Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels
Year: 2020
Type: article
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.
Source: Bioinformatics
Authors: Tianzhong Yang, Peng Wei, Wei Pan
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: Statistical Methods for Genomic Data, G1129490614, Estimation and Inference of Gene Regulatory Networks, Rare variants and NHLBI traits in deeply phenotyped cohorts, G232264632, Discovering causal genes, brain regions and other risk factors for Alzheimer'a disease, G278200998, Genetic Association and Personalized Medicine, G3256118428, G3405256882, The Avon Longitudinal Study of Parents and Children (ALSPAC): A multi- generation, longitudinal resource focusing on life course health and well- being., Integrating genomic and imaging endophenotypes in GWAS, G5164707915, Association analysis of rare variants with sequencing data, The Framingham Heart Study, Studies of Rare Genetic Variation in the Isolated Population of Sardinia, G8491121283
OpenAlex ID: W3093366890