We also performed bivariate analysis on both stress levels (i.e. WCT versus CPT) for all HRV measurements and HR and found the r G significantly different from zero ranging from 0.60 to 1.00 (for low frequency, VLF, and total power: r G = 1), and the r E ranged from 0.30 to 0.81 (Table 4). The genetic correlations for HR, SDNN, and RMSSD were significantly different from 1 as well. Results of the bivariate analyses between WCT and CPT in the smaller and younger subsample limited to participants who participated in the WCT were very similar (Supplementary Table 5, http://links.lww.com/HJH/A911).
In this study, we quantified the contribution of genes to the variance of HRV and HR at rest and under mental and physical stress and the extent to which genes overlap between rest and stress conditions in a large homogeneous Arab population. We found that genetic factors significantly influence HRV and HR at rest in this Arab population and that the heritability estimates increased particularly under mental stress, but not under physical stress. Furthermore, most traits showed large shared genetic influences between rest and stress conditions, but in comparison with rest, there were also modest stress-specific genetic effects.
Moreover, in our study, we did not consider the underlying genetics of normalized units (nu) for high frequency, low frequency, and LFHF ratio because relevant genetic influences captured by the magnitude of total power's dynamic range – which could be of several orders of magnitude – would disappear with such normalization. In other words, individual 1 (who is young and healthy) has a resting low frequency of 4000 ms2 and a high frequency of 5000 ms2; and individual 2 (who is older but healthy and still with considerable sinus arrhythmia) has resting low frequency and high frequency values of 40 and 50 ms2, respectively. Both individuals would then have the same normalized units, but with vastly different magnitude of oscillations.
We also observed striking differences in stress reactivity patterns to the two tests amongst the different HRV measurements in contrast to an almost identical HR reactivity to mental and physical stress. SDNN, low frequency, and total power significantly decreased in response to the WCT, but increased in response to the CPT, whereas RMSSD, high frequency, and HR showed directionally consistent and significant effects to both stressors. The clustering in reactivity patterns among HRV variables might be expected because both SDNN and total power reflect total variability in HR encompassing both short-term high frequency and lower frequency components [4,41], which are influenced by both the parasympathetic (PNS) and sympathetic (SNS) nervous system . In contrast, RMSSD and high frequency encompass short-term HRV changes only [4,41] and reflect only the PNS . Therefore, based on our HRV data, the reactivity to mental stress appears predominantly because of vagal withdrawal, whereas the reactivity to physical stress appears characterized by a decrease in PNS activity accompanied by a simultaneous increase in SNS activity. These diverging response patterns eventually resulted in virtually identical reactivity of HR to mental and physical stress. Our results confirm those from Snieder et al.  who also observed a stronger reduction in respiratory sinus arrhythmia (i.e. vagal withdrawal) in response to mental stress as compared with the CPT and those from Fonkoue and Carter  who, conversely, observed a stronger increase in muscle sympathetic nerve activity in response to the CPT than in response to mental stress.
Interestingly, our bivariate modeling results were confirmed by the genome-wide multipoint linkage analyses. We found significant linkage for HR on chromosome 3 and suggestive linkage on chromosomes 6, 7, and 12 for some HRV traits. Moreover, we observed indeed that some loci were shared between rest and stress whereas others were specific for either rest or stress. However, a downside of using linkage analysis for a complex phenotype such as HRV, is that the effect sizes of the individual causal variants are likely too small to allow for detection via co-segregation . Therefore, the power to detect genes for complex traits with linkage analysis is minimal  and mapping resolution is low , which could explain why most of our linkage results for HRV were only suggestive. A more suitable approach that we recently used for gene identification of HRV is the hypothesis-free genome-wide association study .
Notably, our study is the first to use an Arab population to examine the genetic contribution to HRV variables and HR at rest and stress in a family design using univariate and bivariate analyses. The OFS cohort has a number of major strengths: it is geographically isolated, which provides a more homogenous environmental exposure; the socioeconomic status is similar among OFS participants; the participants have similar health-related habits (e.g. religious abstinence from alcohol); and genealogical records are authentic and well accessible. At the same time, this homogeneity may limit the generalizability of our results. Further investigation in families of other ethnicities could be warranted.
Another limitation of our study is that we only investigated one type of mental and one type of physical stress. For future studies, using a variety of stress tests to represent mental and physical stress could be of interest such as a bicycle exercise challenge to represent physical stress and a virtual reality car driving challenge  to represent mental stress. Using CPT as a representative of physical stress could be considered a drawback because its stress response is largely driven by the experience of pain in response to exposure to cold, which is influenced by an individual's pain tolerance. Individual variations in pain tolerance have been shown in other studies .
Addressing the full complexity of the neural underpinnings of HRV was beyond the scope of our study, given that each RR interval (or IBI) depends on the interaction of vagal and sympathetic efferent activity within its humoral environment and molecular structures  and RR variability does not provide direct measures of autonomic (parasympathetic/sympathetic) activity (i.e. neural firing) but only indices of regulatory modulation [52,53]. However, we do believe that gene-finding studies of HRV may provide novel insights into vagal heart rhythm regulation as shown in our recent meta-analyses of genome-wide association studies for HRV (N = 53 174) .
In conclusion, we showed that genetic factors significantly influence HRV measurements and HR; the heritability estimates of HRV under mental stress, in particular, for SDNN and RMSSD, are higher than those at rest, whereas those during physical stress are similar to the ones at rest; there is evidence for a large overlap of genetic factors influencing HRV at rest and during mental or physical stress; SDNN, RMSSD, and HR also show genetic effects that are specific to one of the stress conditions; and environmental factors contribute more than genetic factors in explaining the phenotypic correlation between HRV and HR at rest and under stress conditions. In addition, our linkage-based gene-finding approach suggested some loci to be shared between HRV and HR at rest and under stress, whereas others were specific for either rest or stress, which confirmed our bivariate modelling results.
Our results provide more information regarding the contribution of the genetic factors of HRV at rest and under physical and mental stress conditions and emphasize the importance of carefully defining the trait and standardizing its conditions in future gene-discovery efforts. Uncovering genes for HRV, for example, in GWASs, would facilitate investigation into their underlying function and impact on clinical outcomes.
There are no conflicts of interest.
Reviewers’ Summary Evaluations
This study has shown that resting heart rate and heart rate variability are influenced by genetic factors and that most of the genetic factors that influence heart rate variability at rest also influence heart rate variability during stressful situations. The peculiarity of this investigation is that the results were obtained in a homogeneous Arab population. Limitations of the study are that important confounding factors such as smoking and alcohol drinking could not be accounted for when establishing genetic associations and that these findings cannot be extended to other ethnic groups.
This paper has two major objectives: a genetic study and a neural one. They are both complex and require simplifications, but nevertheless convincingly show an interaction between genes and HRV (as a proxy of autonomic regulation).
In addition, simplifications carry the risk of becoming over-simplifications or impossible to understand, particularly in the case of issues still being debated, such as the mechanisms explaining HRV.
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† Shared last authors.