Imagine a world where genetic testing is so precise that it can accurately reflect your unique ancestral background, leading to more reliable diagnoses and personalized treatments. But here's where it gets controversial: what if the way we currently interpret genetic data is oversimplified, potentially leading to misdiagnoses for certain populations? Researchers at Texas Children's Neurological Research Institute (NRI) and Baylor College of Medicine have tackled this issue head-on by developing a groundbreaking tool within the Genome Aggregation Database (gnomAD). This innovation promises to revolutionize genetic testing by incorporating local ancestry inference (LAI), a method that dissects the genome into ancestry-specific segments to uncover hidden genetic nuances.
Published in Nature Communications (https://www.nature.com/articles/s41467-025-63340-2), the study titled 'Improved Allele Frequencies in gnomAD through Local Ancestry Inference' addresses a critical gap in genetic research. Traditional genetic testing often relies on broad population averages, which can overlook important differences in individuals with mixed ancestral backgrounds, such as African/African American or Latino/Admixed American populations. Dr. Elizabeth Atkinson, the study's senior author and Assistant Professor in the Department of Molecular and Human Genetics at Baylor College of Medicine, explains, 'This research updates our genomic resources to better reflect the full spectrum of genetic variation. By refining allele frequency estimates for admixed populations, we can improve diagnostic accuracy and reduce misclassification risks, benefiting patients across all backgrounds.'
And this is the part most people miss: LAI doesn't just look at the genome as a whole; it breaks it down into segments tied to specific continental ancestries, such as African, European, or Indigenous American. By calculating the frequency of genetic variants within these segments, the team discovered that many variants previously considered rare globally are actually common in certain ancestral groups. 'These differences aren't just academic,' Dr. Atkinson emphasizes. 'They have real clinical consequences.'
For instance, in African/African American and Latino/Admixed American groups, over 80% of genetic sites showed higher frequencies in at least one ancestry-specific segment than previously reported. This can push variants above clinical thresholds used by organizations like the American College of Medical Genetics and Genomics (ACMG) to classify them as benign, potentially leading to more accurate reclassifications. The new ancestry-specific data is now publicly available through gnomAD, offering researchers, clinicians, and genetic testing labs worldwide a more precise tool for interpreting genetic variation.
But here's the controversial question: Is our current one-size-fits-all approach to ancestry labeling in genetic testing outdated? Dr. Atkinson argues, 'Ancestry is complex, and assigning a single label to patients isn't the most accurate way to diagnose them. With this research, we're moving toward a more nuanced consideration of ancestry.' This shift could spark debates about how we define and use ancestry in medical contexts, raising questions about equity and representation in genetic research.
What do you think? Is a more nuanced approach to ancestry the future of genetic testing, or does it complicate an already complex field? Share your thoughts in the comments below!
More information: Pragati Kore et al, Improved allele frequencies in gnomAD through local ancestry inference, Nature Communications (2025). DOI: 10.1038/s41467-025-63340-2 (https://dx.doi.org/10.1038/s41467-025-63340-2)
Citation: Local ancestry inference method could improve accuracy of genetic testing and patient diagnoses (2025, October 6) retrieved 6 October 2025 from https://medicalxpress.com/news/2025-10-local-ancestry-inference-method-accuracy.html
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