Skip to Content
Uncategorized

Using Spit to Search for a Superathlete Gene

Taiwanese scientists in search of DNA that may account for sports stars’ prowess are building a genetic library of these athletes’ saliva.

For years, geneticists have been poking around in the human genome looking for genes that might contribute to superathletes like road-racing cyclist Lance Armstrong. One geneticist recently told me that Armstrong and other phenomenal athletes are “mutants”–meaning their DNA almost certainly contains supergenes that allow them to, for example, sprint up the Pyrenees at full tilt during the Tour de France or, in the case of baseball players, hit balls traveling at 100 miles per hour.

Scientists at the Taipei Physical Education College have announced that they are developing a gene bank containing DNA from superperforming athletes from Taiwan. Led by researcher Hsu Tai-ke, the team has been collecting gene-laden saliva from top performers, such as last year’s 19-game-winning pitcher Wang Chein-Ming, of the New York Yankees, and Olympic tae-kwon-do medalists Chu Mu-yen and Huang Chih-hsiung.

In each case, the researchers found “polymorphisms,” special genes or stretches of DNA present in some people and not in others, that numerous studies have associated with athletes’ cardio endurance.

Tai-ke suggests that asking big-time athletes for spit rather than blood will increase the number of test subjects and confirm whether or not these genes help superathletes perform.

The Taiwanese scientist also raised the notion that further understanding these genes might lead to testing kids who seem to be developing into great pitchers or karate stars–a Gattaca sort of idea that makes the mind wonder how this information would be used, or if athletes might add their genes to the list of products they endorse.

Apparently, there is already a market for superbeautiful women and supersmart men and women to contribute (in exchange for payment) their eggs and sperm to make beautiful geniuses. What will it cost to buy an actual gene to help you pitch a 19-and-6 season with a 3.63 ERA?

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.