The recent completion of the human genome project and major technical advances have revolutionised the genetics of complex diseases.
Highly effective procedures for genotyping and improvements in appropriate statistical methods have allowed genome-wide association (GWA) studies to be conducted successfully. In the last years, these technologies have enabled identification of an unexpected degree of new genes responsible for the most common endemic diseases.
Researchers were especially successful with the genetic analysis of coronary heart disease (CHD) and cardiac arrests. Researchers at Lübeck Medical School, from the Department for Integrative and Experimental Genomics and the Department for Biometrics and Statistics partook in the first description of twelve of the thirteen genes so far identified for cardiac arrest.
Presently, one has access to data from several GWA studies on a total of four cardiovascular phenotypes: cardiac arrest, main stem stenosis, left ventricular hypertrophy and aortic stenosis.
Hence, Lübeck’s unique expertise in Germany on genome-wide investigation of cardiovascular diseases brought considerable recognition abroad. This led to the founding of a worldwide consortium to conduct meta-analysis of data from GWA studies, involving more than 14 studies with over 80,000 test subjects (CARDIoGRAM).
The challenge ahead is to identify the regulatory networks modulated by genomic, somatic and exogenous factors and which are then misdirected upon precipitation of illnesses. A better understanding of such processes should lead to new preventive measures and therapies. One has already completed the initial steps in this cascade, i.e. functional matching of disease-relevant genetic variants in terms of changes at the transcription or proteomics level. Nevertheless, one still needs to integrate genome-wide typing with data from transcription, proteomics and metabolomics analysis and turn this into a functional network – such that it also accounts for aspects of endemic medicine like exogenous influencing factors.