Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels.

ABSTRACT:

Motivation

The abundance of omics data has facilitated integrative analyses of single and multiple molecular layers with genome-wide association studies focusing on common variants. Built on its successes, we propose a general analysis framework to leverage multi-omics data with sequencing data to improve the statistical power of discovering new associations and understanding of the disease susceptibility due to low-frequency variants. The proposed test features its robustness to model misspecification, high power across a wide range of scenarios and the potential of offering insights into the underlying genetic architecture and disease mechanisms.

Results

Using the Framingham Heart Study data, we show that low-frequency variants are predictive of DNA methylation, even after

SUBMITTER: Yang T 

PROVIDER: S-EPMC7850048 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature