults over 20 years of age suffer from hypertension [1], a major risk factor for cerebrovascular disease, ischemic heart disease, and cardiac and renal failure [2]. The nature and genetic origins of hypertension have long been the subject of scientific inquiry, dating back to the 1940s and the famous debate between Dr. Robert Platt and Dr. George Pickering. Platt argued that hypertension was characterized by a bimodal distribution of blood pressure values, with a single (unknown) genetic defect, transmitted with Mendelian inheritance and leading to a discrete disease state.

Pickering, on the other hand, believed that the spectrum of blood pressure values observed in the population was better described as a unimodal distribution with polygenic inheritance, and that subjects with hypertension were simply those falling at the extreme high end of the continuum [3].

Today it’s known that there’s some truth to both sides. For a variety of rare familial hypertensive diseases, traditional linkage and association has been very successful in identifying genes carrying disease-causing mutations with Mendelian inheritance. For the remaining cases of hypertension – the vast majority – the complex architecture of the genetic and environmental interactions that regulate blood pressure and become pathologic in disease has yet to be elucidated.

However, decades of research, aided by an increasingly powerful and affordable technological arsenal, have yielded substantial insights into the mechanisms of blood pressure regulation, even as they have failed to identify the complete set of genetic polymorphisms, environmental risk factors, and their interactions that underlie these cases of essential hypertension.

Several opportunities for methodological progress have emerged from these insights, of which the first two listed are pursued in the work that follows: 1) test the role of gene-gene and other interactions; 2) reduce the multiple testing burden by focusing on variants concentrated in pathways; 3) test the role of rare variants; and 4) distinguish finer-grained hypertension phenotypes

(i.e. salt-sensitive vs. spontaneous? sympathetic nervous activation?). This chapter will summarize the major findings of two major classes of study designs – candidate gene studies and genome-wide studies – with an emphasis on how each has contributed to our understanding of the genetics of blood pressure regulation.

II. Candidate gene studies

Candidate gene studies typically examine one or a few polymorphisms in one or a few genes, testing for linkage or association with either blood pressure as a continuous trait or with hypertension case/control status. This approach exploits a priori biological information to select the target gene(s), and the limited number of tests performed allows the detection of small effects with only moderate sample sizes. On the other hand, because data are collected on only one or a few polymorphisms, few if any gene-gene interactions can be observed, and the smaller samples generally employed preclude the examination of rare variants as too few subjects have the rare genotypes to perform meaningful tests.

Studies using this basic design have made major contributions to our understanding of the genetics and physiology underlying both Mendelian and essential hypertension. Mendelian forms of hypertension have unambiguously revealed the central importance of several pathways and processes involved in renal sodium reabsorption: channels and transporters for sodium and other ions, ion channel activation by aldosterone through the mineralocorticoid receptor, the synthesis of

aldosterone, and ion channel regulation by kinases (e.g. [4-8], see Table 1). By contrast, candidate gene studies of essential hypertension have expanded the evidence for these pathways and demonstrated the role of several other pathways but have produced much less consistent results across studies and populations. Genes regulating aldosterone secretion, dopaminergic and adrenergic signaling pathways, and adducins have received considerable attention (see Table 2).

Other targets such as NO-dependent and NO-independent vasodilation pathways, and inflammation genes are emerging in BP association studies as well. While inflammation and BP have been connected through their role in metabolic syndrome [9], few BP association studies have been conducted targeting inflammation genes [10].

IIa. Mendelian hypertension candidate genes

Early efforts in blood pressure genetics focused on the genetic basis of several Mendelian disorders causing either hypertension or hypotension. Strikingly, every gene identified on this basis is involved in the renal maintenance of ion concentrations, particularly sodium. A brief summary of the implicated genes and processes follows; Lifton’s excellent review [11] gives more detail on many of these findings.

One major pathway whose disruption can lead to pathologic sodium reabsorption is the synthesis of aldosterone and related steroid hormones. Mutations in aldosterone synthase and 11β- hydroxylase lead to the hypertensive syndrome glucocorticoid-remediable aldosteronism [4], while hypotension can result from recessive mutations in several steroidogenic genes including CYP11B2

(aldosterone synthase; [12]) and CYP21A2 (steroid 21-hydroxylase; [13]). Aberrant activation of aldosterone’s target, the mineralocorticoid receptor (MR), can also result from dysregulation of cortisol caused by mutations in 11β-hydroxysteroid dehydrogenase (HSD11B; [5, 14]) or in

glucocorticoid receptors [15]. Increased levels of other MR-activating steroids, including corticosterone and deoxycorticosterone, due to a steroidogenic shunt caused by genetic deficiencies in 11β-hydroxylase [16] or 17α-hydroxylase [17], can also lead to hypertension. One mutation in the mineralocorticoid receptor itself can cause hyper-activation leading to hypertension exacerbated by pregnancy [18] while a variety of other MR mutations lead to pseudohypoaldosteronism type I, a syndrome including hypotension, hyperkalemia, and acidosis [19]. Notably, these phenotypic consequences result from a gene by diet interaction, appearing in neonates but disappearing in patients with a salt-rich diet [20].

One major downstream effector of mineralocorticoid receptor activation is the sodium channel ENaC. Each of its subunits are known to contain disease causing mutations [21]: hyperactivation leading to hypertension results from mutations in either the β or γ subunits (SCNN1B,

SCNN1G, Liddle syndrome, [22, 6]). Reduced ENaC activity resulting in pseudohypoaldosteronism type I, on the other hand, can result from recessive mutations in either the α, β, or γ subunits

(SCNN1A, SCNN1B, SCNN1G [7, 23]). Other ion channels and transporters also lead to Mendelian

BP dysregulation disorders including the Na-Cl cotransporter NCC (Gitelman’s syndrome, [24,

25]), the Na-K-2Cl cotransporter NKCC2 (Bartter’s syndrome type 1 [8]), the potassium channel

ROMK (Bartter’s syndrome type II, [26]), and the chloride channel CLCNKB (Bartter’s syndrome type III, [27]). Finally, two kinase regulators of these ion channels and transporters, WNK1 and

WNK4 have been implicated in the Mendelian hypertensive disorder pseudohypoaldosteronism type

II [28].

IIb. Essential hypertension candidate genes

While Mendelian hypertensive disorders have provided invaluable clues as to the mechanisms regulating blood pressure, over 95% of all cases of hypertension show more complex inheritance patterns and are believed to have polygenic as well as environmental origins; these are cases of ‘essential hypertension’. Attempts to detect associations between essential hypertension (or blood pressure) and genetic polymorphisms in candidate genes have frequently yielded conflicting results, with numerous genes implicated in many studies while also showing no association in many others. Explanations have been offered appealing to two themes discussed in detail here, epistasis [29] and variant rarity [30], while other issues potentially impacting study design and power have been noted including heterogeneity of the disease and of the studied populations, and choice and accuracy of phenotypes [31-33]. Nevertheless, while individual genes and polymorphisms have tended to be significantly associated in some studies and populations but not others, several pathways have consistently emerged as containing genetic variation relevant to blood pressure regulation, including those discussed above as well as others. In addition, candidate gene studies of essential hypertension, more so than any other type of study, have revealed the important role interactions play in the regulation of blood pressure. These include interactions between genes, sex, age, diet, and the environment. The following section summarizes the evidence implicating several pathways, while a thorough review of the positive and negative findings associated with specific polymorphisms is beyond the scope of this work. Table 2 organizes the referenced studies according to whether or not association was detected for each gene.

Pathways implicated in Mendelian BP disorders

The renal functions implicated in Mendelian BP disorders were among the first to be studied in connection with BP in the general population. Even the mostly conclusively identified

genetic pathways showed relatively inconsistent association with BP among candidate gene studies in various general populations (Table 2). This includes mixed results for the implicated aldosterone synthesis enzymes (CYP11B1 [34-36], CYP11B2 [37-42], HSD11B1/2 [43, 44], and CYP17A1 [45,

46]), ion channels (potassium channel KCNJ1 [47-49], chloride channel CLCNKB [50-53, 49], sodium channel subunits SCNN1A, SCNN1B, and SCNN1G[54-58]), ion transporters (Na-Cl transporter SLC12A3 [47, 59-61, 53], Na-K-2Cl transporter SLC12A1 [47, 62, 49, 63]), and regulatory kinases (WNK1 [33, 64-66, 61] and WNK4 [61, 67, 68]). One of these studies [47] was among the first to demonstrate a role for rare variants, identifying a set of rare coding mutations that were collectively associated with BP. This study is also suggestive of a SNP-age interaction: the average effect of these mutations was a drop in SBP of 5.7 mm Hg at age 40 but increased to a

9.0 mm Hg drop at age 60.

Additional pathways: aldosterone secretion

In addition to those genes implicated in Mendelian BP disorders, candidate gene studies of essential hypertension have targeted several other groups of genes. One related group of genes governs aldosterone secretion, the renin-angiotensin-aldosterone system (RAAS). Renin (REN, [69-

72]), angiotensinogen (AGT, [73-76]), angiotensin converting enzyme (ACE, [76-78]), and the angiotensin type II receptor AGTR1 ([79-86]) have been the subject of numerous studies, with associations detected in some studies but not others for each gene. In addition to associating several genes related to aldosterone metabolism beyond those implicated in Mendelian disorders, several of these studies hint at SNP interactions, including with other SNPs [76, 77] and with race and sex

[75, 86].

Additional pathways: ion transporter regulators

Like WNK1 and WNK4, SGK1 codes for a kinase that regulates Na+ cotransporters, and several studies have investigated its role in BP regulation, with some [87-89] but not others [90, 54] finding evidence of association. A different ion exchanger, the Na+/K+ pump, is the target of regulation by several cytoskeletal proteins including ADD1 and ADD2, which have each been studied for BP associations with both positive (ADD1 [91-94], ADD2 [91, 95]) and negative (ADD1

[96-98], ADD2 [94, 99]) results. Two more thorough reviews of the findings on adducins [100,

101] show that associations were detected more commonly in Asian than in Caucasian populations and that one of the most commonly studied polymorphisms, a missense mutation in ADD1, is more common in Asians as well (MAF = 48% vs 17%) [101]. As with the aldosterone secretion genes, candidate gene studies of BP targeting adducins have revealed several SNP-SNP interactions, including interactions among adducins [91] and interactions between adducins and genes involved in aldosterone synthesis [93] and secretion [92].

Additional pathways: sympathetic nervous signaling

The candidate gene studies of BP discussed above have expanded the list of implicated genes related to aldosterone signaling and the regulation of ion transport. In addition, candidate gene studies have also studied several pathways and functions not implicated in Mendelian BP disorders. One major such function is signaling of the sympathetic nervous system -- dopamine and adrenaline signaling influence BP by regulating both renal sodium transport and vascular tone. This functional relationship is reflected by statistical association of the D1A dopamine receptor detected in both mice [102] and humans [103, 104]. As with the candidate gene results for other pathways, this particular variant was not associated in several other studies [105-107], but those studies did

find associations elsewhere in the same gene [106] and in several other genes regulating dopamine signaling, including COMT and DBH [107] as well as GRK4 [108-110]. Others have investigated the role of adrenoreceptors and, as meta-analyzed by Kitsios et al.[111], 163 studies collectively provided evidence of association in the genes ADRA1A, ADRB1, ADRB2, and ADRB3. The results of these studies on dopamine and adrenaline signaling support the idea that pathways are more consistently associated with BP than are the specific variants they comprise. One of these studies also investigated the role of epistasis and “found no significant single site associations, but the hypertensive class deviated significantly from genotype equilibrium in more than 25% of all multilocus comparisons (2,162 of 8,178), whereas the normotensive class rarely did (11 of 8,178)

[110],” contributing evidence of the importance of SNP interactions to BP genetic architecture and epidemiology.

Additional pathways: other vascular tone regulators

Several other genes believed to affect vascular dilation and contraction have been the targets of candidate gene studies of BP, though to date the evidence for association is not strong.

One of the most commonly studied genes in this group is NOS3, which codes for the endothelial nitric oxide synthase. A commonly studied promoter SNP in NOS3 was associated with hypertension in one study [112], while a missense mutation was associated in adolescents (ages 14-

16) but not children (ages 8-10) [113], possibly suggesting the presence of a SNP-age interaction.

A meta-analysis of 53 NOS3 studies, however, did not support association with the promoter SNP and found association with the missense SNP in Asians only. Furthermore, while it did find evidence for association for a third variant, a 23 base-pair tandem repeat in intron 4, the authors also found evidence of publication bias, calling the result of this meta-analysis into question [114].

Several other vasoactive genes have been infrequently studied for association with BP, including

CYP2C8, CYP2C9, CYP2J2, and sEH, members of the epoxygenase pathway which produces arachidonic acid and vasoactive metabolites [115]. Some evidence for association of CYP2C8 [116,

110] and CYP2J2 has been reported [117], while no associations were found in any of these four genes by Dreisbach et al. [115]. With so few studies of these genes conducted, strong conclusions about their role in BP (or the lack thereof) are not yet warranted.

Inflammation

Inflammatory processes have received considerable attention for their involvement in metabolic syndrome [9, 118, 119], which includes hypertension, and these processes may have other connections to hypertension as well [120, 121]. Most studies have focused on biomarkers, however (e.g. [122]), while relatively few genetic association studies have been performed to investigate the relationship between inflammation genes and BP. The inflammation gene most commonly studied in connection with BP is TGFB1, which has been associated in several studies

[123-126]. Polymorphisms in IL6 have also been targeted, but no associations were detected [126].

Genes comprising the NFκB complex and its IKK regulators, core mediators of inflammation, have not been studied for association with BP, despite the apparent connection between BP and inflammation. Given the multi-faceted physiological relationship between inflammation and BP and relative dearth of association studies, more such studies are needed before reaching conclusions about the role of inflammation genes in architecture of BP is possible.

III. Genome-wide studies

The candidate gene studies discussed above have highlighted several major pathways and biological functions as important determinants of blood pressure. A strength of these studies is that statistically significant results are typically easy to interpret in terms of the biological context in which the associated polymorphisms are found. This focus on genes in pathways already suspected to be relevant to blood pressure, though, is also the primary weakness of these studies: their inability to detect novel BP pathways. This weakness is a major motivator behind two other study designs, genome wide linkage scans and genome wide association studies (GWLS/GWAS). These types of studies take an agnostic approach to gene discovery, querying variants distributed throughout the genome, searching for associations in pathways not previously known to regulate blood pressure. A major disadvantage of this approach is that the large number of variants tested

(as many as one million or more in genome-wide association studies, though usually only several hundred in linkage scans) requires a large multiple testing correction to help filter false positive associations. This correction makes the detection of small effects difficult and contingent on very large sample sizes. Testing all pairs of genes for interactions compounds this problem, and few genome-wide studies test for gene-gene interactions. As with candidate gene studies, rare variants are not typically investigated in genome-wide studies, as the widely used commercial SNP chips focus almost exclusively on common (minor allele frequency >5%) variants.

In some ways, both linkage scans and GWAS have followed a similar trajectory. Early genome-wide studies of both kinds met with little success, detecting few genome-wide significant variants and achieving little or no replication. Subsequent studies of both types, though, have begun to produce replicated genome-wide significant findings. Many of these studies have collaborated, forming consortia to increase sample size, while some have employed strategies such as testing for

epistasis and other interactions, pathway analysis, and targeting advantageous populations. The functional significance of their findings is, however, largely unclear. Linkage peaks tend to be broad and include many plausible candidate genes. Most of the variants detected in GWAS, on the other hand, are intronic, often in genes without an obvious connection to blood pressure, or else intergenic, where the potential mechanism of action is still less clear. Some of these variants have plausible mechanisms linking them to blood pressure regulation via known pathways, while the functional significance of many others remains to be elucidated. As a consequence, whether these studies will achieve the goal of detection of novel pathways affecting blood pressure also remains to be seen. Arguably the biggest result from these studies over the last decade has been the relative dearth of common variants with moderate main effects, with associated variants explaining less than 2% of the phenotypic variance. This result, the only major finding common to all genome- wide studies of blood pressure, implicates an undetermined combination of rare variants, common variants with small (~1 mm Hg or less) effects, and interactions in the genetic architecture of hypertension.

IIIa. Genome-wide linkage

Early GWLS

Over two dozen genome-wide linkage scans (GWLS) were reported between 1999 and

2006. While all but two of these detected at least one suggestive QTL, between them implicating nearly every chromosome, approximately half of these studies failed to report even one QTL reaching genome-wide significance. Furthermore, not one of these early QTL was replicated in other genome scans (reviewed in Binder [127]). For example, one of the largest early GWLS, the

British Genetics of Hypertension Study (BRIGHT, N= 3,599), initially reported just one genome- wide significant QTL in their primary analysis [128], and it was no longer present in an expanded sample (N=3,863) [129], while a new suggestive QTL (LOD = 2.5) was detected on chromosome

5. Early contributions from another major group, the National Heart Lung and Blood Institute’s

(NHLBI) Family Blood Pressure Program (FBPP), were also largely disappointing. A study based on HyperGEN, one of the four FBPP sub-studies, reported no QTL reaching a LOD score of 3 or greater, and just one locus with a LOD greater than 2 [130]. A preliminary meta-analysis of all four

FBPP networks, comprising 6245 relatives, failed to identify any QTL with LOD greater than 2

[131].

Follow up GWLS: interactions

Later GWLS were more fruitful, however, employing refined methods and larger samples and able to replicate other findings. These include several studies incorporating analysis of epistasis and other interactions. Bell et al. [132] analyzed a slightly bigger cohort from the BRIGHT study

(4284 subjects), testing for epistasis with a systematic two-dimensional scan. They detected several interactions with LOD greater than 4 including two involving the 5q13.1 locus reported by Munroe et al. [129], one with 11q22.1 (LOD=5.45) and one with 19q12 (LOD=5.12). Comparison of their

2D results with their 1D results revealed several loci involved in suggestive epistasis despite the absence of any main effect. Their simulations determined that the significance threshold for their two-dimensional scan is a LOD of 5.84, however, indicating that their strongest results fall just short of genome-wide significance [132].

Padmanabhan et al. [133] included medication response in interactions with genes in their analysis of the BRIGHT data, and detected a hypertension QTL on chromosome 2. This was one of

the earliest replicated loci, as it had been reported earlier by the FBPP [134]. Another study, which included over 3000 Caucasians and African Americans from HyperGEN, employed gene-age interactions and detected 15 loci replicated elsewhere in the literature as well as 11 novel loci

[135]. This study realized substantial increases in linkage evidence when using gene-age interactions, with numerous loci otherwise lacking strong evidence (LOD<0.5) demonstrating clear linkage (LOD>3) when interactions with age were included.

Follow up GWLS: specialized population

One GWLS (and several GWAS, see below) have made use of populations with advantageous qualities such as high genetic homogeneity or low exposure to environmental risk factors. Ciullo et al. [136] studied a single, extensive (N=2,180), multi-generational family with

173 individuals with essential hypertension. Linkage analysis using pedigree-breaking techniques detected three significant QTL for essential hypertension, two of which were already known, while the third has since been replicated [137].

Follow up GWLS: meta-analysis and others

Larger sample sizes have also enabled replication of previously reported QTL. Simino et al.

[137] performed a meta-analysis of over 13,000 subjects across all four FBPP networks and detected 5 QTL: These include one QTL from the meta-analysis of all subjects (DBP, 8q23) and 4

QTL specific to particular ethnicities (pulse pressure (PP), African Americans, 6p22; PP and SBP,

Caucasians, 21q21.1; PP and SBP, Caucasians, 21q21.3; PP, Mexican Americans, 20q13). Each enjoys some degree of support from independent GWLS [137-141]. The 8q23.1 mixed-ethnicity

QTL for DBP, for example, was previously reported as a hypertension QTL by Ciullo et al. [136], and also as moderately linked (LOD=1.74) to log-transformed DBP in a Nigerian population [139].

Other replicated QTL have emerged recently as even less well-powered studies have begun producing overlapping results. Chang et al. [142] detected a QTL reaching genome-wide significance on chromosome 1 in one FBPP network, GenNET, and replicated it in two others,

GENOA and HyperGEN. This locus had previously been implicated by three studies reporting suggestive, but not significant, evidence in humans [143-145]. In addition, the homologous region of the mouse genome had also been implicated [146]. Family-based association tests were performed in 9 candidate genes on chromosome 1 and significant associations were detected with either SBP or DBP in 3 genes with clear relationships to blood pressure: ATB1B, RGS5, and SELE.

Notably, each SNP influences 2-5 mm Hg, larger than the 0-1 mm Hg effects usually observed for

SNPs with minor allele frequency greater than 5% [142].

A chromosome 6 QTL has been well replicated but also lacks any obvious candidate genes.

The Veterans Administration Genetic Epidemiology Study (VAGES) performed genome-wide linkage with 385 markers on 1,089 subjects in 266 Mexican-American families [147]. Strong linkage (LOD = 3.6) with SBP was detected on chromosome 6q14, a result that provided replication of results reported earlier (LOD = 3.3) by the National Heart, Lung, and Blood Institute

[144]. Suggestive linkage (LOD = 2.5) at this locus was also reported for DBP by a study of Dutch families [148]. The 1-LOD region is approximately 30 Mb long and contains roughly 180 genes, of which the authors report that none are major candidates for blood pressure [147].

IIIb. Genome-wide association

With the early returns from the BRIGHT and FBPP studies giving disappointing results, attention turned to population and case/control association studies. It was hoped that with sufficient sample size and the relatively dense genotyping afforded by new SNP chips (initially 100k to 500k

SNPs, later 1M and more with imputed SNPs), a substantial portion of the genetic variance in blood pressure would be detected as common variants with moderate effects. These studies investigated the majority of common SNPs (minor allele frequency greater than 5%) but for the most part ignored rarer SNPs and structural variation. While this approach proved successful for certain more homogenous conditions such as Crohn's disease, early GWAS for hypertension were often unable to detect any variants meeting genome-wide significance thresholds, and those that were identified were not replicated in other genome-wide association studies. Later GWAS employing meta- analysis of massive sample sizes were able to identify a handful of variants, each of which have small effects (roughly 1 mm Hg or less) and which collectively explain less than 3% of the phenotypic variability [149]. As with genome-wide linkage studies, the primary conclusion available from these studies is that common variants do not explain much of the genetic variance, and the functional significance of most of those variants which have been detected has yet to be determined.

Early GWAS

The first attempt to evaluate the genetic basis of hypertension using GWAS was the

Wellcome Trust Case Control Consortium (WTCCC). This study simultaneously investigated seven distinct diseases using 2,000 cases each. A group of 3,000 common controls was used for each analysis but, importantly, these controls were drawn from the general population without regard for their disease status for any of the seven diseases. As hypertension is estimated to affect

roughly one in four US adults [150], it is likely that several hundred hypertension cases were present in this common control population, reducing power to detect hypertension associated alleles. This fact may partly explain the absence of hypertension findings while Crohn's disease, which affects less than 1% of the US population [151] produced 9 associated variants. At a genome-wide significance level of 5e-7, no hypertension variants were detected among the 500,000

SNPs evaluated in the WTCCC study [152]. Furthermore, only one of the six most significant

SNPs was associated with blood pressure as a continuous trait in a replication test by the FBPP

[153].

A separate genome-wide analysis by the FBPP employed 371 microsatellite markers in

13,524 subjects across four ethnic groups. This analysis looked for associations with SBP, DBP, and hypertension, and detected 71 loci associated with at least one trait in at least one ethnic group

[154]. These associations were based on a significance level of 0.01 without correction for multiple testing, however, and the authors regard their analysis as hypothesis generating rather than hypothesis testing.

The Framingham Heart Study, like the FBPP, analyzed the continuous phenotypes SBP an

DBP rather than hypertension. They measured these two phenotypes at two different time points and took several measures of arterial stiffness at the second time point (pulse wave velocity, pressure wave amplitude, mean arterial pressure, N = 644). With 100,000 SNPs tested, however, they found twelve associations (with any phenotype) below the 10e-5 significance level but did not detect any associations deemed genome-wide significant [155].

Follow-up GWAS: specialized populations

Sabatti et al. [156] studied a birth cohort of 4,763 individuals born in 1966 in one of two

Finnish provinces. This sample offered an unusually high degree of homogeneity in terms of genetic background, age, and environmental exposures, and allowed the study of life history factors including gestational age and the use of oral contraceptives. Multiple genome-wide significant associations were reported for various metabolic phenotypes but no significant associations were detected for either SBP or DBP. In an exploratory follow-up analysis, however, associations significant at the 5e-7 level were reported for three SNP-sex interactions, each of which had an effect on DBP in the opposite direction between the two sexes. Because of the large number of tests performed and the low power to detect interactions corrected for multiple testing, though, the authors treat these results as hypothesis generating.

Wang et al. [157] also sought to reduce both genetic and lifestyle heterogeneity in their study sample and investigated 542 Amish subjects. Their analysis included 79,447 SNPs and focused on SBP and DBP. They reported one SNP with genome-wide significance on 9p21.3. The functional importance of this locus is unclear as it is located 900 kb away from the nearest known genes, but has also been repeatedly associated with cardiovascular disease [158]. On the other hand, a cluster of SNPs in the STK39 gene reached suggestive levels of significance (8.9e-6 to 9.1 e-5) for association with SBP in their original sample, and also showed evidence of association in two other Amish samples. Furthermore, STK39 SNPs were also associated in subjects from the

Diabetes Genetics Initiative as well as the Framingham Heart Study. Two other samples, a founder population (the Hutterites) and GenNET, showed trends with similar effect sizes and directions but were not significant. A meta-analysis of all six samples revealed an association with rs6749447 at p

= 1.6e-7. In addition to the replication across samples, this study is unique among GWAS in that the authors carried out functional validation on their most convincing result, the association with

STK39. This serine/threonine kinase interacts with WNK kinases and both the Na-Cl and Na-K-2Cl cotransporters [159]. The authors demonstrated that STK39 is expressed in the distal nephron, and that its expression level is influenced by a highly conserved intronic variant [157].

Follow-up GWAS: pathways and epistasis

The results emerging from these GWAS made it clear that any common variants associated with blood pressure have small individual effects (~ 1 mm Hg) on SBP and DBP, making the replication of associations with individual SNPs difficult. One approach to mitigate this problem was used by Adeyemo et. al. [138] in their GWAS in an African American population of 1,017 subjects. In addition to trying to replicate particular SNPs, they assessed the extent to which their top scoring SNPs (p < 1e-5) were clustered in GeneGo categories. They found evidence of enrichment for associated SNPs in several pathways and processes, and these included some with a straight forward role in blood pressure such as PIP3 signaling in cardiac myocytes, potassium transport, and blood vessel morphogenesis, and others (e.g synaptic vessel exocytosis) whose connection is less obvious. Moreover, although there is an absence of replicating SNPs between their study and the WTCCC, the authors note that several pathways were found common to both their pathway analysis of their data and that performed on the WTCCC data [160], and argue that analysis at the level of networks and pathways may be more robust than at the level of individual

SNPs.

Another approach, as was done in the Bell et al. [132] and Shi et al. [135] linkage studies, is to account for potential interactions. A GWAS of Han Chinese subjects examined young-onset hypertension (YOH) in 1,008 case-control pairs [161]. A false-discovery rate (FDR) approach was used to control the type I error rate, and no associations were detected with SNP main effects or

haplotypes. One pair of SNPs participating in an epistatic interaction, however, was significantly associated with YOH. This included a SNP in the IMPG1 gene on 6q14, the same locus implicated in the VAGES study [147], and an intergenic SNP on chromosome 20 [161].

Follow-up GWAS: meta-analysis

The primary response to the realization that individual genes have very small effects has been to increase sample size. In 2009, two closely related papers were published describing the meta-analyses of multiple smaller GWAS studies conducted by two large consortia, CHARGE and

GlobalBPgen [162, 163]. CHARGE (Cohorts for Heart and Aging Research in Genome

Epidemeology) consisted of 6 groups (including the Framingham Heart Study and the

Athersclerosis Risk In Communities (ARIC) groups) totaling over 29,000 subjects. GlobalBPgen included over 34,000 subjects from 13 cohorts and controls from 4 case-control studies. The discovery sample from each study was used as a replication sample in the other study. In addition,

GlobalBPgen genotyped more than 71,000 individuals of European ancestry and nearly 13,000 individuals of Indian ancestry for further replication of particular SNPs.

In spite of these very large samples, there were relatively few variants detected with genome-wide significance, and little consistency between the two studies. Eleven SNPs were reported by CHARGE as genome-wide significant after replication in GlobalBPgen; they fall in or near the genes CYP17A1, PLEKHA7, ATP2B1, and SH2B3 for SBP; ULK4, CACNB2, ATP2B1,

TBX3/TBX5, and CSK/ULK3 for DBP, and ATP2B1 for hypertension [162]. GlobalBPgen reported eight SNPs as genome-wide significant in their combined discovery and replication data sets, of which two were in genes reported by CHARGE: CYP17A1 for SBP and SH2B3 for DBP; the other

SNPS are near MTHFR and PLCD3 for SBP, and FGF5, c10orf107, CYP1A1, and ZNF652 for

DBP [163]. These were significant after Bonferonni correction in the combined datasets, however it is worth nothing that of the ten strongest results for SBP in CHARGE alone, four did not reach even nominal significance (p<0.05) in GlobalBPgen alone, while six of the ten strongest results for

SBP in GlobalBPgen were not nominally significant in CHARGE [31].

A recent GWAS of African Americans replicated associations at three of these loci: SH2B3,

TBX3-TBX5, and CSK-ULK3 [164]. This study, which reported one genome-wide significant SNP each for DBP and SBP, failed to replicate any of the other SNPs reported by

CHARGE/GlobalBPgen. The authors also sought replication of their top results in five other

African American cohorts but were unable to replicate any associations. None of the SNPs previously reported to be associated with blood pressure in people of African ancestry could be replicated either [164].

As with other genome-wide studies, the functional significance of the loci implicated by

CHARGE/GlobalBPgen remains largely undetermined. In addition to the fact that most of the associated SNPs are intronic and have not been subject to functional studies, the relatively large LD blocks in Caucasian populations means that they are also correlated with other SNPs and genes. In some cases more than one such gene is a promising candidate. The 1p36 locus whose marker is closest to the MTHFR gene, for example, also contains natriuretic peptides A and B (NPPA,

NPPB), the angiotensin II receptor-associated protein (AGTRAP), which inhibits angiotensin signaling, and the neuronally-expressed calcium channel CLCN6 [163]. The 12q24 locus, on the other hand, contains over a dozen genes no one of which is a particularly obvious candidate. Of course, part of the promise of genome-wide studies is the discovery of novel pathways not known to be involved in blood pressure and/or the confirmation of pathways with putative, uncertain involvement. The authors note that a missense SNP in SH2B3 is perfectly correlated with the

marker SNP at this locus [163], an observation which may support the emerging proposal that the inflammatory response of the immune system (in which SH2B3 plays a role) is a contributor to blood pressure regulation [154, 165]. Further work will be necessary to determine the mechanisms linking the associations reported by CHARGE and GlobalBPgen to blood pressure.

IV. Discussion

Summary of findings to date

Decades of research has shown us much about the genetic and other factors affecting blood pressure, implicating polymorphisms in several signaling pathways as well as environmental factors such as age, sex, and diet. It’s clear that renal sodium reabsorption is of paramount importance to long term blood pressure regulation, with polymorphisms in the ion channels, hormones, receptors, enzymes, and regulatory proteins involved in this process all playing a role in determining long term blood pressure. Polymorphisms in several pathways affecting vascular tone are also believed to be significant contributors. Inflammatory processes have come under investigation for their role as well, and BP association studies of polymorphisms in inflammation- related genes are becoming more frequent.

In addition to the association and/or linkage of specific pathways and their polymorphisms, several general principles have emerged from the large body of research. A major finding from the recent wave of GWAS studies is that common polymorphisms with moderate to large individual effects on blood pressure play very little role; if there are many common variants with individual effects, they must have very small effect sizes. For this and many other reasons, another general observation is that individual polymorphisms tend to be inconsistently associated with BP across different study populations.

An important related finding, however, is that, while specific polymorphisms are not consistently implicated, the pathways that contain them are more robustly associated. This has been observed among both candidate gene [106, 107] and genome-wide studies [138, 152].

As noted throughout previous sections, there is also considerable support for the role of epistasis and other interactions in the architecture of blood pressure regulation. Much of this evidence has come from candidate gene studies, which can exhaustively analyze interactions among targeted variants without the stringent multiple-testing correction used in GWAS [75, 77,

86, 91-93]. Even some genome-wide studies have reported some evidence of interactions linked or associated with BP phenotypes [132, 135, 161], and used interactions to narrow QTL [132] or dramatically increase the linkage evidence for particular loci [135].

Another aspect of the genetic architecture of blood pressure has recently emerged as important: the role of rare variants. Theoretical modeling based on the interplay between mutations, genetic drift, and selection has been used to support the idea that most variance of complex traits is due to low frequency susceptibility variants [30]. More recently, empirical data on the contribution of rare variants to blood pressure has supported this position [47].

Finally, phenotypic heterogeneity may play a major role in the limited and inconsistent nature of BP association findings. Under the controlled conditions made possible by the use of animal models, a clear phenotypic distinction is observed between salt-sensitive hypertension and spontaneous hypertension [166]. Neurogenic elevation of BP may represent a distinct phenotype with its own genetic architecture, or it may overlap with either of these forms of hypertension [166,

120]; other distinct etiologies such as lead-induced hypertension have been proposed as well [121].

The vast majority of human studies identifies hypertensive cases entirely on the basis of their SBP and DBP values (or assess BP directly) and fails to distinguish these populations.

Future Directions

Virtually all studies performed to detect linkage or association between genetic variants and blood pressure are either candidate gene studies, focusing on just one or a few genes and variants, or genome-wide studies, testing markers distributed throughout the genome. While each of these approaches has its merits and has made substantial contributions to our understanding of the etiology of hypertension, the findings discussed here suggest that new approaches may be required as well. The multiple-testing correction associated with testing one million or more variants in a

GWAS makes the detection of genes with small effect very difficult. This problem is only exacerbated when systematically examining the role of interactions; one million individual variants are involved in half a trillion variant-variant interactions, to say nothing of higher order interactions. Candidate gene studies, on the other hand, by definition have only one or a few variants to test and thus are limited to little if any interaction testing.

Another approach is to test an intermediate set of genes and variants. One type of study might seek to assess the role of a particular pathway, and test 10-20 genes with a few dozen variants. All gene-gene interactions could be tested without requiring a multiple testing correction nearly as strong as that commonly used in GWAS. An attempt to be more comprehensive might include several pathways suspected to be involved, targeting several dozen genes (50-100) containing a few hundred polymorphisms. With this many variants, considerable interaction testing could be performed while still performing many fewer comparisons than a typical GWAS; such an approach is described in the work that follows.

Beyond the scope of the present study, sequencing of this many genes could target both exons and introns and allow the assessment of the role of rare variants while still costing

significantly less than exome sequencing, to say nothing of whole genome sequencing. With or without a focus on pathways, the role of rare variants should continue to be explored. Finally, future studies that assess crucial phenotypic characteristics such as salt-sensitivity and the degree of sympathetic nervous activation may be able to better enrich case populations for particular etiologies and thereby enhance power to detect the genetic basis of the corresponding forms of hypertension.

Chapter 2: Genome-Wide Associations with BP in FHS

I. Introduction

The first aim is to conduct a GWAS for SBP and DBP using the Framingham Heart Study

(FHS) data. These data have been extensively analyzed since the study’s inception in 1948, most recently including a longitudinal analysis [167] and as part of the CHARGE consortium [162].

While these and other existing studies give an indication of what association results to expect, differences in the particular subjects and phenotypes analyzed, as well as in the analytical methods and software employed, make a direct comparison between the proposed analyses of aims 2 and 3 and the published GWAS findings problematic. The primary purpose of the present analysis, therefore, is to provide a baseline against which the results of subsequent analyses may be compared.

The Framingham Heart Study

The Framingham Heart Study was founded in 1948 to identify the factors that contribute to the development of cardiovascular disease (CVD) and related traits. Since its inception, FHS has made considerable contributions to our understanding of the relationship between medical outcomes including coronary disease, stroke, heart failure, and peripheral artery disease and genetic, physiological, and behavioral risk factors such as hypertension, dyslipidemia, glucose intolerance, atrial fibrillation, cigarette smoking, and alcohol consumption for example [168], reviewed in [169].

Subjects and visits

FHS was designed as a longitudinal study, with members of the initial recruitment group

(referred to as the ‘Original Cohort’ or G0: 5,209 men and women from Framingham, MA) returning every 2 years for up to 27 visits. A second cohort was recruited in 1971 consisting of

5,124 offspring (biological or adopted) of the original cohort, and their spouses. This ‘Offspring

Cohort’ (or G1) returned for 6 additional visits spaced 4 years apart beginning in 1979. A final cohort was recruited in 2002 (‘Generation 3’ or G3) consisting of a single visit by 4,095 children of the G1 cohort (grandchildren of the G0 cohort). All cohorts are composed principally of Caucasian subjects. These three cohorts are not strict generations because of the presence of parent-offspring pairs in the G0 cohort, as well as its wide age range (30-62).

Phenotypes and covariates

All three generations underwent extensive physical examination, laboratory work, and lifestyle interviews. This testing included multiple measurements of SBP and DBP as well as other metabolic and cardiovascular phenotypes (total cholesterol, HDL, LDL, triglycerides, glucose). In addition, the lifestyle interviews collected information pertaining to several topics including diet, alcohol, smoking, physical activity, education, and medication use. In general, the metabolic phenotypes were collected for the majority of cohorts and visits, while information on the lifestyle covariates is available for a more limited subset of cohorts and visits.

Genotypes

DNA was extracted from blood samples and immortalized cell lines from all three cohorts.

The Affymetrix 100k GeneChip was used with 1,345 members of the Original and Offspring cohorts. Over 9,300 subjects from all three cohorts were genotyped at 400 microsatellite linkage

markers as well as using the Affymetrix 500k array and the Affymetrix 50k supplemental array.

Imputation of approximately 2 million additional SNPs was also performed (using MACH) for

8,477 subjects. Of the added SNPs, most (1,835,103) were common, with minor allele frequency of at least 5%; an additional 327,367 were rare. The model used for imputation was based on a subset of unrelated subjects. Candidate gene SNPs have also been genotyped in various subsets of study subjects.

Our Framingham Dataset

Only a subset of the entire FHS dataset will be analyzed by our group.

Subjects and visits

For the G0 cohort, 7 clinic visits were selected that most closely matched the dates of the 7 visits made by G1 subjects. The primary analyses, cross-sectional in nature, will focus on ‘visit 7’ data: the G3 data and the most recent visit from G0 and G1. Longitudinal analysis will make use of data from some or all of the 7 visits depending on the specific analytical method used. A total of

9,168 genotyped subjects are represented across these 7 visits, of whom 8,477 had imputations performed. Aim 1 focuses on the 6,890 subjects with data on genotypes, SBP, DBP, and the three

‘basic’ covariates: age, sex, and body-mass index (BMI). Subsequent analyses involving additional covariates will use a subset of these subjects. These and other data on the subjects are summarized in table 3.

Race

The majority of the 6,890 subjects are Caucasian, while 63 are not. However, race data for all G0 subjects and approximately 20% of G1 subjects was not collected and these subjects are assumed to be Caucasian.

Phenotypes and covariates

The thesis will focus on SBP and DBP as the outcome variables. The fit of each of these variables to the assumption of normality was evaluated. Based on the low values of skewness and kurtosis found in the distribution of the raw forms of both SBP and DBP (for visit 7 only, see table

4), no transformation was applied to these variables. These distributions were also examined for possible outliers and none were deemed worthy of removal. No single criterion was used, but whether a data point was 4 or more standard deviations away from the mean and the distance to its nearest neighbor were the primary considerations. Table 4 presents values of skewness and kurtosis used to assess normality for both SBP and DBP in each sex. Figures 1A and 1B present boxplots of

SBP and DBP for visit 7 subjects with GWAS data.

Three ‘basic’ covariates will be used in all aims: age, sex and BMI. All subjects with age less than 20 or greater than 80 at visit 7 were excluded. The distributions of age and BMI among

FHS subjects from visit 7 with GWAS data are presented in figures 1C and 1D.

Genotypes

The thesis will use the Affymetrix 500k GeneChip data as the primary analysis data. For signals of interest warranting fine mapping, imputed data will be used. I have calculated a quality measure (called r^2) for each imputed SNP as the ratio of the observed variance of the dosage calls to that expected under Hardy-Weinberg (2*p*[1-p]). Only those SNPs with r^2 > 0.3 will be included in analysis of imputations. No additional SNP filtering based on minor allele frequency, deviation from Hardy-Weinberg equilibrium or other factors was employed.

II. Methods

GWAS for SBP and DBP were conducted using the R packages GenABEL and ProbABEL.

The first stage generates residuals for SBP and DBP using GenABEL to run a polygenic model with several covariates but no SNP effects. For all such models the covariates included age, age2, sex, and hypertension medication use. In addition, these polygenic models were run both with BMI

(aim 1a for SBP, 1b for DBP) as a covariate and without BMI (aim 1c for SBP, 1d for DBP). This approach allows, in aims 1b and 1d, for the identification of SNPs influencing BP via an influence on BMI (or affecting both independently in a pleiotropic manner), while adjusting for these effects to improve power to detect other effects in aims 1a and 1c. Thus the models evaluated by

GenABEL are of the form:

y     * Age   * Age 2   *Sex   * BMI   * Polygene  e 0 1 2 3 4 5 ij

For aims 1a and 1b, while for aims 1c and 1d they are of the form:

y     * Age   * Age 2   *Sex   * Polygene  e 0 1 2 3 4 ij

Running these polygenic models produces a heritability estimate as well as a residual trait value. The second stage of the GWAS analyzes this residual as the outcome variable using

ProbABEL. The ProbABEL model in aim 1 contains only one independent variable, the main effect of the SNP, on which is regressed the residual trait value.

III. Results

Figures 2A and 2C presents the results of the aims 1a and 1c GWAS for SBP, the first of which included BMI as a covariate while the second did not. The two analyses produced very similar results. No loci contained robust signals reaching genome-wide significance in either case.

A single SNP on chromosome 9 with a p-value less than 1e-10 was too rare (only 8 minor alleles in

8 heterozygous individuals) to meaningfully estimate the effect of the variant.

Several loci from each analysis contained clusters of SNPs reaching suggestive significance levels. A cluster on chromosome 1 reached a p-value < 1e-6 both with and without BMI in the model, as did a dense cluster on chromosome 14. A third cluster, on chromosome 12, also approached this significance level in both analyses.

A handful of loci demonstrated stronger associations (though still not significant at the genome-wide level) when BMI was left out of the model than when it was included. This includes peaks located on chromosomes 3, 9, 11, and 13. The reverse occurred in one weak cluster on chromosome 22, whose most significant p-value increased nearly one full log unit (that is, became less significant) from 3.3e-6 to 2.2e-5 with the removal of BMI from the model.

As with the SBP analyses, neither the DBP GWAS with BMI nor that without produced any results reaching genome-wide significance (Figures 2B and 2D). Clusters on chromosomes 1, 2, and 4 contain SNPs with p-values less than 10-6 both with and without BMI. A cluster on chromosome approaches this suggestive threshold (p=2.7e-5) when BMI is excluded from the model, but not when BMI is included (p=9.1e-5).

IV. Discussion

The Framingham Heart Study data have been previously analyzed using the Affymetrix

100k GeneChip instead of the 500k chip [155] and no significant associations were reported for either SBP or DBP. The absence of significant associations in the present GWAS was, therefore, expected a priori. Why, then, devote an entire thesis to a project with an expectedly null outcome?

