The sequence of the human DNA, or genome, has been extensively studied over the past two decades to understand different aspects of disease and health, from pre-birth to those that develop later in life. The primary focus is to use this knowledge for better healthcare.
Researchers have shown that a person’s genome determines his or her susceptibility towards certain diseases and response to treatment. A person may have gene variants which puts him or her at risk for a particular disease as compared to someone with another variant of the same gene. Identification of such variants ahead of time could enable efficient disease management and better outcomes or even prevention. This has given rise to the idea of ‘personalized’ medicine as against the conventional ‘one size fits all’ approach which treats manifested symptoms based on general clinical guidelines.
With DNA testing technologies becoming affordable, the question now is ‘how fast’ rather than ‘if’ genomics will impact healthcare. Genomic profiling has already shown great value in risk prediction for Huntington’s disease, hereditary form of cancers; and metabolic diseases in which the body is unable to break down certain types of dietary compounds. The treatment of these potentially fatal conditions is invasive, expensive or has severe side effects, thus genetic prediction can be very crucial. Such conditions are determined by a single gene (monogene) and its cause-effect relationship is easy to decipher.
Disease management of conditions like diabetes, which affects 1 in 11 individuals worldwide, is gaining priority. Predicting multi-gene disorders like diabetes and coronary heart disease (CHD) can be difficult using genetic approach since they are determined by cumulative effect of many genes and also environmental factors. Adding to the complexity are co-occurring diseases where the occurrence of one makes an individual susceptible to another disease. For example, diabetes is associated with various general and organ specific complications of which CHD is most prominent. Susceptibility-associated gene variants having high predictive value, either for occurrence of individual condition or co-occurrence with another, may show a low overall predictive value when taken together with other similar gene variants in other parts of the person’s DNA; thereby showing only a marginal increase or decrease in comparison to the average risk factor of the entire community.
A recent multi-country study, headed by researchers from University of Pennsylvania and also involving Indian scientists, was published in journal Nature Genetics which reflects on such associations. Commenting on the study, Dr Mitali Mukerji, a senior principal scientist at the CSIR- Institute of Genomics and Integrative Biology (IGIB), New Delhi, said that although the study shows genetic association between diabetes and risk for CHD in many cases, it also reports exceptions which are difficult to explain. Adding to this, Prof. Dwaipan Bharadwaj of Jawaharlal Nehru University explains “all of these variations are carrying very nominal risk. It is now more or less clear that diabetes or related so-called lifestyle diseases are not overtly genetics based; rather they are manifestations of modifications in the genome. At present all the discovered DNA variations together cannot explain more than 10% risks”.
The study reports new gene variants which might be important. Previous studies have shown that new yet unknown gene pathways and novel intermediate biomarkers may be stronger predictors of disease than the genetic variant that led to its identification. Dr Bharadwaj explains that these new discoveries will additionally help in understanding disease biology better and force researchers to think of alternate risk factors.
On one hand, increasing number of studies are reporting gene variants which potentially predict disease progression in different clinical settings, both for monogenic and multi-gene disorders. One the other hand, many researchers believe that identifying combinations of causal gene variants with high disease predictive value, even within the same population is unlikely for complex multi-gene diseases as diabetes and CHD. This may be true even though the variants by themselves may be common as the combinations are likely to be rare. Effects of gene-gene and gene-environment interaction along with ethnic difference in populations are other factors to consider.
While the jury is out on the use of gene variants for susceptibility prediction for complex diseases like diabetes, the impact of genomics on predicting treatment outcomes looks promising, both in terms of understanding drug potency as well as drug-drug interaction and resultant effects. Citing the study, Dr Mukerji pointed out that genetic variations that elevate levels of a certain biological molecule, LDL (indicating risk for CHD), lower the risk for diabetes. However, drugs that are used to reduce LDL levels increase the risk for diabetes. She adds, “These kind of genetic studies provide a ‘genetic cautionary’ note for management of these diseases.”
India Science Wire