Can molecular diagnostics aid in diagnosis and prognosis in complex chronic diseases?
Vol. 25 • Issue 2 • Page 14
Chronic disease affects more than 75% of the human population, is responsible for 7 out of every 10 deaths and is costing the U.S. economy almost a trillion dollars each year. The Department of Health and Community Services has focused on the following chronic diseases as they affect a large percentage of the population and have a significant negative effect on the quality of life: arthritis, cancer, chronic pain, diabetes, heart disease, kidney disease, lung disease and stroke.
In addition to environmental causes, chronic diseases also have a genetic component that determines each individual’s susceptibility. As the health system shifts its focus from disease management to wellness, there will be an increasing need to encourage lifestyles that are tailored to each individual’s genetic susceptibility to chronic diseases. Recent focus is on the use of single nucleotide polymorphisms (SNPs) in our genome as predictors for chronic disease. There are more than 1.25 million SNPs identified in the human genome, yet relatively few chronic diseases have been linked to individual gene variants, suggesting that many diseases are polygenic (meaning they arise from the presence of many interacting SNPs). Thus, molecular diagnostics (MDx) will provide predictive values for those who are most likely to acquire and succumb to chronic disease based on the presence or absence of various SNPs. When this information is combined with family history and environmental information (e.g., consumption of fat, salt or excess calories), a tailored approach to a healthier lifestyle will be possible.
Haplotype analysis is another form of polygenic testing proving useful for diagnostic testing. Many studies have demonstrated that SNPs are found next to each other in clusters along the single strands of DNA that make up a chromosome. These tightly linked clusters, in blocks of 3 to 92 kilobases, survive a history of inherited chromosomal translocations, crossovers and other recombination events. Thus, tightly linked variants that have remained associated with each other (also referred to as being in linkage disequilibrium) can be predictive of disease. Furthermore, since they are so tightly linked, the analysis of only a few SNPs is often predictive of the entire haplotype.
In cancer, there are already examples of chronic disease prediction using SNPs. Familial adenomatous polyposis (APC) disease leads to colorectal cancer in about 80% of subjects who test positive for the APC gene. BRCA1 and BRCA2 have been associated with a significant increase in the incidence of breast cancer, prompting some women to get prophylactic mastectomies if they test positive for these SNPs. SNPs in five DNA regions have moderate predictability of prostate cancer individually, but in combination, the association is quite strong. Other areas under intense investigation are SNPs predictive of bladder, testicular and lung cancer.
Hypertension and Salt Sensitivity
Novel genes have been shown to be predictive for hypertension and salt sensitivity (the tendency to increase blood pressure following a high salt meal).
Hypertension can result from single SNPs (monogenic hypertension), as well as multiple genetic defects. Monogenic forms of hypertension affecting less than 1% of the population include glucocorticoid-remedial aldosteroneism, apparent mineralocorticoid excess, mineralocorticoid receptor mutations and Liddle’s Syndrome. Essential hypertension (high blood pressure of unknown origin) and/or salt sensitivity of blood pressure affect over 50% of the adult population. Approximately 30-50% of the polygenic form of hypertension is genetically inherited and the remainder is due to environmental factors, such as consumption of excess salt.
Recent advances have identified a number of SNPs associated with these variations of blood pressure and salt sensitivity, suggesting that we can reduce the mortality and morbidity associated with these highly prevalent diseases. There is the potential for a very large number of molecular tests to be necessary to prevent or delay these highly prevalent diseases.
Polygenic testing calls for new mathematical treatment of genetic testing data. To create a mathematical model that would determine that the polygenic test was statistically valid, multifactor dimensionality reduction (MDR) to determine the best genetic model predicts each phenotype. In the case of SS hypertension, a three locus model incorporating three variants in GRK4 successfully predicted the phenotype correctly 94.5% of the time. These results illustrate the important point that interactions among genes result in the expression of complex phenotypes such as hypertension.
Greater Utility of Polygenic Tests
Polygenic testing will become more popular as additional SNP panels are discovered that yield data related to the likelihood of expressing a disease. Even greater utility of polygenic tests will be achieved when used in conjunction with personal and family history data, review of coexisting conditions, physical examination for particular symptoms and when confirmed by other laboratory tests. It can also be anticipated that novel therapeutic approaches to treat disease will be revealed by polygenic testing, since SNPs that are predictive of disease might lie in pathways that may become useful therapeutic targets.