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  Progress in research: The past decade has yielded unprecedented new insights into the role of genetics in disease risk and treatment response. Some of these insights are ready to be utilized in clinical care.

  
  
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  Focus for initial translation: Pharmacogenomics, common disease risk stratification, and rare diseases are the three main conceptual areas being addressed for initial implementation strategies of personalized medicine.

  
  
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  Barriers and solutions: Barriers to translation include a lack of provider education and resources to implement this new knowledge. Solutions involve incorporating personalized medicine into undergraduate and postgraduate curricula and the development of computational resources to simultaneously analyze data and evidence to provide useable clinical decision support at the point of care.

Following a decade of extraordinary advances in genomics and healthcare information technology, a series of fundamental changes in medical practice are coming, promising to transform the way in which health care is delivered, transitioning to the era of Personalized Medicine.

Our knowledge and understanding of the genetic influences on common disease risk and therapeutic efficacy of pharmacologic agents has increased dramatically in the past decade. Since the completion of the Human Genome Project in 2003, genomics research has exploded, yielding an evergrowing number of genomic variants that are associated with an individual’s risk for a disease or their response to a medication. This began in earnest with the development of high-throughput genotyping technology and the subsequent success of genome-wide association studies (GWASs) in identifying large numbers of genomic variants with potential clinical utility. The most common genetic variations among people are single-nucleotide polymorphisms (SNPs) (“snips”). Each SNP denotes a difference in a single nucleotide. For example, in a stretch of DNA, a SNP may replace the nucleotide adenine (A) with guanine (G). A phenotype is an observable or measurable characteristic of an individual, such as height, weight, blood pressure, lipid levels, a disease including its manifestations, or response to medication. Comparison of the frequencies of SNPs between cases and controls for a given phenotype has yielded a large number of variants that are associated with risk for a wide range of phenotypes including cardiovascular disease, diabetes, kidney disease, hyperlipidemia, and hypertension, many of which have been replicated successfully in multiple populations. This phenomenon is likely to continue for some time as the $1000 genome nears reality and comprehensive individual genotype and molecular (“omics”) data becomes the norm in clinical care. Today we stand on the brink of a paradigm shift in health care, transitioning from the era of population-based medicine to the era of Personalized Medicine.

Personalized medicine aims to optimize the health care provided to an individual by basing decisions about their care on all available patient data, including genomic, molecular, clinical, and environmental data. This represents a sea change from the utilitarian approaches of evidence-based medicine to date. On the whole, current clinical guidelines tend to summarize evidence that is applicable on a population level. As an example, the UK National Institute for Health and Clinical Excellence (NICE) recommends that the first-line treatment for elevated blood pressure (>140/90 mm Hg) in a Caucasian male under the age of 55 is an angiotensin-converter enzyme (ACE) inhibitor. Whilst this recommendation is based on the best available evidence, the evidence considers what the best treatment option is for a group, rather than an individual. Because variations in individuals’ genetic profiles may correlate with differences in how individuals develop diseases and respond to treatment, personalized medicine has the potential to facilitate the tailoring of health care to the individual. In the example above, rather than recommending treatment for any Caucasian male under the age of 55 with high blood pressure, personalized medicine would aim to recommend a specific drug at a specific dose with a specific blood pressure threshold for starting treatment in a specific individual.

The transition of both genomics and personalized medicine from the realms of research into mainstream clinical care has begun. As the concept of integrated personalized medicine becomes a reality, genomic and molecular tests that could potentially guide providers in making clinical decisions are being validated and approved for use by professional bodies and federal agencies. There are three major conceptual areas of personalized medicine translation characterized by different opportunities, challenges, and current progress: pharmacogenomics, common disease risk prediction, and rare diseases defined by a prevalence of less than 0.05%.

Pharmacogenomics: