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Cited by 284Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGenomics and Rare Diseases

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Improved human disease candidate gene prioritization using mouse phenotype

Open access
Chen, Jing, Xu, Huan, Aronow, Bruce J, Jegga, Anil G·2007·BMC Bioinformatics
DOI 10.1186/1471-2105-8-392PMC PMC2194797
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Abstract

Background: The majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes. Results: Extending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene http://toppgene.cchmc.org , outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR. Conclusion: The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.

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Figure 1 ROC curves of random-gene cross-validation based on score ranks. Blue curve was generated from the 19 disease gene training sets. Black curve, negative control, was generated from 20 random training sets. See text for the definitions of sensitivity and specificity.
Figure 1 ROC curves of random-gene cross-validation based on score ranks. Blue curve was generated from the 19 disease gene training sets. Black curve, negative control, was generated from 20 random training sets. See text for the definitions of sensitivity and specificity.CC BY · PubMed Central
Figure 2 AUC of different feature sets. Red bars indicate the AUC scores based on each feature set, and blue bars are the corresponding random controls. Yellow bars indicate the coverage of each feature set in the whole genome. For example, mouse phenotype (MP) has AUC score 0.78 and covers 19% of genes in the whole genome. For each feature set, the ROC curve was generated using genes with annotations only.
Figure 2 AUC of different feature sets. Red bars indicate the AUC scores based on each feature set, and blue bars are the corresponding random controls. Yellow bars indicate the coverage of each feature set in the whole genome. For example, mouse phenotype (MP) has AUC score 0.78 and covers 19% of genes in the whole genome. For each feature set, the ROC curve was generated using genes with annotations only.CC BY · PubMed Central
Figure 3 ROC curves of random-gene cross-validation based on scores. The red curve was generated using all features sets (AUC score 0.913). The blue curve was generated without Mouse Phenotype annotations (AUC score 0.893). The orange curve was generated without Mouse Phenotype and Pubmed annotations (AUC score 0.888). See text for the definitions of sensitivity and specificity.
Figure 3 ROC curves of random-gene cross-validation based on scores. The red curve was generated using all features sets (AUC score 0.913). The blue curve was generated without Mouse Phenotype annotations (AUC score 0.893). The orange curve was generated without Mouse Phenotype and Pubmed annotations (AUC score 0.888). See text for the definitions of sensitivity and specificity.CC BY · PubMed Central
Figure 4 The performance of locus-region cross-validation using different feature sets. The average rank ratio (y-axis on the left) indicates the average rank ratio of the "target" genes in the resulting list, thus lower value corresponding to a better performance. At the same time, the higher the number of top 5% ranked "target" genes among total of 150 prioritizations (y-axis on the right), the better the performance. As a result, it's very clear that removing MP, PubMed or both resulted in significant drop of performance.
Figure 4 The performance of locus-region cross-validation using different feature sets. The average rank ratio (y-axis on the left) indicates the average rank ratio of the "target" genes in the resulting list, thus lower value corresponding to a better performance. At the same time, the higher the number of top 5% ranked "target" genes among total of 150 prioritizations (y-axis on the right), the better the performance. As a result, it's very clear that removing MP, PubMed or both resulted in significant drop of performance.CC BY · PubMed Central
Figure 5 Schematic representation of gene prioritization. (A) Genes in the training set are selected based on their attributes or current gene annotations (genes associated with a disease, phenotype, pathway or a GO term). (B) Test gene source can be candidate genes from linkage analysis studies or genes differentially expressed in a particular disease or phenotype. (C) Enriched terms of the eight gene annotations, namely, GO: Molecular Function, GO: Biological Process, Mouse Phenotype, Pathways, Protein Interactions, Protein Domains and Gene Expression, compiled from various data sources, are obtained for the training set of genes. (D) A similarity score is generated for each annotation of each test gene by comparing to the enriched terms in the training set of genes. The final prioritized gene list is then computed based on the aggregated values of the eight similarity scores.
Figure 5 Schematic representation of gene prioritization. (A) Genes in the training set are selected based on their attributes or current gene annotations (genes associated with a disease, phenotype, pathway or a GO term). (B) Test gene source can be candidate genes from linkage analysis studies or genes differentially expressed in a particular disease or phenotype. (C) Enriched terms of the eight gene annotations, namely, GO: Molecular Function, GO: Biological Process, Mouse Phenotype, Pathways, Protein Interactions, Protein Domains and Gene Expression, compiled from various data sources, are obtained for the training set of genes. (D) A similarity score is generated for each annotation of each test gene by comparing to the enriched terms in the training set of genes. The final prioritized gene list is then computed based on the aggregated values of the eight similarity scores.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 2

  1. Entrez Gene. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene]
  2. NCBI Gene Expression Omnibus (GEO). [http://www.ncbi.nlm.nih.gov/projects/geo/]

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Metadata

Authors
Chen, J., Xu, H., Aronow, B. J., & Jegga, A. G.
Affiliations
Cincinnati Children's Hospital Medical Center (US)
Corresponding author
Jegga, Anil G
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2007-10-16
Volume
8
Issue
1
Pages
392-392
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/1471-2105-8-392
PMC
PMC2194797
OpenAlex
W2128245545
Cited by
284
References
42
Citations by year
2026: 7 · 2025: 14 · 2024: 13 · 2023: 21 · 2022: 18 · 2021: 15 · 2020: 13 · 2019: 18 · 2018: 22 · 2017: 7 · 2016: 20 · 2015: 9 · 2014: 19 · 2013: 11 · 2012: 21
FWCI
4.63
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Cancer Institute (U01 CA84291-07, UO1 CA84291-07, CA84291, U01CA084291, CA84291-07)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
candidate gene prioritization, human disease genes, mouse phenotypes, mouse orthologs, data integration, polygenic disease
MeSH terms
Algorithms, Animals, Chromosome Mapping, Humans, Phenotype, Species Specificity, Linkage Disequilibrium, Genetic Predisposition to Disease, Mice

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