Thursday 13/07/23

This session will cover monogenic and complex genetic diseases. We will be discussing how to ascertain if a condition has a genetic component, and then what laboratory and analytical methods to use for gene discovery. The lecture will then cover hypertension as an exemplar. 

Hypertension affects over 1 billion people worldwide, and for a small percentage of individuals their high blood pressure is due to rare inherited mutations, however the majority (95%) have what is called essential hypertension. This is regarded a complex genetic condition, where both genes and environment are important. There has been much progress over the past decade in understanding the genetic component of essential hypertension using genome-wide association studies. We will cover the major advances in this area, key results and briefly the approaches to understand gene-environment interactions for blood pressure traits.  

Further reading:

1. Horton et al. Recent developments in genetics/genomic medicine (Review). Clinical Science 2019

2. Li et al. Whole -exome sequencing identifies a de novo PDE3A variant causing autosomal dominant hypertension with brachydactyly type E syndrome: a case report. BMC Medical Genetics, 2020.

3. Evangelou et al. Genetic analysis in over one million people identifies 535 new loci associated with blood pressure traits. Nature Genetics, 2018.

The Epigenome – the packaging and chemical modifications of the genome – gives an insight to cell-specific activity. This session will focus on the utility of one of these epigenetic marks, DNA methylation, to reveal molecular changes in disease pathology, but also its potential to act as an epidemiological biomarker of long-term environmental exposure.

Quantitative DNA methylation signatures from blood can readily be determined for health-related factors, such as tobacco smoking. Additionally, estimates of age, or DNA methylation ‘clocks’, capture the multisystemic and phenotypic changes occurring with age. These ‘biological’ DNA methylation age measures are associated with both morbidity and mortality.

Thus, the integration of epigenomic data, capturing non-genetic or environmental risk factors, with identified genetic risk, has the potential to be highly informative and bring a deeper understanding of disease susceptibility.

Further reading:

1. Wu H, Eckhardt C & Baccarelli A. (2023) Molecular mechanisms of environmental exposures and human disease. Nature Reviews Genetics 24, 332–344

https://www.nature.com/articles/s41576-022-00569-3

2. Feil R & Fraga M. (2012) Epigenetics and the environment: emerging patterns and implications Nature Reviews Genetics 13(2):97-109.

https://www.nature.com/articles/nrg3142

3. Horvath S & Raj K. (2018) DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19: 371–384

https://www.nature.com/articles/s41576-018-0004-3