Groundbreaking Tool Improves Genetic Testing Accuracy

Genetic testing plays a crucial role in diagnosing disease, but its accuracy depends heavily on understanding how common certain genetic variants are across populations. Most current databases calculate these frequencies using averages across broad groups, which can obscure important ancestry-specific differences. This is especially problematic for people with mixed heritage, such as those with African, European, or Indigenous ancestry, leading to possible variant misclassification. Now, a new approach refines genetic data to make testing more precise across all populations.

Researchers at Texas Children’s Neurological Research Institute (NRI, Houston, TX, USA) and Baylor College of Medicine (Houston, TX, USA) have developed an advanced tool within the Genome Aggregation Database (gnomAD) that uses local ancestry inference (LAI) to improve the accuracy of genetic testing. The method divides the genome into ancestry-specific segments, allowing more accurate allele frequency estimates for individuals with mixed ancestry. By recalculating how common each genetic variant is within each ancestry component, the tool captures genetic variation that global averages miss.

Their study, published in Nature Communications, revealed that in African/African American and Latino/Admixed American populations, over 80% of genetic sites had higher frequencies in at least one ancestry-specific tract than previously estimated. In many cases, variants once considered rare were shown to be common in certain ancestry backgrounds, crossing key thresholds used to classify them as benign by the American College of Medical Genetics and Genomics (ACMG). These adjustments may prevent false interpretations in clinical diagnostics and lead to more accurate genetic risk assessments.

The ancestry-specific data generated through this study is now integrated into gnomAD, making it publicly accessible to researchers, clinicians, and laboratories worldwide. This development ensures that future genetic testing can account for the diverse ancestry composition of patients, leading to improved variant interpretation and fewer diagnostic errors. The research marks a major step forward toward more equitable and precise genomic medicine.

By acknowledging the complexity of ancestry, the researchers demonstrated how nuanced data can improve clinical decision-making and reduce health disparities. The refined allele frequencies will help ensure that patients of mixed or underrepresented backgrounds receive genetic assessments that are as accurate as those for populations historically overrepresented in genomic datasets.

“This research updates our genomic resources to better reflect the full spectrum of genetic variation,” said Dr. Elizabeth Atkinson, principal investigator. “By refining allele frequency estimates for admixed populations, we can improve the accuracy of genetic diagnoses and reduce the risk of misclassification — ultimately benefitting patients across all backgrounds.”

Related Links:
NRI
Baylor College of Medicine