Study demonstrates the potential of precision bioinformatics to improve sensitivity and accuracy of genomic data analysis for individuals from diverse ancestries
The Seven Bridges team provided evidence that genome graphs augmented with next generation sequencing genomes from target populations can significantly improve both read alignment and variant calling accuracy while preserving computational efficiency. The effects are most pronounced in populations that genetically diverge the most from the standard linear reference genome. The authors applied this approach to analyze populations of African ancestry from the 1000 Genomes dataset using a tailored pangenome reference. This approach allowed detection of greater than 6 fold more unique variants than were detected using a linear reference genome. Importantly, when the authors evaluated the potential functional significance of identified variants, the use of tailored pangenome references allowed the detection of 3 to 4 times more variants in coding regions with high or moderate impact. These variants were otherwise undetectable by conventional methods.
“Realizing the promise of genomics requires that we rethink not only the way we practice medicine to incorporate genetic findings, but also the underlying methods by which we identify genetic variants for individuals of diverse ancestries,” said William Moss, CEO at Seven Bridges. “The concept of precision bioinformatics incorporates an individual’s ancestry and familial information to provide a more accurate and comprehensive understanding of their genome.”
In addition to improving variant calling at an individual level, the researchers demonstrate the value of this approach in population data analysis. Using Seven Bridges’ GRAF Suite, the authors analyzed the efficacy of using a tailored pangenome reference compared to joint variant calling. The data showed that the graph-based approach achieved similar improvements as the current state-of-the-art method for rescuing genotype calls across many individuals. These gains were achieved without computationally and time-intensive post-processing steps.
“Today, large genome sequencing projects like the UK BioBank or All of Us refine genome sequencing results using joint calling which requires simultaneous processing of all samples in a dataset. Not only is this process incredibly computationally intensive and costly, it also creates delays in the release of data for the broader research community”, said Brandi Davis-Dusenbery PhD, Chief Science Officer of Seven Bridges, “We believe our approach of incorporating population-level information at the genome reference and alignment step is an important step toward ultimately enabling more rapid sharing of data to accelerate the discoveries that improve health outcomes for all.”
For more information on GRAF, visit https://www.sevenbridges.com/graf/
About Seven Bridges
Seven Bridges enables researchers to extract meaningful insights from genomic and phenotypic data in order to advance precision medicine. The Seven Bridges Ecosystem consists of a compliant analytic platform, intelligently curated content, transformative algorithms, unprecedented access to federated data sets, and expert on-demand professional services. This holistic approach to bioinformatics is enabling researchers — at the world’s leading academic, biotechnology, clinical diagnostic, government, medical centers, and pharmaceutical entities — to increase R&D efficiency, enhance the hypothesis resolution process, isolate critical biomarkers, and even turn a failing clinical trial around while also reducing computational workflow times and data storage costs. To learn more, visit sevenbridges.com or follow us on LinkedIn and Twitter.
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