Mouse genetic and phenotypic resources for human genetics

Variation in the Spectrum of New Mutations among Inbred Strains of Mice

The mouse serves as a mammalian model for understanding the nature of variation from new mutations, a question that has both evolutionary and medical significance. Previous studies suggest that the rate of single-nucleotide mutations (SNMs) in mice is ∼50% of that in humans. However, information largely comes from studies involving the C57BL/6 strain, and there is little information from other mouse strains. Here, we study the mutations that accumulated in 59 mouse lines derived from four inbred strains that are commonly used in genetics and clinical research (BALB/cAnNRj, C57BL/6JRj, C3H/HeNRj, and FVB/NRj), maintained for eight to nine generations by brother-sister mating. By analyzing Illumina whole-genome sequencing data, we estimate that the average rate of new SNMs in mice is ∼μ = 6.7 × 10-9. However, there is substantial variation in the spectrum of SNMs among strains, so the burden from new mutations also varies among strains. For example, the FVB strain has a spectrum that is markedly skewed toward C→A transversions and is likely to experience a higher deleterious load than other strains, due to an increased frequency of nonsense mutations in glutamic acid codons. Finally, we observe substantial variation in the rate of new SNMs among DNA sequence contexts, CpG sites, and their adjacent nucleotides playing an important role.

The Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel

BACKGROUND

Unraveling the relationship between genetic variation and phenotypic traits remains a fundamental challenge in biology. Mapping variants underlying complex traits while controlling for confounding environmental factors is often problematic. To address this, we establish a vertebrate genetic resource specifically to allow for robust genotype-to-phenotype investigations. The teleost medaka (Oryzias latipes) is an established genetic model system with a long history of genetic research and a high tolerance to inbreeding from the wild.

RESULTS

Here we present the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel: the first near-isogenic panel of 80 inbred lines in a vertebrate model derived from a wild founder population. Inbred lines provide fixed genomes that are a prerequisite for the replication of studies, studies which vary both the genetics and environment in a controlled manner, and functional testing. The MIKK panel will therefore enable phenotype-to-genotype association studies of complex genetic traits while allowing for careful control of interacting factors, with numerous applications in genetic research, human health, drug development, and fundamental biology.

CONCLUSIONS

Here we present a detailed characterization of the genetic variation across the MIKK panel, which provides a rich and unique genetic resource to the community by enabling large-scale experiments for mapping complex traits.

Improvement of short straws for sperm cryopreservation: installing an air-permeable filter facilitates handling

Saving space for sperm cryopreservation would aid mouse genetics research. We previously developed the ST (sperm freezing in ShorT STraw to reduce STorage space) method for cryopreserving mouse sperm in a smaller storage space than conventional methods. However, our ST method has two drawbacks: difficulties during freeze-thaw procedures and the potential risk of sperm loss during storage. Here, we refine ST, terming the new method improved ST (iST). In iST, the straw has an air-permeable filter and the straw container (2-ml cryotube) is endowed with air vents. As in our ST method, iST frozen-thawed sperm showed good performance upon in vitro fertilization. Moreover, up to nine straws can be stored in one cryotube, occupying less storage space than conventional methods. This method provides an easy and space-saving cryopreservation method for mouse sperm, and thus will be valuable for mouse genetics researchers.

Mitochondrial calcium uniporter is essential for hearing and hair cell preservation in congenic FVB/NJ mice

Mitochondrial Ca²⁺ regulates a wide range of cell processes, including morphogenesis, metabolism, excitotoxicity, and survival. In cochlear hair cells, the activation of mechano-electrical transduction and voltage-gated Ca²⁺ channels result in a large influx of Ca²⁺. The intracellular rise in Ca²⁺ is partly balanced by the mitochondria which rapidly uptakes Ca²⁺ via a highly selective channel comprised of the main pore-forming subunit, the mitochondrial Ca²⁺ uniporter (MCU), and associated regulatory proteins. MCU thus contributes to Ca²⁺ buffering, ensuring cytosolic homeostasis, and is posited to have a critical role in hair cell function and hearing. To test this hypothesis, Ca²⁺ homeostasis in hair cells and cochlear function were investigated in FVB/NJ mice carrying the knockout allele of Mcu (Mcu^+/− or Mcu^−/−). The Mcu knockout allele, which originated in C57BL/6 strain cosegregated along with Cdh23^ahl allele to the FVB/NJ strain, due to the close proximity of these genes. Neither Mcu^+/− nor Mcu^−/− genotypes affected cochlear development, morphology, or Ca²⁺ homeostasis of auditory hair cells in the first two postnatal weeks. However, Mcu^−/− mice displayed high-frequency hearing impairment as early as 3 weeks postnatal, which then progressed to profound hearing loss at all frequencies in about 6 months. In Mcu^+/− mice, significantly elevated ABR thresholds were observed at 6 months and 9 months of age only at 32 kHz frequency. In three-month-old Mcu^−/− mice, up to 18% of the outer hair cells and occasionally some inner hair cells were missing in the mid-cochlear region. In conclusion, mitochondrial Ca²⁺ uniporter is not required for the development of cochlea in mice, but is essential for hearing and hair cell preservation in congenic FVB/NJ mice.

