https://orcid.org/0000-0003-3006-1229
Figures
Abstract
Genetic predisposition is a risk factor for office hypertension. We sought to determine whether genetic predisposition identifies individuals with ambulatory daytime hypertension. 1444 participants from the GAPP study (ages 25–41) were analyzed. We evaluated two measures of predisposition to hypertension: family history and polygenic risk scores (PRS). We evaluated correlation of predisposition with blood pressure traits and compared incremental value of each predisposition measure to a validated ambulatory BP prediction model. 12% of participants had office hypertension, while 37% had out-of-office hypertension. The correlation between PRS and family history of hypertension was low (R2 = 4.96x10-3), but both were strongly associated with ambulatory blood pressure (2.2 mmHg per 1 SD increase [95% CI: 1.6, 2.7] & 2.4 mmHg increase with positive family history [95% CI: 1.3, 3.4], respectively). PRS provides incremental improvement predicting ambulatory systolic blood pressure beyond a validated blood pressure prediction score (ΔAIC = −33), whereas family history does not (ΔAIC = 1). The difference between a baseline prediction algorithm for identifying ambulatory systolic hypertension (positive likelihood ratio of 6.87 [95% CI: 5.56, 8.49]; negative likelihood ratio of 0.45 [95% CI: 0.39, 0.51]) and the same model with PRS integrated (positive likelihood ratio of 7.69 [95% CI: 6.18, 9.57]; negative likelihood ratio of 0.43 [95% CI: 0.37, 0.49]) was modest. In a white European sample from Liechtenstein, PRS provides incremental information in identification of individuals with ambulatory hypertension, unlike family history. However, these gains are modest and warrant further development to improve predictive utility at the point-of-care.
Citation: Narula S, Mohammadi-Shemirani P, Aeschbacher S, Chong MR, Le A, Thériault S, et al. (2026) Genetic predisposition to high blood pressure and out-of-office hypertension: Insights from a population sample in liechtenstein. PLoS One 21(8): e0354952. https://doi.org/10.1371/journal.pone.0354952
Editor: Marcelo Arruda Nakazone, Faculdade de Medicina de São José do Rio Preto, BRAZIL
Received: December 30, 2025; Accepted: July 14, 2026; Published: August 10, 2026
Copyright: © 2026 Narula et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Summary statistics for the polygenic risk score construction are publicly available on the PGS catalog (PGS ID: PGS002257 and PGS002258; https://www.pgscatalog.org/). Data that underlie the results reported in this paper were collected from study participants from the Principality of Liechtenstein, a very small country, where the risk of subject identification is increased due to the size of the population (less than 40,000 inhabitants). The code underlying the main data analysis is available upon request without restriction. To respect data protection and to prevent the identification of participants, data access is restricted to researchers meeting the criteria for access to confidential data. Data are available from (contact: lorenz.risch@ufl.li, martin.risch@ksgr.ch, and david.conen@phri.ca). Further, the data underlying the results presented in the study are available from Private University of the Principality of Liechtenstein, Institutional Review Board (9495 Triesen; irb@ufl.li).
