A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Targeted gene expression study using TaqMan low density array to gain insights into venous thrombo-embolism (VTE) pathogenesis at high altitude

Venous thrombo-embolism (VTE) is multi-factorial disease involving several genetic and acquired risk factors responsible for its onset. It may occur spontaneously upon climbing at High Altitude (HA). Several studies demonstrated that hypoxic conditions prevailing at HA pose an independent risk factor for VTE; however, molecular mechanism remains unknown. Present study aims to identify genes associated with HA-induced VTE pathophysiology using real time TaqMan Low-Density Array (TLDA) of known candidate genes. Gene expression of total 93 genes were studied and analyzed in patients of VTE from HA (HA-VTE) and from sea level (SL-VTE) in comparison to respective controls. Both HA-VTE and SL-VTE patients showed up-regulation of 37 genes involved in blood coagulation cascade, clot formation, platelet formation, endothelial response, angiogenesis, cell adhesion and calcium channel activity. Seven genes including ACE, EREG, C8A, DLG2, USF1, F2 and PCDHA7 were up-regulated in both HA-controls and VTE patients (both HA-VTE and SL-VTE) indicating their role during VTE event and also upon HA exposure. Ten genes; CDH18, FGA, EDNBR, GATA2, MAPK9, BCAR1, FRK, F11, PCDHA1 and ST8SIA4 were uniquely up-regulated in HA-VTE. The differentially expressed genes from the present study could be determining factors for HA-VTE susceptibility and provide insights into VTE occurrence at HA.

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information

Motivation

Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.

Results

Our gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.

Availability and implementation

The Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Candidate gene prioritization for non-communicable diseases based on functional information: Case studies

Candidate gene prioritization for complex non-communicable diseases is essential to understanding the mechanism and developing better means for diagnosing and treating these diseases. Many methods have been developed to prioritize candidate genes in protein-protein interaction (PPI) networks. Integrating functional information/similarity into disease-related PPI networks could improve the performance of prioritization. In this study, a candidate gene prioritization method was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease PPI networks with weights for nodes and edges based on functional information. Here, three types of non-communicable diseases with pathobiological similarity, Type 2 diabetes (T2D), coronary artery disease (CAD) and dilated cardiomyopathy (DCM), were used as case studies. Literature review and pathway enrichment analysis of top-ranked genes demonstrated the effectiveness of our method. Better performance was achieved after comparing our method with other existing methods. Pathobiological similarity among these three diseases was further investigated for common top-ranked genes to reveal their pathogenesis.

Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.