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Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

ABSTRACT: Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn's disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn's disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

SUBMITTER: Lin PL

PROVIDER: S-EPMC5021324 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Lin Peng-Lin PL Yu Ya-Wen YW Chung Ren-Hua RH

PloS one 20160913 9

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene ...[more]

PMID: 27622767

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Project description:Neurodegenerative diseases of the central nervous system are characterised by pathogenetic cellular and molecular changes in specific areas of the brain that lead to the dysfunction and/or loss of explicit neuronal populations. Despite exhibiting different clinical profiles and selective neuronal loss, common features such as abnormal protein deposition, dysfunctional cellular transport, mitochondrial deficits, glutamate excitotoxicity and inflammation are observed in most, if not all, neurodegenerative disorders, suggesting converging pathways of neurodegeneration. We have generated comparative genome-wide gene expression data for Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, multiple sclerosis, Parkinson’s disease and schizophrenia using an extensive cohort of well characterised post-mortem CNS tissues. The analysis of whole genome expression patterns across these major disorders offers an outstanding opportunity not only to look into exclusive disease specific changes, but more importantly to uncover potential common molecular pathogenic mechanisms that could be targeted for therapeutic gain. Surprisingly, no dysregulated gene that passed our selection criteria was found in common across all 6 diseases using our primary method of analysis. However, 61 dysregulated genes were shared when comparing five and four diseases. Our analysis indicates firstly the involvement of common neuronal homeostatic, survival and synaptic plasticity pathways. Secondly, we report changes to immunoregulatory and immunomodulatory pathways in all diseases. Our secondary method of analysis confirmed significant up-regulation of a number of genes in diseases presenting degeneration and showed that somatostatin was downregulated in all 6 diseases. The latter is supportive of a general role for neuroinflammation in the pathogenesis and/or response to neurodegeneration. Unravelling the detailed nature of the molecular changes regulating inflammation in the CNS is key to the development of novel therapeutic approaches for these chronic conditions. A total of 113 cases were selected retrospectively on the basis of a confirmed clinical and neuropathological diagnosis and snap-frozen brain blocks were provided by various tissue banks within the BrainNet Europe network. Total RNA was extracted from dissected snap-frozen tissue (< 100 mg) by the individual laboratories according to a BNE optimised common protocol using the RNeasy(r) tissue lipid mini kit (Qiagen Ltd, Crawley, UK) according to the manufacturer's instructions, and was stored at -80C until further use. Gene expression analysis was performed on the RNA samples using the Illumina whole genome HumanRef8 v2 BeadChip (Illumina, London, UK). All the labelling and hybridisation of the samples was carried out in a single experiment by the Imperial College group to reduce the technical variability. RNA samples were prepared for array analysis using the Illumina TotalPrep(tm)-96 RNA Amplification Kit (Ambion/Applied Biosystems, Warrington, UK). Finally, the BeadChips we re scanned using the Illumina BeadArray Reader. The data was extracted using BeadStudio 3.2 (Illumina). Data normalisation and gene differential expression analyses were conducted using the Rosetta error models available in the Rosetta Resolver(r) system (Rosetta Biosoftware, Seattle, Wa, USA). Two samples presented very low signal expression most likely due to hybridization problems and did not pass the quality control test. They are not represented here. One of the 2 samples was a replicate, therefore there was loss of only 1 case bringing the grand total of cases used to 112 (total of samples of 118 including 6 replicates).

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Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Publications

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

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