Our hope was that interactions and multi-marker approaches will help advance discovery

One aspect of these results not previously reported on is the effect of including BMI in the analysis model. For most SNPs the presence or absence of BMI as a covariate has very little effect on the p-value. Several loci showed stronger association in the presence of BMI, possibly demonstrating the importance of including variables with a strong effect on BP in order to reduce the amount of un-modeled variance that can weaken other associations. One locus was more strongly associated (by an order of magnitude) in the absence of BMI and is therefore a candidate to influence BP via an effect on BMI. In both cases, however, the lack of significance of the associations both with and without BMI makes it difficult to distinguish these changes in significance from chance effects.

These results provide the baseline against which the results of the subsequent analyses will be compared.

Chapter 3: SNP-SNP interactions with BP in FHS

I. Introduction

In aim 2 we pursued two goals: 1) to focus on two biochemical pathways: renal ion balance and inflammation, and 2) to explore the role of SNP interactions in blood pressure, confining the search to SNPs in these pathways (this chapter focuses on SNP-SNP interactions, results of other

SNP interactions will be discussed in chapter 4). Two primary factors motivated the focus on pathways. The first is the hypothesis that different subsets of the SNPs in a pathway may show association in different populations, such that the pathway as a whole is associated more consistently than are its individual SNPs. Numerous causes contribute to the inconsistency with which a particular SNP is associated with BP, including different allele frequencies across populations, different LD strength between marker and causal loci, and different allele frequencies or exposure levels at other SNPs or environmental covariates with which the SNP interacts. For a particular causal SNP, then, it may be different marker SNPs in different populations that capture its association. In the case of an interaction between SNPs in two different genes, these factors could result in one of the two genes showing a main effect in some populations while the other gene shows such an effect in other populations. We hypothesize, however, that in a pathway with functionally relevant variation, some subset of SNPs and genes will demonstrate BP associations, even if the particular SNPs and genes vary across populations.

The second motivation for a focus on pathways is the gain in statistical power achieved relative to GWAS. While GWAS have the potential to reveal associations in previously unsuspected genes and pathways, this potential comes at the cost of the severe multiple testing burden associated with performing millions of tests. Limiting the SNPs analyzed to those from a

particular pathway obviously can only implicate that pathway, but this more biologically focused investigation allows a significance threshold that is several orders of magnitude more relaxed than that typically used in GWAS. For SNP-SNP interactions, the number of possible tests increases rapidly as more SNPs are included, and for a few hundred SNPs the number of interaction tests approaches the number of main effects analyzed in GWAS. Still, the resulting significance threshold is drastically reduced compared to what would be required were exhaustive interaction testing performed in a GWAS context, which may be one reason why very few investigators have even attempted to analyze the full set of SNP-SNP interactions possible in a GWAS. To date no studies investigating SNP-SNP interactions in a genome-wide setting have achieved experiment- wise significance, and their authors have generally reported their results as hypothesis-generating rather than hypothesis testing [132, 161].

The second goal for aim 2 is the exploration of SNP-SNP interactions within and between the two targeted pathways. This goal was chosen on the basis of both theoretical considerations and empirical evidence suggesting a possible role for interactions in the genetic architecture of complex traits in general and BP specifically. The theoretical motivation stems from the general observation that genes work together and regulate each other, and do so in a complex biochemical context that influences their actions. Moreover, it was not theoretical justifications that led to the predominant focus on main effects in the first place; rather it was pragmatic considerations such as 1) the cost of genotyping the additional loci necessary to evaluate interactions, 2) the increased computational time required for the (dramatically) increased number of tests performed, 3) the lack of well- established statistical models for evaluating interactions, and 4) the decrease in statistical power associated with performing many more tests. While the 4th point remains important (and as noted is a major reason for the focus on pathways), progress is being made on the 3rd [170, 171]; meanwhile

advances in computer technology and high throughput genotyping and sequencing have made the first two points much less burdensome than they used to be.

Thus no theoretical arguments have been advanced denying that interactions might be an important contributor to phenotypic variance; in addition, some empirical evidence has been reported demonstrating interactions associated with BP in both humans and animal models.

Furthermore, this is true in spite of the fact that only a tiny fraction of the BP genetics literature has examined interactions. Much of this evidence has come from candidate gene studies, whose interaction tests reflect targeted hypotheses and employ manageable significance levels. Such studies have shown intragenic interactions leading to Mendelian hypertension disorders [12] and association with BP or essential hypertension [77]. Numerous studies have reported intergenic interactions associated with BP between SNPs in AGT and ACE [76], ADD1 and ADD2 [91],

ADD1 and ACE [92], and ADD1 and CYP11B2 [93]. Several studies in transgenic mice have provided evidence of interactions between dopamine receptors and various components of the renin-angiotensin-aldosterone system (RAAS) [172-174]. Candidate gene studies have also provided many examples of SNP-environment interactions. The most prevalent examples involve known major risk factors for hypertension: race, sex, and body-mass index (BMI). Wang et. al. reported an association found only in African-American men between BP and a variant in AGT

[75], while an AGTR1 association was reported in men only [86]. Numerous studies have found

SNP-BMI interactions associated with BP, including in CYP11B2 [175], ADD1 [176], ADRB2

[177], and others [178-180].

Genome-wide studies, both linkage scans and association studies, have also provided some evidence of interactions associated with BP. Because of the large number of tests involved, few genome-wide studies have exhaustively tested SNP-SNP interactions, and those that have tested

exhaustively have failed to reach experiment-wise significance and have regarded the results as hypothesis-generating rather than hypothesis-testing [132, 161]. Various environmental factors, however, have been shown to interact with SNPs to influence BP. Interactions with age revealed 11 novel linkage peaks whose main effects showed no linkage [135], while GWAS have reported potential interactions between SNPs and sex, use of oral contraceptives, and pre-term birth [156].

Another GWAS found increased signal when considering interactions between SNPs and phenotypic group (latent classes identified using a clustering algorithm called ‘Self Organizing

Maps’) [181].

There is, then, reason to suspect that incorporating interactions can aid in gene discovery, but this hypothesis needs further evaluation and much of the BP genetics literature continues to focus on main effects. The overarching goal of aim 2 was to evaluate the extent to which interactions can aid in identifying novel associations. Interactions were analyzed within the renal pathway, within the inflammation pathway, and across the two pathways, with the first group of analyses targeting a different question than the latter two groups. The involvement in blood pressure regulation of the majority of the targeted renal ion balance genes is taken as given on the basis of knowledge of their physiological activity (even though most failed to show an association in numerous studies). The question explored through analysis of interactions amongst renal SNPs, then, is: among genes that are involved in BP regulation, are SNP-SNP interactions an important contribution to that involvement? A positive answer to this question, particularly a detectable interaction effect in the absence of main effects from the same gene(s), would motivate the search for SNP-SNP interactions in other pathways whose involvement is suspected but not yet conclusively demonstrated. A negative answer would suggest that such an approach is less likely to be fruitful. The within-inflammation and cross pathway renal-inflammation analyses seek to

address this question for one such case, the inflammation pathway: Can interaction effects be used to implicate previously unassociated inflammation genes?

II. Methods

Phenotype and genotype data

The same subjects and phenotype data from aim 1 were analyzed in aim 2. The genotype data comprised all 818 genotyped SNPs found within 10 kb of 64 genes across the two pathways.

These genes were chosen on the basis of their physiological role as well as previous associations with BP and for each pathway belonged to one of several sub-categories as detailed in tables 5A and 5B. Note that the total of all entries in the #SNPs column is 821: 3 SNPs are found within 10 kb of both TNFRSF1A and SCNN1A and were analyzed as both ‘renal’ and ‘inflammation’ snps.

Pathways

Renal Na+ reabsorption into the blood stream, by creating osmotic pressure that leads to increases in blood volume, is a primary determinant of long-term BP [182]. The genes chosen for this pathway (see table 5A) all either form channels that directly mediate the flow of Na+ and several related ions or act in part by regulating these channels. In addition to their clear physiological connection to blood pressure, all four categories of genes targeted for the renal ion balance pathway have demonstrated associations with BP or hypertension: 1) The renin- angiotensin-aldosterone system (RAAS) contains several enzymes (CYP11B1, CYP11B2,

CYP17A1, HSD11B1, HSD11B2) responsible for synthesizing aldosterone and other hormones which have been conclusively linked to Mendelian hyper- or hypotension syndromes. The other

RAAS genes, mostly involved in angiotensin secretion (REN, AGT, ACE, AGTR1, MR), have all been associated with BP (reviewed in [10]). 2) The downstream effectors of RAAS signaling are ion channels and transporters that directly enable and couple the filtering and reasborption of Na+,

K+, and Cl-. The majority, including all three subunits of the epithelial sodium channel (SCNN1A,

SCNN1B, SCNN1G), a Na+-Cl- cotransporter (SCL12A3), a Na+-K+-2Cl- cotransporter (SLC12A1), a K+ channel (KCNJ1), and a Cl- channel (CLCNKB) are associated with Mendelian disorders leading to hypertension or hypotension [10], while the other two, subunits of the Na+-K+-ATPase

(ATP1A1,ATP1B1), have been associated with BP [62, 142, 183]. 3) Dopamine signaling decreases

Na+ reabsorption in the proximal tubule. The targeted genes include three dopamine receptor subunits (DRD1, DRD2, DRD3) as well as adrenoreceptor subunits (ADRA1A, ADRB2), as dopamine activates these receptors as well at high enough concentrations. Of these, all but DRD2 have been previously associated with BP [10]. A modulator of dopamine receptor activity (GRK4) has been implicated in BP [184, 185], as have two genes involved in systemic dopamine synthesis

(COMT, DBH) [107, 186, 187], while a gene involved in renal dopamine synthesis (DDC) has not received prior attention in connection with BP. 4) A handful of other regulators of ion channels were also targeted, including two genes associated with Mendelian hypertension disorders (WNK1,

WNK4) [28] that influence the activity of the Na+-Cl- cotranspoter. Adducins can activate the

Na+/K+ pump, and two subunits have been previously associated with BP (ADD1, ADD2) while a third (ADD3) hasn’t [91, 92].

The physiological relationship between renal ion balance and blood pressure is well established. In contrast, the interactions between inflammation and blood pressure are less well understood. Hypertension is sometimes viewed as one of the contributing causes to inflammatory diseases such as atherosclerosis [188]. Others have presented evidence that inflammation can

contribute to elevated blood pressure in various contexts including spontaneous hypertension in rats

[118], lead-induced hypertension in rats [121], and in a proposed model of neurogenic hypertension

[120]. Recent reviews have emphasized the multi-faceted role inflammation may play not just in hypertension but in other components of metabolic syndrome such as insulin resistance and obesity

[9], and the bi-directional role of inflammation in mediating communication between the cardiovascular system and the kidney [119].

Despite these links between hypertension and inflammation, very few studies have looked for associations between BP and polymorphisms in inflammation genes. Three of the six categories of genes in the targeted inflammation pathway (see table 5B) contain no genes previously associated with BP. 1) A core component of this pathway is the NFκB complex. These are five genes (NFKB1, NFKB2, REL, RELA, RELB) which regulate transcription to mediate a variety of stress and immune responses including inflammation, and do so in a finely regulated, tissue- specific manner [189, 190]. 2) Part of their regulation is accomplished by a group of NFκB inhibitors (the IKK complex, with SNPs analyzed here from the genes: IKBKB, IKBKE, CHUK) which are themselves inhibited by 3) another set of genes (the IKK inhibitors: NFKBIA, NFKBIB,

NFKBIE, NFKBIZ), leading to activation of the NFkB complex. None of these 12 genes have been associated with BP or even targeted in BP studies. Two other major components of the targeted inflammation pathway are a group of 4) pro-inflammatory cytokines, and 5) a group of adipokines, a sub-class of cytokines secreted in adipose tissue. The majority of these genes have also not been studied in connection with BP at all, while a few others have been included in a small number of studies. Among the cytokines, CCL2 has been associated with BP [191] while both CCL2 [192] and

TGFB1 [125] have been associated with hypertension. Linkage with BP was detected and replicated at a QTL containing TNFRSF1B [193, 194], but no association was found there [195].

Among the adipokines, BP associations have been reported for both a common [196, 197] and a rare [198] LEP polymorphism as well as one for LEPR [199]. Some evidence for association with

BP has also been reported for RETN [200] and ADIPOR1 [201], while ADIPOR2 and ADIPOQ have not been associated. The final category is 6) a group of four other genes. Two are cellular adhesion genes (ICAM1, VCAM1) that haven’t been associated with BP. EMILIN1 is a regulator of

TGFB1 and has been associated with hypertension both independently [202, 203] and as part of a gene-age interaction [204]. Finally, the vasodilator NOS3 has been investigated several times for association with BP with a mix of positive [113, 112, 205] and negative [206, 207] results.

Analysis

Analysis of interactions for the complete set of SNP-SNP pairs drawn from this set of 818

SNPs was performed using the ABEL package. As before, this two-step procedure included an initial covariate adjustment for age, age squared, sex, use of hypertension medications, and familial relatedness, and was run both with and without BMI as a covariate. The residuals from this regression were then themselves regressed on both SNP main effects as well as their interaction:

y  0  1 *SNP1 2 *SNP2  3 *SNP1*SNP2  eij

Because the ABEL package requires non-missing data for the SNP designated as the interacting covariate, each interaction test could be performed on two slightly varying sets of subjects. For the within-renal and within-inflammation analyses, each interaction was evaluated both ways, while for the cross-pathway analysis all inflammation SNPs were designated as the interacting covariates and thus determined which subjects were analyzed.

III. Results

Figures 3A through 3H show the SNP main effects for both pathways (renal, inflammation), both phenotypes (sbp, dbp), and both sets of covariates (with and without BMI). The dashed line represents pathway-wide significance (-logP = 4.05 for 573 renal SNPs and 3.68 for 248 inflammation SNPs).

Tables 6A through 6D show the top SNP-SNP interactions for both phenotypes (SBP, DBP) and both sets of covariates (with and without BMI), listing the rs# and gene for each SNP in the interaction, the –log10(p-value) for the main effect of each SNP, and the –log10(p-value) for the 1 d.f. test of the interaction term. Each table lists renal-renal interactions followed by inflammation- inflammation and then renal-inflammation. Only interactions for which both SNPs have a minor allele frequency of at least 1% and the product of their minor allele frequencies is at least 0.5% are shown. Genotypic means are plotted for the interactions in boldface (figures 4A-4M for SBP and

5A-5C for DBP).

Tables 7A-7H show the top SNP-SNP interactions by q-value (False Disovery Rate, FDR) for both phenotypes, pathways, and sets of covariates.

IV. Discussion

For each phenotype, a total of 336,610 SNP-SNP interaction tests were performed within and between the two pathways. On the assumption that each test is independent of the others, the resulting Bonferonni corrected α=1.5e-7 (-log[α] = 6.82); no single SNP-SNP interaction reached this level of significance. A few points should be kept in mind, however, when assessing the extent to which these analyses contribute evidence of association between BP and the queried genes. One, the assumption of independence is weakened by the LD between SNPs within genes, and the total

number of independent tests performed is lower as a result. This is true in a GWAS context as well, but because the linkage is predominantly local its effect is presumed to be small and the assumption of independence is treated as largely valid. The same reasoning holds in the present context, but less so for two reasons. First, because the SNPs are drawn from a small set of genes, the relative fraction of SNP-SNP relationships that are local is considerably higher than for GWAS. Second, the inflation in the number of test statistics produced relative to the number of independent tests performed due to LD is exacerbated when testing for interactions as compared to main effects.

Consider two causal loci each in high LD with four marker SNPs: If both loci have a main effect, the two “true” main effects result in eight significant markers: a four-fold redundancy. If the two causal SNPs interact, a single interaction could result in as many as 16 significant pairs of marker

SNPs: a 16-fold redundancy. The assumption of independence is thus more questionable for the pathway interaction analysis than for GWAS, and the total number of independent tests conducted was thus somewhat lower than the number corrected for. This effect is not likely to be large, however, with only a modest increase in α expected: for example, if the true number of tests conducted was reduced by more than 1/3 to 200,000, the Bonferonni corrected α would still be

2.6e-7 (-log[α]=6.59) – no SNP reached this level of significance either.

A larger issue is that it may not be appropriate in the context of complex trait genetics to focus exclusively on Bonefronni-corrected significance thresholds. This is because the effects of individual variants are so small relative to BP variability that, except for the most focused studies or those with tens or even hundreds of thousands of subjects, many false positives are expected to be generated at or above the significance level that true positives are likely to reach. By the same token, very few true associations reach the level beyond which numerous false positives are expected, i.e. the Bonferonni-corrected significance threshold.

Even when it does happen, a single result reaching this threshold can’t be claimed with certainty to be a true biological signal, rather it represents a 20-fold enrichment of association signals above the number of false positives expected. An examination of the false discovery rate

(FDR; [208]) allows this notion of enrichment to be extended to other significance levels. The

FDR, a function of a list of p-values and a specified α-value threshold, quantifies (as the ‘q-value’

[209]) the fraction of signals below this threshold expected to have occurred by chance. A major disadvantage to this approach is that from a set of signals with an FDR of 0.2, for example, while the majority may reflect genuine biological associations, no one signal can be singled out and each stands a good chance of being a false positive. One consequence is that such an enrichment set (of signals with FDR = 0.2) might not be a cost-effective starting point for drug development.

On the other hand, being able to describe a set of signals as likely containing true associations, even if particular signals can’t be specifically identified from within that set, can have value. This is particularly true if the set is comprised of variables not previously associated with the trait, and if those signals are conceptually related. The present analyses provide an example of a study design that can make use of this type of assessment. Almost none of the inflammation genes studied have been investigated for association with BP, and of those that have, most were not shown to be associated. In fact, there is considerable uncertainty regarding the involvement of inflammatory processes in the modulation of BP. Relative to the existing association literature and to what is known about the underlying physiology, then, showing that a set of association signals comprised entirely of interactions involving inflammation genes is likely to include a substantial fraction of real associations would represent a useful, if modest, contribution to the current understanding of the factors governing BP and its genetic architecture.

Claims of this nature are possible with regard to the inflammation pathway targeted in the present study. To assess this signal enrichment at a particular p-value, it is important to count the number of loci independently associated below that threshold. To avoid counting the same locus (or the same locus-locus interaction) multiple times, a sparse tagging approach was employed in which each gene was represented by the single SNP with the most significant main effect on that phenotype (SNPs with minor allele frequencies less than 5% were not used to tag genes). This method of tagging resulted in 31 renal genes with 465 SNP-SNP interactions and 30 inflammation genes with 435 SNP-SNP interactions. For each combination of phenotype and covariate set (with or without BMI), the interactions confined to a single pathway with the lowest FDR are listed in tables 7A-7H.

The strongest results are the associations between DBP and interactions among inflammation SNPs (table 7D, Figure 3). The top three interactions have a q-value of 0.042. The probability of getting two false positives at this level (the least significant p-value of the three:

3.77e-4) is 1.2e-3, while the probability of getting three (i.e. the number observed) false positives at this level is 6.0e-4. Thus while none of these three results reaches the Bonferonni-corrected threshold for 435 inflammation interaction tests (α = 1.2e-4), the probability that all three are false positives is quite low. Similarly, the top 15 results have a q-value of 0.311 – this is the fraction of these 15 results that is expected to represent false discoveries. The probability of getting all of these results by chance is again rather small: 1.5e-3. These results therefore constitute evidence that some interactions between inflammation genes are associated with DBP.

Any of the genes tested might be expected to interact physiologically in as much as they were chosen because of their role in inflammation. The two strongest interactions, however, are interesting because they are particularly plausible interaction partners: NFKBIA x IKBKB and

IKBKE x CHUK. The first pair includes a member of the NFκB complex and a member of the IκK inhibitors of that complex, while the second pair includes two members of the IκB inhibitor complex. Thus both interactions represent pairs of genes known to have physiological interactions; these data are the first statistical evidence suggesting that these interactions are associated with BP.

Interestingly, these data provide less evidence of involvement of the renal genes than they do for the inflammation genes. Of the 465 renal gene pairs tested, the strongest results are a group of six interactions for DBP with p-values between 1.6e-3 and 6.2e-3. These have a q-value of 0.482 and the probability of generating at least six false positives (at p=6.2-3) in 465 tests is 0.07. While this provides some indication that not all of the most significant results are false positives, the probability that they are false positives remains substantial. One possible explanation for these observations is that the centrally important role of renal ion balance in BP means that genes that influence ion balance are less dependent on the state of other variables for their BP effect, while the consequences of genes involved in physiology less immediately and directly relevant to BP reveal their effects most strongly when taking other genetic variables into account.

Chapter 4: SNP-Environment Interactions with BP in FHS

I. Introduction

As discussed in chapter 3, aim 2 had two goals: 1) focus the investigation on biochemical pathways, and 2) explore the role of SNP interactions with other contexts. In addition to analyzing

SNP-SNP interactions (in chapter 3), the effects due to interactions between SNPs and sex, BMI, and hypertension status on BP were evaluated.

As contrasted with SNP-SNP interactions, interactions between SNPs and non-SNP covariates have two attractive features. Part of the reason to analyze SNP interactions is to more definitively characterize how the SNP effect is modulated by differences in the “context” in which that SNP functions. While other SNPs can change local (and important) aspects of that context, variables such as sex and BMI may correlate with broader systemic differences that can’t be captured by just one or a few SNPs. This difference is reflected in the fact that individual SNPs rarely have BP effects larger than 1 mm Hg, while both sex and BMI typically show much stronger effects: in the polygenic model of our study population that included age, age2, sex, BMI, and hypertension medication use as covariates (discussed in chapter 3) the estimated effect of sex on both SBP and DBP was greater than 5 mm Hg, while for BMI the effect of 1 kg/m2 was estimated to be 0.63 mm Hg for SBP and 0.31 mm Hg for DBP.

