Quantitative cell morphology phenotypes are predicted significantly better than random expectation

<p><b>Copyright information:</b></p><p>Taken from "Broad network-based predictability of gene loss-of-function phenotypes"</p><p>http://genomebiology.com/2007/8/12/R258</p><p>Genome Biology 2007;8(12):R258-R258.</p><p>Published online 5 Dec 2007</p><p>PMCID:PMC2246260.</p><p></p> In contrast, genes whose disruption decreases population co-efficient of variance (CV) were not predictable. A histogram plotting the distribution of the area under the receiver operating characteristic (ROC) curve (AUC) values for 562 quantitative morphological phenotypes shows a significantly higher proportion of high AUC values than for 1,000 size-matched random gene sets. Separate analyses of phenotypes associated with morphologic features and phenotypes associated with cell-to-cell variability in the morphologic features reveals asymmetry in predictability. Sets of genes whose disruption causes the 40 largest or smallest mean values of a morphological feature (middle plots) are significantly more predictable than random gene sets (left side). By contrast, although the sets of genes whose disruption most increase the CV tend to be predictable (high AUC), those that most decrease the CV are not (low AUC). Box-and-whisker plots are drawn as in Figure 3. A comparison of the median phenotypic CVs observed for deletion strains versus replicate analyses of wild-type cells shows that deletion strains with the most reduced CVs are essentially wild-type-like in character, whereas those with the most increased CVs show significantly more cell-to-cell variability than wild-type cells. These latter knockout strains carry deletions for genes predominantly involved in maintaining genomic integrity. This trend is therefore likely to have arisen from nonclonal genetic variation in these strains, recapitulating the classic mutator phenotype

Yeast genes with human orthologs linked to the same diseases are predicted better than random expectation

<p><b>Copyright information:</b></p><p>Taken from "Broad network-based predictability of gene loss-of-function phenotypes"</p><p>http://genomebiology.com/2007/8/12/R258</p><p>Genome Biology 2007;8(12):R258-R258.</p><p>Published online 5 Dec 2007</p><p>PMCID:PMC2246260.</p><p></p> Predictability is measured as the area under a receiver operating characteristic (ROC) curve (AUC), as in Figure 3, measuring the AUC for each of 28 human diseases reported in the Online Mendelian Inheritance in Man (OMIM) disease database [51] that have four or more yeast orthologs annotated in the yeast function network and plotting the resulting AUC distributions. Real disease gene sets are significantly more predictable than size-matched random gene sets drawn from the set of yeast-human orthologs. Box plots are drawn as in Figure 3

Diverse yeast gene loss-of-function phenotypes are predictable using guilt-by-association in a functional gene network

<p><b>Copyright information:</b></p><p>Taken from "Broad network-based predictability of gene loss-of-function phenotypes"</p><p>http://genomebiology.com/2007/8/12/R258</p><p>Genome Biology 2007;8(12):R258-R258.</p><p>Published online 5 Dec 2007</p><p>PMCID:PMC2246260.</p><p></p> Predictability is measured in a receiver operating characteristic plot of the true positive rate (sensitivity) versus false positive rate (1 - specificity) for predicting genes giving rise to ten specific loss-of-function phenotypes, as well as for essential genes whose disruption produces nonviable yeast [4]. For each phenotype, each gene in the yeast genome was prioritized by the sum of the weights of its network linkages to the seed genes associated with the phenotype. Genes with higher scores are more tightly linked to the seed set and therefore more likely to give rise to the phenotype. Each phenotype was evaluated using leave-one-out cross-validation, omitting genes from the seed set for the purposes of evaluation. More predictable phenotypes tend toward the top-left corner of the graph; random predictability is indicated by the diagonal. For clarity, the line connecting the final point of each graph to the top right corner has been omitted. FN, false negative; FP, false positive; TN, true negative; TP, true positive

A plot of seed set size versus predictability of the phenotype shows no significant correlation

<p><b>Copyright information:</b></p><p>Taken from "Broad network-based predictability of gene loss-of-function phenotypes"</p><p>http://genomebiology.com/2007/8/12/R258</p><p>Genome Biology 2007;8(12):R258-R258.</p><p>Published online 5 Dec 2007</p><p>PMCID:PMC2246260.</p><p></p> Thus, there does not appear to be an intrinsic limitation for applying network-guided reverse genetics even when seed set size is small. Each filled circle indicates the prediction strength (area under the receiver operating characteristic [ROC] curve, as calculated in Figure 3) of one of the 100 loss-of-function phenotypes relative to the number of genes in that seed set