A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.

ABSTRACT: With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effect

SUBMITTER: Wang X 

PROVIDER: S-EPMC9310531 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature