IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration.

ABSTRACT:

Motivation

Integrative genomic analysis is a powerful tool used to study the biological mechanisms underlying a complex disease or trait across multiplatform high-dimensional data, such as DNA methylation, copy number variation and gene expression. It is common to perform large-scale genome-wide association analysis of an outcome for each data type separately and combine the results ad hoc, leading to loss of statistical power and uncontrolled overall false discovery rate (FDR).

Results

We propose a multivariate mixture model (IMIX) framework that integrates multiple types of genomic data and allows modeling of inter-data-type correlations. We investigated the across-data-type FDR control in IMIX and demonstrated lower misclassification rates at controlled overall FDR than e

SUBMITTER: Wang Z 

PROVIDER: S-EPMC8016490 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature