Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

A comparative pharmacogenomic analysis of three classic TCM prescriptions for coronary heart disease based on molecular network modeling

Traditional Chinese medicine (TCM) has evolved over several thousands of years, which has been shown to be efficacious in the treatment of ischemic heart disease. Three classical TCM prescriptions, namely Xuefu Zhuyu Decoction, Zhishi Xiebai Guizhi Decoction, and Gualou Xiebai Banxia Decoction, have been extensively used in the treatment of coronary heart disease (CHD). Based on molecular network modeling, we performed a comparative pharmacogenomic analysis to systematically determine the drug-targeting spectrum of the three prescriptions at molecular level. Wide-area target molecules of CHD were covered, which was a common feature of the three decoctions, demonstrating their therapeutic functions. Meanwhile, collective signaling involved metabolic/pro-metabolic pathways, driving and transferring pathways, neuropsychiatric pathways, and exocrine or endocrine pathways. These organized pharmacological disturbance was mainly focused on almost all stages of CHD intervention, such as anti-atherosclerosis, lipid metabolism, inflammation, vascular wall function, foam cells formation, platelets aggregation, thrombosis, arrhythmia, and ischemia-reperfusion injury. In addition, heterogeneity analysis of the global pharmacological molecular spectrum revealed that signaling crosstalk, cascade convergence, and key targets were tendentious among the three decoctions. After all, it is unadvisable to rank the findings on targeting advantages of the three decoctions. Comparative pharmacological evidence may provide an appropriate decoction scheme for individualized intervention of CHD.

Precision medicine validation: identifying the MYBPC3 A31P variant with whole-genome sequencing in two Maine Coon cats with hypertrophic cardiomyopathy

OBJECTIVES

The objective of this study was to perform a proof-of-concept experiment that validates a precision medicine approach to identify variants associated with hypertrophic cardiomyopathy (HCM). We hypothesized that whole-genome sequencing would identify variant(s) associated with HCM in two affected Maine Coon/Maine Coon cross cats when compared with 79 controls of various breeds.

METHODS

Two affected and two control Maine Coon/Maine Coon cross cats had whole-genome sequencing performed at approximately × 30 coverage. Variants were called in these four cats and 77 cats of various breeds as part of the 99 Lives Cat Genome Sequencing Initiative ( http://felinegenetics.missouri.edu/99lives ) using Platypus v0.7.9.1, annotated with dbSNP ID, and variants' effect predicted by SnpEff. Strict filtering criteria (alternate allele frequency >49%) were applied to identify homozygous-alternate or heterozygous variants in the two HCM-affected samples when compared with 79 controls of various breeds.

RESULTS

A total of four variants were identified in the two Maine Coon/Maine Coon cross cats with HCM when compared with 79 controls after strict filtering. Three of the variants identified in genes MFSD12, BTN1A1 and SLITRK5 did not segregate with disease in a separate cohort of seven HCM-affected and five control Maine Coon/Maine Coon cross cats. The remaining variant MYBPC3 segregated with disease status. Furthermore, this gene was previously associated with heart disease and encodes for a protein with sarcomeric function.

CONCLUSIONS AND RELEVANCE

This proof-of-concept experiment identified the previously reported MYBPC3 A31P Maine Coon variant in two HCM-affected cases. This result validates and highlights the power of whole-genome sequencing for feline precision medicine.

Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis

Rheumatoid arthritis (RA) is a complex and systematic autoimmune disease, which is usually influenced by both genetic and environmental factors. Pathway analyses based on a single data type such as microarray data or SNP data have successfully revealed some biology pathways associated with RA. However, we found that the pathway analysis based on a single data type only provide limited understanding about the pathogenesis of RA. Gene-disease association is usually caused by many ways, such as genotype, gene expression and so on. Therefore, the integrative analysis method combining multiple levels of evidence can more precisely and comprehensively identify the pathway associations. In this study, we performed a pathway analysis by integrating GWAS and gene expression analysis to detect the RA-related pathways. The integrative analysis identified 28 pathways associated with RA. Among these pathways, 18 pathways were also found by both GWAS and gene expression analysis, 7 pathways are novel RA-related pathways, such as B cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis and so on. Compared with pathway analyses using only one type genomic data, we found integrative analysis can increase the power to identify the real associations and provided more stable and accurate results. We believe these results will contribute to perform future genetic studies in RA pathogenesis and may promote the development of new therapeutic strategies by targeting these pathways.

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD.

Genetic epidemiology of coronary artery disease: an Asian Indian perspective

Coronary artery disease (CAD) has emerged as a major cause of morbidity and mortality worldwide. Recent findings on the role of genetic factors in the aetiopathology of CAD have implicated novel genes and variants in addition to those involved in lipid and lipoprotein metabolism. However, our present knowledge is limited due to lack of clarity on their exact identity and the quantum of impact on disease susceptibility, and incident risk. It is a matter of great interest to understand the role of genetic factors in ethnic populations that have a strong underlying predisposition to CAD such as the South Asian populations, particularly among Asian Indians living in India and abroad. Although, a number of isolated studies do implicate certain gene polymorphisms towards enhanced disease susceptibility, the available data remains scanty and inconclusive as they have not been validated in large, prospective cohorts. The present review aims to consolidate the available literature on the genetics of CAD in Asian Indians and seeks to provide insights on the concerns that need to be addressed in future studies to generate information having clinical value.

Shared molecular pathways and gene networks for cardiovascular disease and type 2 diabetes mellitus in women across diverse ethnicities

BACKGROUND

Although cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D) share many common risk factors, potential molecular mechanisms that may also be shared for these 2 disorders remain unknown.

METHODS AND RESULTS

Using an integrative pathway and network analysis, we performed genome-wide association studies in 8155 blacks, 3494 Hispanic American, and 3697 Caucasian American women who participated in the national Women's Health Initiative single-nucleotide polymorphism (SNP) Health Association Resource and the Genomics and Randomized Trials Network. Eight top pathways and gene networks related to cardiomyopathy, calcium signaling, axon guidance, cell adhesion, and extracellular matrix seemed to be commonly shared between CVD and T2D across all 3 ethnic groups. We also identified ethnicity-specific pathways, such as cell cycle (specific for Hispanic American and Caucasian American) and tight junction (CVD and combined CVD and T2D in Hispanic American). In network analysis of gene-gene or protein-protein interactions, we identified key drivers that included COL1A1, COL3A1, and ELN in the shared pathways for both CVD and T2D. These key driver genes were cross-validated in multiple mouse models of diabetes mellitus and atherosclerosis.

CONCLUSIONS

Our integrative analysis of American women of 3 ethnicities identified multiple shared biological pathways and key regulatory genes for the development of CVD and T2D. These prospective findings also support the notion that ethnicity-specific susceptibility genes and process are involved in the pathogenesis of CVD and T2D.

Pathway-based analysis of GWAs data identifies association of sex determination genes with susceptibility to testicular germ cell tumors

Genome-wide association (GWA) studies of testicular germ cell tumor (TGCT) have identified 18 susceptibility loci, some containing genes encoding proteins important in male germ cell development. Deletions of one of these genes, DMRT1, lead to male-to-female sex reversal and are associated with development of gonadoblastoma. To further explore genetic association with TGCT, we undertook a pathway-based analysis of SNP marker associations in the Penn GWAs (349 TGCT cases and 919 controls). We analyzed a custom-built sex determination gene set consisting of 32 genes using three different methods of pathway-based analysis. The sex determination gene set ranked highly compared with canonical gene sets, and it was associated with TGCT (FDRG = 2.28 × 10(-5), FDRM = 0.014 and FDRI = 0.008 for Gene Set Analysis-SNP (GSA-SNP), Meta-Analysis Gene Set Enrichment of Variant Associations (MAGENTA) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (i-GSEA4GWAS) analysis, respectively). The association remained after removal of DMRT1 from the gene set (FDRG = 0.0002, FDRM = 0.055 and FDRI = 0.009). Using data from the NCI GWA scan (582 TGCT cases and 1056 controls) and UK scan (986 TGCT cases and 4946 controls), we replicated these findings (NCI: FDRG = 0.006, FDRM = 0.014, FDRI = 0.033, and UK: FDRG = 1.04 × 10(-6), FDRM = 0.016, FDRI = 0.025). After removal of DMRT1 from the gene set, the sex determination gene set remains associated with TGCT in the NCI (FDRG = 0.039, FDRM = 0.050 and FDRI = 0.055) and UK scans (FDRG = 3.00 × 10(-5), FDRM = 0.056 and FDRI = 0.044). With the exception of DMRT1, genes in the sex determination gene set have not previously been identified as TGCT susceptibility loci in these GWA scans, demonstrating the complementary nature of a pathway-based approach for genome-wide analysis of TGCT.

Identification of possible pathogenic pathways in Behçet's disease using genome-wide association study data from two different populations

Behçet's disease (BD) is a multi-system inflammatory disorder of unknown etiology. Two recent genome-wide association studies (GWASs) of BD confirmed a strong association with the MHC class I region and identified two non-HLA common genetic variations. In complex diseases, multiple factors may target different sets of genes in the same pathway and thus may cause the same disease phenotype. We therefore hypothesized that identification of disease-associated pathways is critical to elucidate mechanisms underlying BD, and those pathways may be conserved within and across populations. To identify the disease-associated pathways, we developed a novel methodology that combines nominally significant evidence of genetic association with current knowledge of biochemical pathways, protein-protein interaction networks, and functional information of selected SNPs. Using this methodology, we searched for the disease-related pathways in two BD GWASs in Turkish and Japanese case-control groups. We found that 6 of the top 10 identified pathways in both populations were overlapping, even though there were few significantly conserved SNPs/genes within and between populations. The probability of random occurrence of such an event was 2.24E-39. These shared pathways were focal adhesion, MAPK signaling, TGF-β signaling, ECM-receptor interaction, complement and coagulation cascades, and proteasome pathways. Even though each individual has a unique combination of factors involved in their disease development, the targeted pathways are expected to be mostly the same. Hence, the identification of shared pathways between the Turkish and the Japanese patients using GWAS data may help further elucidate the inflammatory mechanisms in BD pathogenesis.

