5,310 research outputs found
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
BACKGROUND: Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases. METHODOLOGY/PRINCIPAL FINDINGS: We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes. CONCLUSIONS/SIGNIFICANCE: Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations
Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases
Phenotypes are the observable characteristics of an organism arising from its
response to the environment. Phenotypes associated with engineered and natural
genetic variation are widely recorded using phenotype ontologies in model
organisms, as are signs and symptoms of human Mendelian diseases in databases
such as OMIM and Orphanet. Exploiting these resources, several computational
methods have been developed for integration and analysis of phenotype data to
identify the genetic etiology of diseases or suggest plausible interventions. A
similar resource would be highly useful not only for rare and Mendelian
diseases, but also for common, complex and infectious diseases. We apply a
semantic text- mining approach to identify the phenotypes (signs and symptoms)
associated with over 8,000 diseases. We demonstrate that our method generates
phenotypes that correctly identify known disease-associated genes in mice and
humans with high accuracy. Using a phenotypic similarity measure, we generate a
human disease network in which diseases that share signs and symptoms cluster
together, and we use this network to identify phenotypic disease modules
- …