Prediction of gene-phenotype associations in humans, mice, and plants using phenologs

Abstract

BackgroundPhenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such “orthologous phenotypes,” or “phenologs,” are examples of deep homology, and may be used to predict additional candidate disease genes.ResultsIn this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data — from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans — establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene–phenotype associations, as for the Arabidopsis response to vernalization phenotype.ConclusionsWe are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.

Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafish

“…Similar prediction frameworks have been applied to predict associated phenotypes in yeast (King et al, 2003;Saha and Heber, 2006) and worm and to identify putative human disease gene candidates (Linghu et al, 2009;Woods et al, 2013) but have not been systematically coupled with in vivo validation in a vertebrate model organism.…”

Investigating the Importance of Anatomical Homology for Cross-Species Phenotype Comparisons Using Semantic Similarity

“…Nonetheless, these results suggest that it would be worthwhile to explore the impact of "deeper" homology statements, either those sourced from the literature, or those derived computationally, such as by the phenolog approach. 26 In future work, we intend to explore the impact of homology reasoning on measurement of semantic similarity for phenotypes that vary naturally among vertebrate lineages, such as those in the Phenoscape Knowledgebase. 27 Independent of the use of homology axioms, some of the semantic similarity statistics that we examined showed relatively poor discrimination between orthologs and non-orthologs, suggesting the need to take a critical look at the biological accuracy of different phenotype semantic similarity measures.…”

Applications of comparative evolution to human disease genetics

“…Although model organisms will remain a mainstay of human disease genetics, the advent of these novel molecular tools has raised the possibility of high-throughput screens of genotype/phenotype relations in any organism of interest, blurring the lines between model and non-model organism. Importantly, the advances in both forward and reverse genetics have produced hundreds of thousands of gene-phenotype associations across multiple organisms [24, 25, 26 and 27], providing deep datasets that now make computational analyses of new disease genes increasingly possible.…”

“…These methods employ a statistical model to test the significance (commonly, the hypergeometric probability) of two phenotype-associated groupings from two species sharing a set of orthologous genes. This has proven to be an effective approach for identifying extremely divergent model phenotypes which employ the same genetic pathways involved in human diseases [27 and 66], such as, for example, the identification of a plant model of human Waardenburg syndrome [66]. Because these phenotypes employ orthologous genetic mechanisms, they have been termed “phenologs” [66].…”

“…Because these phenotypes employ orthologous genetic mechanisms, they have been termed “phenologs” [66]. The original approach inferred pairwise phenologs [66], but has been subsequently improved upon by extension to multiple species [27]. The success of this extension indicates an important fact: more comparative data means more inferential power for discovering novel genes associated with human disease.…”