Penalized regression and model selection methods for polygenic scores on summary statistics.

ABSTRACT: Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods f

SUBMITTER: Pattee J 

PROVIDER: S-EPMC7553329 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature