Genomic Tools for Prediction and Personalized Care
How Do They Fit Together?
Emerging know-how and technologies from the human genome are enabling predictive and proactive approaches across the spectrum from health to disease (Figure). Disease susceptibility and risk can now be quantified and anticipated during health and even at birth using stable genomics or DNA-based approaches that do not change over a person's lifetime. Individual SNPs or multi-SNP panels of genes are emerging that might be used as part of health risk assessment (see below). DNA variation also provides information about the possibility of being relatively protected from disease development as well as information about one's sensitivity or resistance to certain medications and ability to metabolize nutrients in our diets. All of this can and probably should be done early in life such that a course or strategy to maintain health can be charted well in advance of the development of potentially detrimental lifestyle habits and exposures. The other -omics that are dynamic and interact with and respond to environmental stimuli, lifestyles, diets, and pathogens are rapidly improving capabilities to predict and intervene at an individual level. Transcriptional profiles, protein expression, and levels of metabolites combined with dynamic imaging modalities will provide more precise ways to screen individuals who are at high risk for disease to find the earliest molecular manifestations while the disease is subclinical. This same information may provide a definitive diagnosis and a molecular classification that foretells prognosis. For example, today a HER/2neu-positive breast cancer ascribes that patient to a more aggressive form of the disease and directs care to a much different course than a HER/2neu-negative cancer. Similarly, the selection of drugs can be guided both by the patient's underlying genetic makeup as well as the molecular architecture of the disease in the individual. Given that the evolution of a disease from baseline risk often occurs over many years, healthcare providers must focus on strategic health planning and disease prevention during the most cost-effective times of the disease life cycle to shift the current paradigm of care from disease treatment to personalized care.
Figure. Use of genomic markers to predict, prognose, diagnose, treat, and monitor health and disease. The red line indicates the course of disease from health through death. The black dashed line is the 'symptom horizon' below which the disease remains subclinical. The red dashed line is the desired outcome of an intervention to reverse disease.
Genomics to Identify Disease Susceptibility
Although genetic testing for Mendelian disorders such as cystic fibrosis, Huntington's disease, familial breast cancer, and phenylketonuria, among others, was widely available prior to the genomic era, the genetic basis for complex disease remains unclear. From 1980 through 2002, fewer than 10 genes were associated with complex diseases in humans in contrast to more than 1300 genes that were associated with Mendelian disorders from 1980 through 2001.[22] Testing for Mendelian disorders has been essential to understanding both the genetic basis of disease and the clinical impact of identifying risk prior to the onset of disease. However, Mendelian disorders are rare and for genetic testing to be widely applicable, the genetic basis for complex disease needs to be understood.
Until recently, techniques to identify susceptibility genes have been limited to linkage analysis and association studies. If genetic risk factors are present, studying families with affected sibling pairs provides a stronger genetic effect in these families because these siblings will likely share genetic regions that underlie the disease phenotype. Linkage studies take advantage of this to identify regions of the genome that are more strongly associated with disease than by chance by looking for microsatellites that are more prevalent in affected sibling pairs compared with controls. Microsatellites are short segments of DNA that contain repetitive sequences of nucleotides. Linkage studies have had limited success. For example, of 9 linkage studies trying to identify susceptibility genes for coronary artery disease, only 4 genes were identified and 1 locus (2p11) was replicated.[23] Reasons for the limited success of linkage studies include the polygenic nature of complex diseases, with each gene contributing only a small risk to the phenotype; the relatively low level of heritability of complex diseases compared with Mendelian disorders; and underpowered study designs.[24]
In contrast to linkage studies that are unbiased, association studies look for an increased frequency of a particular genotype at a candidate gene locus in cases compared with controls. In these studies, the candidate genes must be known a priori and are therefore limited by understanding of the genes that contribute to a particular disease. Association studies have been abundant in the literature. For coronary artery disease alone, association of 96 polymorphisms in 75 genes has been reported.[25] However, aside from the initial limitation of a priori knowledge, genetic association studies have been limited by their lack of reproducibility. Even though the contribution of these types of association studies remains uncertain, it has been suggested that common genetic variants may contribute to common diseases, supporting the role for continued association studies.[26] Although appealing, the candidate gene approach has been fraught with design issues, including strict adherence to the definition of the phenotypes, adequate sample size, issues of multiple testing, and lack of replication.[27]
