| Literature DB >> 26561988 |
Celeste A Shelton1,2, David C Whitcomb1,2.
Abstract
Proponents of personalized medicine predict that genetic information will provide pivotal perspectives for the prevention and management of complex disorders. Personalized medicine differs from traditional Western medicine, in that it focuses on more complex disorders that require mechanistic disease modeling and outcome simulation by integrating genomic risk, environmental stressors, and biomarkers as indicators of disease state. This information could be useful to guide targeted therapy and prevent pathologic outcomes. However, gaps exist in the process of linking the pieces together; currently, genetic data are seldom used to assist physicians in clinical decision making. With rapid growth in genetic data and the requirements for new paradigms for complex disorders comes the need to train professionals to understand and manage the impact of genetic information on patients within these clinical settings. Here we describe the challenges, controversies, and opportunities for genetics and genetic counselors in managing complex disorders and discuss the rationale for modifications in genetic counselor training and function. We conclude that a major paradigm shift is underway and a compelling functional, ethical, and financial argument can be made for employing properly trained genetic counselors to be strategically positioned within the health-care industries that are responsible for managing complex disorders.Entities:
Year: 2015 PMID: 26561988 PMCID: PMC4817528 DOI: 10.1038/ctg.2015.46
Source DB: PubMed Journal: Clin Transl Gastroenterol ISSN: 2155-384X Impact factor: 4.488
Figure 1Schematic for evaluating Mendelian and complex disorders. Genetic testing for Mendelian disorders is a well-established discipline with the ultimate goal of identifying, confirming, or ruling out a highly penetrant genetic disorder. In complex disorders, genetic information using single-nucleotide polymorphism (SNP) arrays or next generation sequencing (NGS) panels are combined with other risk factors and markers within a disease model to classify patients based on disease mechanism. The actionable result(s) lead to better management of the patient based on targeted therapies and avoiding adverse outcomes.