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.

Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.

Role of small GTPase protein Rac1 in cardiovascular diseases: development of new selective pharmacological inhibitors

A pathway-based genome-wide association analysis has recently identified Rac1 as one of the biologically important gene in coronary heart diseases. The role of the small GTPase Rac1 in cardiac hypertrophy and atherosclerosis has also been documented in clinical studies with the HMG-CoA reductase inhibitors and in in vitro and in vivo settings using transgenic and knockout mice. Thus, Rac1 has emerged as a new pharmacological target for the treatment of cardiovascular diseases. The activation state of Rac1 depends on the release of guanosine diphosphate and the binding of guanosine triphosphate. This cycling is regulated by the guanine nucleotide exchange factors, as activators, and by the GTPase-activating proteins. Three categories of selective Rac1 inhibitors have been developed affecting different steps of this pathway: antagonists of Rac1-guanine nucleotide exchange factor interaction, allosteric inhibitors of nucleotide binding to Rac1, and antagonists of Rac1-mediated NADPH oxidase activity. These chemical compounds have shown to selectively inhibit Rac1 activation in cultured cell lines without affecting the homologous proteins RhoA and Cdc42. Moreover, pioneer studies have been conducted with Rac1 inhibitors in in vivo experimental models of cardiovascular diseases with encouraging results. The present review summarizes the current knowledge of the role of Rac1 in cardiovascular diseases and the pharmacological approaches that have been developed to selectively inhibit its function.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Association between CDH13 variants and cardiometabolic and vascular phenotypes in a Korean population

PURPOSE

Although some CDH13 single nucleotide polymorphisms (SNPs) have been shown to be determinants of blood adiponectin levels, the clinical implications of CDH13 variants are not yet completely understood. The purpose of this study was to evaluate the effects of SNPs of CDH13 on metabolic and vascular phenotypes.

MATERIALS AND METHODS

We included 238 hypertensive subjects and 260 age- and sex-matched controls. Seven tagging-SNPs were identified in the CDH13 gene by whole gene sequencing. The association between these SNP variants and the risk of hypertension, metabolic traits, and carotid intima-media thickness (IMT) was examined.

RESULTS

Minor allele carriers of rs12444338 had a lower risk of hypertension, but the association turned out just marginal after adjusting confoudners. Blood glucose levels were higher in the minor allele carriers of c.1407C>T (p=0.01), whereas low-density lipoprotein-cholesterol levels were greater in those of rs6565105 (p=0.02). The minor allele of rs1048612 was associated with a higher body mass index (p=0.01). In addition, the mean carotid IMT was significantly associated with rs12444338 (p=0.02) and rs1048612 (p=0.02).

CONCLUSION

These results provide evidence that CDH13 variants are associated with metabolic traits and carotid atherosclerosis in Koreans. This study shows the multifaceted effects of CDH13 variants on cardiometabolic risk.

Cardiomyocyte-specific overexpression of an active form of Rac predisposes the heart to increased myocardial stunning and ischemia-reperfusion injury

The GTP-binding protein Rac regulates diverse cellular functions including activation of NADPH oxidase, a major source of superoxide production (O(2)(·-)). Rac1-mediated NADPH oxidase activation is increased after myocardial infarction (MI) and heart failure both in animals and humans; however, the impact of increased myocardial Rac on impending ischemia-reperfusion (I/R) is unknown. A novel transgenic mouse model with cardiac-specific overexpression of constitutively active mutant form of Zea maize Rac D (ZmRacD) gene has been reported with increased myocardial Rac-GTPase activity and O(2)(·-) generation. The goal of the present study was to determine signaling pathways related to increased myocardial ZmRacD and to what extent hearts with increased ZmRacD proteins are susceptible to I/R injury. The effect of myocardial I/R was examined in young adult wild-type (WT) and ZmRacD transgenic (TG) mice. In vitro reversible myocardial I/R for postischemic cardiac function and in vivo regional myocardial I/R for MI were performed. Following 20-min global ischemia and 45-min reperfusion, postischemic cardiac contractile function and heart rate were significantly reduced in TG hearts compared with WT hearts. Importantly, acute regional myocardial I/R (30-min ischemia and 24-h reperfusion) caused significantly larger MI in TG mice compared with WT mice. Western blot analysis of cardiac homogenates revealed that increased myocardial ZmRacD gene expression is associated with concomitant increased levels of NADPH oxidase subunit gp91(phox), O(2)(·-), and P(21)-activated kinase. Thus these findings provide direct evidence that increased levels of active myocardial Rac renders the heart susceptible to increased postischemic contractile dysfunction and MI following acute I/R.