Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics

Abstract

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse embryonic stem cell knockout resource provides a basis for the characterization of relationships between genes and phenotypes. The EUMODIC consortium developed and validated robust methodologies for the broad-based phenotyping of knockouts through a pipeline comprising 20 disease-oriented platforms. We developed new statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no previous functional annotation. We captured data from over 27,000 mice, finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. New phenotypes were uncovered for many genes with previously unknown function, providing a powerful basis for hypothesis generation and further investigation in diverse systems.

Figure 1a. Effect size versus sample size

Detectable standardized effect size, d, as a function of sample size, under a variety of experimental workflows and analysis approaches (identified in legend). The two qualitative design choices under consideration were: whether mutant animals were phenotyped across multiple days with four animals per day, or all on a single day; and whether baseline animals were phenotyped on the same day(s) as mutants (i.e. whether the mutants were accompanied). Two analytical approaches were compared: analysis of all baseline data (all data); versus analysis restricted to baseline data from animals phenotyped on the same day(s) as mutants (accompanying data only). Calculations were based on attaining 80% power while controlling the FDR at 5%. The variance components used in the power calculations were taken as the average estimates across all parameters and procedures: the variance proportion for day effect was 0.18, for the litter effect 0.12 and for the residual effect 0.69 (Supplementary Figure 2 shows similar plots for procedure-specific variance components).

Figure 1b. Histogram of number of annotations per line

Histogram of number of annotations per line, with each bar split by colour into counts arising from homozygous and heterozygous lines.

Figure 1c. Histogram of number of annotation in each top level MP term

Histogram of number of annotations within each top-level MP ontology term, with each bar split by colour into numbers arising from mutant lines with or without annotations in MGI.

Figure 2. Phenotyping variance

Comparison of estimated variance components across centres. Posterior median (with error bars indicating 95% credible intervals) of total phenotypic SD (top panel), and proportions of variance (bottom three panels), are shown for each quantitative parameter, labelled top, within each test, labelled bottom. For visual comparison the total phenotypic SDs at each test were scaled multiplicatively to a mean of 1.

Figure 3. Heatmap of annotations of reference lines

Reference line comparison of annotations across centres. Colours represent scaled genotype effect (posterior median / SD), with blue/red indicating a decreased/increased mutant phenotype relative to baseline animals. Significant annotations (FDR < 5%) are indicated by a black outline around the corresponding rectangle.

Figure 4. Heatmap of annotations of complete dataset

Heatmap of annotations. Colours represent scaled genotype effect (posterior median / SD), with blue/red indicating a decreased/increased mutant phenotype relative to baseline animals. Significant annotations (FDR < 5%) are indicated by a black outline around the corresponding rectangle. Labels for non-EUCOMM lines are in red. For legibility, the heatmap only displays a subset of parameters for those lines with at least three annotations.

Figure 5. Phenotyping similarity

Classification of EUMODIC-MGI gene pairs into matched or unmatched on the basis of phenotype similarity. The Receiver Operating Characteristic (ROC) curve plots the proportion of (EUMODIC-MGI) matched gene pairs correctly classified as matched against the proportion of unmatched gene pairs incorrectly classified as matched, as the phenotype-similarity threshold is varied (ROC area under curve 0.674).

Figure 6. Analysis of genes with no prior annotations

The Venn diagrams illustrate the distribution of genes with relevant phenotype hits in three disease areas – (a) bone and skeleton; (b) metabolism; (c) neurological and behaviour. For each area, we identified combinations of tests, where a phenotype hit would be indicative of the relevant disease correlate and assigned genes accordingly. A total of 94 genes were identified across the three disease areas.