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Content archived on 2024-05-29

Identifying genes for heart rate variability

Final Activity Report Summary - IGHRV (Identifying genes for heart rate variability)

The main objective of this project is to identify genes contributing to human variation in heart rate variability (HRV), a noninvasive index of cardiac parasympathetic tone and a predictor of all-cause mortality, arrhythmic events and sudden death. The focus was on assessing the effects of eight key genes involved in biosynthesis, transport, breakdown and receptor binding of acetylcholine (the neurotransmitter of the parasympathetic pathway). In this collaborative project we used data from the TRAILS cohort (800 unrelated white youth) and data on 720 black youth drawn from the Georgia Cardiovascular Twin Study and the longitudinal BP Stress cohort. All cohorts had beat-to-beat heart rate measured at rest and during stress tasks and commonly used time and frequency-domain parameters of HRV were calculated.

Intermediate analyses in the Georgia Cardiovascular Twin Study suggested that, independent of ethnicity and gender, HRV regulation at rest and in response to stress is largely influenced by the same genes with a small but significant contribution of stress-specific genetic effects. Analyses of the longitudinal BP Stress cohort showed that blacks show higher resting HRV than whites, and females display greater HRV response to stress than males; and these ethnic and sex differences are consistent across 1.5 years. Resting HRV declined with weight gain.

Genotyping of 96 SNPs in the white population and 147 SNPs in the black (i.e. African American) population in both Georgia and TRAILS cohorts was performed successfully. Thus, phenotype and genotype data are complete and genetic association analysis combining the white samples of the Georgia cohorts and the TRAILS cohort is ongoing and will be completed in the final 3rd year of the project.

The long term objective of this project is to understand the genetic basis of cardiac autonomic function. Findings may lead to a more accurate prediction of individuals at risk, improve the effectiveness of primary interventions and contribute to individualised therapy for cardiovascular disease.