Affecting up to 216,000 studies – a common genetic method found to be severely flawed

The concept of genetic disease research

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

A new study has uncovered flaws in a common analytical method within population genetics.

According to recent research from Sweden Lund UniversityThe analytical method most commonly used in population genetics is highly flawed. This may have caused incorrect results and misconceptions regarding racial and genetic relationships. This method has been used in hundreds of thousands of studies, affecting results in medical genetics and even commercial strain tests. The results were recently published in the journal Scientific Reports.

The pace of scientific data collection is increasing rapidly, resulting in huge and extremely complex databases, which has been dubbed the “Big Data Revolution”. Researchers use statistical techniques to condense and simplify data while preserving the majority of important information in order to make the data more manageable. PCA (principal component analysis) is probably the most widely used approach. Imagine PCA as an oven with flour, sugar, and eggs as the input data. The oven may always do the same thing, but the end result, a cake, depends largely on the proportions of the ingredients and how you mix them.

“This method is expected to give correct results because it is used extensively. It is not a guarantee of reliability and does not produce statistically robust conclusions,” says Dr. Eran El-Hayek, Assistant Professor of Molecular Cell Biology at Lund University.

According to El-Hayek, the method contributed to the development of ancient beliefs about race and ethnicity. It plays a role in making historical tales of who and where people come from, not only by the scientific community but also by commercial companies. A well-known example is when a famous American politician used ancestry testing to support the claims of their ancestors before the 2020 presidential campaign. Another example is the misconception of Ashkenazi Jews as an isolated group or race driven by PCA findings.

“This study shows that these results were unreliable,” says Eran El-Hayek.

PCA is used in many scientific fields, but El-Hayek’s study focuses on its use in population genetics, where the explosion in data set sizes is particularly acute, which is driven by low costs.[{” 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

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