Fig 1.
Framework of network-driven integrative genomics analyses.
(A) Integration of genetics and functional genomics datasets to identify CVD and T2D associated co-expression modules. The GWAS studies for CVD and T2D were derived from three independent cohorts representing three ethnic populations: WHI (AA, EA, HA), FHS (EA), and JHS (AA). These independent datasets were supplemented with GWAS of coronary artery disease from CARDIoGRAMplusC4D and T2D from DIAGRAM to increase power. We also curated a comprehensive list of tissue-specific functional genomics datasets, including 2672 co-expression modules, human eQTLs of various tissues, and ENCODE based variants annotation. The significant modules were identified by MSEA and Meta-MSEA, and then annotated to reveal shared pathways for CVD and T2D. In MSEA, the co-expression modules were used to define data-driven gene sets each containing functionally related genes, tissue-specificity was determined based on the tissue-origins of the human eQTLs, and ethnic specificity was determined based on the ethnicity of each GWAS cohort. (B) Identification of disease key drivers and subnetworks. We utilized multi-tissue graphical networks to capture key drivers for disease associated co-expression modules using wKDA, then prioritized KDs based on consistency and disease relevance of the subnetworks. (C) Validation of the top key drivers and their subnetworks via intersection with known human CVD and T2D genes from DisGeNET and GWAS catalog, in vitro adipocyte siRNA experiments, and cross-validation at both transcriptomic and genomic levels in the hybrid mouse diversity panels (HMDP).
Fig 2.
Venn diagrams of overlap in significant co-expression modules and functional categories between diseases and ethnicities.
A) Count of module overlaps by disease based on Meta-MSEA; B) Count of module overlaps for each disease by ethnicity based on MSEA of individual studies. Co-expression modules captured in CARDIoGRAMplusC4D and DIAGRAM were not counted due to uncertain ethnic origin; C) Count of independent functional category overlaps by disease based on results from Meta-MSEA in panel A.
Table 1.
Summary of top co-expression modules associated with CVD or T2D (FDR < 1% in Meta-MSEA, in column FDRmeta).
Fig 3.
Summary of 41 independent functional categories enriched in both CVD and T2D co-expression modules (Bonferroni-corrected p< 0.05 based on Fisher’s exact test, number of direct overlapping genes > 5).
Independent functional categories were defined as the categories with pair-wise overlapping ratio < 10%. Red and blue block indicates that the significant CVD or T2D co-expression modules identified from the study and ethnicity origin are enriched for the particular functional category term. CAR+C4D: CARDIoGRAMplusC4D; M: mixed ethnicities; AA: African Americans; HA: Hispanic Americans; EA: European Americans.
Table 2.
Summary of the 15 key drivers and their corresponding subnetworks shared by CVD and T2D.
Fig 4.
Subnetworks of the top 15 shared KDs orchestrate known genes for CVD, T2D, obesity and lipids.
A) Fold enrichment of KD subnetwork genes for known genes related to cardiometabolic traits reported in DisGeNET. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. B) Top KD subnetworks with GWAS hits (p < 1e-5 as reported in GWAS Catalog) for cardiometabolic traits. KDs are large nodes. Edge color denotes tissue-origin. Only high-confidence edges (those with weight score in the top 20%) are visualized.
Fig 5.
Validation of CAV1 subnetwork using in vitro siRNA knockdown (A) and in vivo knockout mouse model (B). A) Fold change of expression level for CAV1 subnetwork and negative control genes 2 days after Cav1 knockdown using two siRNAs separately. Twelve CAV1 neighbors were randomly selected from the first and second level neighboring genes of CAV1 in adipose network. Five negative controls were randomly selected from the genes not connected to CAV1 or its first level networks in adipose network. Statistical significance of genes was determined by linear model, adjusting for batch effect and siRNA differences. N = 6/siRNA group, mean ± SEM, *p < 0.05, **p < 0.01, ***p<0.001. B) Overlap of CAV1 neighboring genes in the adipose tissue subnetwork at various distance levels with the differentially expressed genes in the gonadal adipose tissue in Cav1 knockout mice (N = 3/group). Overlap p-value is determined by Fisher’s exact test. *Overlap p < 0.05 after Bonferroni correction.
Fig 6.
Associations of KDs and subnetworks with cardiometabolic traits in mice.
(A) Association between KD expression and cardiometabolic traits in adipose tissue from HF-HMDP (HF) and aorta tissue from atherogenic-HMDP (ATH) as determined by Pearson correlation. *p< 0.05; **p< 0.05 after Bonferroni correction for the KD number; ***p< 0.05 after Bonferroni correction for the number of KDs and traits. (B) Transcriptomic-wide association of KD subnetworks and cardiometabolic traits in adipose tissue from HF-HMDP, and aorta tissue from atherogenic-HMDP, as evaluated by MSEA. (C) Genome-wide genetic association of KD subnetworks and cardiometabolic traits based on adipose eQTL mapping in HF-HMDP, and aorta eQTL mapping in Atherogenic-HMDP, as determined by MSEA. p<0.05, p<3.3–3, and p<4.8e-4 correspond to uncorrected and Bonferroni-corrected p-values (correcting for the number of KDs or for the number of KD and trait combinations).