BACKGROUND: Genes that are co-expressed tend to be involved in the same 
biological process. However, co-expression is not a very reliable predictor of 
functional links between genes. The evolutionary conservation of co-expression 
between species can be used to predict protein function more reliably than 
co-expression in a single species. Here we examine whether co-expression across 
multiple species is also a better prioritizer of disease genes than is 
co-expression between human genes alone.
RESULTS: We use co-expression data from yeast (S. cerevisiae), nematode worm (C. 
elegans), fruit fly (D. melanogaster), mouse and human and find that the use of 
evolutionary conservation can indeed improve the predictive value of 
co-expression. The effect that genes causing the same disease have higher 
co-expression than do other genes from their associated disease loci, is 
significantly enhanced when co-expression data are combined across 
evolutionarily distant species. We also find that performance can vary 
significantly depending on the co-expression datasets used, and just using more 
data does not necessarily lead to better prioritization. Instead, we find that 
dataset quality is more important than quantity, and using a consistent 
microarray platform per species leads to better performance than using more 
inclusive datasets pooled from various platforms.
CONCLUSION: We find that evolutionarily conserved gene co-expression prioritizes 
disease candidate genes better than human gene co-expression alone, and provide 
the integrated data as a new resource for disease gene prioritization tools.