Funding: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Award Number: PP003_133681) Hanela Foundation Swiss Society of Hypertension Swiss Heart Foundation University Hospital Basel Schiller AG Novartis Liechtenstein government The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: DC received consulting fees from Roche Diagnostics and Trimedics, and speaker fees from Servier and BMS/Pfizer, all outside of the current work. LR and MR are key shareholders of the Dr Risch Medical Laboratory. During the course of the project, PMS became a full-time employee at Deep Genomics and subsequently Takeda. However, his role was limited to before he began industry employment and the results/project are not related to the work he conducts in his industry role. ST holds a junior scholar award from the FRQS (Fonds de recherche du Québec–Santé). GP holds the Canada Research Chair in Genetic and Molecular Epidemiology and Cisco Systems Professorship in Integrated Health Biosystems. SN, MRC, KG, AL, SA have nothing to disclose. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Abbreviations: AIC, Akaike information criterion; BP, blood pressure; FHx, family history; GAPP, Genetic and Phenotypic Determinants of Blood Pressure and Other Cardiovascular Risk Factors; GWAS, genome wide association study, PROOF-BP: PRedicting Out of OFfice Blood Pressure; PRS, polygenic risk score; RMSE, root mean square error
Introduction
Organ damage and clinical events resulting from arterial hypertension can be delayed with preemptive intervention, however hypertension remains underdiagnosed especially in young and apparently healthy populations [1–4]. Appropriate identification of those with hypertension requires clinicians to be able to identify those who likely are hypertensive outside of the office regardless of in-office hypertensive status. Indeed blood pressure (BP) derived from ambulatory measurements has a stronger association with mortality, long term-clinical events, and prognostic surrogate endpoints than BP derived from clinic visits [5–7]. Better identifying these patients would help providers prioritize who needs closer blood pressure follow-up and more intense risk factor modification.
Given that barriers still persist for more widespread use of out-of-office BP measurement, ambulatory BP prediction models have been developed and validated, particularly in middle-aged and older populations with cardiovascular comorbidities [8,9]. One such model is the PROOF-BP (PRedicting Out of OFfice Blood Pressure in the clinic) algorithm that uses age, sex, height, weight, diagnosis of hypertension, and history of cardiovascular disease along with office blood pressure measurements to predict out-of-office blood pressure (see S1 Table). However, in younger and healthier adults, some of the risk factors used in models such as PROOF-BP have yet to manifest. We hypothesize that other risk factors (such as genetic predisposition) may further stratify likelihood of ambulatory hypertension among those without overt hypertension risk factors.
Two methods to ascertain genetic predisposition to elevated BP build off the heritability of BP: polygenic risk scores (PRS) and family history of hypertension (FHx). PRS is a single number that captures the predisposition of an individual to a trait attributable to genetic variants and instances of its use have been widely studied in chronic disease [10]. Familial inheritance is a classic risk factor assumed to be a surrogate measure of genetic predisposition to chronic disease despite the fact that those with family history of disease may also be exposed to environments that increase susceptibility. A direct comparative analysis of family history and PRS has yet to be conducted in the context of identifying those with both in-office and out-of-office hypertension. In this study, we sought to characterize two facets of genetic predisposition to elevated BP (BP PRS and self-reported family history of hypertension) as predictors of office-based and ambulatory blood pressure. We hypothesize that incorporation of these tools can improve detection of those with ambulatory hypertension.
Methods
An overview of the study is provided in Fig 1.
The GAPP study enrolled a representative sample of 25-41 year old participants from Liechtenstein. Genetic predisposition to hypertension on the basis of family history and a polygenic risk score were evaluated as predictors of in-office and out-of-office 24-hour blood pressure measurement.
Participants
We performed our analysis on the baseline samples of the genetic and phenotypic determinants of BP and other cardiovascular risk factors (GAPP) study. In short, GAPP is a population-based cohort consisting of residents of the Principality of Liechtenstein with data collection ongoing. Participants were eligible for the sample if they were residents of the Principality of Liechtenstein between the ages of 25 and 41 at baseline. They were excluded if they had a history of major illness, including cardiovascular disease, treated diabetes mellitus, renal disease, atrial fibrillation, heart failure, severe obesity (body mass index>35), or sleep apnea. Full description of inclusion and exclusion criteria as well as other methodological details surrounding study recruitment and variable collection can be found in the previously published protocol [11]. Written informed consent was obtained from each participant and the study was conducted in accordance with a protocol approved by the local ethics committee. A total of 2170 individuals were recruited for the GAPP study. The first patient was recruited June 10, 2010 and recruitment remains ongoing. 1444 participants were analyzed for this study after removing participants who failed genomic quality control (n = 649 [29%] excluded; see quality control procedures below) or had missing covariate traits (n = 76 [3.5%]; complete case analysis).