Differences in both sex and BMI are thus contributors to variation in BP, and we hypothesize that they also modify the effects of SNPs on BP. Some evidence in support of this hypothesis has been reported for both sex and BMI. An association with both SBP and DBP was detected for a SNP in AGT in African American males but not in African American females (nor in other race groups) [75], while a SNP in AGTR1 was associated in men only [86]. A GWAS in a

Finish birth cohort found suggestive evidence of SNP-sex interaction associated with BP for 3

SNPs on chromosome 4 [156]. Interactions between SNPs and BMI have been associated with BP in several studies, including for SNPs in CYP19A1[178], CAPN13[179], MMP3[180],

ADRB2[177], and CYP11B2[175], while a SNP in ADD1 revealed an association with BP only after modeling its interaction with both BMI and sex [176].

In addition, a novel type of interaction, between SNPs and hypertension status (SNP-HT), was investigated. The motivation for this exploratory analysis begins by noting that the kidney, vasculature, brain, and other organs work together to stabilize blood pressure. That is, one of the primary functions of the kidney, in conjunction with other systems, is to ensure that the effect on

BP of a given perturbation (for example an age-related increase in the expression of a gene with a

BP-increasing SNP) is minimized. However, a substantial population of people exists whose systems for stabilizing BP have demonstrated the inability to keep BP below the threshold at which the risk for cardiovascular disease, stroke, etc. begins to rise, namely hypertensives. Because these are people whose BP homeostasis mechanisms have proven less effective, we hypothesize that the effects of SNPs are larger in hypertensives than in those without hypertension.

II. Methods

We have analyzed the same phenotype data as in aim and the same genotype data as described in chapter 3. In all, 818 SNPs drawn from 64 genes in two pathways were used.

Analysis

The SNP-sex and SNP-BMI interaction analyses were performed using the ABEL package.

GenABEL was used to adjust for age, age squared, sex, BMI, and use of hypertension medication as covariates while also adjusting for family relatedness with a polygenic term. The residuals from

this adjustment were then regressed on the SNP, BMI, and SNP-BMI interaction term (or SNP, sex, and SNP-sex). Thus the model for analyzing SNP-BMI interactions was of the form:

y     * SNP   * BMI   * SNP * BMI  e 0 1 2 3 ij

The SNP main effect and the interaction term were tested together with a joint 2 degree-of-freedom test.

The SNP-HT interaction analysis was performed differently. Heteroskedasticity was observed across hypertension status groups, with the variance in hypertensives more than double that of the non-hypertensives for both SBP and DBP. As a result, analyzing SNP-HT interactions with an approach analogous to that used for SNP-BMI and SNP-sex would produce an inflated test statistic. Instead, the motivating hypothesis (that SNP effects are larger in hypertensives than non- hypertensives) was first explored using a stratified analysis in which the main effect of each SNP was analyzed separately within hypertensives and non-hypertensives. For this model, the same analysis pipeline used for aim 1 in Chapter 2 was used to test for SNP main effects with no interactions; namely, an adjustment was performed in GenABEL with a polygenic term and the covariates age, age squared, sex, BMI, and hypertension medication use. The residuals from this adjustment were then regressed on each SNP.

An additional analysis was performed to attempt to capture the difference in SNP effects between hypertensives and non-hypertensives (the SNP-HT interaction) using all subjects simultaneously. To avoid overestimating the precision of the effect estimate for the SNP-HT interaction, a family-based bootstrapping approach was employed. One hundred bootstrap datasets were created by randomly selecting whole families with replacement. Each bootstrap was considered filled when a randomly selected family would push the combined sample size over that of the actual sample size (6,889); that family was not added. Each bootstrap was then analyzed

using a simple linear regression of the phenotype on the SNP, hypertension status, SNP-HT interaction, and the covariates age, age squared, sex, BMI, and hypertension medication use. In addition, a single regression using the same model was performed using the complete dataset (in which each family and individual appears once). For each SNP, the estimate of the SNP-HT interaction effect was taken from this model, while the error term associated with this SNP-HT effect was empirically determined as the standard deviation of the 100 effects estimated from the bootstraps.

III. Results

Association results for SNP-BMI interactions are presented in figure 6. Results for SNP-Sex interactions are presented in figure 7. Comparison of 2 d.f. interaction term results with corresponding main effects is shown for SNP-BMI interactions in figure 8 and for SNP-Sex interactions in figure 9. Results of the SNP-hypertension interaction analyses are presented in figure 10. Figure 11 shows QQ plots for the SNP-hypertension interaction term, while figure 12 shows QQ plots for the SNP main effects assessed separately in hypertensives and normotensives.

Details for one interaction, that between hypertension status and ADIPOR2 variant rs12821401 are shown in figure 13.

IV. Discussion

The SNP-BMI and SNP-sex interactions yielded similar results. Although both kinds of interactions revealed stronger association signals for many SNPs than did main effects alone, no single interaction reaches the significance threshold based on the Bonferonni correction for 818 tests. (A pair of SNPs on chromosome 5 show a strong SNP-sex interaction -- too few copies of the

minor allele are present in this data, however, to have confidence in the reality of the interaction).

This is true both for the 2 d.f. test (Figures 6 and 7) and for the 1 d.f. test of interaction (not shown).

These results suggest that, even in a focused experiment such as this one with a modest multiple testing burden and moderate sample size (~10k), incorporating SNP-BMI and SNP-sex interactions does not improve gene discovery.

There is, however, reason to continue to investigate the role of these interactions in BP regulation. One such reason is that several interactions achieved suggestive significance (table 8).

Three of these interactions had p-value of less than 6.1e-4 while a fourth actually achieved significance (p-value: 2.7e-6) but, as noted above, with too few minor alleles to warrant confidence.

A larger sample size, such as that employed in the major BP consortia [149], might reveal that one or more of these are biologically meaningful. Another reason is the general trend toward stronger associations when using these interactions. Figures 8 and 9 show the –log(p-value) for SNP- interactions (2 d.f.) vs. SNP main effects for SNP-BMI and SNP-sex interactions, respectively. As expected, the majority of SNPs do not show an interaction and the interaction p-value is within a single log-unit of the main effect p-value. Of the renal SNPs whose interaction and main effect p- values differ by more than one log-unit, however, the overwhelming majority show an increase in significance when including the interaction. Inflammation SNPs do not show this trend (very few

SNPs show a difference in effect greater than one log-unit and, of those that do, increases in significance are no more common than decreases), indicating that interactions with BMI and sex are more likely to be relevant for renal SNPs than for inflammation SNPs.

The SNP-Ht interaction analysis using bootstrap-derived robust errors also failed to achieve significance for any SNP (Manhattan plots in Figure 10, QQ plots for the interaction terms in

Figure 11). The analysis of SNP main effects stratified by hypertension status, however, did enable

the discovery of a significant association between SBP and a SNP in a novel gene, ADIPOR2, and provided general support for the hypothesis that SNP effects are larger and more readily detectable in hypertensives than in non-hypertensives. Figure 12 shows QQ plots for SNP main effects within hypertensives and non-hypertensives. The p-values from non-hypertensives (green) reflect a controlled test-statistic primarily lying on or below the expectation line (blue). The hypertensive p- values, by contrast, show substantial inflation for this same statistic. It is particularly striking that the hypertensive p-values are often more significant given that the sample size is much smaller

(1,804 vs. 5,005) and the total variance roughly twice as big.

Figure 13 shows details for the novel discovery, the association among hypertensives between SBP and rs12821401 in ADIPOR2. An effect of approximately 2.3 mm Hg in all subjects gave rise to a non-significant p-value 0f 2.1e-3. Stratifying reveals that this effect reflects the weighted average of an effect of about 2.2 mm Hg in non-hypertensives and 4.1 mm Hg in hypertensives. While neither the 2.3 mm Hg effect in all subjects, nor the 1.9 mm Hg interaction effect (corresponding to the difference in effects between hypertensives and non-hypertensives) reach significance, the 4.1 mm Hg effect in hypertensives did achieve Bonferroni-corrected significance (6.1e-5). This suggests that a 2 d.f. test accounting for the main effect and interaction simultaneously in all subjects might be more strongly significant, but a robust 2 d.f. test was not implemented.

Chapter 5: Multi-marker analysis of SNP Interactions with BP in FHS

I. Introduction

In chapters 2, 3, and 4 we investigated the role a SNP’s “context” has in modifying its effect on BP, with a focus on biological context: the pathway to whose function the SNP-containing gene contributes, the other genetic variants in that pathway, and other systemic characteristics of the surrounding physiology (sex, BMI, hypertension). The analyses in those chapters was motivated by the suggestion that one reason SNP effects on BP have been difficult to definitively identify may be that they differ across these contexts, and hypothesized that accounting for those differences in effect would therefore improve discovery. In chapter 5 we shift to an emphasis on statistical context. Here we recognize that even a SNP effect that is stable across many biological contexts will be difficult to detect if the signal-to-noise ratio is too low because the SNP effect is small relative to other sources of un-modeled variation. This is the reason that major BP-influencing factors such as age, sex, and BMI are routinely included in models of BP. Based on one of the few thoroughly consistent findings in BP genetics, namely that SNP effects are small, it seems unlikely that the addition of one or a few genetic terms will reduce un-modeled variation sufficiently to reveal novel effects. However, the possibility remains that including large numbers of genetic terms could accomplish this goal if they collectively account for enough variation to improve the signal/noise ratio for other variants above the threshold needed for detection. Two analytical methods that could be employed to explore this hypothesis by simultaneously modeling a large number of genetic effects are 1) the least absolute shrinkage and selection operator (LASSO) and 2) stepwise regression modeling. Candidate gene studies have used stepwise models to select main effects of SNPs from among a small set drawn from the targeted gene (e.g.[210, 211]). To our

knowledge, however, few if any BP studies have been published using either of these techniques on genetic datasets drawn from numerous genes and incorporating SNP-SNP interactions.

II. Methods

The first step of our approach was to employ a LASSO on several multi-gene datasets that included SNP-SNP interactions. These analyses were limited to the 693 (of 818) pathway SNPs that were imputed so that the genetic datasets contained no missing data points (see chapter 3,

Tables 5A and 5B). For SBP and DBP, all SNP terms (both main effects and SNP-SNP interactions) were sorted, and four subsets selected: 1) all interactions from among the top 100 terms; 2) these top interactions and their corresponding main effects; 3) all interactions from among the top 1000 terms; and 4) these top interactions and their corresponding main effects. The LASSO was implemented in the Lars R package using a 10-fold cross-validation procedure to determine the optimal value of the penalty parameter.

The LASSO penalty (based on the L1 norm of the effect estimates i.e. their sum) helps avoid over-fitting by reducing parameter estimates, including in many cases to 0. The resulting estimates are biased (downward), however, and the significance of the resulting model is difficult to assess. In the second step of our approach we chose to submit the LASSO’d terms to an un- penalized stepwise regression procedure to select a final subset of SNP main effects and interactions whose significance and BP variance contribution could be estimated in an unbiased manner. For these stepwise models, the covariates (age, age squared, sex, BMI, hypertension medication use) were always included while genetic terms were added (with backward elimination) to this minimum model based on the Aikaike information criterion (AIC). For each of the four sets of LASSO’d terms, two sets of terms were submitted to the stepwise regression analysis: 1) the

LASSO’d terms only, and 2) the LASSO’d terms plus any SNP main effects not already included for each SNP involved in at least one of the LASSO’d interaction terms. A total of 16 stepwise models were therefore created, one for each combination of two phenotypes (SBP, DBP), two set sizes (top 100, top 1000), and four uses of main effects (added to both LASSO and stepwise, to

LASSO only, to stepwise only, and to neither).

III. Results

Table 9 lists each of the 16 stepwise models and the percentage of the phenotypic variance they explain. As a reference, models for SBP and DBP containing only the covariates but no genetic terms are also included. For each model, Tables 10A-P shows the significance of each term in the stepwise model and the significance of the same term in the reduced model (for SNP main effects just the covariates and the SNP, for SNP-SNP interactions both the covariates and both SNP main effects as well as the interaction term).

IV. Discussion

The multi-marker analyses we performed, combining LASSO and stepwise-regression based approaches, was motivated by the hypothesis that by allowing numerous SNPs and SNP-SNP interactions into a single model and thereby accounting for more otherwise unexplained phenotypic variance, numerous signals not otherwise reaching significance could be estimated more precisely

(i.e. with smaller standard errors), thereby becoming significant and collectively explaining a larger fraction of the phenotypic variance. Overall, these analyses do not support this hypothesis. Even the largest models, which included several dozen terms (see Tables 9 and 10) from among over 1,000 possible SNP main effects and SNP-SNP interactions, explain very little variance beyond that

explained by the covariates alone: less than 2% for SBP and less than 3.5% for DBP. This is comparable to the variance explained by large a GWAS consortia [212], but our model’s SNP terms are at least four to seven orders of magnitude less significant than the stringent thresholds employed in that meta-analysis. This approach neither explained more variance by implicating more SNP terms nor yielded stronger significance for the terms it did implicate (relative to the significance obtained in meta-analysis [212]).

Relative to the significance obtained for SNP main effects and SNP-SNP interactions earlier in the present work (chapters 2 and 3), however, one interesting pattern did emerge. The vast majority of SNP-SNP interactions included in the stepwise models described in Tables 10A-P are much less significant in these models than they are in the reduced models that include only the covariates and two corresponding SNP main effects in addition to the interaction term. Conversely, a majority of SNP main effects are more significant in the stepwise model than they are in the reduced model including only the SNP main effect and the covariates.

The interpretation of this observation is not immediately clear. Perhaps the most likely explanation is that the majority of the selected SNP-SNP interactions are false positives and represent the 0.1% - 0.01% of the approximately 330,000 interactions analyzed expected to reach – log(P) of 3-4 by chance. The failure of these terms to remain as significant in a substantially different model (one that includes a variety of other SNPs and SNP-SNP interactions) is therefore not surprising; false positive signals are less likely to replicate across models than are true biological associations. The increase in significance observed in most SNP main effects in the stepwise models relative to the reduced models could result from the decrease in unexplained variance in the presence of these interactions. While this was the hoped-for effect of multi-marker

analysis, however, the likelihood that the interaction terms are spurious calls in to question the validity of this observed increase in main effect significance.

An alternative explanation is that some of the variance accounted for by SNP-SNP interactions in their reduced models is instead being attributed to other terms in the stepwise models, resulting in their reduced significance. The increase in significance observed for most SNP main effects could still be the result of explaining more background variance in this scenario. The nearly perfect consistency with which interaction effects became less significant in the stepwise models, however, makes this possibility less likely.

Chapter 6: Conclusions

Motivated by the large medical and financial challenges posed by elevated BP, researchers have been trying for decades to unravel the relationship between genes and BP. Considerable evidence suggests that such a relationship explains a substantial fraction (at least 30% [212]) of observed BP variation, but less than 3% of this variation has been attributed to specific genetic variants [10, 149]. In the present work, we began by identifying several features of the prevailing methods for investigating the genetic architecture of BP that could explain why the discovery those methods have achieved has been so limited. We then hypothesized that employing alternative approaches designed to address those features would make additional contributions to the discovery of BP-associated genes.

The specific study design features we chose to address as potentially limiting gene discovery are as follows. First, the majority of BP genetic studies (indeed, genetic studies of complex traits generally) assess only the main effects of the targeted genetic variants [10]. This means that variations in a SNP’s effect that are dependent on a subject’s sex, BMI, genetic background, or other characteristics are not captured, which could prevent their detection [29]. A comparatively small number of studies have used SNP interactions to explicitly analyze these variations in effect with promising results that warrant further analyses of this kind [10]. A second study characteristic potentially limiting gene discovery is the analysis of millions of SNPs from throughout the genome. Whereas candidate gene studies begin with an a priori list of genes and/or pathways known or suspected for their involvement in the trait, over the past six years the field of

BP genetics has shifted emphasis towards genome-wide studies with the consequence that SNP effects are assessed relative to a stringent significance threshold (typically α=5e-8). While progress in BP genetics has been slow, one finding that has consistently emerged is that the effects of

individual SNPs are small, usually less than or equal to 1 mm Hg; such effects are unlikely to reach significance at that threshold without the massive sample sizes (approaching or exceeding 100,000 subjects) used in the biggest GWAS consortia [149]. A third feature of nearly all BP genetic studies is the analysis of a single SNP’s effects at a time. By omitting the analysis of other SNPs and SNP-

SNP interactions, this approach ensures that most BP variance is un-modeled, even if opportunities exist to reliably model BP as a function of additional genetic variants. As a result, the association

‘signal’ of a particular SNP under investigation is a lesser fraction of the ‘noise’ comprised of un- modeled variance, thereby decreasing the likelihood of discovering associations.

The present study consisted of several analyses designed to address these issues. After conducting a genome-wide analysis as a reference, all subsequent analyses focused on a small set of SNPs (818) drawn from two biochemical pathways. This approach allowed a dramatically reduced significance threshold for main effects (α=6.3e-5 vs. 5e-8) relative to that employed in

GWAS, and even the threshold for an exhaustive analysis of SNP-SNP interactions (331,453 pairwise tests) among these pathway-derived SNPs is more lenient (α=1.5e-7). We chose to target one pathway, genes involved in renal ion balance, as a weak form of positive control; most of the targeted genes have a clear physiological relationship to BP and have been associated with BP in multiple candidate gene studies, albeit inconsistently, with other studies failing to find association in nearly all cases [10]. Many of these genes have also been conclusively linked to Mendelian hypertension (and hypotension) disorders [10]. We therefore expected that any study designs and analytical approaches that could ultimately prove successful in detecting associations of novel genes and pathways with BP would demonstrate their utility via association of at least some subset of these genes. The second pathway we targeted, genes involved in inflammation, was a more

speculative choice, designed to investigate the postulated but unclear relationship between inflammation and blood pressure [119].

In addition to the analyses of main effects and pairwise interactions among pathway SNPs, several other analyses addressing the issues above were conducted. A pair of model-building techniques, penalized (LARS) and stepwise regression, was used to allow the inclusion of large numbers of genetic (main effect and interaction) terms simultaneously, potentially reducing un- modeled variance and increasing power. Interactions with pathway SNPs between both sex and

BMI were analyzed as well. Finally, differences in SNP effect among hypertensive and non- hypertensive individuals were evaluated. This set of analyses included an approach similar to one previously employed, the separate assessment of SNP main effects in hypertensives and non- hypertensives [213], and a novel approach, a bootstrap-based assessment of the SNP-hypertension interaction in all subjects simultaneously.

Despite the reduced significance threshold associated with the Bonferroni correction for just

818 pathway SNPs, no SNP main effects reached ‘pathway-wide’ significance. A small number of modestly suggestive associations (those within one log(P) unit of significance) were detected, primarily for inflammation SNPs with SBP and renal SNPs with DBP. Relative to the significance threshold determined by the number of pairwise interactions analyzed both within and between the two pathways, not a single SNP-SNP interaction reached within one log(P) unit of significance.

Among all pairwise interactions within a set of 30 inflammation gene tagging SNPs, however, a trio of SNP-SNP interactions (among six genes) with DBP is significantly enriched for association signals: the probability that all three are false positives is 6.5e-4. Despite the absence of any individual SNP-SNP interaction reaching significance on its own, this finding provides evidence

both that inflammation genes contribute to the genetic architecture of BP and that SNP-SNP interactions can aid in discovering associations between BP and novel genes and pathways.

The simultaneous modeling of multiple SNP main effects and interactions did not make a substantial contribution. Instead of an increase in significance driven by a reduction in un-modeled variance, interactions (which constitute the majority of the selected terms) were almost uniformly less significant, most likely reflecting their inclusion in the model by chance alone. Neither SNP- sex nor SNP-BMI interactions were significantly associated with either SBP or DBP, although a mild trend amongst renal SNPs suggests that the use of these interactions may have provided an increase in power, albeit one insufficient to yield significant associations. Of those SNPs whose main effect and 2 d.f. interaction effect (sex or BMI) p-values differed by more than one order of magnitude, a strong majority of interactions were more significant than the corresponding main effects.

The investigation of differences in SNP effects between hypertensives and non- hypertensives did yield a significant association: that between SBP and a common SNP

(MAF=14.2%) in ADIPOR2, one of the six genes implicated in the 3 SNP-SNP interactions that were significantly enriched for association signals with DBP. This main effect in hypertensives reflects a difference in mean SBP between the two homozygote groups of 4.1 mm Hg. The corresponding difference in all subjects was only 2.3 mm Hg, but neither this main effect nor the bootstrapped 1 d.f. interaction term, which roughly captures the difference (~2.2 m Hg) between these two effects, yielded significance. A 2 d.f. test statistic capturing both the main effect in all subjects and the increase in this effect among hypertensives might well reach significance, possibly even more so than the significant main effect in hypertensives alone due to the inclusion of the additional (non-hypertensive) subjects. However, no established method exists to calculate a 2 d.f.

statistic based on a regression of a dependent variable (SBP) on both a SNP and that SNP’s interaction with another independent variable (hypertension) that is highly correlated to the dependent variable. As the development of such an analytical method fell well outside the scope of the original proposal, such a 2 d.f. test was not implemented. This avenue of inquiry, however, represents an intriguing area for further research.

An analysis of SNP effects that considers variation across physiological contexts was able to implicate genes in the inflammatory pathway as associated with BP. This novel association was identified both indirectly, by evidence of association among three SNP-SNP interactions, as well as directly, by evidence of association in hypertensives of one of the members of those interactions,

ADIPOR2. In addition, other forms of analysis involving context-dependent effects (SNP-sex,

SNP-BMI) showed at least some potential to increase statistical power. At the same time, these data make clear that SNP interactions are no panacea for the difficult problem of unraveling the architecture of BP. Further work is necessary developing the statistical tools and study designs appropriate to maximize the potential gains these interaction effects can offer. Contributions from other active areas of research in BP genetics, such as the role of rare variants [214] and an appreciation of finer phenotypic distinctions (e.g. between forms of hypertension), may play a major role as well. The present work represents an incremental step forward in this collective effort.

