A cross-genomic approach for systematic mapping of phenotypic traits to genes

Abstract

We present a computational method for de novo identification of gene function using only cross-organismal distribution of phenotypic traits. Our approach assumes that proteins necessary for a set of phenotypic traits are preferentially conserved among organisms that share those traits. This method combines organism-to-phenotype associations,along with phylogenetic profiles,to identify proteins that have high propensities for the query phenotype; it does not require the use of any functional annotations for any proteins. We first present the statistical foundations of this approach and then apply it to a range of phenotypes to assess how its performance depends on the frequency and specificity of the phenotype. Our analysis shows that statistically significant associations are possible as long as the phenotype is neither extremely rare nor extremely common; results on the flagella,pili, thermophily,and respiratory tract tropism phenotypes suggest that reliable associations can be inferred when the phenotype does not arise from many alternate mechanisms.

Figure 1

Relationship between maximum propensity Φ and minimum estimated as a function of the number of organisms exhibiting phenotype f, given that there are N = 86 total genomes and that we are testing X = 4000 genes.

Figure 2

Receiver Operating Characteristic (ROC) curves comparing our approach with the approaches of Levesque et al. (2003) and Pellegrini et al. (1999) on the same flagella data set. Each ROC curve for our approach is obtained by keeping all genes with propensity scores greater than a fixed cutoff and varying the P-value cutoffs. The ROC curve for the Levesque et al. (2003) approach is obtained by varying the similarity threshold cutoff. The ROC curve for the Pellegrini et al. (1999) approach is obtained by comparing phylogenetic profiles against the FlgL gene (used in their study) and varying the Manhattan distance cutoff.

Figure 3

Average phylogenetic distances between E. Coli proteins at each flagellar propensity level.

Figure 4

Hierarchical clustering (average-linkage) of the top proteins associated with flagella and thermophily (see Tables 2 and 4), on the basis of their phylogenetic profiles. Genomes are on the x-axis, and genes are on the y-axis. Gray coloring indicates the presence of a gene in a genome.