Journal Logo

ORIGINAL PAPERS: Preeclampsia

The antihypertensive MTHFR gene polymorphism rs17367504-G is a possible novel protective locus for preeclampsia

Thomsen, Liv Cecilie V.; McCarthy, Nina S.; Melton, Phillip E.; Cadby, Gemma; Austgulen, Rigmor; Nygård, Ottar K.; Johnson, Matthew P.; Brennecke, Shaun; Moses, Eric K.; Bjørge, Line; Iversen, Ann-Charlotte

Author Information
doi: 10.1097/HJH.0000000000001131

Abstract

INTRODUCTION

Preeclampsia is a disease of pregnancy with potentially severe and fatal outcomes for mother and fetus. The condition occurs in 2–8% of pregnancies and is commonly defined as de novo hypertension and proteinuria after gestational week 20 [1]. Women who experience preeclampsia have a two to eight-fold increased risk of subsequent development of cardiovascular diseases (CVD) [2], a major cause of death in women worldwide [3].

Preeclampsia and CVD share lifestyle-related, pathophysiological, and genetic risk factors [4–10]. For the joint pathophysiologic mechanisms, genetic and molecular functional studies have demonstrated endothelial dysfunction, inflammation, and vascular remodeling to be crucial to the development of chronic hypertension [9,10]. Changes in these biological processes are also thought to play a role in preeclampsia [11]. Aberrant blood pressure (BP) regulation can lead to sustained hypertension and CVD in susceptible individuals [12]. Hypertension developing during pregnancy (gestational hypertension) is the only consistent part of the preeclampsia diagnosis included in all definitions of the disease, whereas other diagnostic criteria of the disorder, including proteinuria, have varied between populations and over time [1,13–15]. Both gestational and nongestational hypertension are usually defined by BP at least 140/90 mmHg, but it is not known whether these two conditions share a common cause. Preeclampsia as well as CVD involves release of angiogenic factors, oxidative stress, and adaptive metabolic changes like hyperlipidemia and increased insulin resistance, resulting in sustained inflammation and endothelial dysfunction [10,16]. The physiologic low-grade systemic inflammation of pregnancy is dysregulated and enhanced in preeclampsia at the maternal–fetal interface in the oxidatively stressed placenta and in the maternal vascular system [16,17]. In CVD periods of reduced tissue perfusion and ischemia with subsequent tissue reperfusion increase the level of cellular oxidative stress and thereby establish a sustained inflammatory response locally in tissues and generalized in the circulatory system [9,10].

The genetic contributions to preeclampsia and CVD have been established separately in large cohorts based on national registries and longitudinal health surveys as well as through family studies [4–7,18,19]. The maternal heritability estimates for preeclampsia are in the range 35–60% [6,19], whereas the heritability of BP regulation is estimated to 16–63% [4–7,19]. Genome-wide association studies (GWAS) on either preeclampsia or BP regulation have identified genetic variants associated with each of these conditions. The first GWAS published on preeclampsia identified two loci near the Inhibin beta B gene that could not be replicated in two other cohorts, whereas the second GWAS had a smaller numbers of cases and did not find any genome-wide significant associations [20,21]. Large GWAS have identified several genetic loci associated with BP regulation [22–30] and other inflammatory diseases such as inflammatory bowel disease, diabetes mellitus type 2, and lung function [31–33].

The concept of pleiotropy is receiving increasing attention in studies on complex diseases. Pleiotropy concerns how a single genetic variant directly regulates the expression of two or more phenotypes, either by inducing the same biological effect on two phenotypes or influencing opposite phenotypic effects through inducing both a protective and a disease promoting phenotype [34,35]. GWAS have resulted in identification of single nucleotide polymorphisms (SNPs) in genetic regions significantly associated with several seemingly unrelated conditions, indicating presence of pleiotropic influences on the development of complex human conditions. Despite an increasing awareness of a pathophysiologic relationship between preeclampsia and chronic hypertension, both risk factors for CVD, few studies have investigated possible pleiotropic genetic effects on these conditions or focused on their shared pathophysiological mechanisms of disease development [36–39].

In this study, our aim was to test the association between 122 SNPs previously associated with the phenotypes BP regulation and inflammation with preeclampsia. These phenotypes were chosen on the basis of their prominent involvement in both CVD and preeclampsia.

METHODS

Ethics statement

The Norwegian Regional Committees for Medical and Health Research Ethics (REK 2012/1876) and the HUNT Research Centre (HUNT 13/4894) have approved the project. Informed consent was obtained from all participants when enrolled in the HUNT surveys.

The Australian study was approved by the Royal Women's Hospital Research and Ethics Committees, Melbourne, Australia, and the Institutional Review Board of the University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA. All study participants gave their written informed consent when enrolled in the study.

