Researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital have developed a novel methodology to enhance the accuracy of genetic testing. Their findings, published in Nature Communications, focus on improving the measurement of genetic variants within the Genome Aggregation Database (gnomAD), a crucial resource in the field of human genetics.
The study utilizes a technique known as local ancestry inference (LAI), which segments the genome into ancestry-specific parts. This approach allows for a more precise understanding of genetic differences across various populations. Dr. Elizabeth Atkinson, assistant professor in the Department of Molecular and Human Genetics at Baylor and principal investigator at the Duncan NRI, emphasized the significance of this research. “This research updates our genomic resources to reflect the full spectrum of genetic variation,” she stated.
The implications of this study are profound. By refining allele frequency estimates for populations with mixed ancestry, the researchers aim to reduce the likelihood of misclassification in genetic diagnoses. This advancement stands to benefit patients from diverse backgrounds significantly.
The study titled “Improved Allele Frequencies in gnomAD through Local Ancestry Inference” marks a significant step forward in the domains of genetic testing and personalized medicine. Dr. Atkinson is the senior author of the study, with Pragati Kore and Michael Wilson serving as co-first authors.
Genetic testing plays a vital role in diagnosing various diseases. Typically, if certain genetic variants are prevalent in the broader population, they are deemed more likely to be benign. However, current population frequency estimates often rely on averages that might overlook critical differences, especially for individuals with ancestors from multiple continents. This is particularly relevant for groups identified as African/African American or Latino/Admixed American in gnomAD. The aggregate method may obscure significant genetic variations specific to these ancestral components.
Dr. Atkinson’s team addressed this issue by applying local ancestry inference to dissect the genome into segments that correspond to different continental ancestries, such as African, European, or Indigenous American. This detailed analysis allowed the researchers to assess the prevalence of each genetic variant within these specific ancestry segments. The findings revealed that many variants previously considered rare on a global scale were, in fact, common within certain ancestry groups.
“These differences are not just academic,” Dr. Atkinson noted. “They have clinical consequences.” The study found that in the African/African American and Latino/Admixed American populations, over 80% of genetic sites exhibited higher frequencies in at least one ancestry-specific segment than what had been reported before. In some instances, this elevated frequency was significant enough to place certain variants above a critical threshold established by the American College of Medical Genetics and Genomics for classifying variants as benign, potentially leading to a more accurate reclassification of genetic variants.
The newly generated ancestry-specific data is now publicly accessible through gnomAD, equipping researchers, clinicians, and genetic testing laboratories with a more precise tool for interpreting genetic variations. Dr. Atkinson remarked on the complexity of ancestry, stating, “Putting a single label on patients is not the most accurate way to diagnose them. With this research, we are moving toward a more nuanced consideration of ancestry.”
As this groundbreaking work continues to unfold, it promises to transform the landscape of genetic testing, paving the way for more effective and accurate medical diagnoses tailored to individual genetic backgrounds.
