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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Methodology for the inference of gene function from phenotype data.

Joao A Ascensao1,2, Mary E Dolan3, David P Hill4

  • 1The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA. Joao.Ascensao@rice.edu.

BMC Bioinformatics
|December 16, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm to infer gene function (Gene Ontology) from mammalian phenotypes (Mammalian Phenotype Ontology) using statistical relationships, not just word similarity. This method predicts 4818 unique gene functions for 1796 genes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biomedical ontologies are crucial for organizing biological data.
  • Independent ontology development can segregate knowledge, limiting data integration.
  • Linking ontologies like Gene Ontology (GO) and Mammalian Phenotype Ontology (MP) can expand information scope.

Purpose of the Study:

  • To develop a statistical methodology for inferring gene function (GO annotations) from existing mammalian phenotype (MP) data.
  • To overcome limitations of semantic similarity measures in relating different biological ontologies.
  • To suggest GO functional annotations from MP phenotype annotations.

Main Methods:

  • Designed and tested algorithms analyzing emergent structure and relationships between gene functions and phenotypes.
  • Algorithms identify cases where multiple phenotype terms arise from a single gene function.
  • Methodology applied to mouse gene data from the Mouse Genome Informatics (MGI) resource.

Main Results:

  • Generated 7444 rule instances from five generalized rules.
  • Produced 4818 unique GO functional predictions for 1796 genes.
  • Demonstrated a novel approach distinct from semantic or lexical similarity measures.

Conclusions:

  • The method infers high-quality functional annotations from curated phenotype data.
  • Potential to uncover unforeseen biological associations between gene function and phenotypes.
  • Future work includes applying algorithms to other model organism databases.