A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study.

Autor: Acharya S; Division of Computational and Data Sciences, Washington University, St Louis, MO, USA., Liao S; Department of Computer Science and Engineering, Washington University, St Louis, MO, USA., Jung WJ; Department of Computer Science and Engineering, Washington University, St Louis, MO, USA., Kang YS; Department of Computer Science and Engineering, Washington University, St Louis, MO, USA., Moghaddam VA; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Feitosa MF; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Wojczynski MK; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Lin S; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Anema JA; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Schwander K; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Connell JO; Department of Medicine, University of Maryland, Baltimore, MD, USA., Province MA; Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA., Brent MR; Department of Computer Science and Engineering, Washington University, St Louis, MO, USA. brent@wustl.edu.
Jazyk: angličtina
Zdroj: Human genetics [Hum Genet] 2024 Oct; Vol. 143 (9-10), pp. 1241-1252. Date of Electronic Publication: 2024 Sep 14.
DOI: 10.1007/s00439-024-02701-1
Abstrakt: The Long Life Family Study (LLFS) enrolled 4953 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 ).
(© 2024. The Author(s).)
Databáze: MEDLINE
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