Study population

In this report, the HUNT Study (HUNT), a longitudinal health study containing a biorepository, was linked to the Medical Birth Registry of Norway (MBRN) to identify female HUNT participants who had been diagnosed with preeclampsia and a control group with unaffected pregnancies.

The Medical Birth Registry of Norway

The MBRN is a national databank based on unique identification numbers and compulsory notification that contains information collected since 1967 on all pregnancies in Norway of at least 16 weeks gestation. The registered data detail maternal health before and during pregnancy, complications during pregnancy and delivery, and pregnancy outcomes [40].

The HUNT Study

The HUNT Study is a collaboration between the HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology), Nord-Trøndelag County Council, the Central Norway Health Authority, and the Norwegian Institute of Public Health. The HUNT Study consists of three separate multipurpose health surveys (HUNT1–3) in the adult population of Nord-Trøndelag County in Norway and has been described in detail elsewhere [41,42]. The ethnicity of participants is not requested in the surveys, but the population in Nord-Trøndelag is stable and ethnically homogenous with more than 97% of Northern European origin. The main purpose of the HUNT Study is to investigate etiological factors of human disease. The three parts of the study took place in 1984–1986 (HUNT1), 1995–1997 (HUNT2), and 2006–2008 (HUNT3), and the available data on participants consist of health information based on questionnaires and physical examinations, as well as biological samples from individuals taking part in HUNT2 and HUNT3. Approximately 41,500 women participated and donated biological samples in at least one part of the study. Of these, over 20,000 women took part in both HUNT2 and HUNT3, and about 15,300 women participated in all three surveys (http://www.ntnu.no/hunt/oppmote, accessed 11 May 2015).

Data linkage

By means of the unique national identification numbers, data from the MBRN have been linked with the HUNT Databank to identify a preeclampsia case–control cohort of female participants in HUNT2 and HUNT3. These women gave birth between 1967 and 2009 and have pregnancies registered in the MBRN either with preeclampsia (cases) or as having had only pregnancies unaffected by preeclampsia, gestational hypertension, and chronic hypertension (controls).

The Australian preeclampsia case–control cohort

The replication cohort from Australia has previously been described [20]. Briefly, 1092 women (545 preeclampsia cases and 547 controls) of European ancestry were recruited at the Royal Women's Hospital, Melbourne, Australia. Of the 1092 individuals included 1018 women (471 preeclampsia cases and 547 controls) were ascertained from a larger case–control cohort assembled over a 5-year period (2007–2011), and an additional 74 unrelated preeclampsia cases were from a family-based cohort recruited over a 15-year period (1984–1999) [43].

Clinical definition of phenotypes

Data on gestational health and outcomes from the MBRN and well defined clinical and laboratory information from the HUNT Databank were used to identify phenotypes and covariates related to preeclampsia. Time of participation in HUNT was not related in time to the pregnancies and births of the included women.

Data from the MBRN contained information on all pregnancies and births of each woman, including maternal health prior to and during the pregnancies. For the women who had participated in more than one HUNT survey, all available relevant information was utilized, including data on diabetes, asthma, inflammatory diseases, use of medication, risk factors of CVD, established CVD and family history of CVD, as well as BP measurements and results from biochemical blood analysis.

In the MBRN preeclampsia is registered according to the set criteria of the repository: hypertension (BP ≥ 140/90 mmHg) with proteinuria (≥+1 protein on a urine dipstick). We have previously found the diagnostic validity of these criteria to be 88.3% when examined against medical records of women in HUNT who gave birth at two hospitals (Levanger Hospital and Namsos Hospital) [15].

Cases and controls lacking phenotypic information on pregnancies and births in the MBRN were excluded. Individuals originally selected as controls were omitted if they had any pregnancies registered in the MBRN as stillbirths or complicated by placental abruption, or if they, prior to pregnancy, had an established kidney disorder, maternal heart disease, or a chronic inflammatory disorder such as rheumatoid arthritis. Numbers for excluded cases and controls can be found in SDC2 Table 2, http://links.lww.com/HJH/A681 in Supplemental digital content.

In the Australian cohort preeclampsia was defined as de novo hypertension with proteinuria after 20 weeks of gestation. Hypertension was defined as BP at least 140/90 mmHg or, an increase in SBP and/or DBP of at least 25 and/or 15 mmHg, respectively. Proteinuria was defined as either at least 0.3 g/l in a 24-h urine specimen, at least a ‘2+’ dipstick reading from a random urine sample, or a spot protein : creatinine ratio at least 0.03 g/mmol. The preeclampsia diagnosis was determined by clinicians examining information in the medical hospital records of the participant. Exclusion criteria were preexisting hypertension or other medical conditions known to predispose for preeclampsia such as renal disease and diabetes mellitus. Further, women pregnant with twins or fetuses with chromosomal abnormalities were excluded.

