WHO SHOULD READ THIS?
As the field of genomics evolves, knowledge of how to apply these technologies to address patient health will affect the practice of all PAs.
WHAT ARE GENOMIC
MEDICINE AND GENOME-WIDE ASSOCIATION STUDIES?
Genomic medicine involves the use of information derived from chromosomal DNA as well as RNA, proteins, and metabolites to guide and tailor clinical decision making, leading to personalized medicine. Together this information may be used to assess disease risk, support clinical diagnosis and choice of treatment, assess disease prognosis, and correlate with other traditional risk factors such as family history. We frequently use health risk assessments to estimate risk for developing disease and reinforce disease prevention; examples include the Framingham Heart Study models, the Gail model for breast cancer risk assessment, and others. These risk assessment tools utilize surrogates of genome-influenced measures—family history, metabolite concentrations, body habitus, BP, and others.1 The advent of today's genomic technologies is beginning to allow the practical use of genetic patterns for health care decision making.
Genome-wide association studies (GWAS) are based on comparing the genotype of large numbers of genes from large numbers of case and control subjects, looking for the presence of variants of genes (called polymorphisms). The relationship of these gene variants to diseases appearing in the case subjects (called phenotypes) is calculated to determine risk associated with the identified polymorphisms. Up to several thousand genes may be found to contribute to disease risk, most in a small way.2 GWAS are revolutionizing genomic medicine by identifying genetic risk factors for chronic diseases such as diabetes, heart disease, and some cancers like prostate, breast, and colorectal cancer.3 There is a tendency for overlap in identified susceptibility loci among numerous diseases, particularly among inflammatory disorders.4
WHAT ARE CURRENT
PRACTICES?
Applications of medical genomics to patient care are currently modest but growing. Genomic testing is generally ordered by providers, in consultation with genetic counselors, for evaluating the presence of polymorphisms following Mendelian inheritance patterns that are associated with a high risk for developing disease (called high-penetrance polymorphisms). Examples are the defective breast cancer suppressor genes BRCA1 and BRCA2, which increase the risk for breast and ovarian cancer.5 Knowledge of the relationship between other genetic polymorphisms is beginning to show direct clinical applicability as well, for example in the assessment and management of disease risk and treatment optimization.
The area of pharmacogenetics likely offers the greatest promise in improving health care within the next decade. Pharmacogenetics involves the use of genetic information to tailor pharmacotherapeutic decision making for an individual. At present, the most developed pharmacogenetic model revolves about the genetics of warfarin sensitivity and use of this information to design algorithms for optimal, safe use.
Certain polymorphisms of the CYP2C9 (cytochrome P-450 2C9) and VKORC1 (vitamin K epoxide reductase complex, subunit 1) genes have been associated with moderately to greatly increased sensitivity to warfarin. Certain polymorphisms in these genes are more commonly associated with Caucasian, Asian, or African ancestry. Variation in these genes explains 30% to 40% of the variability of warfarin dosing requirements. The mean daily dose of warfarin needed to maintain a therapeutic international normalized ratio (INR) varies considerably from a mean of 2.0 mg/d to more than 6.0 mg/d, depending on the CYP2C9 and VKORC1 genotypes of the individual, especially during drug initiation.6 Pharmacogenetic algorithms tailored to patient genotypes may contribute to selecting an ideal target warfarin dose, although current algorithms offer at best modest evidence-based outcome improvements.7-9 Other polymorphisms associated with either increased or reduced warfarin sensitivity have been identified. Over the next few years, PAs should be on the lookout for improved pharmacogenetic algorithms for optimizing the prescribing for warfarin and other medications.