Machine learning platform for generating risk models
OwnerJared Michael O'Connell, Sahar Victoria Mozaffari +4inventors
InventorsJared Michael O'Connell, Sahar Victoria Mozaffari, Wei Wang, Suyash S. Shringarpure, Adam Auton, Jingchunzi Shi
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Abstract
The disclosed embodiments concern methods, apparatus, systems, and computer program products for developing polygenic risk score (PRS) models with improved performance across different ethnicities and for different target phenotypes.
Claims23
A method for generating a cross-traits polygenic risk score (PRS) model, comprising:
- selecting a phenotype of interest having a set of summary statistics from a genome wide association study (GWAS);
- selecting a plurality of candidate phenotypes, each candidate phenotype having a set of summary statistics from a corresponding GWAS for that candidate phenotype;
- determining a set of genetic correlations between the phenotype of interest and each candidate phenotype of the plurality of candidate phenotypes;
- filtering the plurality of candidate phenotypes based on the set of genetic correlations to assemble a cohort of filtered candidate phenotypes;
- retrieving a plurality of PRS models, each PRS model corresponding to a phenotype of the cohort of filtered candidate phenotypes;
- and
- determining the cross-traits PRS model based at least in part on the plurality of PRS models.
(canceled)
A method for generating a cross-traits polygenic risk score (PRS) model, comprising:
- obtaining, for a phenotype of interest, GWAS statistical data relating the phenotype of interest to genetic information;
- identifying one or more filtered candidate phenotypes to form a cohort of filtered candidate phenotypes, wherein each filtered candidate phenotype has GWAS statistical data, and wherein each filtered candidate phenotype has a genetic correlation with the phenotype of interest and the genetic correlation exceeds a defined threshold;
- retrieving a plurality of PRS models, each PRS model corresponding to a phenotype of the cohort of filtered candidate phenotypes;
- and
- determining the cross-traits PRS model based at least in part on the plurality of PRS models.
A method for generating a transethnic polygenic risk score (PRS) model, comprising:
- selecting a target population of interest having genotype data available for individuals within the target population;
- analyzing the genotype data for the target population and one or more population-specific genetic datasets to determine one or more sets of SNPs that are statistically associated with a phenotype of interest, wherein the population-specific genetic datasets are for populations other than the target population,
- applying SNP filtering criteria to the one or more set of SNPs to generate a plurality of training SNP sets with each training SNP set corresponding to a different population of the one or more population-specific genetic datasets;
- training a plurality of PRS models based on the genotype data for the one or more population-specific genetic datasets and the plurality of training SNP sets to generate a PRS model for each of the one or more populations in the one or more population specific genetic datasets;
- and
- determining the transethnic PRS model based at least in part on training the plurality of PRS models using the target population training set to generate the transethnic PRS model.
-45. (canceled)
Dependencies are inferred from the claim text.