Popular genetic method is deeply flawed, study reveals

The most common analytical method within population genetics is deeply flawed, according to a new study from Lund University in Sweden. This may have led to incorrect results and misconceptions about ethnicity and genetic relationships. The method has been used in hundreds of thousands of studies, affecting results within medical genetics and even commercial ancestry tests. The study is published in Scientific Reports.

The rate at which scientific data can be collected is rising exponentially, leading to massive and highly complex datasets, dubbed the "Big Data revolution." To make these data more manageable, researchers use statistical methods that aim to compact and simplify the data while still retaining most of the key information. Perhaps the most widely used method is called PCA (principal component analysis). By analogy, think of PCA as an oven with flour, sugar and eggs as the data input. The oven may always do the same thing, but the outcome, a cake, critically depends on the ingredients' ratios and how they are combined.

It is expected that this method will give correct results because it is so frequently used. But it is neither a guarantee of reliability nor produces statistically robust conclusions."

Dr. Eran Elhaik, Associate Professor in molecular cell biology at Lund University

According to Elhaik, the method helped create old perceptions about race and ethnicity. It plays a role in manufacturing historical tales of who and where people come from, not only by the scientific community but also by commercial ancestry companies. A famous example is when a prominent American politician took an ancestry test before the 2020 presidential campaign to support their ancestral claims. Another example is the misconception of Ashkenazic Jews as a race or an isolated group driven by PCA results.

"This study demonstrates that those results were unreliable," says Eran Elhaik.

PCA is used across many scientific fields, but Elhaik's study focuses on its usage in population genetics, where the explosion in dataset sizes is particularly acute, which is driven by the reduced costs of 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.