TY - JOUR
T1 - Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels
AU - Yang, Tianzhong
AU - Wei, Peng
AU - Pan, Wei
N1 - Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85105119458&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105119458&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btaa898
DO - 10.1093/bioinformatics/btaa898
M3 - Article
C2 - 33070182
AN - SCOPUS:85105119458
SN - 1367-4803
VL - 36
SP - 5223
EP - 5228
JO - Bioinformatics
JF - Bioinformatics
IS - 21
ER -