Figures

Figure 1A

Figure 1B

Figure 1C

Figure 1D

Figure 2A

Figure 2B

Figure 2C

Figure 2D

Figure 3A

Figure 3B

Figure 3C

Figure 3D

Figure 3E

Figure 3F

Figure 3G

Figure 3H

Figure 4A: SLC12A3 x ADRA1A, SBP, -log(p-value) = 4.33 SLC12A3 (rs2289114) x ADRA1A (rs6557946), SBP 128

126

124

122 SLC12A3: 0 120

mean SBP mean SLC12A3: 1 118 SLC12A3: 2

116

114 0 1 2 # minor alleles (ADRA1A)

Figure 4B: ATP1A1 x KCNJ1, SBP, -log(p-value) = 4.31 ATP1A1 (rs12133039) x KCNJ1 (rs11221484), SBP

156

146

136 ATP1A1: 0

mean SBP mean ATP1A1: 1 126 ATP1A1: 2 116

106 0 1 2 # minor alleles (KCNJ1)

Figure 4C: ACE x TNFRSF1B, SBP, -log(p-value) = 4.81 ACE (rs12709426) x TNFRSF1B (rs5746074), SBP 140

135

130

125 ACE: 0 120 mean SBP mean ACE: 1 ACE: 2 115

110

105 0 1 2 # minor alleles (TNFRSF1B)

Figure 4D: CYP11B2 x IL6, SBP, -log(p-value) = 4.58 CYP11B2 (rs7831617) x IL6 (rs1880242), SBP 123.5 123 122.5 122

121.5 121 CYP11B2: 0

mean SBP mean 120.5 CYP11B2: 1 120 CYP11B2: 2 119.5 119 118.5 0 1 2 # minor alleles (IL6)

Figure 4E: AGTR1 x KCNJ1, DBP, -log(p-value) = 5.39 AGTR1 (rs12695918) x KCNJ1 (rs17137982), DBP 86

76 AGTR1: 0 mean DBP mean 74 AGTR1: 1

68 0 1 2 # minor alleles (KCNJ1)

Figure 4F: ADRB2 x WNK1, DBP, -log(p-value) = 4.64 ADRB2 (rs877741) x WNK1 (rs16931965), DBP 82

76 ADRB2: 0

mean DBP mean ADRB2: 1 74 ADRB2: 2

70 0 1 2 # minor alleles (WNK1)

Figure 4G: RETN x ADIPOR2, DBP, -log(p-value) = 4.22 RETN (rs12610253) x ADIPOR2 (rs4766415), DBP 77

74 RETN: 0

mean DBP mean RETN: 1 73 RETN: 2

71 0 1 2 # minor alleles (ADIPOR2)

Figure 4H: NKKBIA x LEPR, DBP, -log(p-value) = 4.22 NFKBIA (rs4982270) x LEPR (rs3806318), DBP 79 78 77 76

75 74 NFKBIA: 0

mean DBP mean 73 NFKBIA: 1 72 NFKBIA: 2 71 70 69 0 1 2 # minor alleles (LEPR)

Figure 4I: ADD2 x NFKBIZ, DBP, -log(p-value) = 4.43 ADD2 (rs17006246) x NFKBIZ (rs694936), DBP 79

75 ADD2: 0 74

mean DBP mean ADD2: 1 73 ADD2: 2 72

70 0 1 2 # minor alleles (NFKBIZ)

Figure 4J: ADD2 x CYP11B1, SBP (no BMI), -log(p-value) = 4.64 ADD2 (rs3755353) x CYP11B1 (rs9657021), SBP (no BMI) 126

124

122

120 ADD2: 0 118

mean SBP mean ADD2: 1 116 ADD2: 2

114

112 0 1 # minor alleles (CYP11B1)

Figure 4K: GRK4 x WNK1, SBP (no BMI), -log(p-value) = 4.26 GRK4 (rs2798285) x WNK1 (rs9804992), SBP (no BMI) 128 126 124

122

120 GRK4: 0 118

mean SBP mean GRK4: 1 116 GRK4: 2 114 112 110 0 1 2 # minor alleles (WNK1)

Figure 4L: AGTR1 x TNFRSF1B, SBP (no BMI), -log(p-value) = 5.59 AGTR1 (rs2933249) x TNFRSF1B (rs652625), SBP 145

140

135

130 AGTR1: 0 125 mean SBP mean AGTR1: 1 AGTR1: 2 120

115

110 0 1 2 # minor alleles (TNFRSF1B)

Figure 4M: SCNN1G x EMILIN1, SBP (no BMI), -log(p-value) = 4.89 SCNN1G (rs407390) x EMILIN1 (rs2304681), SBP (no BMI) 126

124

122

120 SCNN1G: 0 118

mean SBP mean SCNN1G: 1 116 SCNN1G: 2

114

112 0 1 2 # minor alleles (EMILIN1)

Figure 5A: NFKBIA x IKBKB, DBP, -log(p-value) = 3.52 NFKBIA (rs4982270) x IKBKB (rs5029748), DBP 78 77.5 77

76.5

76 75.5 NFKBIA: 0

mean DBP mean 75 NFKBIA: 1 74.5 NFKBIA: 2 74 73.5 73 0 1 2 # minor allels (IKBKB)

Figure 5B: IKBKE x CHUK, DBP, -log(p-value) = 3.49 IKBKE (rs2871360) x CHUK (rs7358099), DBP

79 IKBKE: 0 77

mean DBP mean IKBKE: 1 75 IKBKE: 2 73

69 0 1 2 # minor alleles (CHUK)

Figure 5C: ADIPOR2 x RETN, DBP, -log(p-value) = 3.42

ADIPOR2 (rs11612383) x RETN (rs12610253), DBP 77 76.5 76

75.5

75 74.5 ADIPOR2: 0 74 mean DBP mean ADIPOR2: 1 73.5 ADIPOR2: 2 73 72.5 72 0 1 2 # minor alleles (RETN)

Figure 6: SNP-BMI interactions Figures 6A-6D show the –log(P) for association between the specified phenotype and SNP-BMI interactions for each snp in the specified pathway, sorted by chromosome and position. The gray dashed line is the Bonferroni-corrected significance level for 818 independent tests (-log[P] = 4.2).

Figure 6A: SNP-BMI interaction for SBP (renal pathway)

Figure 6B: SNP-BMI interaction for SBP (inflammation pathway)

Figure 6C: SNP-BMI interaction for DBP (renal pathway)

Figure 6D: SNP-BMI interaction for DBP (inflammation pathway)

Figure 7: SNP-Sex interactions Figures 7A-6D show the –log(P) for association between the specified phenotype and SNP-Sex interactions for each snp in the specified pathway, sorted by chromosome and position. The gray dashed line is the Bonferroni-corrected significance level for 818 independent tests (-log[P] = 4.2).

Figure 7A: SNP-Sex interaction for SBP (renal pathway)

Figure 7B: SNP-Sex interaction for SBP (inflammation pathway)

Figure 7C: SNP-Sex interaction for DBP (renal pathway)

Figure 7D: SNP-Sex interaction for DBP (inflammation pathway)

Figure 8 Figures 8A-8D show the 2d.f. SNP-BMI interaction –log(P) vs. the SNP main effect –log(P) for the specified phenotype for each SNP in the specified pathway. The green dashed lines show where SNPs would be whose main effect and interaction p-values are different by one order of magnitude (one log unit).

Figure 8A: SNP-BMI vs. SNP main effect for SBP (renal pathway)

Figure 8B: SNP-BMI vs. SNP main effect for SBP (inflammation pathway)

Figure 8C: SNP-BMI vs. SNP main effect for DBP (renal pathway)

Figure 8D: SNP-BMI vs. SNP main effect for DBP (inflammation pathway)

Figure 9 Figures 9A-9D show the 2d.f. SNP-Sex interaction –log(P) vs. the SNP main effect –log(P) for the specified phenotype for each SNP in the specified pathway. The green dashed lines show where SNPs would be whose main effect and interaction p-values are different by one order of magnitude (one log unit).

Figure 9A: SNP-Sex vs. SNP main effect for SBP (renal pathway)

Figure 9B: SNP-Sex vs. SNP main effect for SBP (inflammation pathway)

Figure 9C: SNP-Sex vs. SNP main effect for DBP (renal pathway)

Figure 9D: SNP -Sex vs. SNP main effect for DBP (inflammation pathway)

Figure 10 Figure 10 shows SNP main effects in hypertensives only (panel 1), SNP main effects in non- hypertensives only (panel 2), and bootstrapped SNP-hypertension interaction effects (1 d.f., panel 3) for the specified phenotype for each SNP in the specified pathway.

Figure 10A: SNP-Hypertension – interaction effects and stratified main effects for SBP (renal pathway)

Figure 10B: SNP-Hypertension – interaction effects and stratified main effects for SBP (inflammation pathway)

Figure 10C: SNP-Hypertension – interaction effects and stratified main effects for DBP (renal pathway)

Figure 10D: SNP-Hypertension – interaction effects and stratified main effects for SBP (inflammation pathway)

Figure 11 Figure 11 shows the quantile-quantile plot for association of the 1 d.f. SNP-Hypertension interaction term with the specified phenotype for each SNP in the specified pathway. The SNPs are shown in red and the null expectation is shown in blue.

Figure 11A: QQ plot for 1 d.f. SNP x HT association with SBP (renal pathway)

Figure 11B: QQ plot for 1 d.f. SNP x HT association with SBP (inflammation pathway)

Figure 11C: QQ plot for 1 d.f. SNP x HT association with DBP (renal pathway)

Figure 11D: QQ plot for 1 d.f. SNP x HT association with DBP (inflammation pathway)

Figure 12 Figure 12 shows the quantile-quantile plot for association of the SNP main effect on the specified phenotype (SBP/DBP) within hypertensives only and within non-hypertensives only for each SNP in the specified pathway (renal/inflammation). The SNPs are shown in red for hypertensives and green for non-hypertensives while the null expectation is shown in blue.

Figure 12A: QQ plot for SNP main effect on SBP in hypertensives only and in non-hypertensives only (renal pathway)

Figure 12B: QQ plot for SNP main effect on SBP in hypertensives only and in non-hypertensives only (inflammation pathway)

Figure 12C: QQ plot for SNP main effect on DBP in hypertensives only and in non-hypertensives only (renal pathway)

Figure 12D: QQ plot for SNP main effect on DBP in hypertensives only and in non-hypertensives only (inflammation pathway)

Figure 13. Mean SBP by ADIPOR2 genotype (rs12821401) and hypertension status. ADIPOR2 rs12821401, SBP 140 137.2745 134.058 135 133.1502

130

125 Ht

mean SBP mean 120.92584 119.4891307 Nt 120 All 118.6618657 114.6496 114.3964 115 112.4966 110 0 1 2 # minor alleles

Tables

Table 1: Mendelian Hypertension Genes Syndrome Pathway Gene(s) Mechanism Reference Unequal crossing over results in a 11β-hydroxylase chimera with glucocorticoid- and aldosterone Steroid/aldosterone aldosterone remediable synthase [4] synthesis synthase activity aldosteronism (CYP11B1 and driven by CYP11B2) adrenocorticotrop ic hormone Enzyme corticosterone aldosterone Steroid/aldosterone dysfunction methyloxidase II synthase [12] synthesis results in reduced deficiency (CYP11B2) aldosterone levels Enzyme steroid 21- steroid 21- steroid/aldosterone dysfunction hydroxylase hydroxlase [13] synthesis results in reduced deficiency (CYP21A2) aldosterone levels Impaired conversion of 11β- cortisol to apparent steroid/aldosterone hydroxysteroid cortisone results mineralocorticoid [14, 5] synthesis dehydrogenase in cortisol- excess (11B-HSD) mediated hyperactivation of MR Glucocorticoid recepor dysfunction leads familial steroid/aldosterone glucocorticoid to increased glucocorticoid [15] synthesis receptor (NR3C1) cortisol and resistance cortisol-mediated hyperactivation of MR

steroid 11β- Enzyme steroid/aldosterone 11β-hydroxylase hydroxylase dysfunction leads [16] synthesis (CYP11B1) deficiency to increased levels of MR-

activating hormones Enzyme 17 α- dysfunction leads hydroxylase and/or steroid/aldosterone 17-α-hydroxylase to increased [17] 17,20- synthesis (CYP17A1) levels of MR- lyase deficiency activating hormones Missense mutation makes hypertension MR active aldosterone mineralocorticod exacerbated by without ligand, [18] signaling receptor (MR) pregnancy further activated by progesterone in pregnancy mineralocorticoid receptor, or electrogenic loss-of-function pseudo- aldosterone sodium channel α, mutation leading hypoaldosteronism signaling/ renal ion [19, 7, 23] β or γ subunit to reduced ENaC type I channel (MR, SCNN1A, activity SCNN1B, SCNN1G) kinase mutations pseudo- with-no-lysine ion channel lead to hypoaldosteronism kinase 1 or [28] regulation upregulated type II 4(WNK1, WNK4) SLC12A3 C terminus electrogenic deletion leads to sodium channel β reduced ENaC Liddle syndrome renal ion channel or γ subunit [22, 6] clearance and (SCNN1B, increased ENaC SCNN1G) activity loss-of-function Na-Cl Gitelman's mutation leads to renal ion channel cotransporter [24] syndrome lower sodium (SLC12A3) reabsorption loss-of-function Na-K-2Cl Bartter's syndrome mutations leads to renal ion channel cotransporter [8] type I lower sodium (SLC12A1) reabsorption

100

reduced potassium potassium Bartter's syndrome inwardly- renal ion channel recycling leads to [26] type II rectifying channel impaired sodium (KCNJ1) reabsorption reduced chloride Bartter's syndrome chloride channel transport leads to renal ion channel [27] type III kb (CLCNKB) impaired sodium reabsorption loss-of-function peroxisome mutation leads to insulin resistance transcriptional proliferator- insulin resistance [215] and hypertension regulation activated receptor and hypertension; gamma (PPARγ) vascular effects postulated mutation in hypertension, mitochondrially conserved base hypercholesterolem encoded tRNA protein synthesis near anti-codon [216] ia, and isoleucine (MT- impairs ribosome hypomagnesemia TI) binding

101

Table 2: Selected Essential Hypertension Candidate Genes Association Pathway Gene(s) Association refuted confirmed aldosterone signaling renin (REN) [69, 70, 63] [72, 71] angiotensinogen aldosterone signaling [74, 76, 75] [217-219] (AGT) angiotensin aldosterone signaling converting enzyme [220, 77, 76] [221, 78, 222] (ACE) angiotensin II aldosterone signaling receptor, type 1 [79, 81, 80] [84, 82, 85] (AGTR1) Na-Cl cotransporter renal ion channel [59, 47] [60, 61, 53] (SLC12A3) Na-K-2Cl renal ion channel cotransporter [62, 44, 47] [63] (SLC12A1) potassium inwardly- rectifying channel, renal ion channel [47, 48] [49] subfamily J, member 1 (KCNJ1) with-no-lysine kinase ion channel regulation [33, 64-66] [61] 1 (WNK1) with-no-lysine kinase ion channel regulation [61, 64] [67, 68] 4 (WNK4) serum/glucocorticoid ion channel regulation regulated kinase 1 [87-89] [90, 54] (SGK1) Electrogenic sodium channel, α, β, and γ renal ion channel [54-56] [57, 58] subunits (SCNN1A, SCNN1B, SCNN1G) chloride channel kb renal ion channel [50, 51] [49, 52, 53] (CLCNKB) ion channel regulation adducin 1 (ADD1) [93, 91, 92, 94] [96-98]

102

ion channel regulation adducin 2(ADD2) [91, 95] [94, 99] ion channel tyrosine hydroxylase [223] [224] regulation/vasoconstriction (TH) ion channel dopamine receptor D1 [103, 104, 106] [105, 225] regulation/vasoconstriction (DRD1) ion channel dopamine receptor D2 [226] --- regulation/vasoconstriction (DRD2) catechol-O- ion channel methyltransferase [107, 186] --- regulation/vasoconstriction (COMT) ion channel dopamine beta- [187, 107] --- regulation/vasoconstriction hydroxylase (DBH) G protein-coupled ion channel regulation receptor kinase 4 [205] [106] (GRK4) ion channel adrenergic receptor, [227, 63] [228] regulation/vasoconstriction beta 2 (ADRB2) ion channel adrenergic receptor, [229, 230] [231, 63] regulation/vasoconstriction alpha 1A (ADRA1A) ion channel adrenergic receptor, [63, 232] [233] regulation/vasoconstriction beta 1 (ADRB1) ion channel adrenergic receptor, [234, 111] --- regulation/vasoconstriction beta 3 (ADRB3) nitric oxide synthase 3 vasoconstriction [113, 112] [206, 207] (NOS3) vasoconstriction endothelin 1 (EDN1) [235, 236] [237] endothelin receptor vasoconstriction [67, 238] --- type A (EDNRA) cytochrome P450, family 2, subfamily C, vasoconstriction [110, 116] [115] polypeptide 8 (CYP2C8) inflammation interleukin 6 (IL6) --- [126] transforming growth inflammation [126, 125] --- factor beta 1(TGFB1)

103

Table 3. Number of Subjects, Families and Sibpairs Visit 7 Original Cohort Sample size 518 Age range 79-103 Offspring Cohort Sample size 3,305 Age range 33-89 G3 Cohort Sample size 3,893 Age range 19-72 Total # of Subjects 7,716 # Subjects w/genotypes and age, 6,890 sex, BMI data # of Singletons 1,282 # of Nuclear Families 2,329 # of Sibpairs 5,326

Table 4: visit 7 normality check Skewness Kurtosis SBP-males 1.03 1.81 SBP-females 0.97 1.06 DBP-males 0.22 0.31 DBP-females 0.34 0.42

104

Table 5A: Renal ion balance genes # Gene Chromosome SNPs Category REN 1 10 RAAS HSD11B1 1 16 RAAS AGT 1 14 RAAS AGTR1 3 33 RAAS MR 4 91 RAAS CYP11B1 8 5 RAAS CYP11B2 8 5 RAAS CYP17A1 10 6 RAAS HSD11B2 16 1 RAAS ACE 17 7 RAAS CLCNKB 1 2 Ion channel/transporter ATP1A1 1 17 Ion channel/transporter ATP1B1 1 24 Ion channel/transporter KCNJ1 11 14 Ion channel/transporter SCNN1A 12 6 Ion channel/transporter SLC12A1 15 13 Ion channel/transporter SCNN1G 16 5 Ion channel/transporter SCNN1B 16 15 Ion channel/transporter SLC12A3 16 15 Ion channel/transporter Catecholamine DRD3 3 12 signaling Catecholamine GRK4 4 19 signaling Catecholamine ADRB2 5 11 signaling Catecholamine DRD1 5 7 signaling Catecholamine DDC 7 65 signaling Catecholamine ADRA1A 8 25 signaling Catecholamine DBH 9 8 signaling Catecholamine DRD2 11 18 signaling Catecholamine COMT 22 9 signaling ADD2 2 39 Ion channel regulators

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ADD1 4 22 Ion channel regulators ADD3 10 11 Ion channel regulators WNK1 12 27 Ion channel regulators WNK4 17 1 Ion channel regulators

Table 5B: Inflammation genes Gene Chromosome #SNPs Category REL 2 1 NFkB complex NFKB1 4 27 NFkB complex NFKB2 10 3 NFkB complex RELA 11 2 NFkB complex RELB 19 2 NFkB complex IKBKE 1 3 IKK complex IKBKB 8 7 IKK complex CHUK 10 10 IKK complex NFKBIZ 3 6 IKK inhibitors NFKBIE 6 4 IKK inhibitors NFKBIA 14 5 IKK inhibitors NFKBIB 19 3 IKK inhibitors Pro-inflammatory TNFRSF1B 1 17 cytokines Pro-inflammatory CRP 1 5 cytokines Pro-inflammatory TNF 6 1 cytokines Pro-inflammatory IL17A 6 4 cytokines Pro-inflammatory IL6 7 8 cytokines Pro-inflammatory TNFRSF1A 12 3 cytokines Pro-inflammatory CCL2 17 8 cytokines Pro-inflammatory CCL3 17 3 cytokines Pro-inflammatory TGFB1 19 4 cytokines LEPR 1 49 Adipokines ADIPOR1 1 6 Adipokines ADIPOQ 3 10 Adipokines

106

LEP 7 16 Adipokines ADIPOR2 12 21 Adipokines RETN 19 2 Adipokines VCAM1 1 8 Other EMILIN1 2 3 Other NOS3 7 6 Other ICAM1 19 1 Other

