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Scientist Smackdown: Experts Question Study of Longevity Genetics

July 8th, 2010 07:11 admin Leave a comment Go to comments

genechipWe can predict your chances of living exceptionally long, with 77 percent accuracy, by looking at 150 tiny genetic variants. That’s what researchers claimed in a Science paper that we described last week. Those predictive powers have left some feeling a little uneasy–and not just about what futures are buried in their genomes. Where the paper’s authors saw correlations, some experts are now seeing errors from DNA testing chips.

No DNA chip is perfect; it can get things wrong as it sorts through hundreds of thousands of genetic variants. In fact, certain chips might even make the same error repeatedly. That could cause problems, because what looks like a genetic variant common to a group of people could instead just be an echoed flaw in one chip’s testing capabilities.

Newsweek, which broke this story, reports that the Boston University researchers who led the study did, in fact, use different chips, but not enough different chips to rule out this potential error. They used two different types of DNA chips to test the centenarian group (about 1,000 people whose ages ranged from 95 to 119): a 370 chip that examines 370,000 genetic variants and a 610-Quad that examines 610,000 variants. The control group (of about 1,200 younger people) was tested with those two chips and a few others, thus possibly hiding any shared errors.

David Goldstein, the Duke University geneticist who first questioned this method at Newsweek last week, says that using similar chips (both made by Illumina) for the centenarian group and a collection of different ones for the control group could have left the researchers wide open for errors.

“Unfortunately, different chips have their own little problems for specific [genetic variants],” he says. The key to keeping false positives at bay is to ensure that cases and control groups are analyzed using exactly the same techniques. If you use one type of chip to analyze your cases and a different type to analyze your control group, “you can see any [variants] that are genotyped differently on the different chips ‘lighting up’ as apparently associated with the trait,” says Goldstein, when in fact that pattern is just an experimental artifact. [Newsweek]

DISCOVER’s Razib Khan has also posted in Gene Expression on various hints that this study might not be all that it’s cracked up to be. Khan takes note of skeptical claims from other experts and the commercial testing company 23andMe, which, after the study’s fanfare, tried to see the relationship between these tiny variants (called SNPs) highlighted by the study and their older clients’ genomes.

We took a preliminary look in our customer data to see if the proposed SNP-based model described in Sebastiani et al. is predictive of exceptional longevity. . . . [B]ased on our data, performance of this model is not significantly better than random. [23andMe]

The study’s authors led by Paola Sebastiani and Thomas Perls released a statement yesterday apparently in response to the Newsweek article and comments from Kari Stefansson, the leader of a similar study quoted in the New York Times. The Boston University researchers are checking their results.

“We have been made aware that there is a technical error in the lab test used on approximately 10% of the centenarian sample that involved the two of the 150 variants. Our preliminary analysis of this issue suggests that the apparent error would not effect the overall accuracy of the model. Because the issue has been raised since the publication of the paper, we are now closely re-examining the analysis. Another question that was raised concerns the criteria used to determine if an association between a genetic variant and exceptional longevity was statistically significant. We used standard criteria for the analysis, and we are confident that the appropriate threshold was used.” [Newsweek]

Image: Wikimedia / Schutz

Source: Scientist Smackdown: Experts Question Study of Longevity Genetics

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