Genotype to Phenotype: CRISPR Gene Editing Reveals Genetic Compensation as a Mechanism for Phenotypic Disjunction of Morphants and Mutants

Forward genetic screens have shown the consequences of deleterious mutations; however, they are best suited for model organisms with fast reproductive rates and large broods. Furthermore, investigators must faithfully identify changes in phenotype, even if subtle, to realize the full benefit of the screen. Reverse genetic approaches also probe genotype to phenotype relationships, except that the genetic targets are predefined. Until recently, reverse genetic approaches relied on non-genomic gene silencing or the relatively inefficient, homology-dependent gene targeting for loss-of-function generation. Fortunately, the flexibility and simplicity of the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system has revolutionized reverse genetics, allowing for the precise mutagenesis of virtually any gene in any organism at will. The successful integration of insertions/deletions (INDELs) and nonsense mutations that would, at face value, produce the expected loss-of-function phenotype, have been shown to have little to no effect, even if other methods of gene silencing demonstrate robust loss-of-function consequences. The disjunction between outcomes has raised important questions about our understanding of genotype to phenotype and highlights the capacity for compensation in the central dogma. This review describes recent studies in which genomic compensation appears to be at play, discusses the possible compensation mechanisms, and considers elements important for robust gene loss-of-function studies.

Generation of Monogenic Candidate Genes for Human Nephrotic Syndrome Using 3 Independent Approaches

Introduction

Steroid-resistant nephrotic syndrome (SRNS) is the second most common cause of chronic kidney disease during childhood. Identification of 63 monogenic human genes has delineated 12 distinct pathogenic pathways.

Methods

Here, we generated 2 independent sets of nephrotic syndrome (NS) candidate genes to augment the discovery of additional monogenic causes based on whole-exome sequencing (WES) data from 1382 families with NS.

Results

We first identified 63 known monogenic causes of NS in mice from public databases and scientific publications, and 12 of these genes overlapped with the 63 known human monogenic SRNS genes. Second, we used a set of 64 genes that are regulated by the transcription factor Wilms tumor 1 (WT1), which causes SRNS if mutated. Thirteen of these WT1-regulated genes overlapped with human or murine NS genes. Finally, we overlapped these lists of murine and WT1 candidate genes with our list of 120 candidate genes generated from WES in 1382 NS families, to identify novel candidate genes for monogenic human SRNS. Using this approach, we identified 7 overlapping genes, of which 3 genes were shared by all datasets, including SYNPO. We show that loss-of-function of SYNPO leads to decreased CDC42 activity and reduced podocyte migration rate, both of which are rescued by overexpression of wild-type complementary DNA (cDNA), but not by cDNA representing the patient mutation.

Conclusion

Thus, we identified 3 novel candidate genes for human SRNS using 3 independent, nonoverlapping hypotheses, and generated functional evidence for SYNPO as a novel potential monogenic cause of NS.

Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.

Innate Immunity and Cell Surface Receptors in the Pathogenesis of COPD: Insights from Mouse Smoking Models

Chronic obstructive pulmonary disease (COPD) is mainly associated with smoking habit. Inflammation is the major initiating process whereby neutrophils and monocytes are attracted into the lung microenvironment by external stimuli present in tobacco leaves and in cigarette smoke, which promote chemotaxis, adhesion, phagocytosis, release of superoxide anions and enzyme granule contents. A minority of smokers develops COPD and different molecular factors, which contribute to the onset of the disease, have been put forward. After many years of research, the pathogenesis of COPD is still an object of debate. In vivo models of cigarette smoke-induced COPD may help to unravel cellular and molecular mechanisms underlying the pathogenesis of COPD. The mouse represents the most favored animal choice with regard to the study of immune mechanisms due to its genetic and physiological similarities to humans, the availability of a large variability of inbred strains, the presence in the species of several genetic disorders analogous to those in man, and finally on the possibility to create models “made-to-measure” by genetic manipulation. The review outlines the different response of mouse strains to cigarette smoke used in COPD studies while retaining a strong focus on their relatability to human patients. These studies reveal the importance of innate immunity and cell surface receptors in the pathogenesis of pulmonary injury induced by cigarette smoking. They further advance the way in which we use wild type or genetically manipulated strains to improve our overall understanding of a multifaceted disease such as COPD. The structural and functional features, which have been found in the different strains of mice after chronic exposure to cigarette smoke, can be used in preclinical studies to develop effective new therapeutic agents for the different phenotypes in human COPD.

Reciprocal White Matter Changes Associated With Copy Number Variation at 15q11.2 BP1-BP2: A Diffusion Tensor Imaging Study

BACKGROUND

The 15q11.2 BP1-BP2 cytogenetic region has been associated with learning and motor delays, autism, and schizophrenia. This region includes a gene that codes for the cytoplasmic FMR1 interacting protein 1 (CYFIP1). The CYFIP1 protein is involved in actin cytoskeletal dynamics and interacts with the fragile X mental retardation protein. Absence of fragile X mental retardation protein causes fragile X syndrome. Because abnormal white matter microstructure has been reported in both fragile X syndrome and psychiatric disorders, we looked at the impact of 15q11.2 BP1-BP2 dosage on white matter microstructure.

METHODS

Combining a brain-wide voxel-based approach and a regional-based analysis, we analyzed diffusion tensor imaging data from healthy individuals with the deletion (n = 30), healthy individuals with the reciprocal duplication (n = 27), and IQ-matched control subjects with no large copy number variants (n = 19), recruited from a large genotyped population sample.

RESULTS

We found global mirror effects (deletion > control > duplication) on fractional anisotropy. The deletion group showed widespread increased fractional anisotropy when compared with duplication. Regional analyses revealed a greater effect size in the posterior limb of the internal capsule and a tendency for decreased fractional anisotropy in duplication.

CONCLUSIONS

These results show a reciprocal effect of 15q11.2 BP1-BP2 on white matter microstructure, suggesting that reciprocal chromosomal imbalances may lead to opposite changes in brain structure. Findings in the deletion overlap with previous white matter differences reported in fragile X syndrome patients, suggesting common pathogenic mechanisms derived from disruptions of cytoplasmic CYFIP1-fragile X mental retardation protein complexes. Our data begin to identify specific components of the 15q11.2 BP1-BP2 phenotype and neurobiological mechanisms of potential relevance to the increased risk for disorder.

Mouse Models and Online Resources for Functional Analysis of Osteoporosis Genome-Wide Association Studies

Osteoporosis is a complex genetic disease in which the number of loci associated with the bone mineral density, a clinical risk factor for fracture, has increased at an exponential rate in the last decade. The identification of the causative variants and candidate genes underlying these loci has not been able to keep pace with the rate of locus discovery. A large number of tools and data resources have been built around the use of the mouse as model of human genetic disease. Herein, we describe resources available for functional validation of human Genome Wide Association Study (GWAS) loci using mouse models. We specifically focus on large-scale phenotyping efforts focused on bone relevant phenotypes and repositories of genotype-phenotype data that exist for transgenic and mutant mice, which can be readily mined as a first step toward more targeted efforts designed to deeply characterize the role of a gene in bone biology.

Mouse Cre-LoxP system: general principles to determine tissue-specific roles of target genes

Genetically engineered mouse models are commonly preferred for studying the human disease due to genetic and pathophysiological similarities between mice and humans. In particular, Cre-loxP system is widely used as an integral experimental tool for generating the conditional. This system has enabled researchers to investigate genes of interest in a tissue/cell (spatial control) and/or time (temporal control) specific manner. A various tissue-specific Cre-driver mouse lines have been generated to date, and new Cre lines are still being developed. This review provides a brief overview of Cre-loxP system and a few commonly used promoters for expression of tissue-specific Cre recombinase. Also, we finally introduce some available links to the Web sites that provides detailed information about Cre mouse lines including their characterization.

Neuronal Migration During Development of the Cerebellum

Neuronal migration is a fundamental process in central nervous system (CNS) development. The assembly of functioning neuronal circuits relies on neuronal migration occurring in the appropriate spatio-temporal pattern. A defect in the neuronal migration may result in a neurological disorder. The cerebellum, as a part of the CNS, plays a pivotal role in motor coordination and non-motor functions such as emotion, cognition and language. The excitatory and inhibitory neurons within the cerebellum originate from different distinct germinal zones and migrate through complex routes to assemble in a well-defined neuronal organization in the cerebellar cortex and nuclei. In this review article, the neuronal migration modes and pathways from germinal zones to the final position in the cerebellar cortex and nuclei will be described. The cellular and molecular mechanisms involved in cerebellar neuronal migration during development will also be reviewed. Finally, some diseases and animal models associated with defects in neuronal migration will be presented.

OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.

Post-genomic behavioral genetics: From revolution to routine

What was once expensive and revolutionary-full-genome sequence-is now affordable and routine. Costs will continue to drop, opening up new frontiers in behavioral genetics. This shift in costs from the genome to the phenome is most notable in large clinical studies of behavior and associated diseases in cohorts that exceed hundreds of thousands of subjects. Examples include the Women's Health Initiative (www.whi.org), the Million Veterans Program (www.

RESEARCH

va.gov/MVP), the 100 000 Genomes Project (genomicsengland.co.uk) and commercial efforts such as those by deCode (www.decode.com) and 23andme (www.23andme.com). The same transition is happening in experimental neuro- and behavioral genetics, and sample sizes of many hundreds of cases are becoming routine (www.genenetwork.org, www.mousephenotyping.org). There are two major consequences of this new affordability of massive omics datasets: (1) it is now far more practical to explore genetic modulation of behavioral differences and the key role of gene-by-environment interactions. Researchers are already doing the hard part-the quantitative analysis of behavior. Adding the omics component can provide powerful links to molecules, cells, circuits and even better treatment. (2) There is an acute need to highlight and train behavioral scientists in how best to exploit new omics approaches. This review addresses this second issue and highlights several new trends and opportunities that will be of interest to experts in animal and human behaviors.

Integrating phenotype ontologies with PhenomeNET

BACKGROUND

Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.

RESULTS

Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.

CONCLUSIONS

PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.

Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability

BACKGROUND

Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders.

METHODS

Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data to explore the molecular mechanism at play.

RESULTS

Integrating genomics, transcriptomics, and epigenomics data, we describe two novel DLG2 promoters and coding first exons expressed in human fetal brain. Their murine conservation and protein-level evidence allowed us to produce new DLG2 gene models for human and mouse. These new genic elements are deleted in 90% of 29 patients (public and in-house) showing partial deletion of the DLG2 gene. The patients' clinical characteristics expand the neurodevelopmental phenotypic spectrum linked to DLG2 gene disruption to cognitive and behavioral categories.

CONCLUSIONS

While protein-coding genes are regarded as well known, our work shows that integration of multiple omics datasets can unveil novel coding elements. From a clinical perspective, our work demonstrates that two new DLG2 promoters and exons are crucial for the neurodevelopmental phenotypes associated with this gene. In addition, our work brings evidence for the lack of cross-annotation in human versus mouse reference genomes and nucleotide versus protein databases.

Rapid phenotyping of knockout mice to identify genetic determinants of bone strength

The genetic determinants of osteoporosis remain poorly understood, and there is a large unmet need for new treatments in our ageing society. Thus, new approaches for gene discovery in skeletal disease are required to complement the current genome-wide association studies in human populations. The International Knockout Mouse Consortium (IKMC) and the International Mouse Phenotyping Consortium (IMPC) provide such an opportunity. The IKMC generates knockout mice representing each of the known protein-coding genes in C57BL/6 mice and, as part of the IMPC initiative, the Origins of Bone and Cartilage Disease project identifies mutants with significant outlier skeletal phenotypes. This initiative will add value to data from large human cohorts and provide a new understanding of bone and cartilage pathophysiology, ultimately leading to the identification of novel drug targets for the treatment of skeletal disease.

PiggyBac transposon-based polyadenylation-signal trap for genome-wide mutagenesis in mice

We designed a new type of polyadenylation-signal (PAS) trap vector system in living mice, the piggyBac (PB) (PAS-trapping (EGFP)) gene trapping vector, which takes advantage of the efficient transposition ability of PB and efficient gene trap and insertional mutagenesis of PAS-trapping. The reporter gene of PB(PAS-trapping (EGFP)) is an EGFP gene with its own promoter, but lacking a poly(A) signal. Transgenic mouse lines carrying PB(PAS-trapping (EGFP)) and protamine 1 (Prm1) promoter-driven PB transposase transgenes (Prm1-PBase) were generated by microinjection. Male mice doubly positive for PB(PAS-trapping (EGFP)) and Prm1-PBase were crossed with WT females, generating offspring with various insertion mutations. We found that 44.8% (26/58) of pups were transposon-positive progenies. New transposon integrations comprised 26.9% (7/26) of the transposon-positive progenies. We found that 100% (5/5) of the EGFP fluorescence-positive mice had new trap insertions mediated by a PB transposon in transcriptional units. The direction of the EGFP gene in the vector was consistent with the direction of the endogenous gene reading frame. Furthermore, mice that were EGFP-PCR positive, but EGFP fluorescent negative, did not show successful gene trapping. Thus, the novel PB(PAS-trapping (EGFP)) system is an efficient genome-wide gene-trap mutagenesis in mice.

From classic ethology to modern neuroethology: overcoming the three biases in social behavior research

A typical current study investigating the neurobiology of animal behavior is likely restricted to male subjects, of standard inbred mouse strains, tested in simple behavioral assays under laboratory conditions. This approach enables the use of advanced molecular tools, alongside standardization and reproducibility, and has led to tremendous discoveries. However, the cost is a loss of genetic and phenotypic diversity and a divergence from ethologically-relevant behaviors. Here we review the pros and cons in behavioral neuroscience studies of the new era, focusing on reproductive behaviors in rodents. Recent advances in molecular technology and behavioral phenotyping in semi-natural conditions, together with an awareness of the critical need to study both sexes, may provide new insights into the neural mechanisms underlying social behaviors.

Brain imaging and genetic risk in the pediatric population, part 1: inherited metabolic diseases

In this article, the genotype-MR phenotype correlation of the most common or clinically important inherited metabolic diseases (IMD) in the pediatric population is reviewed. A nonsystematic search of the PubMed/Medline database of relevant studies about "genotype-phenotype correlation" in IMD was performed. Some MR phenotypes related to specific gene mutations were found, such as bilateral hypertrophy of inferior olives in patients harboring POLG and SURF1 mutations, and central lesions in the cervical spinal cord in patients with nonketotic hyperglycinemia harboring GLRX5 gene mutation.

A cross-species genetic analysis identifies candidate genes for mouse anxiety and human bipolar disorder

Bipolar disorder (BD) is a significant neuropsychiatric disorder with a lifetime prevalence of ~1%. To identify genetic variants underlying BD genome-wide association studies (GWAS) have been carried out. While many variants of small effect associated with BD have been identified few have yet been confirmed, partly because of the low power of GWAS due to multiple comparisons being made. Complementary mapping studies using murine models have identified genetic variants for behavioral traits linked to BD, often with high power, but these identified regions often contain too many genes for clear identification of candidate genes. In the current study we have aligned human BD GWAS results and mouse linkage studies to help define and evaluate candidate genes linked to BD, seeking to use the power of the mouse mapping with the precision of GWAS. We use quantitative trait mapping for open field test and elevated zero maze data in the largest mammalian model system, the BXD recombinant inbred mouse population, to identify genomic regions associated with these BD-like phenotypes. We then investigate these regions in whole genome data from the Psychiatric Genomics Consortium's bipolar disorder GWAS to identify candidate genes associated with BD. Finally we establish the biological relevance and pathways of these genes in a comprehensive systems genetics analysis. We identify four genes associated with both mouse anxiety and human BD. While TNR is a novel candidate for BD, we can confirm previously suggested associations with CMYA5, MCTP1, and RXRG. A cross-species, systems genetics analysis shows that MCTP1, RXRG, and TNR coexpress with genes linked to psychiatric disorders and identify the striatum as a potential site of action. CMYA5, MCTP1, RXRG, and TNR are associated with mouse anxiety and human BD. We hypothesize that MCTP1, RXRG, and TNR influence intercellular signaling in the striatum.

Using genetic mouse models to gain insight into glaucoma: Past results and future possibilities

While all forms of glaucoma are characterized by a specific pattern of retinal ganglion cell death, they are clinically divided into several distinct subclasses, including normal tension glaucoma, primary open angle glaucoma, congenital glaucoma, and secondary glaucoma. For each type of glaucoma there are likely numerous molecular pathways that control susceptibility to the disease. Given this complexity, a single animal model will never precisely model all aspects of all the different types of human glaucoma. Therefore, multiple animal models have been utilized to study glaucoma but more are needed. Because of the powerful genetic tools available to use in the laboratory mouse, it has proven to be a highly useful mammalian system for studying the pathophysiology of human disease. The similarity between human and mouse eyes coupled with the ability to use a combination of advanced cell biological and genetic tools in mice have led to a large increase in the number of studies using mice to model specific glaucoma phenotypes. Over the last decade, numerous new mouse models and genetic tools have emerged, providing important insight into the cell biology and genetics of glaucoma. In this review, we describe available mouse genetic models that can be used to study glaucoma-relevant disease/pathobiology. Furthermore, we discuss how these models have been used to gain insights into ocular hypertension (a major risk factor for glaucoma) and glaucomatous retinal ganglion cell death. Finally, the potential for developing new mouse models and using advanced genetic tools and resources for studying glaucoma are discussed.