Genotyping and quality control

Single nucleotide polymorphism selection

The SNP selection was based on results from GWAS performed in populations of European descent to match our study cohort. The studies were registered in one of two databases: the National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies (NHGRI Catalog), containing published GWAS data [44], and the NCBI resource phenotype–genotype integrator (PheGenI) [45]. PheGenI contains genetic results assembled from several sources and by several methods, including published and unpublished raw data from GWAS. In the NHGRI Catalog (http://www.genome.gov/gwastudies/, accessed 25 February 2013), we utilized search filters ‘blood pressure’ and ‘hypertension’ as well as P value 10−6 or less, at which point 87 SNPs mapped to 65 unique genic regions were reported at this P value.

We next applied the following inflammatory genes one by one as filters in the PheGenI (http://www.ncbi.nlm.nih.gov/gap/phegeni, accessed 25 March 2013) including only results with P value 10−6 or less: Toll-like receptor (TLR) 1, TLR2, TLR3, TLR4, TLR5, TLR9, nucleotide-binding oligomerization domain (NOD) 1, NOD2, advanced glycosylation end product-specific receptor (AGER), NOD-like receptor containing pyrin domain (NLRP)3, NLRP12, vascular endothelial growth factor A (VEGF)-A, VEGF-B, regulator of G-protein signaling 5 (RGS5), and NLR family CARD domain-containing protein 4 (NLRC4). For 10 of these genes, GWA results below the set P value were identified: TLR1, TLR4, TLR5, NOD1, NOD2, AGER, NLRP3, NLRP12, VEGF-A, and RGS5, resulting in n = 163 inflammatory SNPs for replication.

Of the in total 250 identified SNPs, 61 were found in duplicate and 74 were in linkage disequilibrium with another SNP in the dataset (SNAP [46]r2 ≥ 0.8, HapMap release 21, Northern Europeans from Utah population). The SNP with the highest rs-number in each linkage disequilibrium pair was excluded, leaving 115 SNPs for genotyping (SDC1 Table 1, http://links.lww.com/HJH/A681).

Genotyping

DNA was extracted by the HUNT Biobank from whole blood or blood clots sampled at inclusion in the HUNT2 and HUNT3 and stored at the HUNT Biobank, Levanger, Norway [41,42]. The genotyping was performed on the Sequenom MassARRAY system (Sequenom iPLEXassay, Sequenom Inc., San Diego, California, USA) by the Australian Genome Research Facility Ltd., St Lucia, Queensland, Australia. Of the 115 SNPs, 12 failed designability, 11 of which were replaced by all suitable surrogates (r2 = 1). Finally a total of 122 SNPs were successfully genotyped, effectively covering 114 of the 115 SNPs originally planned genotyped.

Quality control

Standard quality control methods were applied as described in Supplementary material SDC2 Table 2, http://links.lww.com/HJH/A681 and SDC3 Table 3, http://links.lww.com/HJH/A681. Of 2187 samples genotyped, we excluded 125 individuals with very low quality DNA who failed all genotyping assays. From the remaining samples only individuals with average call rates more than 95% were included in the genetic analysis (SDC2 Table 2, http://links.lww.com/HJH/A681). SNPs failing genotyping and SNPs with call rates less than 98% were excluded from further analysis (SDC3 Table 3, http://links.lww.com/HJH/A681). Of the 122 SNPs successfully genotyped in the cohort, 119 SNPs passed the quality control and were analyzed in the 1822 individuals who passed quality control (n = 1006 cases and n = 816 controls).

Statistical analyses

All analyses in the Norwegian cohort were performed using the software packages PLINK version 1.07 [47] and R version 2.15.2 [48]. Quality control checks were performed with the R software. For phenotyping and identification of covariates the various birth-related outcomes were treated separately for each pregnancy, whereas data regarding the total obstetric history of a woman (including ever having had a pregnancy resulting in a stillbirth) were treated as binary variables. Longitudinal measures like BMI and BP were averaged. Participants were classified as smokers if they acknowledged to ever have smoked.

Variables associated with increased risk for developing preeclampsia were examined against the main phenotype preeclampsia in a generalized linear model (SDC5 Table 5, http://links.lww.com/HJH/A681). Adjustment for smoking as a risk factor was made based on whether participants acknowledged having ever smoked (ever vs never smokers). Only those variables significantly associated with the phenotype at P values 0.01 or less were incorporated as covariates in the final model: maternal age, total number of births per woman, average BMI at HUNT participation of the woman, twin pregnancies, year of giving birth, and if a woman had ever smoked (SDC5 Table 5, http://links.lww.com/HJH/A681).

