Methodology for the inference of gene function from phenotype data

Abstract

Background: Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.

Results: We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.

Conclusions: We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Figure 1

ROC curve. Evaluation of the efficacy of a rule: if MP_i then GO_j, using the receiver operating characteristic.

Figure 2

A graphical representation of the plus and minus rule. The highlighted nodes represent the ‘successes’ identified by the corresponding rule. a, The plus rule-a pattern whereby if MP₁ and MP₂, then GO₁. b, The minus rule-another pattern whereby if MP₁ and not MP₂, then GO₁.

Figure 3

Example of rule instances. The highlighted nodes represent the ‘successes’ identified by the corresponding rule. a, Plus rule instance. b, Minus rule instance.

Figure 4

A graphical representation of the three rules emerging from the plus and minus rules. The highlighted nodes represent the ‘successes’ identified by the corresponding rule. a, The plus-plus rule-if MP₁ and MP₂ or MP₃, then GO₁. b, The minus-minus rule-if MP₁ and not either MP₂ or MP₃, then GO₁. c, The plus-minus rule-if MP₁ and MP₂ and not MP₃, then GO₁.

Figure 5

Comparison of the Positive Predictive Value for various p-value cutoffs of the composite rules for the reviewed annotation predictions. The PPV for the first three composite rules is markedly better for a given p-value than for the last two rules.