Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, “Wnt signaling” and “mammalian target of rapamycin (mTOR) signaling” pathways were found to be unique to SBP, while “cytokine network”, and “tryptophan catabolism” to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions

The pathogenesis of obesity from a genomic and systems biology perspective.

The recent obesity epidemic has imposed significant health, economical, and societal concerns. However, effective preventive and therapeutic strategies are currently lacking, primarily due to a lack of comprehensive understanding of the underlying molecular mechanisms. Recent genome-wide scans of genetic variants, transcriptome, and epigenome have uncovered >50 genetic loci that predispose individuals to obesity and revealed hundreds of genes with altered transcriptional activity and/or epigenetic variations in obesity-related tissues upon various environmental challenges such as high caloric diets, lack of physical activity, and environmental chemicals. These discoveries highlight the importance of genes involved in the control of energy homeostasis and food intake by the central nervous system, as well as genes contributing to lipid metabolism, adipogenesis, fat cell differentiation, and immune response in peripheral tissues, in obesity development. Future studies that are directed to obtain a more comprehensive, systems-level understanding of disease mechanisms and that test novel therapeutic strategies aiming at systems-level normalization of the obesity-related molecular alterations are warranted

Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions

Key driver genes of six CAD-associated supersets, and their adjacent regulatory partners.

<p>Key driver genes were denoted as larger nodes in the network. Genes were colored based on their membership in the six CAD-associated supersets. A) ‘Lipid II’ superset in red. B) ‘Lipid I’ superset in yellow. C) ‘Unknow II’ superset in lime. D) ‘Immunity’ superset in green. E) ‘Antigen’ superset in blue. F) ‘Unknown I’ superset in magenta. Only edges that were present in at least two Bayesian networks constructed from independent studies were included.</p

Top five genes whose eSNPs show strongest association with CAD in GWAS (termed “GWAS signal genes”) and key driver genes for selected CAD-associated supersets.

<p>^*Genes within superset whose eSNPs (i.e. putative functional SNPs that affect gene expression) show best association with CAD in the GWAS meta-analysis.</p>#<p>The key driver genes were ascertained by combining key driver analyses of all available Bayesian networks, and taking into account both the consistency across datasets and the KDA statistics.</p

Knowledge-based grouping of canonical pathways that were significantly enriched for CAD genetic loci.

<p>The enrichment score was defined as the mean of negative log-transformed Kolmogorov-Smirnov and Fisher P-values for over-representation of high-ranking GWAS SNPs among the eSNPs that affect the expression of the pathway member genes. The number in parentheses in the first column indicates the number of CAD-associated pathways (detailed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004502#pgen.1004502.s004" target="_blank">Table S1</a>).</p><p>*FDR<20% in Stage 1 and 2 respectively, and FDR<5% in combined Stage 1 & 2.</p

CAD enrichment scores for selected non-overlapping supersets after the merging of CAD-associated canonical pathways and co-expression modules.

<p>The enrichment score was defined as the mean of negative log-transformed Kolmogorov-Smirnov and Fisher P-values for over-representation of high-ranking GWAS SNPs among the eSNPs that affect the expression of the superset member genes.</p><p>*P<0.05 in either Fisher's exact test or Kolmogorov-Smirnov test after Bonferroni correction for the 3,539 original gene sets.</p

Schematic overview of the study design.

<p>A) The SNP set enrichment analysis (SSEA) comprised four steps. First, gene sets from knowledge-driven pathways and data-driven co-expression modules were collected. Second, the gene sets were converted to expression SNP (eSNP) sets according to genetics of gene expression or eQTL studies. Third, P-values from CAD GWAS were extracted for each eSNP. Fourth, the GWAS P-values within eSNP sets were compared against random expectation to derive pathways and network modules enriched for CAD genetic signals. B) Overlapping CAD-associated gene sets were merged and trimmed into non-overlapping supersets. C) Integration of Bayesian gene-gene network models with CAD-associated supersets to determine key driver genes based on network topology.</p