Predicting candidate genes from phenotypes, functions and anatomical site of expression.
Ontology highlight
ABSTRACT: Motivation
Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease-gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.Results
We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression.Availability and implementation
Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Chen J
PROVIDER: S-EPMC8248315 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Phenotypes resulting from mutations in genetic model organisms can help reveal candidate genes for evolutionarily important phenotypic changes in related taxa. Although testing candidate gene hypotheses experimentally in nonmodel organisms is typically difficult, ontology-driven information systems can help generate testable hypotheses about developmental processes in experimentally tractable organisms. Here, we tested candidate gene hypotheses suggested by expert use of the Phenoscape Knowledgebase, specifically looking for genes that are candidates responsible for evolutionarily interesting phenotypes in the ostariophysan fishes that bear resemblance to mutant phenotypes in zebrafish. For this, we searched ZFIN for genetic perturbations that result in either loss of basihyal element or loss of scales phenotypes, because these are the ancestral phenotypes observed in catfishes (Siluriformes). We tested the identified candidate genes by examining their endogenous expression patterns in the channel catfish, Ictalurus punctatus. The experimental results were consistent with the hypotheses that these features evolved through disruption in developmental pathways at, or upstream of, brpf1 and eda/edar for the ancestral losses of basihyal element and scales, respectively. These results demonstrate that ontological annotations of the phenotypic effects of genetic alterations in model organisms, when aggregated within a knowledgebase, can be used effectively to generate testable, and useful, hypotheses about evolutionary changes in morphology.
| S-EPMC4693980 | biostudies-literature
Project description:The discovery of human obesity-associated genes can reveal new mechanisms to target for weight loss therapy. Genetic studies of obese individuals and the analysis of rare genetic variants can identify novel obesity-associated genes. However, establishing a functional relationship between these candidate genes and adiposity remains a significant challenge. We uncovered a large number of rare homozygous gene variants by exome sequencing of severely obese children, including those from consanguineous families. By assessing the function of these genes in vivo in Drosophila, we identified 4 genes, not previously linked to human obesity, that regulate adiposity (itpr, dachsous, calpA, and sdk). Dachsous is a transmembrane protein upstream of the Hippo signalling pathway. We found that 3 further members of the Hippo pathway, fat, four-jointed, and hippo, also regulate adiposity and that they act in neurons, rather than in adipose tissue (fat body). Screening Hippo pathway genes in larger human cohorts revealed rare variants in TAOK2 associated with human obesity. Knockdown of Drosophila tao increased adiposity in vivo demonstrating the strength of our approach in predicting novel human obesity genes and signalling pathways and their site of action.
| S-EPMC8575313 | biostudies-literature
Project description:BackgroundLarge-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory.ResultsApplication of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction.ConclusionOur rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.
| S-EPMC2212665 | biostudies-literature
Project description:This article provides an overview of (a) issues in definition and diagnosis of specific reading disabilities at the behavioral level that may occur in different constellations of developmental and phenotypic profiles (patterns); (b) rapidly expanding research on genetic heterogeneity and gene candidates for dyslexia and other reading disabilities; (c) emerging research on gene-brain relationships; and (d) current understanding of epigenetic mechanisms whereby environmental events may alter behavioral expression of genetic variations. A glossary of genetic terms (denoted by bold font) is provided for readers not familiar with the technical terms.
| S-EPMC3538356 | biostudies-literature
Project description:Sensation seeking is a heritable personality trait that has been reliably linked to behavioral disorders. The dopamine system has been hypothesized to contribute to variations in sensation seeking between different individuals, and both experimental and observational studies in humans and nonhuman animals provide evidence for the involvement of the dopamine system in sensation-seeking behavior. In this study, we took a candidate-system approach to genetic association analysis of sensation-seeking behavior. We analyzed single-nucleotide polymorphisms (SNPs) from a number of dopaminergic genes. Using 273 SNPs from eight dopamine genes in a sample of 635 unrelated individuals, we examined the aggregate effect of SNPs that were significantly associated with sensation-seeking behavior. Multiple SNPs in four dopamine genes accounted for significant variance in sensation-seeking behavior between individuals. These results suggest that multiple SNPs, aggregated within genes that are relevant to a specific neurobiological system, form a genetic-risk score that may explain a significant proportion of observed variance in human traits such as sensation-seeking behavior.
| S-EPMC3031097 | biostudies-literature
Project description:Nanoscale miRNAs regulate the synthesis of most human proteins involved in differentiation, proliferation, cell cycle, apoptosis, and other processes associated with the growth and the development of an organism. miRNAs also play a number of important roles in the development of gastric cancer. In this work, we studied the quantitative characteristics of miRNA interactions with 69 candidate gastric cancer genes using bioinformatics approaches. To this end, the MirTarget program was used, which determines the characteristics of miRNA binding to mRNA in the 5'UTR, CDS, and 3'UTR. Associations of miRNAs with alternative target genes and associations of genes with alternative miRNAs were established. The cluster organization of miRNA binding sites (BSs) in mRNA was revealed, leading to the emergence of miRNA competition for binding to the mRNA of a target gene. Groups of target genes with clusters of overlapping BSs include miR-5095, miR-619-5p, miR-1273 family, miR-466, ID01030.3p-miR, ID00436.3p-miR, miR-574-5p, and ID00470.5p-miR. In the defined associations of target genes and miRNAs, miRNA BSs are organized into clusters of multiple BSs, which facilitate the design and the development of a system of chips that can be used to control the state of miRNA and target genes associations in gastric cancer.
