Dataset Information

Phenomics research on coronary heart disease based on human phenotype ontology.

ABSTRACT: The characteristics of holistic, dynamics, complexity, and spatial and temporal features enable "Omics" and theories of TCM to interlink with each other. HPO, namely, "characterization," can be understood as a sorting and generalization of the manifestations shown by people with diseases on the basis of the phenomics. Syndrome is the overall "manifestation" of human body pathological and physiological changes expressed by four diagnostic methods' information. The four diagnostic methods' data could be the most objective and direct manifestations of human body under morbid conditions. In this aspect, it is consistent with the connation of "characterization." Meanwhile, the four diagnostic methods' data also equip us with features of characterization in HPO. In our study, we compared 107 pieces of four diagnostic methods' information with the "characterization database" to further analyze data of four diagnostic methods' characterization in accordance with the common characteristics of four diagnostic methods' information and characterization and integrated 107 pieces of four diagnostic methods' data to relevant items in HPO and finished the expansion of characterization information in HPO.

SUBMITTER: Shi Q

PROVIDER: S-EPMC4279366 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Phenomics research on coronary heart disease based on human phenotype ontology.

Shi Qi Q Gao Kuo K Zhao Huihui H Wang Juan J Zhai Xing X Lu Peng P Chen Jianxin J Wang Wei W

BioMed research international 20141215

The characteristics of holistic, dynamics, complexity, and spatial and temporal features enable "Omics" and theories of TCM to interlink with each other. HPO, namely, "characterization," can be understood as a sorting and generalization of the manifestations shown by people with diseases on the basis of the phenomics. Syndrome is the overall "manifestation" of human body pathological and physiological changes expressed by four diagnostic methods' information. The four diagnostic methods' data co ...[more]

PMID: 25610858

Similar Datasets

Project description:Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.

Project description:BackgroundExome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patient's sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each term's information content.ResultsModel validation was performed via simulation and with clinical data. We simulated 33 Mendelian diseases with 100 patients per disease. We modeled clinical conditions by adding noise and imprecision, i.e. phenotypic terms unrelated to the disease and terms less specific than the actual disease terms. We ranked the causative gene against all 2488 HPO annotated genes. The median causative gene rank was 1 for the optimal and noise cases, 12 for the imprecision case, and 60 for the imprecision with noise case. Additionally, we examined a clinical cohort of subjects with hearing impairment. The disease gene median rank was 22. However, when also considering the patient's exome data and filtering non-exomic and common variants, the median rank improved to 3.ConclusionsSemantic similarity can rank a causative gene highly within a gene list relative to patient phenotype characteristics, provided that imprecision is mitigated. The clinical case results suggest that phenotype rank combined with variant analysis provides significant improvement over the individual approaches. We expect that this combined prioritization approach may increase accuracy and decrease effort for clinical genetic diagnosis.

Dataset Information

Phenomics research on coronary heart disease based on human phenotype ontology.

Publications

Phenomics research on coronary heart disease based on human phenotype ontology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets