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 conditioning on the nearby common variants. In addition, DNA methylation and gene expression provide complementary information to functional genomics. In the Avon Longitudinal Study of Parents and Children with a sample size of 1497, one gene CLPTM1 is identified to be associated with low-density lipoprotein cholesterol levels by the proposed powerful adaptive gene-based test integrating information from gene expression, methylation and enhancer-promoter interactions. It is further replicated in the TwinsUK study with 1706 samples. The signal is driven by both low-frequency and common variants.

Availability and implementation: Models are available at https://github.com/ytzhong/DNAm.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

(A) The observed $R^2$ of the LFVs-DNAm models in comparison to the expected $R^2$ under the null hypothesis of no association between DNAm and low-frequency variants (115 213 models with $R^2>0.01$); (B) the average of observed $R^2$ of LFVs-DNAm models in comparison with $R^2$ of LFVs-GX models in an independent test dataset for the same gene; (C) the average observed $R^2$ of the CVs-DNAm models in comparison with that of CVs-GX models in an independent test dataset; (D) the observed $R^2$ of CVs-DNAm models in comparison with that of LFVs-DNAm models for the same CpG site in the independent test dataset; E shows the comparison of the predictive performance of low-frequency variants to DNAm variations with (y-axis) and without (x-axis) taking into account CVs in the testing dataset for 28 634 models predictable by low-frequency and common variants; F shows the comparison of the contribution of common variants to DNAm variations with (y-axis) and without (x-axis) taking into account the low-frequency variants in the testing dataset (28 634 models). The dashed lines are the 45-degree identity lines