A Flaw in Most Common Population Analytical Method Raises Questions about Population Information

The massive and highly complex sets of data, called the “Big Data revolution,” has allowed for more research into genetics and other population studies. To manage data effectively, scientists use statistical approaches that compress and simplify these data without losing most of the vital information. The most commonly used method of population genetic data analysis is principal component analysis (PCA). PCA is the first analysis type of data investigation and description in a majority of population genetic analyses. It has a wide range of applications in genetic biology. According to new findings, researchers show PCA is deeply flawed, casting doubt on many results of genetic analyses. With PCA applied in thousands of research works, the current findings claim results of these researches may be incorrect.

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Evaluating the Accuracy of PCA Clustering

Evaluating the Accuracy of PCA Clustering. Credit: Scientific Reports

The discrepancy in PCA can affect genetic science

The principal component analysis method is the most common for genetic data management. Analysis using PCA has given ethnic and genetic relationships to many studies. PCA can also analyze medical genetics and ancestry tests. Scientists have shown that this widely applied method is flawed, causing a potential problem in genetic sciences.

Among the use of PCA is to examine the population structure of a person or group to determine their ancestry. It can also analyze demographic history, infer kinship, and identify ancestral origin in data. These very significant elements of population genetics require a dependable system. The current study show discrepancies in using PCA. Because of the prevalence of PCA, there is a belief it gives correct results. However, a new study has shown the method is not reliable nor produces excellent statistical conclusions on the data. The researchers focused on PCA use in population genetics. They discovered that unknown data could appear similar to any population by changing the numbers and types of the reference samples. The analysis method generates endless historic versions; although all are mathematically correct, only one can be biologically correct. The research also examined the flexibility of PCA. The flexibility of PCA shows the lack of trust in the method because any change in the reference or test samples will lead to a different outcome. A large number of studies have analyzed population studies using PCA. The researchers in this novel study argue that those results may not be entirely accurate. They propose other methods of analysis for population genetics to make the science more reliable.

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Clinical significance

Understanding ancestry and evolutionary trends is vital in Population genetics. With some medical conditions, investigation can require digging into ancestry lineage and developing more effective treatment. With the current study showing limitations of PCA, scientists can develop newer and better methods of population data analysis to address population concerns for clinical use.

Conclusion

People are curious about their ancestry and lineage. While science seeks to promote and offer solutions to varying concerns, it is vital it adopts the right approach to seeking information. Following the results of this current study, the limitations of PCA have now come to light. Scientists can now aim to develop newer and better methods for genetic science. The need to trace ancestry and other population information can be done without fear of getting false results.

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References

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

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