Affects up to 216,000 studies – Popular genetic method recognized as deeply flawed

The flawed method has been used in hundreds of thousands of studies.

A new study reveals flaws in a common analytical method for population genetics.

According to recent research in Sweden Lund University, the most commonly used analytical method in population genetics, is deeply flawed. This could lead to incorrect results and misconceptions about ethnicity and genetic relationships. The method has been used in hundreds of thousands of studies, influencing findings in medical genetics and even commercial ancestry tests. The findings were recently published in a journal Scientific reports.

The pace of scientific data collection is rapidly increasing, leading to the creation of huge and highly complex databases, which has been called the “big data revolution”. Researchers use statistical techniques to condense and simplify data while retaining most of the important information to make the data more usable. PCA (Principal Component Analysis) is perhaps the most widely used approach. Think of PCA as an oven with flour, sugar and eggs serving as inputs. The oven can always do the same thing, but the end result, the cake, depends a lot on the ratio of the ingredients and the way they are mixed.

“This method is expected to give correct results because it is used so often. But this is neither a guarantee of reliability nor does it provide statistically reliable conclusions,” says Dr. Eran Elheik, Associate Professor of Molecular Cell Biology at Lund University.

According to Elhayk, the method contributed to the development of old ideas about race and ethnicity. It plays a role in the creation of historical stories about where and whence people come from, not only by the scientific community but also by commercial rank and file companies. A famous example is when a prominent American politician used an ancestry test to support his claims for the 2020 presidential campaign. Another example is the misperception of Ashkenazi Jews as an isolated group or race due to PCA results.

“This study demonstrates that these results were unreliable,” says Eran Elhayk.

PCA is used in many scientific fields, but Elheik’s research focuses on its use in population genetics, where the explosion in the size of datasets is particularly dramatic, driven by the reduction in cost[{” attribute=””>DNA sequencing.

The field of paleogenomics, where we want to learn about ancient peoples and individuals such as Copper age Europeans, heavily relies on PCA. PCA is used to create a genetic map that positions the unknown sample alongside known reference samples. Thus far, the unknown samples have been assumed to be related to whichever reference population they overlap or lie closest to on the map.

However, Elhaik discovered that the unknown sample could be made to lie close to virtually any reference population just by changing the numbers and types of the reference samples (see illustration), generating practically endless historical versions, all mathematically “correct,” but only one may be biologically correct.

In the study, Elhaik has examined the twelve most common population genetic applications of PCA. He has used both simulated and real genetic data to show just how flexible PCA results can be. According to Elhaik, this flexibility means that conclusions based on PCA cannot be trusted since any change to the reference or test samples will produce different results.

Between 32,000 and 216,000 scientific articles in genetics alone have employed PCA for exploring and visualizing similarities and differences between individuals and populations and based their conclusions on these results.

“I believe these results must be re-evaluated,” says Elhaik.

He hopes that the new study will develop a better approach to questioning results and thus help to make science more reliable. He spent a significant portion of the past decade pioneering such methods, like the Geographic Population Structure (GPS) for predicting biogeography from DNA and the Pairwise Matcher to improve case-control matches used in genetic tests and drug trials.

“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”, adds Professor William Amos, from the Univesity of Cambridge, who was not involved in the study.

Reference: “Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated” by Eran Elhaik, 29 August 2022, Scientific Reports.
DOI: 10.1038/s41598-022-14395-4 Affects up to 216,000 studies – Popular genetic method recognized as deeply flawed

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