Genetic testing has become increasingly popular in recent years, offering valuable insights into people’s health, ancestry, and potential genetic risks. However, the accuracy of these tests has come under scrutiny, particularly because of the incidence of false positive results.
False positives occur when a test indicates the presence of a genetic variant or condition, but the person doesn’t actually have that variant or condition. This article explores the statistics surrounding false positives in genetic testing, shedding light on the extent of the issue and its consequences.
Key Statistics on False Positive Rates
Direct-to-Consumer Genetic Tests
Direct-to-consumer (DTC) genetic tests, often marketed as ways to explore ancestry or assess health risks, have been found to have a significant rate of false positive results.
- A study comparing results from DTC tests to clinical laboratories found that 40% of the results from consumer tests were false positives
- Another analysis revealed a high false-positive rate (40%) in genes with potential clinical impact in the raw genotyping data provided by DTC genetic tests
- The clinical false-negative rate for any BRCA1/2 mutations in a screening cohort was found to be 88.5%, indicating a high false positive rate for tests limited to specific variants
- The emotional and psychological impact of false positives from DTC genetic tests is significant, as most people with apparent “positive” results will not go on to develop the related condition, leading to unnecessary anxiety and stress
Prenatal Screenings
Non-invasive prenatal testing (NIPT) is a popular method for screening for chromosomal abnormalities during pregnancy. However, the accuracy of these tests varies, particularly for rare genetic conditions.
- For Down syndrome, the positive predictive value (PPV) of NIPT is about 90%, meaning about 10% of positive results may be false. However, for microdeletions like Di George syndrome, the PPV can be as low as 2% to 30%, indicating a much higher false positive rate.
- A study found a 27:1 rate of false-positive compared to false-negative NIPT results, suggesting that more than 90% of abnormal fetuses identified by NIPT would end up in abortion during further pregnancy.
- Another report highlighted that NIPT detects 10.8 times more aberrant cases than expected to be born, indicating a significant discrepancy between NIPT positive results and actual birth outcomes.
- For sex chromosome aneuploidies, the false positive rate (FPR) was reported to be 0.12% in a meta-analysis. Still, another study reported that 8.6% of positive sex chromosome aneuploidies were due to an abnormal maternal chromosome X karyotype.
Multi-Cancer Early Detection Test (Galleri)
The Galleri test, a multi-cancer early detection test, has also shown high rates of false positive results. Initially reported a PPV of 44.6%, which later fell to 38.0%, indicating many false positives.
Consequences of False Positives
The consequences of false positive results in genetic testing can be far-reaching, impacting individuals emotionally, socially, and financially.
- Emotional and Psychological Impact: False positive results from prenatal screening tests can lead parents to believe their babies have major disorders when they are, in fact, healthy, causing durable impact and anxiety throughout the pregnancy.
- Social and Familial Tensions: Genetic testing can create tension within families because the results can reveal information about other family members in addition to the person who is tested, potentially leading to emotional distress and strained relationships.
- Financial Consequences: The costs associated with follow-up testing and procedures to confirm false positive results can be significant, leading to increased medical expenses and potential financial burden.
- Healthcare and Medical Implications: False positives can lead to unnecessary medical interventions, including surgeries. For instance, a case was mentioned where a woman was scheduled for surgery due to a false positive result for a BRCA1 variant, which was later found to be incorrect, highlighting the potential for unnecessary medical procedures.
Strategies for Minimizing False Positives
Efforts are being made to reduce the incidence of false positives in genetic testing through various methods and strategies.
- Machine Learning in Newborn Screening: Machine learning has been used to reduce false positives in newborn screening, with one study showing a reduction in false-positive cases for certain conditions
- Genetic Testing Methodologies: The use of second-tier tests (2-TT) and next-generation sequencing (NGS) can minimize false positives, but they also raise ethical considerations due to the detection of variants of unknown significance
- Strategies to Reduce False Positives: Best practices to reduce the likelihood of false positives include strict laboratory hygiene, good pipetting techniques, sterile labware, and dedicated PCR stations or rooms
- Adjusting Cut-off Values: Adjusting cut-off values in newborn screening tests can impact the sensitivity and specificity of the tests, potentially reducing false positives. However, this often increases the risk of false negatives
- False-Positive Investigation Toolkit: The CDC provides a toolkit for investigating false positives in TB testing, emphasizing the importance of genotyping results and reviewing epidemiology within genotype clusters to aid in determining a false-positive result