The associations between phenotypes and SNPs were analyzed under an additive genetic model and tested in a multiple logistic regression model with the PLINK software [47]. Statistical tests were two-sided, and associations with a Bonferroni-corrected P value less than 0.05 for 119 independent tests (SNPs) (P < 0.00042) were considered statistically significant.

We had 90% power to detect SNPs with an effect size equivalent to odds ratio (OR) at least 1.6 with minor allele frequency (MAF) 0.05, or OR at least 1.4 with MAF 0.10 under an additive model assuming a type 1 error rate of 0.05 (adjusted for 119 SNP tests) and a two-sided test (QUANTO power calculator [49], http://biostats.usc.edu/Quanto.html).

The Australian preeclampsia genome-wide association study

The preeclampsia GWAS in Australian samples has been described in detail elsewhere [20,43]. Briefly, DNA was extracted from the 1092 Australian case–control blood samples using Qiagen's Blood & Cell Culture DNA Midi Kit (Qiagen Pty Ltd, Doncaster, Victoria, Australia) and samples were genotyped on Illumina's Human OmniExpress-12 BeadChip (Illumina Inc., San Diego, California, USA). Quality control performance measures for individual SNPs (genotype success rate, Hardy Weinberg equilibrium, and minor allele copy number) and samples (genotype call rate, random genotype error, and cryptic relatedness) were conducted in PLINK [47]. Samples were excluded when average call rates fell below 95%, where X chromosome homozygosity rates were at least 0.2 (PLINK [47]), or if estimates in PLINK indicated possible distant relations between women in the cohort. The potential confounding effect of population structure in the Australian preeclampsia GWAS cohort was negligible (genomic inflation factor = 1.002). Point-wise, asymptotic association tests were performed in PLINK assuming an additive model of gene action. The GWAS analyses included only SNPs passing genotyping and demonstrating call rates above 98%.

RESULTS

Descriptive characteristics of the selected study population from HUNT (n = 1822) are shown in Tables 1 and 2, containing continuous and binary variables, respectively. In the genetic association analysis, 1006 cases with preeclampsia and 816 controls with unaffected pregnancies were included (SDC2 Table 2, http://links.lww.com/HJH/A681). Significantly higher values were identified for preeclampsia cases compared with controls at time of participation in HUNT for BMI (P < 0.001), weight (P < 0.001), SBP and DBP (P < 0.001), diabetes of undefined type (P < 0.001), and waist circumference (P < 0.001) (Table 1). In addition, cases had higher parity (P < 0.001) and were younger at time of participation in HUNT when compared with controls (Table 1). Finally, a significantly higher percentage of the case women reported chronic hypertension (P < 0.001), a diagnosis of diabetes mellitus (P < 0.001), as well as a family history of hypertension (P < 0.01) in the HUNT surveys. A higher proportion of controls reported to ever have smoked compared with the women who had experienced preeclampsia (P < 0.05) (Table 2).

TABLE 1
TABLE 1:
Continuous descriptive variables for the HUNT cohort included in the genotype-phenotype analyses
TABLE 2
TABLE 2:
Binary descriptive variables for the HUNT cohort included in the genotype-phenotype analyses

Of the 122 SNPs genotyped in the cohort, one SNP, rs17367504, was significantly associated with preeclampsia with the G (minor) allele demonstrating a protective effect on disease development after correction for multiple testing [P = 3.52 × 10−5, OR 0.65, 95% confidence interval (CI) 0.53–0.80, allele frequency 13%]. The SNP rs17367504 is located in intron 10 of the methylenetetrahydrofolate reductase (MTHFR) gene on chromosome 1.

The top 10 association results for the preeclampsia phenotype are given in Table 3, whereas a complete list of association results for this trait can be found in Supporting information (SDC4 Table 4, http://links.lww.com/HJH/A681). Three further SNPs demonstrated nominally significant associations with preeclampsia: the intergenic SNP rs17249754 located just downstream of the ATPase, Ca2+ transporting, plasma membrane 1 (ATP2B1) gene (P = 0.02, OR 0.80, CI 95% 0.66–0.97), the intronic SNP rs16933812 in the paired box 5 (PAX5) gene (P = 0.04, OR 0.85, CI 95% 0.74–0.99), and the intronic SNP rs12946454, located in the phospholipase C delta 3 gene (PLCD3) (P = 0.046, OR 0.85, CI 95% 0.73–0.80).