Today we are in the era of the 'genomic gold rush,' with genome-wide association (GWA) studies using high-density genotyping technologies that allow for assays of 500,000 to 1,000,000 SNPs per individual at relatively low cost.[28] The identification of millions of SNPs, development of high-throughput sequencing methodologies, completion of the HapMap, and creation of large genotyped cohorts have made GWA studies possible. The results in the past year have been no less than astounding, with genetic loci being identified for many complex diseases, including breast cancer, coronary artery disease, myocardial infarction, obesity, diabetes, and prostate cancer.[28]
These are indeed encouraging data, many of which have been replicated. GWA studies look for an increased frequency of SNPs distributed throughout the genome in cases compared with controls. The use of genome-wide SNPs means that little to no a priori knowledge of genes contributing to an outcome is needed. The completion of the HapMap means that fewer SNPs need to be genotyped to characterize genomic variation, facilitating the use of technologies such as high-density SNP microarrays. Finally, large genotyped cohorts mean that results in one population can be validated in other populations. Thus far, GWA studies have produced robust and reproducible findings. For example, 3 GWA studies for coronary artery disease have been published using 3 different populations, and all have identified a locus at 9p21.[29,30] This region is not known to contain coding sequence and therefore may be important in helping further our understanding of the molecular basis for coronary artery disease. Type 2 diabetes has been extensively studied, and independent genome-wide scans have identified several loci for diabetes susceptibility: CDKN2A/CDKN2B, CDKAL1, and IGSF2BP2, as well as confirming TCF7L2, PPARG, and KCNJ11, which had been previously identified by other methods.[31-35] A susceptibility gene for obesity, FTO, was identified by the same groups studying type 2 diabetes.[36] Genes for Crohn's disease, rheumatoid arthritis, adult macular degeneration, and prostate cancer all have been identified in the past year using genome-wide approaches.[37-40]
As a result of the above types of studies, large numbers of additional susceptibility markers are sure to emerge in the coming years. However, before these markers can be applied to practice, their clinical utility must be demonstrated; that is, the impact that using the markers might have on health outcomes. First, markers must be shown to provide additional estimates of disease risk over current clinical models. The use of any one SNP for screening complex diseases such as cardiovascular disease has only a minor probability of providing much of a predictive or correlative significance. However, if combined, multiple SNPs, each with only a small predictive value, might provide enough power to be clinically significant.
In addition, we need to know what to do with the results of genetic tests and define actionable options that our patients can take with these results in hand. Screening for markers of susceptibility offers the unique opportunity for the prevention of disease prior to the onset of clinical manifestations or mitigation of the clinical course of disease. One type of intervention includes lifestyle modification. For example, adherence to a low phenylalanine diet in neonates identified to have phenylketonuria can lead to normal brain development. One could hope for a scenario where knowledge of susceptibility to coronary disease or chronic obstructive pulmonary disease may facilitate smoking cessation. Preliminary data suggest that a personalized approach to smoking cessation improves quitting.[41] Another type of intervention includes aggressive screening programs to identify preclinical disease. This has been the approach used in patients with pathologic mutations of BRCA1 and BRCA2, although there are few data to suggest that intense screening reduces mortality in these patients. For rare disorders, consensus opinion may be sufficient to justify such an approach, but for more prevalent disorders and susceptibility markers, outcomes data will be needed to provide justification for an intensive screening approach. This has important ramifications for the types of susceptibility markers that are used clinically: markers must be prevalent enough to select enough individuals to adequately power outcomes-based clinical trials. A third type of intervention is either curative or prophylactic therapy, such as prophylactic thyroidectomy in patients with multiple endocrine neoplasia type 2 or colectomy in patients with familial adenomatous polyposis. Again, for more prevalent disorders and susceptibility markers, markers must be prevalent enough to adequately power outcomes-based clinical trials.
Finally, for genetic markers of susceptibility to gain acceptance into clinical practice, the costs of testing must be manageable. With efforts such as the personal genome project[42] and rapid advances in sequencing technology, the cost of screening will likely become more affordable. However, reimbursement strategies for genomic testing must still be established. Thus, although there is no question that genomic data are pointing toward novel pathways and mechanisms underlying complex diseases, it remains to be seen whether these data will also translate into useful clinical recommendations.
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