Historically, Biesecker said, hypothesis testing in basic research, clinical research, and clinical practice research has relied on methods that produced relatively little data. This is changing, however. Biesecker said that in contrast to the low-throughput assays that have been employed to test a single hypothesis with a single assay and that are both expensive and time-consuming, genomics is emerging as a high-throughput, hypothesis-generating research1 paradigm that can address these limitations. As a hypothesis-generating research tool, genomics will be used to narrow the focus to answer research questions with more classical, hypothesis-testing validation experiments.
Biesecker described four clinical scenarios in which the hypothesisgenerating paradigm could be applied to improve patient care. First, a newborn’s genome could be tested for life-threatening congenital or metabolic disorders, and any genetic abnormality would be followed up with a specific test. Second, if an older patient presented with symptoms of a certain disorder, such as asthma, genomic data could be used to determine the disease subtype and to identify relevant pharmacogenomic information for treatment. Third, when two people are interested in conceiving a child, the genomes of both partners could be tested for recessive alleles for serious congenital disorders. Finally, during adulthood a patient may also be interested in learning about disease risk for particular cancers, such as breast or ovarian. These are lofty ideas about what a clinician could do with genomic data and a set of analytic tools to affect patient health, said Biesecker.
Altering the way that clinical medicine is conducted to include genomic analyses will take some work, Biesecker said. A major problem is that genomic analyses generate much more data than any clinician or patient can use, and clinicians are generally not likely to order tests that provide more data than they are seeking. Additionally, as more tests are performed, the likelihood of a false-positive signal increases. For example, many physicians resist doing a full panel of blood tests when only a specific test is needed. However, clinical laboratories routinely perform multiple tests when a single test is ordered because it is more cost-effective. If one of the non-requested tests on the resultant panel reaches a “panic” value, this is
1 Hypothesis-generating research is defined as an exploratory approach to science whereby an initial experiment, designed with a broad question in mind, produces a large amount of data or observations that can be used to formulate hypotheses that can be tested by directed experiments in the future.