107

Table 6A: Top SBP SNP-SNP interactions by –log(p-value) rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 RENAL-RENAL rs2289114 SLC12A3 0.19 rs6557946 ADRA1A 0.45 0.23 1.06 4.33 rs12133039 ATP1A1 0.03 rs11221484 KCNJ1 0.41 0.45 0.03 4.31 rs41464748 ATP1A1 0.03 rs11221484 KCNJ1 0.41 0.43 0.03 4.29 rs3755353 ADD2 0.36 rs9657021 CYP11B1 0.03 0.68 0.09 4.22 rs11221484 KCNJ1 0.41 rs41434350 ATP1A1 0.03 0.03 0.35 4.12 rs1451375 DDC 0.13 rs2036108 ADRA1A 0.23 1.03 0.01 4.12 rs265972 DRD1 0.44 rs4681444 AGTR1 0.17 0.57 0.17 4.11 rs1892094 ATP1B1 0.47 rs17055965 ADRA1A 0.07 0.58 0.49 4.11 rs2515933 GRK4 0.09 rs7205273 SCNN1B 0.11 0.18 0.03 4.1 rs17055954 ADRA1A 0.07 rs1892094 ATP1B1 0.47 0.46 0.58 4.09 rs2501574 ADD3 0.14 rs12596831 SCNN1B 0.04 0.15 0.15 4.09 rs2036108 ADRA1A 0.23 rs4585697 DDC 0.12 0.01 0.55 4.04 rs2036108 ADRA1A 0.23 rs4452748 DDC 0.12 0.01 1.1 4.03 rs265972 DRD1 0.44 rs4681440 AGTR1 0.17 0.57 0.19 4 RENAL-INFLAMMATION INFLAMMATION-INFLAMMATION rs12709426 ACE 0.27 rs5746074 TNFRSF1B 0.04 0.89 1.33 4.81 rs7831617 CYP11B2 0.48 rs1880242 IL6 0.49 1.5 0.05 4.58 rs2933249 AGTR1 0.2 rs652625 TNFRSF1B 0.06 0.4 2.29 4.5 rs41425546 CYP11B1 0.41 rs2286379 ADIPOR2 0.42 1.19 0.66 4.31 rs2933251 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.36 2.29 4.17 rs409742 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.36 2.29 4.17 rs4073930 SCNN1G 0.24 rs2304681 EMILIN1 0.41 0.47 0.96 4.16 rs2638358 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.38 2.29 4.15 rs4073291 SCNN1G 0.24 rs2304681 EMILIN1 0.41 0.5 0.96 4.12 rs2638359 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.44 2.29 4.09 rs926517 ATP1B1 0.45 rs791608 LEP 0.26 0.11 0.25 4.08 rs275651 AGTR1 0.2 rs652625 TNFRSF1B 0.06 0.2 2.29 4.07 rs2071404 AGT 0.13 rs17875671 IKBKB 0.08 1.41 0.12 4.07 rs11194981 ADD3 0.16 rs9436746 LEPR 0.4 0.82 0.52 4.03 rs41425546 CYP11B1 0.41 rs10744552 ADIPOR2 0.43 1.19 0.9 4.03

Table 6B: Top DBP SNP-SNP interactions by –log(p-value) rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 RENAL-RENAL rs12695918 AGTR1 0.04 rs17137982 KCNJ1 0.15 0.39 0.41 5.39 rs12695918 AGTR1 0.04 rs3758766 KCNJ1 0.16 0.39 0.58 5.38 rs12695918 AGTR1 0.04 rs1148058 KCNJ1 0.16 0.39 0.41 5.27

108

rs12133039 ATP1A1 0.03 rs11221484 KCNJ1 0.41 0.15 0.06 5.02 rs877741 ADRB2 0.2 rs16931965 WNK1 0.15 0.04 0.35 4.64 rs41464748 ATP1A1 0.03 rs11221484 KCNJ1 0.41 0.04 0.06 4.62 rs11221484 KCNJ1 0.41 rs41434350 ATP1A1 0.03 0.06 0.03 4.52 rs877743 ADRB2 0.2 rs16931965 WNK1 0.15 0.14 0.35 4.49 rs3025425 DBH 0.02 rs1512343 MR 0.37 0.15 0.61 4.47 rs877743 ADRB2 0.2 rs12369414 WNK1 0.16 0.14 0.29 4.43 rs877741 ADRB2 0.2 rs10849573 WNK1 0.15 0.04 0.29 4.39 rs12134095 ATP1A1 0.03 rs11221484 KCNJ1 0.41 0.05 0.06 4.38 rs4073930 SCNN1G 0.24 rs2002453 DRD2 0.23 0.44 0.2 4.37 rs4073930 SCNN1G 0.24 rs2245805 DRD2 0.23 0.44 0.14 4.35 rs11575557 DDC 0.1 rs11064580 WNK1 0.43 2.17 0.2 4.35 rs12369414 WNK1 0.16 rs877741 ADRB2 0.2 0.29 0.04 4.32 rs10919065 ATP1B1 0.39 rs6535584 MR 0.41 0.42 0.23 4.31 rs250567 SCNN1B 0.06 rs4580999 DDC 0.38 1.22 0.05 4.31 rs17620330 MR 0.06 rs10519963 MR 0.13 0.28 0.17 4.25 rs877743 ADRB2 0.2 rs10849573 WNK1 0.15 0.14 0.29 4.23 rs2002453 DRD2 0.23 rs4073291 SCNN1G 0.24 0.2 0.45 4.23 rs10503799 ADRA1A 0.06 rs11221484 KCNJ1 0.41 1.2 0.06 4.22 rs17620330 MR 0.06 rs10434100 MR 0.13 0.28 0.3 4.2 rs17620330 MR 0.06 rs17484873 MR 0.12 0.28 0.3 4.19 rs2245805 DRD2 0.23 rs4073291 SCNN1G 0.24 0.14 0.45 4.11 rs11221484 KCNJ1 0.41 rs6986973 ADRA1A 0.06 0.06 1.2 4.09 rs11221484 KCNJ1 0.41 rs6986328 ADRA1A 0.06 0.06 1.2 4.06 rs16928108 WNK1 0.15 rs877741 ADRB2 0.2 0.31 0.04 4.05 rs275651 AGTR1 0.2 rs4245549 DDC 0.11 0.3 0.02 4.05 rs12708966 SLC12A3 0.02 rs1891710 GRK4 0.37 1.66 0.07 4 rs265973 DRD1 0.43 rs17137982 KCNJ1 0.15 0.35 0.41 4 INFLAMMATION-INFLAMMATION rs4982270 NFKBIA 0.5 rs3806318 LEPR 0.27 0.41 0.11 4.22 rs12610253 RETN 0.33 rs4766415 ADIPOR2 0.47 0.91 0.4 4.22 rs17112751 CHUK 0.02 rs3181092 VCAM1 0.35 0.88 0.14 4.08 rs3754734 EMILIN1 0.39 rs2304681 EMILIN1 0.41 0.54 0.41 4.04 RENAL-INFLAMMATION rs929358 ATP1A1 0.02 rs17412368 LEPR 0.47 0.07 0.64 4.56 rs929358 ATP1A1 0.02 rs7524834 LEPR 0.47 0.07 0.47 4.53 rs17006246 ADD2 0.16 rs694936 NFKBIZ 0.44 0.82 0.63 4.43 rs17006246 ADD2 0.16 rs9844218 NFKBIZ 0.33 0.82 0.04 4.37 rs929358 ATP1A1 0.02 rs7513047 LEPR 0.47 0.07 0.47 4.27 rs17006285 ADD2 0.16 rs694936 NFKBIZ 0.44 0.52 0.63 4.27 rs3771466 ADD2 0.3 rs2530797 CCL2 0.16 0.2 0.77 4.18

109

rs929358 ATP1A1 0.02 rs2025805 LEPR 0.47 0.07 0.36 4.16 rs17006285 ADD2 0.16 rs9844218 NFKBIZ 0.33 0.52 0.04 4.06

Table 6C: Top SBP (no BMI) SNP-SNP interactions by –log(p-value) rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 RENAL-RENAL rs2289114 SLC12A3 0.19 rs6557946 ADRA1A 0.45 0.57 1.01 4.97 rs3755353 ADD2 0.36 rs9657021 CYP11B1 0.03 0.68 0.02 4.64 rs2289114 SLC12A3 0.19 rs6998591 ADRA1A 0.47 0.57 1.05 4.42 rs2798285 GRK4 0.47 rs9804992 WNK1 0.16 0.13 0.33 4.26 rs2036108 ADRA1A 0.23 rs4452748 DDC 0.12 0.05 1.01 4.23 rs2036108 ADRA1A 0.23 rs4585697 DDC 0.12 0.05 0.93 4.19 rs1451375 DDC 0.13 rs2036108 ADRA1A 0.23 0.84 0.05 4.18 rs2036108 ADRA1A 0.23 rs10276473 DDC 0.12 0.05 0.06 4.15 rs265972 DRD1 0.44 rs4681444 AGTR1 0.17 0.44 0.38 4.09 INFLAMMATION-INFLAMMATION RENAL-INFLAMMATION rs2933249 AGTR1 0.2 rs652625 TNFRSF1B 0.06 0.31 2.51 5.59 rs2933251 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.34 2.51 5.04 rs409742 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.34 2.51 5.04 rs2638358 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.36 2.51 5.01 rs2638359 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.41 2.51 4.96 rs4073930 SCNN1G 0.24 rs2304681 EMILIN1 0.41 0.52 1.38 4.92 rs275651 AGTR1 0.2 rs652625 TNFRSF1B 0.06 0.18 2.51 4.91 rs4073291 SCNN1G 0.24 rs2304681 EMILIN1 0.41 0.55 1.38 4.89 rs2638357 AGTR1 0.21 rs652625 TNFRSF1B 0.06 0.58 2.51 4.71 rs275652 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.26 2.51 4.6 rs12709426 ACE 0.27 rs5746074 TNFRSF1B 0.04 0.48 0.88 4.54 rs4732908 ADRA1A 0.03 rs230520 NFKB1 0.33 0.19 0.36 4.52 rs1492103 AGTR1 0.19 rs652625 TNFRSF1B 0.06 0.37 2.51 4.51 rs7831617 CYP11B2 0.48 rs1880242 IL6 0.49 1.28 0.08 4.43 rs41425546 CYP11B1 0.41 rs2286379 ADIPOR2 0.42 0.9 0.76 4.19 rs1529930 SLC12A3 0.15 rs5029748 IKBKB 0.27 0.19 0.01 4.08 rs4732908 ADRA1A 0.03 rs230493 NFKB1 0.32 0.19 0.4 4.01

Table 6D: Top DBP (no BMI) SNP-SNP interactions by –log(p-value) rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 RENAL-RENAL rs12695918 AGTR1 0.04 rs17137982 KCNJ1 0.15 0.31 0.61 5.02 rs12695918 AGTR1 0.04 rs3758766 KCNJ1 0.16 0.31 0.73 4.94 rs12695918 AGTR1 0.04 rs1148058 KCNJ1 0.16 0.31 0.58 4.91

110

rs877741 ADRB2 0.2 rs16931965 WNK1 0.15 0.01 0.35 4.46 rs877743 ADRB2 0.2 rs16931965 WNK1 0.15 0.18 0.35 4.24 rs877741 ADRB2 0.2 rs10849573 WNK1 0.15 0.01 0.33 4.16 rs877743 ADRB2 0.2 rs12369414 WNK1 0.16 0.18 0.24 4.11 rs4073930 SCNN1G 0.24 rs2002453 DRD2 0.23 0.46 0.11 4.09 rs12369414 WNK1 0.16 rs877741 ADRB2 0.2 0.24 0.01 4.07 rs4073930 SCNN1G 0.24 rs2245805 DRD2 0.23 0.46 0.06 4.06 rs3025425 DBH 0.02 rs1512343 MR 0.37 0.28 0.44 4.05 rs10919065 ATP1B1 0.39 rs6535584 MR 0.41 0.5 0.27 4.02 rs250567 SCNN1B 0.06 rs2167364 DDC 0.24 1.08 0.1 4.01 INFLAMMATION-INFLAMMATION RENAL-INFLAMMATION rs929358 ATP1A1 0.02 rs7524834 LEPR 0.47 0.1 0.37 4.44 rs929358 ATP1A1 0.02 rs17412368 LEPR 0.47 0.1 0.49 4.39 rs929358 ATP1A1 0.02 rs7513047 LEPR 0.47 0.1 0.33 4.12 rs929358 ATP1A1 0.02 rs2025805 LEPR 0.47 0.1 0.23 4.05 rs3771466 ADD2 0.3 rs2530797 CCL2 0.16 0.1 0.55 4.04

111

Table 7A: Top SBP renal gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs10919065 ATP1B1 0.393 rs1486009 DRD3 0.072 1.084 1.492 2.177 0.779 rs10919065 ATP1B1 0.393 rs11571468 WNK1 0.068 1.084 0.763 1.788 0.779 rs1486009 DRD3 0.072 rs13116347 MR 0.117 1.492 1.581 1.755 0.779 rs2116715 ADRB2 0.382 rs7956915 SCNN1A 0.47 1.211 0.342 1.728 0.779 rs17115583 DRD2 0.105 rs12709435 ACE 0.285 0.691 1.092 1.703 0.779 rs3771424 ADD2 0.193 rs4681157 AGTR1 0.411 1.473 0.846 1.675 0.779 rs1486009 DRD3 0.072 rs12709435 ACE 0.285 1.492 1.092 1.664 0.779 rs1531916 SLC12A1 0.179 rs11074555 SCNN1B 0.241 1.473 1.152 1.653 0.779 rs6271 DBH 0.075 rs4646312 COMT 0.409 1.488 1.061 1.626 0.779 rs4499238 SCNN1G 0.188 rs37027 SLC12A3 0.452 0.681 1.279 1.551 0.779 rs1134095 CYP11B1 0.417 rs4646312 COMT 0.409 2.023 1.061 1.542 0.779 rs7956915 SCNN1A 0.47 rs4499238 SCNN1G 0.188 0.342 0.681 1.527 0.779 rs6696416 CLCNKB 0.069 rs850617 ATP1A1 0.421 0.49 2.031 1.513 0.779 rs1997034 REN 0.252 rs1376523 DDC 0.238 0.532 1.619 1.512 0.779 rs16843773 GRK4 0.399 rs4919683 CYP17A1 0.415 0.684 0.453 1.494 0.779 rs13116347 MR 0.117 rs17115583 DRD2 0.105 1.581 0.691 1.461 0.779 rs10919065 ATP1B1 0.393 rs7956915 SCNN1A 0.47 1.084 0.342 1.436 0.779 rs1486009 DRD3 0.072 rs4919683 CYP17A1 0.415 1.492 0.453 1.436 0.779 rs7548781 HSD11B1 0.227 rs11568030 AGT 0.059 0.464 1.764 1.391 0.779 rs3771424 ADD2 0.193 rs11074555 SCNN1B 0.241 1.473 1.152 1.361 0.779

Table 7B: Top SBP inflammation gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs10919065 ATP1B1 0.393 rs1486009 DRD3 0.072 1.084 1.492 2.177 0.779 rs10919065 ATP1B1 0.393 rs11571468 WNK1 0.068 1.084 0.763 1.788 0.779 rs1486009 DRD3 0.072 rs13116347 MR 0.117 1.492 1.581 1.755 0.779 rs2116715 ADRB2 0.382 rs7956915 SCNN1A 0.47 1.211 0.342 1.728 0.779 rs17115583 DRD2 0.105 rs12709435 ACE 0.285 0.691 1.092 1.703 0.779 rs3771424 ADD2 0.193 rs4681157 AGTR1 0.411 1.473 0.846 1.675 0.779 rs1486009 DRD3 0.072 rs12709435 ACE 0.285 1.492 1.092 1.664 0.779 rs1531916 SLC12A1 0.179 rs11074555 SCNN1B 0.241 1.473 1.152 1.653 0.779 rs6271 DBH 0.075 rs4646312 COMT 0.409 1.488 1.061 1.626 0.779 rs4499238 SCNN1G 0.188 rs37027 SLC12A3 0.452 0.681 1.279 1.551 0.779 rs1134095 CYP11B1 0.417 rs4646312 COMT 0.409 2.023 1.061 1.542 0.779 rs7956915 SCNN1A 0.47 rs4499238 SCNN1G 0.188 0.342 0.681 1.527 0.779 rs6696416 CLCNKB 0.069 rs850617 ATP1A1 0.421 0.49 2.031 1.513 0.779 rs1997034 REN 0.252 rs1376523 DDC 0.238 0.532 1.619 1.512 0.779 rs16843773 GRK4 0.399 rs4919683 CYP17A1 0.415 0.684 0.453 1.494 0.779

112

rs13116347 MR 0.117 rs17115583 DRD2 0.105 1.581 0.691 1.461 0.779 rs10919065 ATP1B1 0.393 rs7956915 SCNN1A 0.47 1.084 0.342 1.436 0.779 rs1486009 DRD3 0.072 rs4919683 CYP17A1 0.415 1.492 0.453 1.436 0.779 rs7548781 HSD11B1 0.227 rs11568030 AGT 0.059 0.464 1.764 1.391 0.779 rs3771424 ADD2 0.193 rs11074555 SCNN1B 0.241 1.473 1.152 1.361 0.779

Table 7C: Top DBP renal gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs11575557 DDC 0.097 rs11064524 WNK1 0.232 2.173 0.663 2.789 0.482 rs3766031 ATP1B1 0.129 rs1531916 SLC12A1 0.179 2.162 0.551 2.379 0.482 rs7539947 ATP1A1 0.075 rs11575557 DDC 0.097 1.202 2.173 2.334 0.482 rs6696416 CLCNKB 0.069 rs17529477 DRD2 0.321 1.073 0.391 2.315 0.482 rs16860812 AGTR1 0.057 rs2883930 MR 0.267 1.342 1.121 2.211 0.482 rs167770 DRD3 0.291 rs4351 ACE 0.464 0.271 0.691 2.206 0.482 rs167770 DRD3 0.291 rs34020100 GRK4 0.052 0.271 1.393 2.075 0.559 rs7539947 ATP1A1 0.075 rs675482 KCNJ1 0.292 1.202 1.347 2.009 0.569 rs41528047 ADD1 0.178 rs34020100 GRK4 0.052 1.992 1.393 1.916 0.624 rs41528047 ADD1 0.178 rs6271 DBH 0.075 1.992 2.56 1.848 0.624 rs16860812 AGTR1 0.057 rs11575557 DDC 0.097 1.342 2.173 1.755 0.624 rs6696416 CLCNKB 0.069 rs41528047 ADD1 0.178 1.073 1.992 1.743 0.624 rs6271 DBH 0.075 rs1531916 SLC12A1 0.179 2.56 0.551 1.739 0.624 rs10093618 CYP11B1 0.417 rs10883782 CYP17A1 0.169 2.85 1.308 1.703 0.624 rs7548781 HSD11B1 0.227 rs11568030 AGT 0.059 1.017 1.974 1.687 0.624 rs4736317 CYP11B2 0.415 rs10883782 CYP17A1 0.169 3.162 1.308 1.668 0.624 rs10093618 CYP11B1 0.417 rs11064524 WNK1 0.232 2.85 0.663 1.511 0.835 rs167770 DRD3 0.291 rs11575557 DDC 0.097 0.271 2.173 1.49 0.835 rs4736317 CYP11B2 0.415 rs11064524 WNK1 0.232 3.162 0.663 1.453 0.84 rs3766031 ATP1B1 0.129 rs10883782 CYP17A1 0.169 2.162 1.308 1.373 0.84

Table 7D: Top DBP inflammation gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs5029748 IKBKB 0.272 rs4982270 NFKBIA 0.5 0.413 0.414 3.523 0.042 rs2871360 IKBKE 0.48 rs7358099 CHUK 0.07 0.718 1.017 3.487 0.042 rs11612383 ADIPOR2 0.296 rs12610253 RETN 0.328 1.249 0.913 3.424 0.042 rs3754734 EMILIN1 0.387 rs4982270 NFKBIA 0.5 0.542 0.414 2.415 0.311 rs3754734 EMILIN1 0.387 rs5029748 IKBKB 0.272 0.542 0.413 2.319 0.311 rs3918186 NOS3 0.08 rs5029748 IKBKB 0.272 1.232 0.413 2.15 0.311 rs3732179 REL 0.325 rs3918186 NOS3 0.08 0.14 1.232 1.969 0.311 rs3754734 EMILIN1 0.387 rs12610253 RETN 0.328 0.542 0.913 1.95 0.311 rs3917891 CCL2 0.199 rs11083616 TGFB1 0.416 0.869 0.26 1.933 0.311 rs4982270 NFKBIA 0.5 rs12610253 RETN 0.328 0.414 0.913 1.921 0.311 rs1466462 RELA 0.374 rs2889490 RELB 0.491 0.155 0.117 1.91 0.311

113

rs2625288 NFKBIZ 0.188 rs11612383 ADIPOR2 0.296 0.89 1.249 1.908 0.311 rs2625288 NFKBIZ 0.188 rs2069837 IL6 0.338 0.89 1.35 1.882 0.311 rs7358099 CHUK 0.07 rs4802998 NFKBIB 0.378 1.017 0.109 1.856 0.311 rs2871360 IKBKE 0.48 rs2889490 RELB 0.491 0.718 0.117 1.853 0.311 rs4711998 IL17A 0.264 rs5029748 IKBKB 0.272 0.939 0.413 1.759 0.361 rs4711998 IL17A 0.264 rs10954174 LEP 0.303 0.939 1.615 1.726 0.367 rs182052 ADIPOQ 0.327 rs504697 NFKBIE 0.287 1.183 1.553 1.695 0.372 rs2625288 NFKBIZ 0.188 rs4699030 NFKB1 0.387 0.89 1.472 1.589 0.435 rs5029748 IKBKB 0.272 rs2569702 ICAM1 0.402 0.413 0.366 1.571 0.435

Table 7E: Top SBP (no BMI) renal gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs10919065 ATP1B1 0.393 rs1486009 DRD3 0.072 1.215 1.647 2.477 0.863 rs10919065 ATP1B1 0.393 rs11571468 WNK1 0.068 1.215 0.795 2.373 0.863 rs17115583 DRD2 0.105 rs12709435 ACE 0.285 0.737 0.737 1.96 0.863 rs1134095 CYP11B1 0.417 rs4646312 COMT 0.409 1.95 0.881 1.863 0.863 rs1486009 DRD3 0.072 rs12709435 ACE 0.285 1.647 0.737 1.702 0.863 rs1486009 DRD3 0.072 rs13116347 MR 0.117 1.647 1.553 1.682 0.863 rs2235543 HSD11B1 0.131 rs265975 DRD1 0.342 0.631 0.558 1.656 0.863 rs16843535 ADD1 0.062 rs12709435 ACE 0.285 0.921 0.737 1.642 0.863 rs850617 ATP1A1 0.421 rs7956915 SCNN1A 0.47 2.402 0.382 1.608 0.863 rs7956915 SCNN1A 0.47 rs152733 SCNN1B 0.205 0.382 0.956 1.596 0.863 rs7956915 SCNN1A 0.47 rs4499238 SCNN1G 0.188 0.382 0.554 1.591 0.863 rs675482 KCNJ1 0.292 rs4646312 COMT 0.409 0.592 0.881 1.565 0.863 rs10786712 CYP17A1 0.395 rs11571468 WNK1 0.068 0.171 0.795 1.565 0.863 rs2116715 ADRB2 0.382 rs11575557 DDC 0.097 1.125 2.03 1.55 0.863 rs4736317 CYP11B2 0.415 rs4646312 COMT 0.409 2.104 0.881 1.507 0.863 rs2116715 ADRB2 0.382 rs7956915 SCNN1A 0.47 1.125 0.382 1.466 0.863 rs13116347 MR 0.117 rs17115583 DRD2 0.105 1.553 0.737 1.445 0.863 rs6696416 CLCNKB 0.069 rs1486009 DRD3 0.072 0.507 1.647 1.397 0.863 rs2235543 HSD11B1 0.131 rs152733 SCNN1B 0.205 0.631 0.956 1.37 0.863 rs1531916 SLC12A1 0.179 rs152733 SCNN1B 0.205 1.677 0.956 1.351 0.863