| S-EPMC8000878 | biostudies-literature
Project description:BACKGROUND: Common fragile sites (cfs) are specific regions in the human genome that are particularly prone to genomic instability under conditions of replicative stress. Several investigations support the view that common fragile sites play a role in carcinogenesis. We discuss a genome-wide approach based on graph theory and Gene Ontology vocabulary for the functional characterization of common fragile sites and for the identification of genes that contribute to tumour cell biology. RESULTS: Common fragile sites were assembled in a network based on a simple measure of correlation among common fragile site patterns of expression. By applying robust measurements to capture in quantitative terms the non triviality of the network, we identified several topological features clearly indicating departure from the Erdos-Renyi random graph model. The most important outcome was the presence of an unexpected large connected component far below the percolation threshold. Most of the best characterized common fragile sites belonged to this connected component. By filtering this connected component with Gene Ontology, statistically significant shared functional features were detected. Common fragile sites were found to be enriched for genes associated to the immune response and to mechanisms involved in tumour progression such as extracellular space remodeling and angiogenesis. Moreover we showed how the internal organization of the graph in communities and even in very simple subgraphs can be a starting point for the identification of new factors of instability at common fragile sites. CONCLUSION: We developed a computational method addressing the fundamental issue of studying the functional content of common fragile sites. Our analysis integrated two different approaches. First, data on common fragile site expression were analyzed in a complex networks framework. Second, outcomes of the network statistical description served as sources for the functional annotation of genes at common fragile sites by means of the Gene Ontology vocabulary. Our results support the hypothesis that fragile sites serve a function; we propose that fragility is linked to a coordinated regulation of fragile genes expression.
| S-EPMC1601973 | biostudies-literature
Project description:BackgroundWe reviewed systematically the results of genetic studies investigating associations between putative susceptibility genes for attention-deficit hyperactivity disorder (ADHD) and neuropsychological traits relevant for this disorder.MethodsWe identified papers for review through the PubMed database.ResultsTwenty-nine studies examined 10 genes (DRD4, DAT1, COMT, DBH, MAOA, DRD5, ADRA2A, GRIN2A, BDNF and TPH2) in relation to neuropsychological traits relevant for ADHD. For DRD4, the continuous performance test (CPT) and derived tasks were the most used tests. Association of high reaction time variability with the 7-repeat allele absence appears to be the most consistent result and seems to be specific to ADHD. Speed of processing, set-shifting and cognitive impulsiveness were less frequently investigated but seem to be altered in the 7-repeat allele carriers. No effect of genotype was found on response inhibition (the stop and go/no-go tasks). For DAT1, 4 studies provide conflicting results in relation to omission and commission errors from CPT and derived tasks. High reaction time variability seems to be the most replicated cognitive marker associated with the 10-repeat homozygosity. The other genes have attracted fewer studies, and the reported findings need to be replicated.LimitationsAlthough we aimed to perform a formal meta-analysis, this was not possible because the number of studies using the same neurocognitive endophenotypes was limited. We referred only minimally to the various theoretical frameworks in this field of research; more detail would have been beyond the scope of our systematic review. Finally, sample sizes in most of the studies we reviewed were small. Thus, some negative findings could be attributed to a lack of statistical power, and positive results should be considered preliminary until they are replicated in extended samples.ConclusionSeveral methodological issues, including measurement errors, developmental changes in cognitive abilities, sex, psychostimulant effects and presence of comorbid conditions, represent confounding factors and may explain conflicting results.
| S-EPMC2647566 | biostudies-literature
Project description:BACKGROUND: Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases. RESULTS: In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates. CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.
| S-EPMC2818071 | biostudies-literature
Project description:Metastatic associated proteins (MTA) are integrators of upstream regulatory signals with the ability to act as master coregulators for modifying gene transcriptional activity. The MTA family includes three genes and multiple alternatively spliced variants. The MTA proteins neither have their own enzymatic activity nor have been shown to directly interact with DNA. However, MTA proteins interact with a variety of chromatin remodeling factors and complexes with enzymatic activities for modulating the plasticity of nucleosomes, leading to the repression or derepression of target genes or other extra-nuclear and nucleosome remodeling and histone deacetylase (NuRD)-complex independent activities. The functions of MTA family members are driven by the steady state levels and subcellular localization of MTA proteins, the dynamic nature of modifying signals and enzymes, the structural features and post-translational modification of protein domains, interactions with binding proteins, and the nature of the engaged and resulting features of nucleosomes in the proximity of target genes. In general, MTA1 and MTA2 are the most upregulated genes in human cancer and correlate well with aggressive phenotypes, therapeutic resistance, poor prognosis and ultimately, unfavorable survival of cancer patients. Here we will discuss the structure, expression and functions of the MTA family of genes in the context of cancer cells.
| S-EPMC4785049 | biostudies-literature