Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.

ABSTRACT: Polygenic risk scores (PRS) aim to predict a trait from genetic information, relying on common genetic variants with low to medium effect sizes. As genotype data are high-dimensional in nature, it is crucial to develop methods that can be applied to large-scale data (large n $$ n $$ and large p $$ p $$ ). Many PRS tools aggregate univariate summary statistics from genome-wide association studies into a single score. Recent advancements allow simultaneous modeling of variant effects from individual-level genotype data. In this context, we introduced snpboost, an algorithm that applies statistical boosting on individual-level genotype data to estimate PRS via multivariable regression models. By processing variants iteratively in batches, snpboost can deal with large-scale cohort data. Ha

SUBMITTER: Klinkhammer H 

PROVIDER: S-EPMC11586906 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature