Concepedia

Systematic discovery of nonobvious human disease models through orthologous phenotypes

Kriston L. McGary, Tae Joo Park, John O. Woods, Hye Young Ji, John B. Wallingford, Edward M. Marcotte

Proceedings of the National Academy of Sciences · 2010 · 312 citations · 31 references

Open access

TL;DR

Biologists have long used model organisms to study human diseases, especially when the model closely resembles the disease. The study introduces a quantitative method to systematically identify nonobvious equivalences between mutant phenotypes across species using overlapping orthologous gene sets. The method analyzes overlapping orthologous gene sets from human, mouse, yeast, worm, and plant to detect phenotype equivalences. The approach identifies phenologs that predict novel disease genes, proposes new model organisms for angiogenesis defects, breast cancer, autism, and Waardenburg syndrome, demonstrates SOX13’s role in angiogenesis and SEC23IP as a candidate Waardenburg gene, and reveals evolutionarily conserved gene networks that can uncover disease-associated genes.

Abstract

Biologists have long used model organisms to study human diseases, particularly when the model bears a close resemblance to the disease. We present a method that quantitatively and systematically identifies nonobvious equivalences between mutant phenotypes in different species, based on overlapping sets of orthologous genes from human, mouse, yeast, worm, and plant (212,542 gene-phenotype associations). These orthologous phenotypes, or phenologs, predict unique genes associated with diseases. Our method suggests a yeast model for angiogenesis defects, a worm model for breast cancer, mouse models of autism, and a plant model for the neural crest defects associated with Waardenburg syndrome, among others. Using these models, we show that SOX13 regulates angiogenesis, and that SEC23IP is a likely Waardenburg gene. Phenologs reveal functionally coherent, evolutionarily conserved gene networks—many predating the plant-animal divergence—capable of identifying candidate disease genes.