A scientific study published this past spring came with damning implications for Chinese scientist He Jiankui, who created the world’s first gene-edited babies: People with the rare genetic variants that He tried to engineer into embryos, the study asserted, had an increased death rate.

On Friday, the paper’s senior author said his study was wrong.

“The one thing that all scientists fear the most is to find out that a major result they have published was based on erroneous data,” Rasmus Nielsen, of the University of California, Berkeley, wrote on Twitter. He said that he had been notified of an error in the data from the massive genetic database that Nielsen and his collaborator, Xinzhu Wei, had analyzed to reach their conclusion. 

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The error “likely explains most or all of our results” regarding the genetic variant that He tried to alter, he said.

 

Nielsen told STAT that he and Wei have requested that the journal that published their study, Nature Medicine, retract the paper.

The study centers around the effects of a variant of the gene known as CCR5, called Δ32, which is best known for protecting against infection with HIV, the virus that causes AIDS. In explaining his choice to target CCR5 in his controversial experiment last year, He said he aimed to protect the pair of twin babies he experimented on from infection with HIV. HIV infection is extremely stigmatized in China.

When news of his experiment stunned the world last fall, He acknowledged that the CCR5 variant had been associated with a higher risk of serious complications and flu death from influenza as well as infection with West Nile virus.

In their study, Nielsen and Wei sought to better understand the health effects of the CCR5 variant. They probed data from 409,693 people of British ancestry in the U.K. Biobank, an ambitious initiative to study the effects of DNA on health and other characteristics. The dataset is widely used by genetic researchers.

Nielsen told STAT that the error stemmed from the specific single nucleotide polymorphism, or genetic marker, that he and Wei looked at. In the U.K. Biobank data, the marker they chose to work with had systematic errors related to “genotype calling” at that site in the DNA; that’s the process by which the genotype is determined for each individual in the sample at each site.

The way the genotypes were being called caused certain genotypes to show up less frequently than they should have, Nielsen said, apparently generating the erroneous signal around increased mortality.

“So if there is an effect on mortality,” Nielsen told STAT, “it is certainly not as strong as we previously reported.” He added: “We are very sorry for the confusion we might have spread by publishing these results.”

Nielsen said that he and Wei tried to replicate their results in several other databases, both freely available to researchers and proprietary, and that work reinforces their conclusion that their published finding was erroneous. In doing that work, they also turned up independent errors related to genotype calling at that same SNP in another genetic database, known as gnomAD, Nielsen said.

Nielsen said the evidence suggests that the problem is likely specific to the marker they analyzed and “not a general sign of poor data quality in the U.K. Biobank.”

When the paper was published in June, it generated some pushback online from scientists who took issue with technical aspects of Nielsen and Wei’s approach.

One of those critics was Sean Harrison, a University of Bristol epidemiologist and statistician who has worked with data from the U.K. Biobank. Shortly after the paper’s publication, Harrison wrote in a blog post that he tried to replicate the analysis using U.K. Biobank data. When he did so, “I found… Nothing,” he wrote.

Nielsen told STAT that he and Wei later worked with Harrison to determine that the reason for the discrepancy in their analyses was that Harrison used a different SNP than they did. That led to the alarming conclusion that while the SNP they looked at generated the signal around increased mortality, that signal wasn’t present in nearby markers.

Nielsen said on Twitter that it was Harrison who inspired Harvard scientist David Reich, who has previously studied the CCR5 variant, to probe the results of the study.

Nielsen said that Reich and his team have “shown quite clearly and convincingly that the error is in the marker” that Nielsen and Wei chose to work on.

Reached by email, Reich told STAT: “I think it’s best just to have a scientific discussion and not to comment to the press while this discussion is going on.”

This story has been updated to include comment from Nielsen.

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  • Last 2 decades, Scientist have created and cremated many new legacies. e.g. Stem cells: cure all legacy, Genomic research: genes responsible for all that goes wrong legacy, Organs in the dish:brain kidneys, RNA interference wonder drug legacy, Personalized medicine legacy, Circulating DNA legacy, Organoids legacy and list will keep going… Therefore until it has been tried, tested and proven in a clinical trial all is hype and hope. That’s coming from a scientist himself.

  • Of course, this correction won’t get rid of the tidal wave of disdainful news about China’s scientific community published in June when this inaccurate study came out.
    I see only a couple news articles today. No news organization cares to correct its mistakes. What a shame

  • I never believed the quick negative report. Just too knee jerk.

    I think the Chinese scientist, though, made a poor choice for what to edit. He should not have changed something to an exotic allele. He should have targeted an exotic and clearly harmful allele and made it normal.

    Some day though, this first human modification may be celebrated and his punishments seen as backward not unlike what happened to Galileo. Granted He did not do anything that a thousand other scientists could have easily done…but didn’t. But that’s the thing, someone had to be first.

    • By who? It is what it is, and is greatly accelerating genetic modification of organisms for thousands of scientific experiments.

    • Eating and drinking may lead to cancer. Going outside may lead to cancer. Staying indoors may lead to cancer. Doing nothing may lead to cancer. Life may lead to cancer.