Table 7F: Top SBP (no BMI) inflammation gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs4982270 NFKBIA 0.5 rs2241704 NFKBIB 0.214 0.547 0.783 2.611 0.602 rs10244329 LEP 0.263 rs11574851 NFKB2 0.054 1.342 0.271 2.543 0.602 rs182052 ADIPOQ 0.327 rs4711998 IL17A 0.264 1.85 0.821 2.375 0.602 rs10244329 LEP 0.263 rs2241704 NFKBIB 0.214 1.342 0.783 2.222 0.643 rs2625288 NFKBIZ 0.188 rs1466462 RELA 0.374 1.401 0.76 1.955 0.775 rs2871360 IKBKE 0.48 rs3786507 RELB 0.421 1.023 0.146 1.934 0.775 rs1634506 CCL3 0.163 rs2569702 ICAM1 0.402 0.125 0.498 1.63 0.775

114

rs1634506 CCL3 0.163 rs2241704 NFKBIB 0.214 0.125 0.783 1.627 0.775 rs6474388 IKBKB 0.065 rs11867200 CCL2 0.083 0.092 1.05 1.607 0.775 rs6474388 IKBKB 0.065 rs11574851 NFKB2 0.054 0.092 0.271 1.565 0.775 rs652625 TNFRSF1B 0.063 rs1634506 CCL3 0.163 2.507 0.125 1.528 0.775 rs12062820 LEPR 0.179 rs2069837 IL6 0.338 1.579 0.522 1.518 0.775 rs4711998 IL17A 0.264 rs11867200 CCL2 0.083 0.821 1.05 1.473 0.775 rs3732179 REL 0.325 rs3786507 RELB 0.421 0.309 0.146 1.449 0.775 rs11867200 CCL2 0.083 rs2241713 TGFB1 0.423 1.05 0.429 1.439 0.775 rs2871360 IKBKE 0.48 rs2241704 NFKBIB 0.214 1.023 0.783 1.4 0.775 rs4711998 IL17A 0.264 rs12821401 ADIPOR2 0.143 0.821 2.848 1.337 0.775 rs2304681 EMILIN1 0.409 rs12610253 RETN 0.328 1.38 2.496 1.33 0.775 rs3732179 REL 0.325 rs3918188 NOS3 0.355 0.309 0.563 1.324 0.775 rs2304681 EMILIN1 0.409 rs3732179 REL 0.325 1.38 0.309 1.315 0.775

Table 7G: Top DBP (no BMI) renal gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs2883930 MR 0.267 rs11957757 ADRB2 0.451 1.183 0.709 2.775 0.397 rs11575557 DDC 0.097 rs11064524 WNK1 0.232 2.486 0.429 2.768 0.397 rs3766031 ATP1B1 0.129 rs1531916 SLC12A1 0.179 1.952 0.648 2.527 0.423 rs6696416 CLCNKB 0.069 rs17529477 DRD2 0.321 1.039 0.495 2.374 0.423 rs16860812 AGTR1 0.057 rs2883930 MR 0.267 1.031 1.183 2.329 0.423 rs167770 DRD3 0.291 rs4351 ACE 0.464 0.251 0.903 2.241 0.423 rs7539947 ATP1A1 0.075 rs675482 KCNJ1 0.292 1.194 1.44 2.162 0.423 rs167770 DRD3 0.291 rs34020100 GRK4 0.052 0.251 1.111 2.129 0.423 rs41528047 ADD1 0.178 rs6271 DBH 0.075 2.006 2.358 2.069 0.423 rs7539947 ATP1A1 0.075 rs11575557 DDC 0.097 1.194 2.486 2.041 0.423 rs41528047 ADD1 0.178 rs34020100 GRK4 0.052 2.006 1.111 1.919 0.509 rs10093618 CYP11B1 0.417 rs10883782 CYP17A1 0.169 2.682 1.15 1.878 0.513 rs17529477 DRD2 0.321 rs4149570 SCNN1A 0.394 0.495 0.262 1.799 0.551 rs4736317 CYP11B2 0.415 rs10883782 CYP17A1 0.169 2.959 1.15 1.78 0.551 rs11194988 ADD3 0.187 rs889299 SCNN1B 0.233 1.745 1.359 1.567 0.841 rs10883782 CYP17A1 0.169 rs889299 SCNN1B 0.233 1.15 1.359 1.429 0.89 rs167770 DRD3 0.291 rs11575557 DDC 0.097 0.251 2.486 1.414 0.89 rs6271 DBH 0.075 rs889299 SCNN1B 0.233 2.358 1.359 1.363 0.89 rs10093618 CYP11B1 0.417 rs11064524 WNK1 0.232 2.682 0.429 1.358 0.89 rs16853059 REN 0.133 rs675482 KCNJ1 0.292 0.354 1.44 1.343 0.89 rs4736317 CYP11B2 0.415 rs11064524 WNK1 0.232 2.959 0.429 1.34 0.89

Table 7H: Top DBP (no BMI) inflammation gene-gene interactions by q-value rs#1 Gene1 MAF1 rs#2 Gene2 MAF2 Main1 Main2 Int1x2 Q1x2 rs5029748 IKBKB 0.272 rs4982270 NFKBIA 0.5 0.45 0.466 3.212 0.143 rs11612383 ADIPOR2 0.296 rs12610253 RETN 0.328 1.269 0.626 3.124 0.143

115

rs2871360 IKBKE 0.48 rs7358099 CHUK 0.07 0.742 1.06 2.82 0.146 rs2304681 EMILIN1 0.409 rs12610253 RETN 0.328 0.641 0.626 2.814 0.146 rs2625288 NFKBIZ 0.188 rs11612383 ADIPOR2 0.296 1.007 1.269 2.384 0.315 rs2304681 EMILIN1 0.409 rs4982270 NFKBIA 0.5 0.641 0.466 2.303 0.316 rs3918186 NOS3 0.08 rs5029748 IKBKB 0.272 0.997 0.45 1.908 0.468 rs2304681 EMILIN1 0.409 rs5029748 IKBKB 0.272 0.641 0.45 1.881 0.468 rs3732179 REL 0.325 rs3918186 NOS3 0.08 0.06 0.997 1.871 0.468 rs4982270 NFKBIA 0.5 rs12610253 RETN 0.328 0.466 0.626 1.801 0.468 rs182052 ADIPOQ 0.327 rs3917891 CCL2 0.199 1.27 0.917 1.8 0.468 rs4711998 IL17A 0.264 rs10954174 LEP 0.303 0.583 1.428 1.783 0.468 rs2625288 NFKBIZ 0.188 rs2069837 IL6 0.338 1.007 0.964 1.771 0.468 rs1466462 RELA 0.374 rs2889490 RELB 0.491 0.218 0.206 1.746 0.468 rs5029748 IKBKB 0.272 rs2569702 ICAM1 0.402 0.45 0.234 1.732 0.468 rs7358099 CHUK 0.07 rs4802998 NFKBIB 0.378 1.06 0.267 1.7 0.468 rs182052 ADIPOQ 0.327 rs504697 NFKBIE 0.287 1.27 1.241 1.68 0.468 rs2871360 IKBKE 0.48 rs4699030 NFKB1 0.387 0.742 1.44 1.593 0.495 rs3918186 NOS3 0.08 rs7358099 CHUK 0.07 0.997 1.06 1.591 0.495 rs504697 NFKBIE 0.287 rs3917891 CCL2 0.199 1.241 0.917 1.577 0.495

116

Table 8: Top SNP-BMI and SNP-Sex interaction (2 d.f.) results Gene Chromosome rs # Phenotype Type p-value ATP1B1 1 rs10405024 SBP SNP-BMI 3.1 x 10-4 ADD1 4 rs2097081 SBP SNP-Sex 3.7 x 10-4 DRD1 5 rs6878159 SBP SNP-Sex 2.7 x 10-6 ADIPOR2 12 rs2058033 SBP SNP-Sex 6.1 x 10-4 Table 8 shows SNP-BMI and SNP-Sex interaction (2 d.f.) results for those SNPs within one order of magnitude of Bonferroni-corrected significance (6.3 x 10-5).

Table 9: Summary of final models from LASSO  stepwise procedure.

Mains Mains Initial set of added in added in Phenotype interactions LASSO stepwise # mains # interactions Multiple r^2 covariates SBP only NA NA NA NA 0.3054 SBP top 100 no no 0 11 0.3111 SBP top 100 no yes 6 9 0.3129 SBP top 100 yes no 10 9 0.3149 SBP top 100 yes yes 12 8 0.3153 SBP top 1,000 no no 0 29 0.3231 SBP top 1,000 no yes 4 27 0.3238 SBP top 1,000 yes no 0 29 0.3231 SBP top 1,000 yes yes 4 27 0.3238 covariates DBP only NA NA NA NA 0.1959 DBP top 100 no no 0 8 0.1984 DBP top 100 no yes 3 5 0.1987 DBP top 100 yes no 4 4 0.1992 DBP top 100 yes yes 4 4 0.1992 DBP top 1,000 no no 0 50 0.2185 DBP top 1,000 no yes 16 37 0.2204 DBP top 1,000 yes no 19 51 0.228 DBP top 1,000 yes yes 25 53 0.2305

117

Table 10A: SBP, top 100, no mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs2501574 ADD3 rs12596831 SCNN1B 3.36 4.09 rs6540547 HSD11B1 rs529948 NFKBIE 2.7 3.82 rs1858416 MR rs17055965 ADRA1A 2.56 3.48 rs265975 DRD1 rs9657021 CYP11B1 1.71 3.59 rs4681443 AGTR1 rs265972 DRD1 1.49 4.08 rs6668161 ATP1A1 rs11221484 KCNJ1 1.4 3.51 rs926517 ATP1B1 rs791608 LEP 1.08 4.08 rs2376018 LEPR rs10744552 ADIPOR2 1.13 3.52 rs2304681 EMILIN1 rs4073930 SCNN1G 1.08 4.16 rs2515933 GRK4 rs7205273 SCNN1B 0.95 4.1 rs3766042 ATP1B1 rs17055954 ADRA1A 0.91 3.73

Table 10B: SBP, top 100, no mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs2501574 ADD3 rs12596831 SCNN1B 3.3 4.09 rs6540547 HSD11B1 rs529948 NFKBIE 2.6 3.82 rs1858416 MR rs17055965 ADRA1A 2.46 3.48 rs2052273 ADD2 NA NA 2.41 2.29 rs6668161 ATP1A1 rs11221484 KCNJ1 1.5 3.51 rs265975 DRD1 rs9657021 CYP11B1 1.71 3.59 rs926517 ATP1B1 NA NA 1.61 0.29 rs4681443 AGTR1 rs265972 DRD1 1.54 4.08 rs10744552 ADIPOR2 NA NA 1.33 0.28 rs2515933 GRK4 NA NA 1.11 0.09 rs2304681 EMILIN1 rs4073930 SCNN1G 1.09 4.16 rs11571093 REN NA NA 0.93 1.33 rs3766042 ATP1B1 rs17055954 ADRA1A 0.94 3.73 rs2071404 AGT rs17875671 IKBKB 1.4 4.07 rs2071404 AGT NA NA 1.08 0.47

Table 10C: SBP, top 100, mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3755353 ADD2 NA NA 4.16 0.06 rs2501574 ADD3 rs12596831 SCNN1B 3.31 4.09 rs6540547 HSD11B1 rs529948 NFKBIE 2.66 3.82 rs1858416 MR rs17055965 ADRA1A 2.54 3.48 rs6668161 ATP1A1 rs11221484 KCNJ1 1.46 3.51 rs926517 ATP1B1 NA NA 1.58 0.19 rs265975 DRD1 rs9657021 CYP11B1 1.59 3.59 rs4681443 AGTR1 rs265972 DRD1 1.85 4.08

118

rs10744552 ADIPOR2 NA NA 1.37 0.28 rs2241704 NFKBIB NA NA 1.33 0.09 rs1492103 AGTR1 NA NA 1.16 0.97 rs4982270 NFKBIA NA NA 1.04 1.33 rs2515933 GRK4 NA NA 1.15 0.49 rs2625288 NFKBIZ NA NA 1.03 0.06 rs2304681 EMILIN1 rs4073930 SCNN1G 1.08 4.16 rs1782755 LEPR NA NA 1.01 0.05 rs1892094 ATP1B1 rs17055965 ADRA1A 0.88 4.11 rs11571093 REN NA NA 0.83 0.2 rs2071404 AGT rs17875671 IKBKB 0.8 4.07

Table 10D: SBP, top 100, mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3755353 ADD2 NA NA 4.22 0.06 rs2501574 ADD3 rs12596831 SCNN1B 3.49 4.09 rs6540547 HSD11B1 rs529948 NFKBIE 2.64 3.82 rs1858416 MR rs17055965 ADRA1A 2.49 3.48 rs6668161 ATP1A1 rs11221484 KCNJ1 1.42 3.51 rs926517 ATP1B1 NA NA 1.57 0.19 rs265975 DRD1 rs9657021 CYP11B1 1.63 3.59 rs4681443 AGTR1 rs265972 DRD1 1.8 4.08 rs10744552 ADIPOR2 NA NA 1.4 0.28 rs2241704 NFKBIB NA NA 1.27 0.09 rs1492103 AGTR1 NA NA 1.12 0.97 rs4982270 NFKBIA NA NA 0.98 1.33 rs2515933 GRK4 NA NA 1.16 0.49 rs2625288 NFKBIZ NA NA 1.09 0.06 rs2304681 EMILIN1 rs4073930 SCNN1G 0.99 4.16 rs1782755 LEPR NA NA 1 0.05 rs1892094 ATP1B1 rs17055965 ADRA1A 2.27 4.11 rs1892094 ATP1B1 NA NA 1.96 0.2 rs17055965 ADRA1A NA NA 1.3 0.24 rs11571093 REN NA NA 0.86 0.39

Table 10E: DBP, top 100, no mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs7035577 DBH rs12363125 DRD2 1.94 4.04 rs17777307 ADD1 rs12596831 SCNN1B 1.37 3.58 rs2245805 DRD2 rs4073291 SCNN1G 1 4.11 rs3783621 VCAM1 rs11575501 DDC 0.99 3.7 rs2342295 GRK4 rs12708966 SLC12A3 0.97 4.08

119

rs1376523 DDC rs250567 SCNN1B 0.9 3.9 rs17620330 MR rs17484873 MR 0.88 4.19 rs17006246 ADD2 rs9844218 NFKBIZ 0.81 4.37

Table 10F: DBP, top 100, no mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs7035577 DBH NA NA 1.13 0.03 rs12596831 SCNN1B NA NA 1.59 0.42 rs7035577 DBH rs12363125 DRD2 1.68 4.04 rs3783621 VCAM1 NA NA 1.01 1.83 rs2342295 GRK4 rs12708966 SLC12A3 0.98 4.08 rs1376523 DDC rs250567 SCNN1B 0.94 3.9 rs2245805 DRD2 rs4073291 SCNN1G 0.88 4.11 rs17620330 MR rs17484873 MR 0.85 4.19

Table 10G: DBP, top 100, mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs4766415 ADIPOR2 NA NA 1.91 0.03 rs7035577 DBH NA NA 1.31 0.42 rs12596831 SCNN1B NA NA 1.55 0.42 rs7035577 DBH rs12363125 DRD2 1.26 4.04 rs3783621 VCAM1 NA NA 1 0.98 rs2342295 GRK4 rs12708966 SLC12A3 0.96 4.08 rs1376523 DDC rs250567 SCNN1B 1.01 3.9 rs3732179 REL rs16891206 IKBKB 0.88 4.51

Table 10H: DBP, top 100, mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs4766415 ADIPOR2 NA NA 1.91 0.03 rs7035577 DBH NA NA 1.31 0.42 rs12596831 SCNN1B NA NA 1.55 0.42 rs7035577 DBH rs12363125 DRD2 1.26 4.04 rs3783621 VCAM1 NA NA 1 0.98 rs2342295 GRK4 rs12708966 SLC12A3 0.96 4.08 rs1376523 DDC rs250567 SCNN1B 1.01 3.9 rs3732179 REL rs16891206 IKBKB 0.88 4.51

Table 10I: SBP, top 1,000, no mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3755353 ADD2 rs16928112 WNK1 2.03 2.53 rs17412368 LEPR rs926517 ATP1B1 2.81 2.35 rs2501574 ADD3 rs12596831 SCNN1B 3.17 4.09

120

rs536401 ADRA1A rs7819943 CYP11B2 3.5 2.89 rs6540547 HSD11B1 rs529948 NFKBIE 2.9 3.82 rs970467 LEPR rs11611246 WNK1 2.59 2.51 rs11122577 AGT rs8044970 SCNN1B 2.17 2.76 rs17032779 NFKB1 rs2286382 ADIPOR2 2.49 2.78 rs1858416 MR rs17055965 ADRA1A 2.56 3.48 rs11737660 MR rs4543 CYP11B2 1.69 2.52 rs10889567 LEPR rs10276473 DDC 2.12 2.74 rs17127838 LEPR rs10744552 ADIPOR2 1.89 2.48 rs11718446 NFKBIZ rs3910044 MR 1.91 3.12 rs265975 DRD1 rs9657021 CYP11B1 1.7 3.59 rs864265 ADIPOQ rs6975312 DDC 1.82 2.79 rs3766031 ATP1B1 rs2143290 ATP1B1 1.5 2.64 rs4681443 AGTR1 rs265972 DRD1 1.21 4.08 rs6668161 ATP1A1 rs11221484 KCNJ1 1.34 3.51 rs1014947 ADD1 rs4802998 NFKBIB 1.61 2.53 rs522807 TNFRSF1B rs12695902 AGTR1 1.04 2.53 rs6590354 KCNJ1 rs1634506 CCL3 1.19 3.3 rs7788818 LEP rs11064153 SCNN1A 1.31 2.64 rs17484259 MR rs2900865 ADD3 1.16 2.5 rs17024387 MR rs10503800 ADRA1A 1.06 2.9 rs7599454 ADD2 rs4690002 ADD1 1.07 2.41 rs2119026 EMILIN1 rs4073291 SCNN1G 1.04 3.03 rs5746074 TNFRSF1B rs3790426 LEPR 1.02 2.86 rs13097326 AGTR1 rs2675513 AGTR1 0.94 2.37 rs2232847 ADIPOR1 rs11611246 WNK1 0.86 2.45

Table 10J: SBP, top 1,000, no mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3755353 ADD2 NA NA 2.13 0.06 rs17412368 LEPR rs926517 ATP1B1 2.83 2.35 rs2501574 ADD3 rs12596831 SCNN1B 3.17 4.09 rs536401 ADRA1A rs7819943 CYP11B2 3.58 2.89 rs6540547 HSD11B1 rs529948 NFKBIE 2.89 3.82 rs970467 LEPR rs11611246 WNK1 3.26 2.51 rs11122577 AGT rs8044970 SCNN1B 2.09 2.76 rs17032779 NFKB1 rs2286382 ADIPOR2 2.52 2.78 rs1858416 MR rs17055965 ADRA1A 2.57 3.48 rs11737660 MR rs4543 CYP11B2 1.66 2.52 rs10889567 LEPR rs10276473 DDC 2.13 2.74 rs17127838 LEPR rs10744552 ADIPOR2 1.85 2.48

121

rs11718446 NFKBIZ rs3910044 MR 1.82 3.12 rs265975 DRD1 rs9657021 CYP11B1 1.74 3.59 rs864265 ADIPOQ rs6975312 DDC 1.84 2.79 rs3766031 ATP1B1 rs2143290 ATP1B1 1.52 2.64 rs4681443 AGTR1 rs265972 DRD1 1.25 4.08 rs6668161 ATP1A1 rs11221484 KCNJ1 1.31 3.51 rs1014947 ADD1 rs4802998 NFKBIB 1.63 2.53 rs522807 TNFRSF1B rs12695902 AGTR1 1.04 2.53 rs6590354 KCNJ1 NA NA 0.85 0.52 rs2232847 ADIPOR1 NA NA 1.25 0.1 rs7788818 LEP rs11064153 SCNN1A 1.42 2.64 rs2241713 TGFB1 NA NA 1.17 0.14 rs17484259 MR rs2900865 ADD3 1.15 2.5 rs5746074 TNFRSF1B rs3790426 LEPR 1.03 2.86 rs2119026 EMILIN1 rs4073291 SCNN1G 1.04 3.03 rs7599454 ADD2 rs4690002 ADD1 1.01 2.41 rs17024387 MR rs10503800 ADRA1A 1 2.9 rs13097326 AGTR1 rs2675513 AGTR1 0.94 2.37 rs6590354 KCNJ1 rs1634506 CCL3 0.81 3.3

