Mining OMIM™ for insight into complex diseases

Michael N. Cantor, Yves A Lussier

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

Understanding clinical phenotypes through their corresponding genotypes is one of the principal goals of genetic research. Though achieving'this goal is relatively simple with single gene syndromes, more complex diseases often consist of varied clinical phenotypes that may be the result of interactions among multiple genetic loci. Microarray technology has brought the phenotype-genotype relationship to the molecular level, using differently behaving cancers, for example, as the basis for comparing patterns of gene expression. With this feasibility study, we attempted to use similar methods of analysis at the clinical level, in order to evaluate our hypothesis that the clustering of clinical phenotypes would provide information that would be useful in elucidating their underlying genotypes. Because of its breadth of content and detailed descriptions, we used OMIM as our source materialfor phenotypic and genetic information. After processing the source material, we then performed self-organizing map and hierarchical clustering analysis on representative diseases by phenotypic category. Through predetermined queries over this analysis, we made two findings of potential clinical significance, one concerning diabetes and another concerning progressive neurologic diseases. Our methods provide a formal approach to analyzing phenotypes among diverse diseases, and may help indicate fruitful areas for further research into their underlying genetic causes.

Original languageEnglish (US)
Title of host publicationStudies in Health Technology and Informatics
Pages753-757
Number of pages5
Volume107
DOIs
StatePublished - 2004
Externally publishedYes

Fingerprint

Genetic Databases
Phenotype
Genotype
Cluster Analysis
Self organizing maps
Microarrays
Medical problems
Genetic Research
Gene expression
Genetic Loci
Feasibility Studies
Nervous System Diseases
Genes
Technology
Gene Expression
Processing
Research
Neoplasms

Keywords

  • cluster analysis
  • Databases
  • genetic
  • genotype
  • phenotype

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Cantor, M. N., & Lussier, Y. A. (2004). Mining OMIM™ for insight into complex diseases. In Studies in Health Technology and Informatics (Vol. 107, pp. 753-757) https://doi.org/10.3233/978-1-60750-949-3-753

Mining OMIM™ for insight into complex diseases. / Cantor, Michael N.; Lussier, Yves A.

Studies in Health Technology and Informatics. Vol. 107 2004. p. 753-757.

Research output: Chapter in Book/Report/Conference proceedingChapter

Cantor, MN & Lussier, YA 2004, Mining OMIM™ for insight into complex diseases. in Studies in Health Technology and Informatics. vol. 107, pp. 753-757. https://doi.org/10.3233/978-1-60750-949-3-753
Cantor MN, Lussier YA. Mining OMIM™ for insight into complex diseases. In Studies in Health Technology and Informatics. Vol. 107. 2004. p. 753-757 https://doi.org/10.3233/978-1-60750-949-3-753
Cantor, Michael N. ; Lussier, Yves A. / Mining OMIM™ for insight into complex diseases. Studies in Health Technology and Informatics. Vol. 107 2004. pp. 753-757
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