Genomic Research Unlocking New Avenues for Chronic Disease Treatment

Abstract

As the world’s population ages, mortality increasingly reflects the ravages of complex chronic diseases, particularly cancer and heart disease. A person’s risk of succumbing to a chronic disease is linked to his or her genetics. This genome-centric view of causation is motivated by the technologic ability to detect and manipulate genes, and fosters the notion that genetic factors are necessary determinants of disease that operate in a causal background of diverse exposures. Chronic venous disease is highly prevalent in the Western side. The recent developments in sequencing technology, clinicians and geneticists alike are embarking on a journey to identify and unravel the genetic candidates of chronic venous disease. Currently substantial evidence to suggest the presence of genetic influences in the aetiology and pathology of venous disease. The researchers should be urged to foster collaborative links and design a genome-wide case-control association study as an international consortium to provide a statistically robust paradigm in the field of chronic venous disease genetics.  Family history is a well-known risk factor for developing many common chronic diseases, such as diabetes, asthma, and coronary heart disease, and reflects inherited genetic and shared environmental contribution in disease. The future directions include incorporating genotype information to partition DNA-transmitted genetic versus environmental variance in phenotype in family history to decompose the various components of risk influenced by familial disease. An accurate diagnosis is also crucial for genetic counseling and family planning and allows reproductive options, such as prenatal or pre-implantation genetic testing. It allows screening of at-risk family members, which may also be important in determining their eligibility as kidney transplant donors. At present, phenotype-associated multigene panels and ES are the preferred diagnostic MPS-based testing modalities, but it is expected that when GS becomes more feasible, both in terms of cost-effectiveness and complex data interpretation, GS-based diagnostic testing will replace most current testing modalities.