Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.

ABSTRACT:

Motivation

The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed.

Results

We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer's Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction.

Availability and implementation

The R-package is available at https://github.com/YaluWen/OmicPred.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Li J 

PROVIDER: S-EPMC7523642 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature