Conclusions About Ancient Populations May be Drastically Wrong Due to Dodgy Method - Article

[jayrtom]

This an interesting article, albeit complex, that may bring explanation to all the claims of ancient populations dating thousands of years and ancient pre-humans in the evolutionary tree

 

Conclusions About Ancient Populations May be Drastically Wrong Due to Dodgy Method | Ancient Origins (ancient-origins.net)

 

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Hundreds of thousands of peer-reviewed papers in genetics employ a method called Principal Component Analysis. But new research shows this method is highly biased. This means that multitudes of major studies regarding ancient populations may be drastically wrong!

 

What is Principal Component Analysis?

PCA is a mathematical transformation that takes a complex dataset, like 10,000 genomes of 2,000 people around the world, and transforms it so that it can be represented by a colorful X-Y scatter plot at the click of a button. It is the best friend of the procrastinating student who has a conference tomorrow and needs to slap some results together quickly, the lecturer looking to produce papers in a rush, and the professor looking for a promotion by making hyped claims without evidence. The number of friends that PCA has is a reminder of the good ol’ MySpace days – with citations numbering around 200,000 in genetics alone, times an average number of 10 authors per paper, we get 2,000,000 scholars who authored a paper that used PCA.

PCA is used to examine the population structure of a group of individuals to determine their ancestry, analyze the demographic history and admixture, decide on the genetic similarity of individuals and exclude outliers, decide how to model populations, describe the ancient and modern genetic relationships between individuals, infer familial relatedness, identify ancestral trends in the data, detect genomic signatures of natural selection , identify evolutionary trends , support genetic studies of diseases, geolocalize individuals, draw historical and ethnobiological conclusions, and more. It is “The Little Scatterplot that Could.”

The problem with PCA was also its greatest advantage. It always told everyone what they wanted to hear, so no one dared to challenge it.

 

“Techniques that offer such flexibility encourage bad science and are particularly dangerous in a world where there is intense pressure to publish.  If a researcher runs PCA several times, the temptation will always be to select the output that makes the best story”, added Professor William Amos, Professor of Evolutionary Genetics at the University of Cambridge, who was not involved in the study.