TABLE 3
TABLE 3:
Top associations with preeclampsia after multiple logistic regression analysis

The top SNP (rs17367504) in our HUNT cohort was successfully genotyped in the Australian preeclampsia GWAS cohort but unlike in the HUNT cohort, the G-allele was not associated with preeclampsia (P = 0.68, OR 1.05, 95% CI 0.83–1.32, MAF = 0.15). A possible risk for carriers of the G-allele in the Australian cohort was not supported by the CI.

A holistic examination of the associations with preeclampsia of the 122 SNPs previously demonstrating association with BP regulation or inflammation does not indicate that using this selection strategy introduces an overall enrichment for associations with preeclampsia. In fact, we saw fewer significant associations than we would have expected due to chance (SDC6 Fig. 1, http://links.lww.com/HJH/A681).

DISCUSSION

Despite an increased focus on preeclampsia as a risk factor for CVD development, few studies have investigated whether preeclampsia and CVD-related risk factors share a genetic background. Here we have performed a candidate SNP study in a preeclampsia-based cohort with candidates selected at loci previously associated with BP regulation and inflammation in GWAS. We identified one intronic SNP, rs17367504, in the MTHFR gene in which the G allele is significantly associated with a protective effect on preeclampsia development in the HUNT cohort, although not in the Australian cohort. The rs17367504 SNP is located within the DNase1 hypersensitivity cluster in 125 cell types from the Encyclopedia of DNA Elements and in a binding site for 16 transcription factors according to the UCSC Genome Browser (http://genome.ucsc.edu/, accessed 5 July 2015) [50]. Despite this strong evidence for a regulatory role for this SNP, no significant expression quantitative trait loci were found for rs17367504 in the Genotype-Tissue Expression Project portal (http://gtexportal.org, accessed 5 July 2015) [51]. The G-allele of this SNP has repeatedly been associated with lower SBP as well as DBP in different ethnic populations [25,26], but this is the first time this SNP has been associated with preeclampsia.

The pleiotropic nature of the MTHFR gene is indicated by more than 2900 publications on the term MTHFR gene. Changes in the MTHFR gene have been indicated in conditions such as hyperhomocysteinemia (a risk factor for CVD), stroke, neural tube defects, cancer, and obstetric complications [38,52–54]. The MTHFR gene produces the enzyme MTHFR, which converts 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, the active form of folate. Connections between the folate cycle and lipid metabolism have been demonstrated repeatedly including that excess folate leads to reduced MTHFR activity and increased lipogenesis [55], and that folate deficiency is associated with elevated homocysteine due to excess methionine scavenging by glycine N-methyltransferase, a key regulator of intracellular cholesterol trafficking shown associated with increased risk for CVD [56–58]. The similarities in preeclampsia and CVD pathophysiology support a role for MTHFR in preeclampsia, but so far no studies have investigated the functional implications of changes in rs17367504 in preeclampsia or CVD.

The novel association suggested by our findings in the HUNT cohort, between the BP-associated SNP rs17367504 and preeclampsia, adds to the increasing evidence that the MTHFR gene influences the development of preeclampsia and may point to a central role of the MTHFR gene in the pathophysiology of both preeclampsia and chronic hypertension. Genetic studies of MTHFR in relation to development of preeclampsia, hypertensive gestational diseases, or CVD have mainly investigated genetic associations with the separate disorders, not focusing on possible pleiotropic effects on both conditions. The preeclampsia studies conducted to date have largely focused on a missense MTHFR SNP, rs1801133, and a regulatory MTHFR SNP, rs1801131 [38,59] and identified an association between the minor alleles and an increased risk of preeclampsia. The MTHFR SNP associated with preeclampsia in our HUNT cohort, rs17367504, is not in linkage disequilibrium with either of the two SNPs (rs1801133 and rs1801131, r2 < 0.5). Further, a recent study has identified the MTHFR gene promoter to be hypermethylated in placental tissue and maternal blood of preeclamptic women [60].

Despite the increasing evidence from candidate gene research and other genetic studies, no SNPs in the MTHFR gene did reach genome-wide significance in the two GWAS on preeclampsia reported to date [20,21]. In our study, the top association from the HUNT Study was examined for replication in the Australian GWAS cohort [20], resulting in a nonsignificant finding not supporting the link between preeclampsia and rs17367504 and rising the question about a possible false positive finding. The lack of replication of the association could also be due to the relatively small sample size, which, coupled with the diagnostic and genetic complexity of preeclampsia, leaves these studies underpowered to detect SNPs with modest effect sizes. The design of GWAS chips means that genotyped SNPs are likely to tag causative variants rather than be functional themselves, and different patterns of linkage disequilibrium between populations means these associations may weaken or disappear altogether. Indeed, both published GWAS on preeclampsia were unable to replicate their findings in independent cohorts [20,21]. To validate the finding identified in the HUNT Study, further replication and functional studies on the SNP rs17367504 are required in new preeclampsia cohorts.

