High-throughput mouse phenomics for characterizing mammalian gene function

Abstract

We are entering a new era of mouse phenomics, driven by large-scale and economical generation of mouse mutants coupled with increasingly sophisticated and comprehensive phenotyping. These studies are generating large, multidimensional gene-phenotype data sets, which are shedding new light on the mammalian genome landscape and revealing many hitherto unknown features of mammalian gene function. Moreover, these phenome resources provide a wealth of disease models and can be integrated with human genomics data as a powerful approach for the interpretation of human genetic variation and its relationship to disease. In the future, the development of novel phenotyping platforms allied to improved computational approaches, including machine learning, for the analysis of phenotype data will continue to enhance our ability to develop a comprehensive and powerful model of mammalian gene-phenotype space.

Fig. 1.. Pleiotropy is central to our understanding of mammalian gene function.

Pleiotropy, the multiple functions of a gene, is manifest through the exploration of disease models and a variety of other phenomena. These include genome-wide association studies (GWAS) where for complex traits the association signals are widely spread across numerous genes and not simply in core disease pathways. The implication is that network pleiotropy is rife and that all genes expressed in a particular tissue are likely to affect phenotype outcome – the “omingenic” hypothesis. Pleiotropy is also revealed in phenome-wide association studies (PheWAS) where the associations of individual genetic variants with multiple phenotypes, known as cross-phenotype associations, are uncovered. The well-known phenomenon of phenotypic expansion in human genetics also exemplifies the pervasiveness of human pleiotropy. Finally, the well-known phenomenon of variable expressivity by which the expression of different aspects of phenotype varies across individuals with identical genotype is also revealing of pleiotropy. Uncovering pleiotropy to its fullest extent is a critical ambition for high-throughput mouse phenomics with the aim of improving our knowledge of pleiotropy and developing datasets where multiple functions are documented. Currently, for most loci we have limited knowledge of pleiotropy and for most genes our knowledge of phenotypes is limited (a). The challenge for genetics is to extend our knowledge of the multiple functions of genes to an increasing number of loci (b) and ultimately to most of the genes in the genome (c).

Fig. 2.. The IMPC phenotyping pipeline.

The International Mouse Phenotyping Consortium (IMPC) pipeline provides an exemplar of the potential of high-throughput pipelines for the acquisition of broad-based phenotype data at both embryonic and adult time-points. The range of phenotyping platforms ensures the recovery of phenotype data across multiple systems and disease states. The key systems areas that are analysed are indicated along with the relevant phenotype tests that impact upon that area. Each phenotyping test is underpinned by a standard operating procedure (SOP) in the International Mouse Phenotyping Resource of Standardised Screens (IMPReSS) database (www.mousephenotype.org/impress) that defines the phenotyping procedure and the associated metadata that is required. μCT, micro-computed tomography; CSD, combined SHIRPA (SmithKline Beecham, Harwell, Imperial College, Royal London Hospital, phenotype assessment) and dysmorphology; DEXA, dual-energy X-ray absorptiometry; E, embryonic day, ECG, electrocardiography; ECHO, echocardiography; FACS, fluorescence-activated cell sorting; HREM, high-resolution episcopic microscopy; OCT, optical coherence tomography; OPT, optical projection tomography; PPI, pre-pulse inhibition.

Fig. 3.. Home-cage monitoring and machine learning.

The figure illustrates a supervised learning feedback loop. This type of automation is essential in order to analyse longitudinal changes in the patterns of behaviour in genetically altered (GA) mice, potentially extending into months and years. Experienced animal researchers and technical staff watch many hours of video recording during which time they record the specific behaviours of individual mice (such as climbing, feeding and drinking). Subsequent machine learning from the manual annotation data generates algorithms that are validated by using test data and performance analysis. This is represented by the data plot (bottom right), where the pattern of behaviours detected by human annotation is compared to those of the machine-learning algorithms. Where the data is non-comparable, further refinement of the algorithms ensues.

Fig. 4.. Ageing as a new dimension of high-throughput mouse phenotyping.

There is increasing interest in the use of ageing pipelines to reveal novel phenotypes, particularly those that might model age-related disease. We illustrate a typical plan for an ageing phenotyping pipeline (mouse age: week 8 to week 60). Cohorts of mutant mice would as usual enter a phase of early-adult phenotyping, including where appropriate embryo analyses. At the end of early-adult phenotyping, some mice from the cohort may be removed from the pipeline for terminal assays. The remaining cohort proceeds to ageing, and subsequently, at around one year or older, adult phenotyping is repeated (late-adult phenotyping) followed by terminal tests. The intervening period between early and late adult phenotyping provides a window for additional phenotyping tests that might not be part of the standard adult phenotyping pipeline.

Fig. 5.. Overview of data flow for large-scale, broad-based mouse phenotyping programmes.

Mouse clinics acquire diverse multi-dimensional datasets, including categorical and continuous data alongside a variety of image data. Data is uploaded routinely to a data coordination centre where it undergoes processing through a standardized pipeline including data validation, robust quality control, statistical analysis, annotation and data integration followed by dissemination to the scientific community. MGI, Mouse Genome Informatics OMIM, Online Mendelian Inheritance in Man

Fig. 6.. Integration of human and mouse data for rare disease genetics.

The figure exemplifies the data sources and algorithms available from the Monarch Initiative portal and variant prioritization software suite (Exomiser). Candidate, rare, pathogenic variants from patient genomes are identified by comparison against reference variant sources such as the Exome Aggregation Consortium (ExAC) to determine the population frequency of variants, and the use of algorithms such as Jannovar and Polyphen2 for predicting which variants are likely to have deleterious, potentially pathogenic, effects. Candidate genes are identified by semantic comparisons of the patient’s phenotypic profile against reference genotype-to-phenotype datasets for human disease as well as model organisms as produced by phenomics programmes, such as the International Mouse Phenotyping Consortium (IMPC). The final set of prioritized, rare pathogenic variants in genes with functional evidence from the phenotype comparisons are presented back to the clinician for a final diagnostic decision dbNSFP, database of nonsynonymous SNPs and their functional predictions ESP, Exome Sequencing Project; GnomAD, Genome Aggregation Database; 1000g, 1000 Genomes Project; HPO, Human Phenotype Ontology; MGI, Mouse Genome Informatics; SIFT, Sorts Intolerant From Tolerant database; VCF, variant call format.