Assessing genetic risk of hypertension at an early age: future research directions

KEYWORDS:

• Hypertension
• blood pressure
• genetic risk
• children

It has long been known that genetic factors play an important role in determining an individual’s propensity to develop hypertension. In recent years, there has been major progress in gene-finding efforts for blood pressure (BP) and hypertension in adults. Of particular note, findings from three new large-scale genome-wide association studies (GWASs) [1–3] each including over 300,000 subjects have expanded the list of genetic loci for systolic BP (SBP), diastolic BP, and pulse pressure to almost 400. This raises the possibility of assessing genetic risk of hypertension at an early age (i.e. in childhood), providing opportunities for early lifestyle intervention to offset the impact of an elevated BP on future cardiovascular risk. However, both 24 h BP levels and BP trajectories during a child’s development may vary widely. Moreover, family and twin studies have firmly established the age-dependency of genetic effects on BP [4,5]. This means that the effect of the newly identified adult BP loci on development of childhood BP still needs to be verified. In this editorial expert review, we will provide an update on the current state of the science and propose four directions needed to move the research in this field forward in order to realize improved assessment of genetic risk of hypertension at an early age.

1. Applying adult BP GWAS findings to children

After the first BP GWAS meta-analysis by the International Consortium for BP GWASs (ICBP), which found 29 loci for adult BP in 2011 [6], several studies explored whether these genetic variants are also associated with BP in children and adolescents [7–9]. Findings were consistent in that the multi-SNP genetic risk score (GRS) based on these loci not only associated with BP levels in children but was also predictive of hypertension development in young adulthood. However, these studies also showed that the adult-based GRSs explained less variance in childhood than in adult BP and that not all of the individual loci displayed significant associations with childhood BP. Now that the list of genetic loci for adult BP has expanded dramatically, reevaluation of the effect of adult GWAS findings on BP in children is warranted.

2. Expanding sample size of BP GWAS in children

The age dependency of genetic effects on BP was well documented by our longitudinal twin study in which we observed that only about 60% of the genetic influence on BP was shared between childhood and early adulthood [4]. Unfortunately, all the GWASs on BP have been limited to participants of 18 years or older with only one exception. In that study [10], genome-wide data from participating European ancestry cohorts of the Early Genetics and Lifecourse Epidemiology (EAGLE) Consortium was analyzed across three epochs: prepuberty (4–7 years, n = 10,090), puberty (8–12 years, n = 8423), and postpuberty (13–20 years, n = 5176). Due to the limited sample size, no signals were consistently observed across all different age epochs although two genome-wide significant loci for SBP were found for specific age periods: one locus for prepuberty and one for puberty. Therefore, larger GWAS consortia specifically focusing on childhood BP are urgently needed. In addition to expanding the sample size of consortia that use existing studies with childhood BP data, electronic health records may constitute another important resource to dramatically increase the sample size. For example, in the US, BP is measured from age 4 to 18 during well-child doctor’s visits. Although such measures may be influenced by somewhat varying circumstances and obtained under a less stringent protocol, the recent study by Hoffmann et al. [1] using longitudinal electronic health record data clearly demonstrated their value in identifying genetic variants for adult BP.

Furthermore, recent progress in statistical methodology allows the detection of the age dependency of genetic effects on BP from GWAS data at both the level of individual loci and the overall genetic architecture. For individual loci, a joint meta-analysis (JMA) approach on SNP main effects and SNP–environment interactions has been described to have larger power than other methods when both main and interaction effects are present [11]. Even if main or interactions effects are absent, power of JMA is close to that of meta-analyses only targeting main effects or interactions. This approach has successfully identified additional novel loci associated with fasting insulin and glucose accounting for interactions with body mass index (BMI) [12]. It also led to the identification of additional loci for general (BMI) and central adiposity (waist circumference and waist-to-hip ratio) accounting for smoking [13]. It would be interesting to also use this approach to examine gene–age interactions in BP regulation. The overall age-dependency of the genetic architecture of BP regulation can be investigated using (bivariate) linkage disequilibrium (LD) score regression [14]. This method only requires summary statistics of GWAS results to estimate the genetic correlation between different traits or traits at different ages and is not biased by sample overlap. Thus, by calculating the genetic correlation, LD score regression can determine to what extent the genetic variants contributing to BP in childhood and in adulthood are the same or different. However, the prerequisite for both of these statistical approaches is the availability of GWAS data for BP in children. This further emphasizes the importance of large GWAS meta-analyses of childhood BP.