The minor alleles of the three SNPs that were nominally associated with a protective role against preeclampsia development in our study have previously been associated with BP regulation [25,26,28]. These SNPs are located in or close to the ATP2B1, PAX5, and PLCD3 genes. Given the nominal P values for association achieved by these variants in this study, both further replication and molecular functional studies are warranted to identify a possible role as disease modifiers in preeclampsia and CVD.

The main strength of this study is the deep phenotyping including longitudinal variables that was performed on all cases and controls. This might be one of the reasons why we, in a cohort of this size, are able to identify a significant association between preeclampsia and a genetic locus with an acknowledged link to BP regulation. A weakness of the present study is the lack of replication of the link between preeclampsia and the SNP rs17367504 in an independent preeclampsia cohort. Although nonsignificant, the G allele in the Australian cohort did not demonstrate a trend toward protection against preeclampsia, as identified in the HUNT cohort. The result could have been strengthened further had the result replicated in an independent preeclampsia cohort. Further, the pleiotrophic effect of the SNP would have been strengthened had the study aim included replication of the associations with BP regulation identified through GWAS, but as the cohort was selected on the basis of the preeclampsia diagnosis, not hypertension, the study contained inadequate power for such a replication. A relatively small number of women had developed chronic hypertension at time of participation in HUNT, probably because the preeclampsia cohort selected from HUNT contained relatively young women (mean age 40.3 years in HUNT2 and 52.1 years in HUNT3).

In summary, we have comprehensively assessed SNPs previously reported to be associated with the phenotypes BP regulation and inflammation for association with preeclampsia. Women who carry the G (minor) allele of the intronic MTHFR SNP rs17367504 in the Norwegian HUNT Study are significantly less prone to develop preeclampsia than other women. Ours is the first study linking this BP associated MTHFR SNP to preeclampsia, thereby identifying a possible pleiotropic protective effect of the minor allele of this variant on preeclampsia and hypertension. Further genetic and functional studies in other cohorts are required for validation of the association. Discovery of common genetic associations between preeclampsia and hypertension will help identify shared etiological pathways. Increased comprehension of pathophysiologic mechanisms may lead to improved screening for preeclampsia and for subsequent risk of CVD.

ACKNOWLEDGEMENTS

The study was funded by grants from the Norwegian Women's Public Health Association and the Norwegian ExtraFoundation for Health and Rehabilitation through EXTRA funds (2010/2/0252), the Norwegian Association of Heart and Lung Patients, the Nordic Federation of Societies of Obstetrics and Gynecology, Sigurd K. Thoresen's Foundation and the Regional Health Authorities. This work was partly supported by the Research Council of Norway through FUGE II (183234), FRIMED (205400/V50), and its Centres of Excellence funding scheme (223255/F50). Statistical genetic analyses were performed on iVEC, the advanced computing resources provided by the Western Australian Advanced Computing Consortia.

Previous presentation of the work: None.

Conflicts of interest

There are no conflicts of interest.

