Journal Article

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Wilson, James G., Sofer, Tamar, Li, Yun, Cornell, Elaine, Love, Michael I., Taylor, Kent D., Johnson, Craig W., Lappalainen, Tuuli, Kasela, Silva, Smith, Josh, Raffield, Laura M., Cruz, Dan, Chen, Jiawen, VanDenBerg, David J., Liu, Yongmei, Durda, Peter, Guo, Xiuqing, Jiang, Min-Zhi, Gerszten, Robert E., Lange, Leslie A., Rotter, Jerome I., Aguet, François, Tracy, Russell P., Rich, Stephen S., Gabriel, Stacey B., Ardlie, Kristin, Reiner, Alex P.

2023

Overview

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Arteriosclerosis / Associations / Biological analysis / Biology and life sciences / Blood / Body mass index / Canonical Correlation Analysis / Cohort analysis / Cohort Studies / Correlation analysis / Data mining / Datasets / DNA methylation / Genetic research / Genetic variation / Humans / Leukocytes / Medical genetics / Methods / Multiomics / Multivariate analysis / Phenotypic variations / Physical Sciences / Proteomics / Proteomics - methods / Research and Analysis Methods / Variables