Abstract
The promise of personalized genomic medicine is that knowledge of a person’s gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.
Highlights
$80,000 per patient per year, or in low-GDP countries with developing healthcare infrastructure) and potentially harmful. Both relative and absolute risk can be used to assess efficacy of medications: the former focuses on reducing the rate of incidents, the latter on reducing the number needed to treat (NNT, say from 50 to 20 incidents prevented for each person taking the drug) [4]
A randomized control trial involving over 9,000 patients [19] evaluated the effectiveness of intensive therapy with an average of almost three drugs targeting systolic blood pressure (SBP) less than 120 mmHg compared with standard treatment with an average of two drugs targeting SBP less than 140 in elderly individuals with incident or preclinical heart disease
Response may be highly correlated with risk of disease, but that cannot be assumed, so more genome-wide association study (GWAS) of disease progression therapeutic response are needed
Summary
I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity and with respect to potential clinical efficacy. $80,000 per patient per year, or in low-GDP countries with developing healthcare infrastructure) and potentially harmful Both relative and absolute risk can be used to assess efficacy of medications: the former focuses on reducing the rate of incidents (say from 5% to 4%), the latter on reducing the number needed to treat (NNT, say from 50 to 20 incidents prevented for each person taking the drug) [4].
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