REFERENCES

1. Steegers EA, von Dadelszen P, Duvekot JJ, Pijnenborg R. Preeclampsia. Lancet 2010; 376:631–644.
2. Brown MC, Best KE, Pearce MS, Waugh J, Robson SC, Bell R. Cardiovascular disease risk in women with preeclampsia: systematic review and meta-analysis. Eur J Epidemiol 2013; 28:1–19.
3. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380:2095–2128.
4. Mitchell BD, Almasy LA, Rainwater DL, Schneider JL, Blangero J, Stern MP, et al. Diabetes and hypertension in Mexican American families: relation to cardiovascular risk. Am J Epidemiol 1999; 149:1047–1056.
5. Vik KL, Romundstad P, Nilsen TI. Tracking of cardiovascular risk factors across generations: family linkage within the population-based HUNT Study, Norway. J Epidemiol Community Health 2013; 67:564–570.
6. Ros HS, Cnattingius S, Lipworth L. Comparison of risk factors for preeclampsia and gestational hypertension in a population-based cohort study. Am J Epidemiol 1998; 147:1062–1070.
7. North KE, Howard BV, Welty TK, Best LG, Lee ET, Yeh JL, et al. Genetic and environmental contributions to cardiovascular disease risk in American Indians: the strong heart family study. Am J Epidemiol 2003; 157:303–314.
8. Oparil S, Zaman MA, Calhoun DA. Pathogenesis of hypertension. Ann Intern Med 2003; 139:761–776.
9. Luscher TF. The endothelium. Target and promoter of hypertension? Hypertension 1990; 15:482–485.
10. Intengan HD, Schiffrin EL. Vascular remodeling in hypertension: roles of apoptosis, inflammation, and fibrosis. Hypertension 2001; 38:581–587.
11. Borzychowski AM, Sargent IL, Redman CW. Inflammation and preeclampsia. Semin Fetal Neonatal Med 2006; 11:309–316.
12. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Bohm M, et al. 2013 ESH/ESC Guidelines for the management of arterial hypertension: the Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens 2013; 31:1281–1357.
13. Visintin C, Mugglestone MA, Almerie MQ, Nherera LM, James D, Walkinshaw S, et al. Management of hypertensive disorders during pregnancy: summary of NICE guidance. BMJ 2010; 341:c2207.
14. Tranquilli AL, Dekker G, Magee L, Roberts J, Sibai BM, Steyn W, et al. The classification, diagnosis and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP. Pregnancy Hypertens 2014; 4:97–104.
15. Thomsen LC, Klungsoyr K, Roten LT, Tappert C, Araya E, Baerheim G, et al. Validity of the diagnosis of preeclampsia in the Medical Birth Registry of Norway. Acta Obstet Gynecol Scand 2013; 92:943–950.
16. Redman CW, Sargent IL. Placental stress and preeclampsia: a revised view. Placenta 2009; 30 (Suppl A):S38–42.
17. Roberts JM. Pathophysiology of ischemic placental disease. Semin Perinatol 2014; 38:139–145.
18. Hunt SC, Hasstedt SJ, Kuida H, Stults BM, Hopkins PN, Williams RR. Genetic heritability and common environmental components of resting and stressed blood pressures, lipids, and body mass index in Utah pedigrees and twins. Am J Epidemiol 1989; 129:625–638.
19. Thomsen LC, Melton PE, Tollaksen K, Lyslo I, Roten LT, Odland ML, et al. Refined phenotyping identifies links between preeclampsia and related diseases in a Norwegian preeclampsia family cohort. J Hypertens 2015; 33:2294–2302.
20. Johnson MP, Brennecke S, East C, Göring H, Kent J Jr, Dyer T, et al. Genome-wide association scan identifies a risk locus for preeclampsia on 2q14, near the inhibin, beta B gene. PLoS One 2012; 7:e33666.
21. Zhao L, Bracken MB, DeWan AT. Genome-wide association study of preeclampsia detects novel maternal single nucleotide polymorphisms and copy-number variants in subsets of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study cohort. Ann Hum Genet 2013; 77:277–287.
22. Levy D, Larson MG, Benjamin EJ, Newton-Cheh C, Wang TJ, Hwang SJ, et al. Framingham Heart Study 100K Project: genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet 2007; 8 (suppl 1):S3.
23. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet 2009; 41:677–687.
24. Wang Y, O’Connell JR, McArdle PF, Wade JB, Dorff SE, Shah SJ, et al. From the cover: whole-genome association study identifies STK39 as a hypertension susceptibility gene. Proc Natl Acad Sci U S A 2009; 106:226–231.
25. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 2009; 41:666–676.
26. Wain LV, Verwoert GC, O’Reilly PF, Shi G, Johnson T, Johnson AD, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet 2011; 43:1005–1011.
27. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011; 478:103–109.
28. Melka MG, Bernard M, Mahboubi A, Abrahamowicz M, Paterson AD, Syme C, et al. Genome-wide scan for loci of adolescent obesity and their relationship with blood pressure. J Clin Endocrinol Metab 2012; 97:E145–E150.
29. He J, Kelly TN, Zhao Q, Li H, Huang J, Wang L, et al. Genome-wide association study identifies 8 novel loci associated with blood pressure responses to interventions in Han Chinese. Circ Cardiovasc Genet 2013; 6:598–607.
30. Franceschini N, Fox E, Zhang Z, Edwards TL, Nalls MA, Sung YJ, et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet 2013; 93:545–554.
31. Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 2006; 314:1461–1463.
32. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008; 40:638–645.
33. Repapi E, Sayers I, Wain LV, Burton PR, Johnson T, Obeidat M, et al. Genome-wide association study identifies five loci associated with lung function. Nat Genet 2010; 42:36–44.
34. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 2013; 14:483–495.
35. Williams GC. Pleiotropy, natural selection, and the evolution of senescence. Evolution 1957; 11:398–411.
36. Kvehaugen AS, Melien O, Holmen OL, Laivuori H, Oian P, Andersgaard AB, et al. Single nucleotide polymorphisms in G protein signaling pathway genes in preeclampsia. Hypertension 2013; 61:655–661.
37. Johansson Å, Curran J, Johnson M, Freed K, Fenstad M, Bjørge L, et al. Identification of ACOX2 as a shared genetic risk factor for preeclampsia and cardiovascular disease. Eur J Hum Genet 2011; 19:796.
38. Yang B, Fan S, Zhi X, Li Y, Liu Y, Wang D, et al. Associations of MTHFR gene polymorphisms with hypertension and hypertension in pregnancy: a meta-analysis from 114 studies with 15411 cases and 21970 controls. PLoS One 2014; 9:e87497.
39. Johnson MP, Brennecke SP, East CE, Dyer TD, Roten LT, Proffitt JM, et al. Genetic dissection of the preeclampsia susceptibility locus on chromosome 2q22 reveals shared novel risk factors for cardiovascular disease. Mol Hum Reprod 2013; 19:423–437.
40. Irgens LM. The Medical Birth Registry of Norway. Epidemiological research and surveillance throughout 30 years. Acta Obstet Gynecol Scand 2000; 79:435–439.
41. Krokstad S, Langhammer A, Hveem K, Holmen TL, Midthjell K, Stene TR, et al. Cohort profile: the HUNT Study, Norway. Int J Epidemiol 2013; 42:968–977.
42. Holmen J, Midthjell K, Krüger Ø, Langhammer A, Holmen TL, Bratberg GH, et al. The Nord-Trøndelag Health Study 1995–97 (HUNT 2): objectives, contents, methods and participation. Norsk Epidemiol 2003; 13:19–32.
43. Moses EK, Lade JA, Guo G, Wilton AN, Grehan M, Freed K, et al. A genome scan in families from Australia and New Zealand confirms the presence of a maternal susceptibility locus for preeclampsia, on chromosome 2. Am J Hum Genet 2000; 67:1581–1585.
44. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014; 42:D1001–1006.
45. Ramos EM, Hoffman D, Junkins HA, Maglott D, Phan L, Sherry ST, et al. Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur J Hum Genet 2014; 22:144–147.
46. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O’Donnell CJ, de Bakker PI. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008; 24:2938–2939.
47. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81:559–575.
48. Core R, R: Team. A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.
49. Gauderman WJ. Sample size requirements for matched case–control studies of gene–environment interaction. Stat Med 2002; 21:35–50.
50. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res 2002; 12:996–1006.
51. Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015; 348:648–660.
52. Kelly PJ, Rosand J, Kistler JP, Shih VE, Silveira S, Plomaritoglou A, et al. Homocysteine, MTHFR 677C→T polymorphism, and risk of ischemic stroke: results of a meta-analysis. Neurology 2002; 59:529–536.
53. Zhang D, Wen X, Wu W, Guo Y, Cui W. Elevated homocysteine level and folate deficiency associated with increased overall risk of carcinogenesis: meta-analysis of 83 case–control studies involving 35,758 individuals. PLoS One 2015; 10:e0123423.
54. van Meurs JB, Pare G, Schwartz SM, Hazra A, Tanaka T, Vermeulen SH, et al. Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease. Am J Clin Nutr 2013; 98:668–676.
55. Christensen KE, Mikael LG, Leung KY, Lévesque N, Deng L, Wu Q, et al. High folic acid consumption leads to pseudo-MTHFR deficiency, altered lipid metabolism, and liver injury in mice. Am J Clin Nutr 2015; 101:646–658.
56. Liao Y, Chen T, Lee T, Wang H, Wang C, Liao L, et al. Glycine N-methyltransferase deficiency affects Niemann-Pick type C2 protein stability and regulates hepatic cholesterol homeostasis. Mol Med 2012; 18:412–422.
57. Sleat DE, Wiseman JA, El-Banna M, Price SM, Verot L, Shen MM, et al. Genetic evidence for nonredundant functional cooperativity between NPC1 and NPC2 in lipid transport. Proc Natl Acad Sci U S A 2004; 101:5886–5891.
58. Davis SR, Quinlivan EP, Shelnutt KP, Ghandour H, Capdevila A, Coats BS, et al. Homocysteine synthesis is elevated but total remethylation is unchanged by the methylenetetrahydrofolate reductase 677C→T polymorphism and by dietary folate restriction in young women. J Nutr 2005; 135:1045–1050.
59. Rahimi Z, Malek-Khosravi S, Rahimi Z, Jalilvand F, Parsian A. MTHFR C677T and eNOS G894T variants in preeclamptic women: contribution to lipid peroxidation and oxidative stress. Clin Biochem 2013; 46:143–147.
60. Ge J, Wang J, Zhang F, Diao B, Song ZF, Shan LL, et al. Correlation between MTHFR gene methylation and preeclampsia, and its clinical significance. Genet Mol Res 2015; 14:8021–8028.
Keywords:

blood pressure regulation; hypertension; methylenetetrahydrofolate reductase; pleiotropy; preeclampsia; single nucleotide polymorphism

Supplemental Digital Content

Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.