Table 10K: SBP, top 1,000, mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3755353 ADD2 rs16928112 WNK1 2.03 2.53 rs17412368 LEPR rs926517 ATP1B1 2.81 2.35 rs2501574 ADD3 rs12596831 SCNN1B 3.17 4.09 rs536401 ADRA1A rs7819943 CYP11B2 3.5 2.89 rs6540547 HSD11B1 rs529948 NFKBIE 2.9 3.82 rs970467 LEPR rs11611246 WNK1 2.59 2.51 rs11122577 AGT rs8044970 SCNN1B 2.17 2.76 rs17032779 NFKB1 rs2286382 ADIPOR2 2.49 2.78 rs1858416 MR rs17055965 ADRA1A 2.56 3.48 rs11737660 MR rs4543 CYP11B2 1.69 2.52 rs10889567 LEPR rs10276473 DDC 2.12 2.74 rs17127838 LEPR rs10744552 ADIPOR2 1.89 2.48 rs11718446 NFKBIZ rs3910044 MR 1.91 3.12 rs265975 DRD1 rs9657021 CYP11B1 1.7 3.59 rs864265 ADIPOQ rs6975312 DDC 1.82 2.79 rs3766031 ATP1B1 rs2143290 ATP1B1 1.5 2.64 rs4681443 AGTR1 rs265972 DRD1 1.21 4.08 rs6668161 ATP1A1 rs11221484 KCNJ1 1.34 3.51 rs1014947 ADD1 rs4802998 NFKBIB 1.61 2.53

122

rs522807 TNFRSF1B rs12695902 AGTR1 1.04 2.53 rs6590354 KCNJ1 rs1634506 CCL3 1.19 3.3 rs7788818 LEP rs11064153 SCNN1A 1.31 2.64 rs17484259 MR rs2900865 ADD3 1.16 2.5 rs17024387 MR rs10503800 ADRA1A 1.06 2.9 rs7599454 ADD2 rs4690002 ADD1 1.07 2.41 rs2119026 EMILIN1 rs4073291 SCNN1G 1.04 3.03 rs5746074 TNFRSF1B rs3790426 LEPR 1.02 2.86 rs13097326 AGTR1 rs2675513 AGTR1 0.94 2.37 rs2232847 ADIPOR1 rs11611246 WNK1 0.86 2.45

Table 10L: SBP, top 1,000, mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3755353 ADD2 NA NA 2.13 0.06 rs17412368 LEPR rs926517 ATP1B1 2.83 2.35 rs2501574 ADD3 rs12596831 SCNN1B 3.17 4.09 rs536401 ADRA1A rs7819943 CYP11B2 3.58 2.89 rs6540547 HSD11B1 rs529948 NFKBIE 2.89 3.82 rs970467 LEPR rs11611246 WNK1 3.26 2.51 rs11122577 AGT rs8044970 SCNN1B 2.09 2.76 rs17032779 NFKB1 rs2286382 ADIPOR2 2.52 2.78 rs1858416 MR rs17055965 ADRA1A 2.57 3.48 rs11737660 MR rs4543 CYP11B2 1.66 2.52 rs10889567 LEPR rs10276473 DDC 2.13 2.74 rs17127838 LEPR rs10744552 ADIPOR2 1.85 2.48 rs11718446 NFKBIZ rs3910044 MR 1.82 3.12 rs265975 DRD1 rs9657021 CYP11B1 1.74 3.59 rs864265 ADIPOQ rs6975312 DDC 1.84 2.79 rs3766031 ATP1B1 rs2143290 ATP1B1 1.52 2.64 rs4681443 AGTR1 rs265972 DRD1 1.25 4.08 rs6668161 ATP1A1 rs11221484 KCNJ1 1.31 3.51 rs1014947 ADD1 rs4802998 NFKBIB 1.63 2.53 rs522807 TNFRSF1B rs12695902 AGTR1 1.04 2.53 rs6590354 KCNJ1 NA NA 0.85 0.52 rs2232847 ADIPOR1 NA NA 1.25 0.1 rs7788818 LEP rs11064153 SCNN1A 1.42 2.64 rs2241713 TGFB1 NA NA 1.17 0.14 rs17484259 MR rs2900865 ADD3 1.15 2.5 rs5746074 TNFRSF1B rs3790426 LEPR 1.03 2.86 rs2119026 EMILIN1 rs4073291 SCNN1G 1.04 3.03 rs7599454 ADD2 rs4690002 ADD1 1.01 2.41

123

rs17024387 MR rs10503800 ADRA1A 1 2.9 rs13097326 AGTR1 rs2675513 AGTR1 0.94 2.37 rs6590354 KCNJ1 rs1634506 CCL3 0.81 3.3

Table 10M: DBP, top 1,000, no mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs10954175 LEP rs10744552 ADIPOR2 3.36 2.64 rs929358 ATP1A1 rs10244329 LEP 2.87 2.51 rs1858416 MR rs10066266 ADRB2 1.26 2.59 rs2471327 GRK4 rs152733 SCNN1B 2.5 2.45 rs17777307 ADD1 rs12596831 SCNN1B 2.56 3.58 rs2052273 ADD2 rs3755353 ADD2 1.81 2.6 rs1419046 GRK4 rs12708966 SLC12A3 1.52 2.78 rs430349 AGTR1 rs13097326 AGTR1 1.85 2.63 rs3771424 ADD2 rs10032020 MR 3.6 3.17 rs11737660 MR rs504697 NFKBIE 3.81 2.43 rs4711998 IL17A rs3739216 ADRA1A 1.02 2.87 rs11571093 REN rs3739216 ADRA1A 1.84 2.88 rs275643 AGTR1 rs11728788 MR 1.52 2.62 rs12363125 DRD2 rs4459610 ACE 0.9 2.76 rs3771408 ADD2 rs9314328 ADRA1A 1.52 2.42 rs522807 TNFRSF1B rs1997034 REN 1.81 3.15 rs235219 TNFRSF1B rs4073930 SCNN1G 1.2 2.79 rs10128072 LEPR rs11957757 ADRB2 1.27 2.45 rs3176879 VCAM1 rs2569702 ICAM1 1.08 2.83 rs6878159 DRD1 rs12596776 SLC12A3 1.15 2.88 rs16853059 REN rs2471857 DRD2 1.59 2.94 rs6963591 IL6 rs11194981 ADD3 1.06 2.88 rs12405556 LEPR rs11122577 AGT 0.87 2.38 rs3766039 ATP1B1 rs2900865 ADD3 1.02 2.37 rs1892094 ATP1B1 rs9804992 WNK1 2.27 2.36 rs3771439 ADD2 rs9804992 WNK1 1.3 2.4 rs3783621 VCAM1 rs11575501 DDC 1.11 3.7 rs877743 ADRB2 rs10774461 WNK1 1.12 2.97 rs11936376 MR rs2060762 DDC 1.16 2.49 rs2863801 ADD2 rs4260521 MR 1.8 2.53 rs1997034 REN rs4260521 MR 1.54 2.81 rs12343735 DBH rs765250 WNK1 1.11 2.39 rs926517 ATP1B1 rs3887856 MR 1.63 2.67 rs230519 NFKB1 rs17484259 MR 1.27 3.01 rs850617 ATP1A1 rs876538 CRP 0.87 2.56

124

rs17032989 NFKB1 rs12343735 DBH 0.97 2.7 rs1805096 LEPR rs6590354 KCNJ1 1.29 2.53 rs9436748 LEPR rs11959615 ADRB2 1.71 2.6 rs4690001 ADD1 rs11168066 ADRB2 1.16 2.77 rs3806318 LEPR rs4982270 NFKBIA 0.98 4.22 rs11575511 DDC rs250567 SCNN1B 1.44 3.11 rs11575510 DDC rs2501574 ADD3 1.08 2.5 rs1922924 ATP1B1 rs11575536 DDC 0.96 2.57 rs4681157 AGTR1 rs7693077 MR 0.94 2.41 rs1541582 ADD2 rs678354 NFKBIZ 1.27 2.8 rs17006285 ADD2 rs11718446 NFKBIZ 1.04 3.2 rs846906 HSD11B1 rs4581480 DRD2 0.87 2.82 rs10499563 IL6 rs3739216 ADRA1A 0.93 2.76 rs550523 TNFRSF1B rs4711998 IL17A 0.91 2.64 rs2007471 CLCNKB rs1512335 MR 0.85 2.39

Table 10N: DBP, top 1,000, no mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3771423 ADD2 rs2471857 DRD2 1.31 2.43 rs10954175 LEP rs10744552 ADIPOR2 3.51 2.64 rs10244329 LEP NA NA 3.05 0.42 rs16998733 MR rs10066266 ADRB2 1.83 2.48 rs17024482 MR NA NA 1.9 0.98 rs2052273 ADD2 rs3755353 ADD2 2.12 2.6 rs3771424 ADD2 rs10032020 MR 3.95 3.17 rs430349 AGTR1 rs13097326 AGTR1 1.79 2.63 rs3771408 ADD2 rs9314328 ADRA1A 1.69 2.42 rs12596776 SLC12A3 NA NA 1.79 0.05 rs2471327 GRK4 rs152733 SCNN1B 2.98 2.45 rs12596831 SCNN1B NA NA 2.36 0.04 rs1419046 GRK4 rs12708966 SLC12A3 1.47 2.78 rs4711998 IL17A rs3739216 ADRA1A 1.04 2.87 rs11571093 REN rs3739216 ADRA1A 1.68 2.88 rs16853059 REN rs2471857 DRD2 1.68 2.94 rs2569702 ICAM1 NA NA 1.29 0.33 rs522807 TNFRSF1B NA NA 1.38 0.45 rs235219 TNFRSF1B rs4073930 SCNN1G 1.33 2.79 rs11122577 AGT rs10519963 MR 1.16 3.03 rs11936376 MR rs2060762 DDC 0.91 2.49 rs275643 AGTR1 rs11728788 MR 1.58 2.62 rs6963591 IL6 rs11194981 ADD3 1.05 2.88

125

rs7035577 DBH NA NA 1.23 0.64 rs877741 ADRB2 rs17800834 WNK1 1.51 2.72 rs968304 ATP1B1 rs9804992 WNK1 2.1 2.89 rs17032989 NFKB1 rs12343735 DBH 0.98 2.7 rs250567 SCNN1B NA NA 1.05 0.36 rs3783621 VCAM1 NA NA 1.04 0.47 rs1858416 MR rs10066266 ADRB2 1.19 2.59 rs846906 HSD11B1 rs4581480 DRD2 1.13 2.82 rs12343735 DBH rs765250 WNK1 1.09 2.39 rs926517 ATP1B1 rs3887856 MR 1.65 2.67 rs1892094 ATP1B1 rs9804992 WNK1 1.87 2.36 rs10158279 LEPR rs10919065 ATP1B1 1.08 2.39 rs850617 ATP1A1 rs876538 CRP 0.92 2.56 rs10774461 WNK1 NA NA 0.98 0.98 rs10484879 IL17A NA NA 1.29 0.1 rs3766039 ATP1B1 rs2900865 ADD3 0.94 2.37 rs10499563 IL6 rs3739216 ADRA1A 0.81 2.76 rs4982270 NFKBIA NA NA 0.83 0.03 rs6986328 ADRA1A rs588472 KCNJ1 1.47 2.78 rs12133039 ATP1A1 rs4529890 KCNJ1 1.3 2.5 rs3766031 ATP1B1 NA NA 0.94 0.02 rs1922924 ATP1B1 rs11575536 DDC 1.06 2.57 rs1541582 ADD2 rs678354 NFKBIZ 1.35 2.8 rs17006285 ADD2 rs11718446 NFKBIZ 1.21 3.2 rs4681157 AGTR1 rs7693077 MR 0.87 2.41 rs7205273 SCNN1B NA NA 1.14 0.03 rs6540547 HSD11B1 NA NA 0.83 0.06 rs11737660 MR NA NA 4.25 0.39 rs3732179 REL rs16891206 IKBKB 0.86 4.51 rs6461662 IL6 rs11074555 SCNN1B 0.85 3.28

Table 10O: DBP, top 1,000, mains added in LASSO, no mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3771423 ADD2 rs878931 ADD1 3.25 2.46 rs1200120 ATP1B1 NA NA 1.78 0.42 rs10032020 MR NA NA 4.22 0.42 rs4766415 ADIPOR2 NA NA 2.43 1.83 rs12363125 DRD2 rs4459610 ACE 1.05 2.76 rs430349 AGTR1 rs13097326 AGTR1 1.69 2.63 rs275643 AGTR1 rs11728788 MR 1.12 2.62 rs17024482 MR NA NA 1.43 0.1

126

rs3771408 ADD2 rs9314328 ADRA1A 1.37 2.42 rs2052273 ADD2 rs3755353 ADD2 1.93 2.6 rs12596776 SLC12A3 NA NA 1.73 0.4 rs10244329 LEP NA NA 4.2 0.04 rs2363768 ADIPOR1 rs4731429 LEP 3.06 2.67 rs2569702 ICAM1 NA NA 1.27 0.03 rs2471327 GRK4 rs152733 SCNN1B 2.25 2.45 rs12596831 SCNN1B NA NA 2.9 0.26 rs6535594 MR rs7788818 LEP 1.49 2.64 rs1419046 GRK4 rs12708966 SLC12A3 1.66 2.78 rs4711998 IL17A rs3739216 ADRA1A 1.16 2.87 rs11571093 REN rs3739216 ADRA1A 1.69 2.88 rs16853059 REN rs2471857 DRD2 1.45 2.94 rs235219 TNFRSF1B rs4073930 SCNN1G 1.15 2.79 rs522807 TNFRSF1B NA NA 1.54 0.33 rs11575511 DDC rs250567 SCNN1B 1.65 3.11 rs11575510 DDC rs2501574 ADD3 0.95 2.5 rs17032989 NFKB1 rs1879827 MR 1.01 2.74 rs4681157 AGTR1 rs7693077 MR 1.21 2.41 rs10499563 IL6 rs3739216 ADRA1A 1.06 2.76 rs7035577 DBH NA NA 1.15 0.47 rs2883930 MR rs11957757 ADRB2 2.2 2.63 rs10484879 IL17A NA NA 1.45 0.52 rs11568030 AGT NA NA 0.95 0.37 rs877741 ADRB2 rs17800834 WNK1 1.47 2.72 rs968304 ATP1B1 rs9804992 WNK1 0.94 2.89 rs10774461 WNK1 NA NA 1.65 1.04 rs926517 ATP1B1 rs3887856 MR 2.23 2.67 rs1997034 REN rs4260521 MR 2.46 2.81 rs1892094 ATP1B1 rs9804992 WNK1 1.72 2.36 rs1541582 ADD2 rs678354 NFKBIZ 1.45 2.8 rs17006285 ADD2 rs11718446 NFKBIZ 1.4 3.2 rs12343735 DBH rs765250 WNK1 1.04 2.39 rs10493936 VCAM1 rs6540547 HSD11B1 1 2.59 rs2242041 DDC rs11064524 WNK1 0.96 2.38 rs2501577 ADD3 NA NA 1.43 0.02 rs1922924 ATP1B1 rs11575536 DDC 0.92 2.57 rs3783621 VCAM1 NA NA 2.41 0.22 rs3783621 VCAM1 rs11575457 DDC 1.71 3.13 rs850617 ATP1A1 rs876538 CRP 1.01 2.56 rs11737660 MR NA NA 4.82 0.03

127

rs3766039 ATP1B1 rs2900865 ADD3 1.43 2.37 rs10128072 LEPR rs1512335 MR 1.66 2.68 rs11099678 MR rs17484873 MR 2.06 2.74 rs10128072 LEPR rs11957757 ADRB2 3.14 2.45 rs10010375 GRK4 rs152733 SCNN1B 1.82 2.83 rs10010375 GRK4 rs4633 COMT 1.42 2.47 rs7190829 SCNN1B rs2279023 HSD11B2 0.93 3.57 rs7531110 LEPR rs11122577 AGT 1.53 2.6 rs7205273 SCNN1B NA NA 1.24 0.28 rs6461662 IL6 rs11074555 SCNN1B 1.09 3.28 rs4648069 NFKB1 rs11728788 MR 0.98 3.23 rs3846329 MR NA NA 1.74 0.02 rs926517 ATP1B1 rs17620822 MR 1.44 2.62 rs230519 NFKB1 rs17484259 MR 1.44 3.01 rs1858416 MR rs10066266 ADRB2 2.13 2.59 rs9436748 LEPR rs11959615 ADRB2 2.52 2.6 rs8032420 SLC12A1 NA NA 0.86 0.51 rs1805096 LEPR rs6590354 KCNJ1 2 2.53 rs7531867 LEPR rs1858416 MR 1.6 2.68 rs4690001 ADD1 rs11168066 ADRB2 2.03 2.77 rs3806318 LEPR rs4982270 NFKBIA 0.81 4.22

Table 10P: DBP, top 1,000, mains added in LASSO, mains added in stepwise rs1 gene1 rs2 gene2 -log(P)_stepwise -log(P)_reduced rs3771423 ADD2 rs878931 ADD1 3.22 2.46 rs1200120 ATP1B1 NA NA 3.31 0.42 rs10032020 MR NA NA 4.53 0.42 rs4766415 ADIPOR2 NA NA 2.24 1.83 rs12363125 DRD2 rs4459610 ACE 0.89 2.76 rs430349 AGTR1 rs13097326 AGTR1 1.81 2.63 rs275643 AGTR1 rs11728788 MR 1.87 2.62 rs17024482 MR NA NA 1.63 0.1 rs3771408 ADD2 rs9314328 ADRA1A 1.36 2.42 rs2052273 ADD2 rs3755353 ADD2 1.89 2.6 rs12596776 SLC12A3 NA NA 1.75 0.4 rs10244329 LEP NA NA 4.1 0.04 rs2363768 ADIPOR1 rs4731429 LEP 3.09 2.67 rs2569702 ICAM1 NA NA 1.27 0.03 rs2471327 GRK4 rs152733 SCNN1B 2.44 2.45 rs12596831 SCNN1B NA NA 2.75 0.26 rs6535594 MR rs7788818 LEP 1.56 2.64

128

rs1419046 GRK4 rs12708966 SLC12A3 1.51 2.78 rs4711998 IL17A rs3739216 ADRA1A 1.12 2.87 rs11571093 REN rs3739216 ADRA1A 1.71 2.88 rs16853059 REN rs2471857 DRD2 1.63 2.94 rs11122577 AGT rs10519963 MR 0.9 3.03 rs235219 TNFRSF1B rs4073930 SCNN1G 1.19 2.79 rs522807 TNFRSF1B NA NA 1.35 0.64 rs250567 SCNN1B NA NA 1.04 0.7 rs17032989 NFKB1 rs1879827 MR 1.01 2.74 rs4681157 AGTR1 rs7693077 MR 1.14 2.41 rs10499563 IL6 rs3739216 ADRA1A 1.12 2.76 rs2883930 MR rs11957757 ADRB2 2.31 2.63 rs7035577 DBH NA NA 1.21 0.47 rs2501577 ADD3 NA NA 1.47 0.52 rs11957757 ADRB2 NA NA 2.33 0.37 rs2883930 MR NA NA 0.98 0.59 rs3732179 REL rs2239728 ADD1 1.38 2.87 rs877741 ADRB2 rs17800834 WNK1 1.54 2.72 rs968304 ATP1B1 rs9804992 WNK1 2.76 2.89 rs926517 ATP1B1 rs3887856 MR 3.63 2.67 rs1922924 ATP1B1 NA NA 1.88 0.1 rs1997034 REN rs4260521 MR 2.09 2.81 rs3783621 VCAM1 NA NA 2.54 0.05 rs3783621 VCAM1 rs11575457 DDC 1.76 3.13 rs10484879 IL17A NA NA 1.45 0.41 rs11568030 AGT NA NA 1.08 0.04 rs6461662 IL6 rs11074555 SCNN1B 1.18 3.28 rs7205273 SCNN1B NA NA 1.2 0.12 rs1541582 ADD2 rs678354 NFKBIZ 1.49 2.8 rs17006285 ADD2 rs11718446 NFKBIZ 1.15 3.2 rs12343735 DBH rs765250 WNK1 1.05 2.39 rs3846329 MR NA NA 1.6 0.03 rs230519 NFKB1 rs17484259 MR 2.16 3.01 rs10128072 LEPR rs1512335 MR 1.81 2.68 rs3766039 ATP1B1 rs2900865 ADD3 1.17 2.37 rs10493936 VCAM1 rs6540547 HSD11B1 0.99 2.59 rs10010375 GRK4 rs152733 SCNN1B 1.6 2.83 rs10010375 GRK4 rs4633 COMT 1.32 2.47 rs1858416 MR rs10066266 ADRB2 2.14 2.59 rs7531867 LEPR rs1858416 MR 1.45 2.68 rs1805096 LEPR rs6590354 KCNJ1 1.57 2.53

129

rs4648055 NFKB1 rs10774461 WNK1 2.51 2.51 rs9804992 WNK1 NA NA 2.13 0.13 rs10158279 LEPR rs10919065 ATP1B1 1.32 2.39 rs16998733 MR NA NA 2.45 0.17 rs17484259 MR rs488323 ADRA1A 0.88 2.83 rs850617 ATP1A1 rs876538 CRP 1.06 2.56 rs7190829 SCNN1B rs2279023 HSD11B2 0.85 3.57 rs11737660 MR NA NA 4.84 0.51 rs4732908 ADRA1A NA NA 0.86 0.21 rs4648069 NFKB1 rs11728788 MR 1.33 3.23 rs4698861 NFKB1 rs2116715 ADRB2 1.88 2.54 rs2116715 ADRB2 NA NA 1.57 1.2 rs9436748 LEPR rs11959615 ADRB2 1.21 2.6 rs7531110 LEPR rs11122577 AGT 0.85 2.6 rs2242041 DDC rs11064524 WNK1 0.92 2.38 rs3806318 LEPR rs4982270 NFKBIA 0.81 4.22 rs846906 HSD11B1 rs4581480 DRD2 1.94 2.82 rs2167364 DDC rs4581480 DRD2 1.69 2.66 rs1922924 ATP1B1 rs11575536 DDC 1.11 2.57 rs7808025 DDC rs6489756 WNK1 0.81 3.04

130

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