Slashdot Mirror
Consumer Genetic Tests May Have a Lot of False Positives (theverge.com)
A new study, published in the journal Genetics in Medicine, found that consumer genetic tests bring up a lot of false positives. "In this case, 40 percent of the results from the consumer tests were false positives," reports The Verge, noting that the findings "cover a very small sample size and don't show that consumer tests always have a 40 percent false positive rate." From the report: The research was done by scientists at Ambry Genetics, a medical laboratory in California. By looking through their own database, they found that 49 people had been referred to them because of some worrying results from their consumer genetic tests. Still, scientists at Ambry were able to confirm only 60 percent of the results when they compared the raw data from consumer tests with more thorough genetic tests done by themselves and other clinical laboratories. So, 40 percent of variants in a variety of genes reported in DTC raw data were false positives, meaning that they said a genetic variant was there when it wasn't. (Most of these turned out to be variants linked to cancer.) Additionally, the authors write, some variants classified as "increased risk" were not only classified as "benign" by clinical laboratories, but they were actually common variants.
Most "serious" genetic indicators are either quite rare, or their effect has already become apparent to consumer through other means. Common consumer genotyping tests test hundreds of thousands of SNPs. The rate of errors being at least 0.1-0.6% for these methods, there are bound to be hundreds of errors in a typical test result. People are not interested of benign errors, but are very interested on errors which would indicate a health-threatening condition, and this small fraction of test results gets lots of attention. IMHO, there's nothing strange on the fact that large portion of tests leading to a second round are false positives; it's how statistics and bias in such a situation happens to work.
When it comes to whole genome sequencing, both amount of data and error rates can be considerably higher; people just have to get used to the fact noise in big datasets causes strange effects...