Integrative Pathway Analysis of SNP and Metabolite Data Using a Hierarchical Structural Component Model

Frontiers in Genetics

Min Kyong Moon¹  Oran Kwon²  Taesung Park³  Taeyeong Jung⁴  Youngae Jung⁵  Geum-Sook Hwang⁵ 

[1] Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea;Department of Nutritional Science and Food Management, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea;Department of Statistics, Seoul National University, Seoul, South Korea;Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea;Korea Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, South Korea;

关键词: pathway analysis;    multi-omics integration;    mGWAS;    metabolite;    SNP;   

DOI  :  10.3389/fgene.2022.814412

来源: DOAJ

【 摘 要 】

Integrative multi-omics analysis has become a useful tool to understand molecular mechanisms and drug discovery for treatment. Especially, the couplings of genetics to metabolomics have been performed to identify the associations between SNP and metabolite. However, while the importance of integrative pathway analysis is increasing, there are few approaches to utilize pathway information to analyze phenotypes using SNP and metabolite. We propose an integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model considering the structural relationships of SNPs, metabolites, pathways, and phenotypes. The proposed method utilizes genome-wide association studies on metabolites and constructs the genetic risk scores for metabolites referred to as genetic metabolomic scores. It is based on the hierarchical model using the genetic metabolomic scores and pathways. Furthermore, this method adopts a ridge penalty to consider the correlations between genetic metabolomic scores and between pathways. We apply our method to the SNP and metabolite data from the Korean population to identify pathways associated with type 2 diabetes (T2D). Through this application, we identified well-known pathways associated with T2D, demonstrating that this method adds biological insights into disease-related pathways using genetic predispositions of metabolites.

【 授权许可】

Unknown