Procedures
BP ascertainment.
Conventional office BP was measured in all participants by a trained study nurse using the Microlife BP3AG1 device with appropriate cuff size after at least 5 minutes of rest for each participant in a quiet setting. Three measurements were obtained in a seated position on the non-dominant arm.
Twenty-four hour ambulatory BP was measured with a validated Schiller BR-102 automated device fitted to the participant’s non-dominant arm [12]. Over the designated 24-hour period, BP was measured every 15 minutes during daytime (between the hours of 0730 and 2200) and every 30 minutes during nighttime (between the hours of 2200 and 0730). All participants were instructed to engage in their usual activities during the measurement period, but to keep their arm still during recordings. Ambulatory BP recordings were repeated if <80% of possible recordings were available. Daytime and nighttime measurements were additionally ascertained through a patient diary kept by each participant during the recording period. Individuals were classified according to their out-of-office hypertension status (daytime ambulatory BP ≥ 135/85 mmHg) and in-office-hypertension status (office BP ≥ 140/90 as determined by the average of the last two office measurements).
Sample collection and genotyping pipeline.
Venous blood sample collection was performed by a trained study nurse and samples were centrifuged and immediately stored in a specialized freezer (-80o C). Genotyping was done using the HumanCoreExome BeadChip designed by Illumina Inc (San Diego, CA), a validated genotyping chip that has been used in several large common variant studies [13,14]. Standard genotyping quality control procedures were undertaken. Analysis was limited to unrelated participants with European ancestry. Additional individual level checks were performed for sex inconsistencies and outlier heterozygosity rates. Genetic markers with increased missing call rate (>5%), Hardy Weinberg equilibrium departures (p-value < 1*10−6), and low minor allele frequency (minor allele frequency< 1%) were excluded. Further genetic quality control information is available in S1 Fig.
Genetic predisposition ascertainment.
We evaluated two traits of genetic predisposition to elevated BP: the polygenic risk score using external weights from publicly available GWAS and self-reported family history of hypertension.
The BP polygenic risk score (PRS) is a function of the GWAS SNP-phenotype effect estimate as well as the genotype of a given individual at that particular locus. These two pieces of information are combined to yield a single scalar value in each participant. This value can be considered representative of the additive genetic risk captured by genetic variants common in the population (minor allele frequency >0.01). We used PRS from weights available from the large GWAS meta-analysis (n = 757,601) of the UK Biobank study and the International Consortium of Blood Pressure conducted by Evangelou et al available through the PGS catalog (PGS ID: PGS002257 and PGS002258) [15]. We note that these weights/effect parameters were derived from the relationship of each SNP with office derived average systolic BP (in the case of the systolic BP PRS) and diastolic BP (in the case of the diastolic BP PRS). After harmonization, our systolic BP PRS used 867 SNPs and our diastolic BP PRS used 868 SNPs.
Family history of hypertension was classified based on participant self-report. A participant was denoted as having a positive family history if they reported at least one first-degree family member having hypertension.
Statistical analysis
Baseline characteristics were presented with median and interquartile range for continuous variables and numbers with proportions for categorical variables. For each analysis involving PRS, we utilized the concordant BP PRS, i.e., the PRS derived from the diastolic BP GWAS summary statistics when evaluating the ambulatory diastolic BP outcome and a PRS derived from systolic BP GWAS summary statistics when evaluating the ambulatory systolic BP outcome.
Assessing the relationship between PRS/family history and BP traits.
We examined the relationship of family history and PRS with BP traits in two linear regression models: 1) a minimally adjusted model (age with a cube root transform, sex); 2) a risk factor adjusted model (age with a cube root transform, sex, hemoglobin A1c, low density lipoprotein cholesterol, high density lipoprotein cholesterol, triglycerides, body mass index, smoking status) with mutual adjustment for PRS and family history. Effect measures for PRS were reported per 1 standard deviation (sample) increase.
Assessing PRS and family history as an incremental predictor of ambulatory hypertension.
We calculated the positive and negative predictive value of both PRS and family historyas it relates to identifying out-of-office systolic and diastolic hypertension. To do this, we evaluated the predictive value of family historyand PRS relative to the PROOF-BP algorithm, a validated clinical model for out-of-office BP that uses as features: demographics, clinical risk factors, and three office BP readings [8,16,17]. The PROOF-BP predicted blood pressure was calculated for each GAPP participant. Then we compared the three linear regression models: 1) PROOF-BP, 2) PROOF-BP + PRS, and 3) PROOF-BP + FHx using the Akaike Information Criteria (AIC) and root mean square error (RMSE) as metrics of model performance. Model output (predicted blood pressure) was used to calculate positive (sensitivity/[1-specificity]) and negative ([1-sensitivity]/specificity) likelihood ratios for ambulatory systolic and diastolic hypertension classification (as defined in S2 Table).
Analyses were performed using the statistical software R version 4.0.2 (Vienna, Austria).
Results
Participant characteristics are presented in Table 1. The median age in our study was 37 years (IQR: 31, 40). 588 participants (41%) self-reported a family history of hypertension in a first degree relative, 177 showed either office systolic or diastolic hypertension (12%), and 530 showed either ambulatory systolic or diastolic hypertension (37%). Family history and systolic BP PRS were uncorrelated (R2 = 4.96x10-3). We observed a similar pattern with family history and diastolic BP PRS (R2 = 5.74x10-3).
The relationships between genetic predisposition traits and BP are listed in Table 2. PRS was associated with an increased office-based systolic BP (1.8 mmHg per 1 SD increase; 95% CI: 1.3–2.4) and diastolic BP (1.5 mmHg per 1 SD increase; 95% CI: 1.1–1.9). PRS was also associated with a higher ambulatory daytime systolic BP (2.2 mmHg per 1 SD increase; 95% CI: 1.6–2.7) and diastolic BP (1.6 mmHg per 1 SD increase; 95% CI: 1.2–2.0). Positive family history was associated with a 3.5 mmHg higher office systolic BP (95% CI: 2.4–4.7), a 2.6 mmHg higher office diastolic BP (95% CI: 1.8–3.5), a 2.4 mmHg higher ambulatory daytime systolic BP (95% CI: 1.3–3.4), and a 1.8 mmHg higher ambulatory diastolic pressure (95% CI: 1.0–2.7). For those with a first degree male family member, positive predictive value and negative predictive value were 41% and 65%, respectively. For those with a first degree female family member, positive predictive value and negative predictive value were 38% and 64% respectively. Having multiple family members with hypertension likewise yielded a positive predictive value and negative predictive value of 43% and 64%, respectively.
We found consistent effects after adjustment for both family history status and PRS in addition to adjustment for other risk factors (S3 Table). Compared to family history, we found that PRS explains more variation in each BP trait than family history (S4 Table).
In our cohort, we found that the uncalibrated PROOF-BP algorithm performs comparably to a naïve model consisting only of average office BP measurements in predicting ambulatory systolic BP, and markedly worse in predicting ambulatory diastolic BP (Table 3). As a result, we refit the PROOF-BP equation using the GAPP participants and assessed the incremental value of genetic predisposition compared to a model with these GAPP-recalibrated coefficients, i.e., we refit a linear regression model with the same predictors and interactions as the original model onto the GAPP data. Compared to the predicted blood pressure from the recalibrated PROOF-BP model, the model incorporating the genetic score for systolic BP had an improved fit for prediction of ambulatory daytime systolic BP (change in AIC = −32; see Table 3). Relative to the recalibrated PROOF-BP score, a family history of hypertension provided no incremental value in predicting ambulatory systolic BP. Compared to the recalibrated PROOF-BP model, the model incorporating the genetic score for diastolic BP had an improved fit for prediction of ambulatory daytime diastolic BP (change in AIC = −22; see Table 3). Likewise, a family history of hypertension did not improve model fit relative to the recalibrated PROOF-BP for diastolic BP prediction. These findings were also consistent when using average blood pressure as a baseline model rather than the output of the GAPP-recalibrated PROOF-BP model (see S5 Table).
