A genetic test can be highly accurate and still leave an important question unanswered. Its performance depends on which changes it looks for, where it looks and what its methods can reliably detect. Understanding those boundaries helps explain why a negative result sometimes reduces uncertainty and sometimes leaves a possible genetic cause unresolved. MedlinePlus explains these limits of negative results.
What does analytical sensitivity measure?
Analytical sensitivity describes how reliably a test detects a particular genetic change when that change is present in the sample. It concerns the laboratory measurement. It does not, by itself, tell us how often the test will explain someone’s symptoms or identify everyone with a particular condition.
Clinical sensitivity asks a different question: among people who have a condition, how many receive a positive result? A condition may have several genetic causes, and a test may cover only some of them. Strong performance for its intended targets therefore does not guarantee that it will identify every affected person. These distinctions are described in NHGRI’s framework for evaluating genetic tests.
An accuracy percentage needs a clear description of what was measured. Performance established for one kind of DNA change cannot automatically describe performance for every other kind. The relevant evidence comes from validation: testing how the method performs with known samples under conditions that reflect its intended clinical use. A percentage without that context gives an incomplete picture.
Three boundaries that matter
1. Scope: what was included?
Tests with similar names can include different genes and methods. A panel may examine selected genes even when the laboratory has generated broader sequence data. Some tests include deletion and duplication analysis; others require an additional assay. A gene appearing on a list does not establish that every possible change in it was assessed.
2. Coverage: where was the measurement reliable?
Sequencing reads DNA fragments. Read depth describes how many times a position is read. Coverage can vary across a test, so an average can hide poorly measured regions. Repeated DNA and regions resembling other genes can be difficult to resolve. Genome sequencing also has technical gaps; its broad name does not promise complete detection.
3. Sample: was the change present there?
In mosaicism, a genetic change is present in some cells. It may be absent from the sampled tissue or present at a level the assay cannot reliably detect. These scope, coverage and sample limitations are described in GeneReviews’ guide to current genetic testing approaches.
A missed change differs from an uncertain finding
A technical blind spot means a relevant change may not have been detected. An interpretation limit means a change was found, but its effect is unclear. A variant of uncertain significance belongs to the second category. Better measurement alone cannot always supply the missing biological or clinical evidence.
A negative report also needs the original clinical question. Testing for a known family variant is different from searching for an explanation for symptoms without an established family finding. Medical history, family history and the test performed all contribute to interpretation. MedlinePlus describes why results require this context.
Questions that make a report easier to understand
A discussion with the clinical team can clarify:
- Which genes, regions and types of change were assessed?
- Were any relevant regions inadequately measured?
- What limitations remain for the sample and method used?
- Would further testing address a specific unresolved question?
Genetic counselling can help explain the test, its possible benefits and the uncertainty that remains. Testing may support diagnosis or care decisions, but it can also produce limited or inconclusive information. MedlinePlus outlines these benefits and limitations.
Research methods and established clinical testing
Research testing is performed within a study, while clinical testing is intended to inform health care. A promising research method does not automatically have established performance for every clinical purpose. Evaluating a new test requires evidence about reliable measurement, its relationship to disease and whether its results are useful. These are separate questions, even when the technology is impressive. MedlinePlus distinguishes testing settings, and NHGRI describes the evidence needed to evaluate tests.