Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

Abstract

The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10^-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).

Fig 1:. Pipeline diagram.

A) Inputs to GWAS, TWAS, and RVA. B) GWAS output is fed into PASCAL, which calculates gene-level p-values. TWAS outputs gene-level p-values. STAAR splits variants into ten functional categories and outputs 10 p-values per gene. C) Correlated meta-analysis (CMA) is run 10 times. Each run uses outputs from PASCAL and TWAS together with one variant category of STAAR, outputting 10 p-values. D) For each gene, the minimum p-value from 10 CMA runs is fed into module enrichment analysis, which is also performed by PASCAL. PASCAL outputs enriched modules and their p-values. E) Gene ontology over-representation analysis identifies biological processes with significant over-representation among genes in each module.

Fig 2:. Sub-modules within enriched modules.

Genes with P < 10⁻⁴ are annotated as suggestive. Module enrichment analysis identifies trait-related genes missed by association analysis. (A) Module cma-STRING-104 is enriched for both TG and HDL. APOB is not significant for TG but directly interacts with genes with suggestive or significant p-values for TG. APOB and MSR1 participate in macrophage-derived foam cell differentiation with two genome-wide significant genes (CETP and ABCG1). (B) SYK is not genome-wide significant after CMA. SYK interacts with significant genes for TG, FCER1A and MS4A2, and participates in mast-cell degranulation.