Diagnostic test characteristics for predicting ambulatory hypertension traits are presented in Table 4. The recalibrated PROOF-BP model had a positive likelihood ratio of 6.87 (95% CI: 5.56–8.49) and negative likelihood ratio of 0.45 (95% CI: 0.39–0.51) for identifying individuals with ambulatory systolic hypertension (i.e., > 135 mmHg) and a positive likelihood ratio of 4.65 (95% CI: 3.89–5.56) and negative likelihood ratio of 0.46 (95% CI: 0.41–0.52) for identifying individuals with ambulatory diastolic hypertension (i.e., > 85 mmHg). The PROOF-BP model with PRS integrated had a positive likelihood ratio of 7.69 (95% CI: 6.18–9.57) and negative likelihood ratio of 0.43 (95% CI: 0.37–0.49) for identifying individuals with ambulatory systolic hypertension and a positive likelihood ratio of 4.72 (95% CI: 3.94–5.66) and negative likelihood ratio of 0.46 (95% CI: 0.41–0.52) for identifying individuals with ambulatory diastolic hypertension.
Discussion
In this study, we conducted a thorough evaluation of the relationship between predisposition to elevated BP with both in-office and ambulatory BP traits in a sample with a high burden of ambulatory and masked hypertension. We found a significant relationship between family history and PRS with elevated BP in both the office and ambulatory setting. We also found that PRS explained more variation in BP phenotypes (both ambulatory and in-office) than family history. However, for identifying individuals with ambulatory hypertension, PRS provided only modest improvement in prediction, while family history provided no incremental information. Importantly, family history had little correlation with PRS suggesting that although both traits are used to capture predisposition to a given trait, they likely provide clinicians with orthogonal information.
An extensive corpus describing the heritable nature of hypertension serves as the basis for understanding the relevance of genetic predisposition in patient assessment [15,18]. For example, an intergenerational analysis of the Framingham cohort shows that early onset hypertension in antecedent generations is predictive of hypertension in subsequent generations [19]. Expert statements posit family history as a risk factor for masked hypertension, along with male sex, diabetes status, and cardiovascular risk factor burden [20]. However, our findings show that family history provides no incremental information in the identification of individuals with ambulatory hypertension and are inferior to PRS for identifying those with ambulatory hypertension. Moreover, PRS-based methods have shown promise in the realm of hypertension. An analysis of two Swedish cohorts found that a blood pressure PRS was associated with an increased incidence of hypertension with an effect size comparable to that of body-mass index [21]. Another analysis of the UKBiobank showed that a PRS for office BP was associated with incident cardiovascular disease independent of measured office BP, suggesting that it may provide additional information on cardiovascular risk [22]. In fact, within our own data, blood pressure PRS is completely uncorrelated with indices such as BMI and lipid measures suggesting that there are separate axes on which this particular PRS is exerting its predictive capabilities. Despite the strong association with blood pressure elevation and its robustness relative to family history, PRS does not provide enough incremental information, as currently constructed, to improve identification of those with ambulatory hypertensive phenotypes in our study.
Strengths of this study include the large sample size of well-characterized apparently healthy young adults with high participant compliance. This is an otherwise understudied demographic in hypertension cohorts. Previous models developed for ambulatory blood pressure prediction have focused on relatively older patients with higher comorbidity burdens. Most interestingly, we note that the high prevalence of ambulatory hypertension (in particular masked hypertension) in our cohort allowed us to document the informativeness of these ‘predisposition’ traits in those for whom we would otherwise have little clinical suspicion for hypertension. Furthermore, detecting hypertension in this group with low comorbidity burden carries meaningful clinical implications even among individuals who appear healthy at baseline. It allows for more refined cardiovascular risk factor assessment and stratification; opens the door to more aggressive risk factor modification with modification of diet, weight management, and abstinence from alcohol; and permits more structured surveillance and follow-up. This group would presumably have the largest incremental yield from genetic susceptibility testing. The integration of ambulatory BP monitoring to parse out distinct patterns between office-based and ambulatory measures provides us with unique insights into the relationship between the genetics of BP that have not been described in previous literature. On the other hand, there are limitations that must be pointed out for our work. First, our BP PRS construction is limited to SNPs associated with office BP measurements. GWAS requires large sample sizes for precise estimation of SNP-phenotype relationships, but large sample sizes mean that higher quality phenotypes (like ambulatory BP) are swapped out with noisier phenotypes like office BP. Conduct of large genome wide meta-analyses with higher quality phenotypes like ambulatory BP may improve future gene discovery efforts and the predictiveness of the resulting secondary tools such as PRS. Second, our measure of family history relied on participant self-report which is subject to recall bias. This may be less reliable than previous studies which rely on confirmed inter-generational ascertainment of hypertension status and can more clearly document age of onset across generations. Despite this, self-report of family history is often the only information available at the bedside and thus may be more relevant as a clinical operationalization of family history than those with family history observed and verified by study teams. Furthermore, hypertension definitions have evolved with time, which may further impact self-report. Despite these limitations, self-reported family history remains a pragmatic clinical standard that in our data still predicts out-of-office hypertension status even though it does not contribute beyond a standard clinical risk score (PROOF-BP). Third, our analysis was limited to PRS constructed from European only source GWAS and applied to a sample of residents from the Principality of Liechtenstein. The consequence of this is that our reported estimates of PRS performance are likely to be optimistic compared to a similar analysis that would be conducted in a more diverse population. Finally, a PRS is not a static entity. In other words, PRS performance for blood pressure can continue to improve with larger GWAS studies, improved phenotyping (as described above), and improved methodology for fitting scores.
Conclusion
Genetic predisposition traits are strongly associated with BP in both the office and ambulatory setting. PRS provides modest incremental information in ascertaining out-of-office hypertension status in young and healthy individuals, but likely not at a level that sufficiently informs clinical management. Family history, on the other hand, serves as a poor surrogate of genetic predisposition in this setting and does not yield incremental information in identifying those with out-of-office hypertension. This contrasts with recommendations from expert statements suggesting that family history can be used as a factor in identifying those with masked hypertension. Our investigation suggests PRS may require further progress before it can be used as a tool for identifying ambulatory daytime hypertension. As such, we cannot exclude the possibility of PRS having clinical utility in future applications as methodology improves or GWAS become larger with improved phenotyping.
Supporting information
S2 Table. Blood pressure phentype definitions.
https://doi.org/10.1371/journal.pone.0354952.s003
(DOCX)
S3 Table. Association between genetic predisposition to elevated blood pressure and blood pressure traits with each model adjusted for hypertension risk factors.
https://doi.org/10.1371/journal.pone.0354952.s004
(DOCX)
S4 Table. Variation explained (R2) by blood pressure genetic score and family history with office/ambulatory blood pressure traits.
https://doi.org/10.1371/journal.pone.0354952.s005
(DOCX)
S5 Table. Model performance compared to office measurements.
https://doi.org/10.1371/journal.pone.0354952.s006
(DOCX)
Acknowledgments
We thank those who enrolled in the GAPP study. We also thank the government of the Principality of Liechtenstein, the health ministers, and the Liechtenstein Office of Public Health for their support. Finally, our thanks are especially due to the Princely House of Liechtenstein, which gave decisive support that enabled the initiation of this project.
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