3. Improving BP prediction using GWAS and EWAS findings of BP risk factors: obesity and low birthweight

Obesity and low birthweight are known risk factors for higher BP in childhood. Family and twin studies have observed that the link between either of these two factors and BP can be partly attributed to a common set of genetic factors [15]. A GRS based on obesity susceptibility loci was associated with childhood BP and even explained a greater proportion of the variation in BP at certain ages than the BP 29-SNP GRS itself [9]. Epigenome-wide association studies (EWASs) constitute another area in which major progress has been made in obesity research in the past several years. EWASs have identified more than 200 methylation markers or CpG sites consistently associated with BMI across multiple ethnicities and age groups [16–19]. Unlike genetic variants, epigenetic markers are dynamic; that is, associations may reflect the consequence of obesity rather than the cause. This is less of a concern if these signals can be used to improve prediction at an early age of diseases for which obesity is a risk factor. For example, a methylation risk score that integrated 187 BMI-related CpG sites was not only a strong predictor of new onset type 2 diabetes but also displayed an independent effect even after adjustment for adiposity and glycemic measures [17]. It is anticipated that the value of these obesity-related epigenetic signals in the prediction of hypertension will also be assessed and reported soon. Birthweight is another example. Among the lead SNPs of 60 loci identified for birthweight in the most recent GWAS in 153,781 individuals [20], most birthweight-raising alleles were associated with reduced BP. Using LD score regression, a strong inverse genetic correlation of −0.22 between birthweight and SBP was found and based on UK Biobank data it was estimated that 85% of the negative relation between birthweight and SBP was explained by shared genetic associations captured by the genotyped SNPs, providing strong evidence that the relationship between early growth and later BP development has an appreciable genetic component. The association of birthweight and related intrauterine environmental exposures with genome-wide DNA methylation changes and whether these changes may mediate the long-term effects of birthweight on adult disease is also an active area of research [21]. Findings from such studies may very well contribute to the prediction of hypertension risk at an early age.

Thus, combining genetic and epigenetic information not only of risk factors, but also of BP itself may have the potential to further improve the phenotypic prediction. Small-scale EWASs on BP and hypertension have already been reported with promising findings [22]. Undoubtedly, in the near future, findings from large-scale EWASs will be reported, enabling joint prediction with both genetic and epigenetic signals.

4. Using improved phenotyping: hypertension classification, ambulatory BP, and BP trajectories

Secondary hypertension is common in children, which is very different from adults in which essential hypertension is the predominant form (95%). In a study [23] of 275 hypertensive children in a pediatric hypertension clinic, 57% had secondary hypertension. Although the percentage of children with secondary hypertension from population studies is expected to be lower than this, this study emphasizes the importance of excluding secondary hypertension cases in future GWASs on childhood BP or essential hypertension. Also, since childhood BP is influenced by height (in addition to age and sex) [24], it should be considered in the analysis. Furthermore, some other methods are also helpful in determining the phenotype. The evaluation of brachial artery endothelial function (i.e. flow-mediated dilation), smooth muscle function (i.e. nitroglycerin-mediated dilation), and pulse wave analysis is now increasingly used for pediatric hypertension risk evaluation [24–26]. So, further study should combine multiple tests to establish a more accurate phenotype.

Previous GWASs on BP have almost exclusively focused on BP measured in the clinic (or doctor’s office). However, BP fluctuates over 24 h and shows circadian rhythmicity. Ambulatory BP monitoring offers a number of advantages over clinic BP readings, including the ability to measure BP in real-life settings, track BP at night, avoid the ‘white coat’ phenomenon, and study BP rhythmicity (i.e. dipping, morning surge, and variability). Our twin studies in youth and young adults have shown that different BP rhythmicity parameters and BP measured at different setting (clinic vs. 24 h) and different times of day (day vs. night) are influenced by partly different genes [27,28], requiring separate GWASs on these specific ambulatory BP parameters to fully understand the underlying genetic mechanisms of BP regulation. Small-scale GWASs have been conducted on ambulatory BP measures with promising results. For example, in a two-stage GWAS with overall sample size of 941, one SNP was identified to be associated with nighttime pulse pressure in early onset hypertensives [29]. The role of clock genes on BP rhythmicity has also been explored. Leu et al. [30] studied diurnal BP changes in relation to 23 tag SNPs in 11 clock genes and observed that 5 SNPs were significantly associated with the nondipper pattern with independent replication in another cohort. Large-scale GWAS meta-analyses of cohorts with ambulatory BP data will have the potential to move this field forward.

In addition to BP levels, which are usually measured only once at baseline, recent longitudinal studies indicate that patterns of BP over time (i.e. BP trajectories) are associated with increased risk of cardiovascular disease. We recently observed that different BP trajectories exist in childhood with participants in the rapidly increasing group showing more target organ damage in young adulthood, adding yet another important piece of evidence in supporting the arguments for routine BP screening in childhood [31]. The underlying genetic factors determining the degree of BP rise over time might be different from those that determine initial BP levels. This was supported by previous studies [9,32], which observed that a multi-SNP BP GRS was significantly associated with BP levels but showed little or weak evidence in association with BP changes over time. We are currently investigating this question using our Georgia Cardiovascular Twin study which has BP measured at multiple time points from childhood to young adulthood. Obtaining GWAS data in cohorts of children with longitudinal BP measurements as well as those with electronic health records from well-child doctor’s visits will have enormous potential to shed light on this topic.

5. Promising future for genomics-based personalized prediction and prevention

Although much more gene finding is still needed to bridge the heritability gap of complex traits like BP [33], the most recent encouraging findings from BP GWAS in adults have moved us one step closer to more accurate prediction of hypertension in early life. A concerted effort to integrate genomics and epigenomics of both hypertension and its underlying risk factors in combination with much larger GWAS efforts in longitudinal cohorts of children with much more detailed phenotypes as well as accurate recording of relevant environmental exposures will be key to advancing this research field in the near future.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

X wang was supported in part by grants from the national heart lung and blood institute [hl104125 & hl105689].