Myriad Genetics. has been granted a patent for a computer-implemented method that infers genetic ancestry from low-coverage genomic data. The method involves generating a reference matrix, decomposing it, and deriving ancestral attribution vectors to determine a patient’s genetic ancestry based on their genetic data. GlobalData’s report on Myriad Genetics gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Myriad Genetics, Personalized medicine biomarkers was a key innovation area identified from patents. Myriad Genetics's grant share as of July 2024 was 30%. Grant share is based on the ratio of number of grants to total number of patents.
Genetic ancestry inference from low-coverage genomic data
The patent US12033722B2 outlines a method for genetic screening that utilizes advanced computational techniques to analyze a patient's genetic data. The process begins with deriving genetic data, including raw sequencing data and genetic markers, from the patient's genome. A computing device identifies a genetic reference dataset that includes dosage information for various subjects at specific loci. This dataset is then transformed into a reference matrix, which serves as a foundation for generating an ancestral attribution matrix. This matrix attributes genetic descent from proposed ancestral populations to the subjects, allowing for the creation of simulated ancestral attribution vectors stored in a K-dimensional tree. The method ultimately derives ancestral attribution vectors for the patient, which reflect the admixture of populations contributing to the patient's genetic ancestry.
Additionally, the patent describes various enhancements to the genetic screening method, such as the use of low-coverage next-generation sequencing and high-throughput sequencing procedures. It includes techniques for imputing missing dosage data and modeling reference genomes to ensure accurate representation of ancestral proportions. The method also incorporates probabilistic values in the reference matrix and employs least squares approximations to refine ancestral attribution vectors. Furthermore, it details the generation of reference clusters and dines that connect different populations, facilitating a more nuanced understanding of the patient's genetic ancestry. Overall, this patent presents a comprehensive approach to genetic screening that leverages computational power to enhance the accuracy and depth of ancestry analysis.
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