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【社会人文】当患者带着基因序列来看你……
It may happen soon. A patient, perhaps one you have known for years, who is overweight and does not exercise regularly, shows up in your office with an analysis of his whole genome at multiple single-nucleotide polymorphisms (SNPs). His children, who were concerned about his health, spent $1,000 to give him the analysis as a holiday gift. The test report states that his genomic profile is consistent with an increased risk of both heart disease and diabetes, and because the company that performed the analysis stated that the test was "not a clinical service to be used as the basis for making medical decisions," he is in the office for some "medical direction." What should you do?
This year has seen a dizzying number of genomewide association studies demonstrating associations between novel gene variants or chromosomal loci and common diseases and phenotypes. These studies rely on microarrays that can assess 300,000 or more SNPs in each DNA sample; researchers use these microarrays to examine interpersonal differences in inherited genetic variability and to compare the prevalence of gene variants among patients who have a given disease with that among controls. Such studies have identified associations with many gene variants that were not previously suspected to be related to the phenotypes under consideration. The new technologies involved have been a boon to researchers who needed unbiased clues as to the causation of diseases that may be used to develop new therapeutic and preventive interventions. The test undergone by the patient described above is one of the products of this new knowledge.
As of November 2007, two companies have made available direct-to-consumer "personal genome services" (www.23andme.com) or "gene profiles" (www.decodeme.com) that rely on the same arrays of 500,000 to 1 million SNPs used in genomewide association studies. A third company (www.navigenics.com) has announced that it will offer similar services later this year. Essentially, a client sends a DNA sample to one of these firms, which analyzes the sample by means of SNP array; the data are stored in an online private account, the results are compared with allele–phenotype databases maintained and updated by the company, and the customer receives a readout of his or her levels of risk for specific conditions.
But such premature attempts at popularizing genetic testing seem to neglect key aspects of the established multifaceted evaluation of genetic tests for clinical applications. First, there is the question of a test's analytic validity, "its ability to accurately and reliably measure the genotype of interest."1 Although appropriate monitoring and oversight of the analytic validity of genetic tests remain largely unaddressed,2 most researchers report that the analytic validity of these platforms is very high. It is likely that sample-handling errors are a greater threat to the validity of results than are genotypic misclassification errors. Yet even very small error rates per SNP, magnified across the genome, can result in hundreds of misclassified variants for any individual patient. Without transparent quality-control monitoring and proficiency testing, the real-world performance of these platforms is uncertain.
Second, one must consider clinical validity, or the ability of the test to detect or predict the associated disorder.1 Components of clinical validity include the test's sensitivity, specificity, and positive and negative predictive value. This is the area in which the data are in the greatest flux, and even the ardent proponents of genomic susceptibility testing would agree that for most diseases, we are still at the early stages of identifying the full list of susceptibility-associated variants. Most of the diseases listed by the direct-to-consumer testing companies (e.g., diabetes, various cancers, and heart disease) are so-called complex diseases thought to be caused by multiple gene variants, interactions among these variants, and interactions between variants and environmental factors. Thus, a full accounting of disease susceptibility awaits the identification of these multiple variants and their interactions in well-designed studies. What we have now is recognition of a limited number of variants associated with relative risks of diseases on the order of 1.5 or lower. Risk factors with this level of relative risk clearly do a poor job of distinguishing people who will develop these diseases from those who will not.3,4
Finally, there is the issue of the test's clinical utility, or the balance of its associated risks and benefits if it were to be introduced into clinical practice.1 Measures of utility address the question at the heart of the clinical application of a test: If a patient is found to be at risk for a disease, what can be done about it? This is the arena in which there are virtually no data available on the health impact of genomewide analysis. There are very few observational studies and almost no clinical trials that demonstrate the risks and benefits associated with screening for individual gene variants — let alone testing for many hundreds of thousands of variants. Thus, any claim to clinical utility currently rests on the assumption that interventions that have proven successful in the general population will behave the same way in a genetically at-risk population. Many of these interventions — such as smoking cessation, weight loss, increased physical activity, and control of blood pressure — are likely to be broadly beneficial in relation to many diseases, regardless of a person's genetic susceptibility to a specific disease.
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作者:admin@医学,生命科学 2011-05-05 17:01
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