© Masafumi Nozawa, Penn State (Adapted from Yokoyama et al. 2008 PNASThe image depicts the structure of the bovine rhodopsin protein. The blue circles represent amino acid sites that have undergone natural selection as determined through experiments, while the red circles represent amino acid sites that have undergone natural selection as determined through statistical analyses.
Scientists at Penn State and the National Institute of Genetics in Japan have demonstrated that several statistical methods commonly used by biologists to detect natural selection at the molecular level tend to produce incorrect results.
"Our finding means that hundreds of published studies on natural selection may have drawn incorrect conclusions," said Masatoshi Nei, Penn State Evan Pugh Professor of Biology and the team's leader. The team's results will be published in the Online Early Edition of the journal
Proceedings of the National Academy of Sciences during the week ending Friday 3 April 2009 and also in the journal's print edition at a later date.
Nei said that many scientists who examine human evolution have used faulty statistical methods in their studies and, as a result, their conclusions could be wrong. For example, in one published study the scientists used a statistical method to demonstrate pervasive natural selection during human evolution. "This group documented adaptive evolution in many genes expressed in the brain, thyroid, and placenta, which are assumed to be important for human evolution," said Masafumi Nozawa, a postdoctoral fellow at Penn State and one of the paper's authors. "But if the statistical method that they used is not reliable, then their results also might not be reliable," added Nei. "Of course, we would never say that natural selection is not happening, but we are saying that these statistical methods can lead scientists to make erroneous inferences," he said.
Comment: Interesting. Using statistical models appears to have been a hidden weakness in genetics research. Yet we are expected to take the statistical models proving 'global warming' as ironclad. This article ought to serve as a warning against such dogmatism. As this article says; Or maybe it might squash a beautiful (profitable) theory with ugly facts.