Similarity-based search of model organism, disease and drug effect phenotypes

Background

Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity.

Results

We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet.

Conclusions

Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.

PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes

We describe a new method, PhenomeExpress, for the analysis of transcriptomic datasets to identify pathogenic disease mechanisms. Our analysis method includes input from both protein-protein interaction and phenotype similarity networks. This introduces valuable information from disease relevant phenotypes, which aids the identification of sub-networks that are significantly enriched in differentially expressed genes and are related to the disease relevant phenotypes. This contrasts with many active sub-network detection methods, which rely solely on protein-protein interaction networks derived from compounded data of many unrelated biological conditions and which are therefore not specific to the context of the experiment. PhenomeExpress thus exploits readily available animal model and human disease phenotype information. It combines this prior evidence of disease phenotypes with the experimentally derived disease data sets to provide a more targeted analysis. Two case studies, in subchondral bone in osteoarthritis and in Pax5 in acute lymphoblastic leukaemia, demonstrate that PhenomeExpress identifies core disease pathways in both mouse and human disease expression datasets derived from different technologies. We also validate the approach by comparison to state-of-the-art active sub-network detection methods, which reveals how it may enhance the detection of molecular phenotypes and provide a more detailed context to those previously identified as possible candidates.

Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes

Electronic health records and scientific articles possess differing linguistic characteristics that may impact the performance of natural language processing tools developed for one or the other. In this paper, we investigate the performance of four extant concept recognition tools: the clinical Text Analysis and Knowledge Extraction System (cTAKES), the National Center for Biomedical Ontology (NCBO) Annotator, the Biomedical Concept Annotation System (BeCAS) and MetaMap. Each of the four concept recognition systems is applied to four different corpora: the i2b2 corpus of clinical documents, a PubMed corpus of Medline abstracts, a clinical trails corpus and the ShARe/CLEF corpus. In addition, we assess the individual system performances with respect to one gold standard annotation set, available for the ShARe/CLEF corpus. Furthermore, we built a silver standard annotation set from the individual systems’ output and assess the quality as well as the contribution of individual systems to the quality of the silver standard. Our results demonstrate that mainly the NCBO annotator and cTAKES contribute to the silver standard corpora (F1-measures in the range of 21% to 74%) and their quality (best F1-measure of 33%), independent from the type of text investigated. While BeCAS and MetaMap can contribute to the precision of silver standard annotations (precision of up to 42%), the F1-measure drops when combined with NCBO Annotator and cTAKES due to a low recall. In conclusion, the performances of individual systems need to be improved independently from the text types, and the leveraging strategies to best take advantage of individual systems’ annotations need to be revised. The textual content of the PubMed corpus, accession numbers for the clinical trials corpus, and assigned annotations of the four concept recognition systems as well as the generated silver standard annotation sets are available from http://purl.org/phenotype/resources. The textual content of the ShARe/CLEF (https://sites.google.com/site/shareclefehealth/data) and i2b2 (https://i2b2.org/NLP/DataSets/) corpora needs to be requested with the individual corpus providers.

Combining RNA and protein profiling data with network interactions identifies genes associated with spermatogenesis in mouse and human

Genome-wide RNA profiling studies have identified hundreds of transcripts that are highly expressed in mammalian male germ cells, including many that are undetectable in somatic control tissues. Among them, genes important for spermatogenesis are significantly enriched. Information about mRNAs and their cognate proteins facilitates the identification of novel conserved target genes for functional studies in the mouse. By inspecting genome-wide RNA profiling data, we manually selected 81 genes for which RNA is detected almost exclusively in the human male germline and, in most cases, in rodent testicular germ cells. We observed corresponding mRNA/protein patterns in 43 cases using immunohistochemical data from the Human Protein Atlas and large-scale human protein profiling data obtained via mass spectroscopy. Protein network information enabled us to establish an interaction map of 38 proteins that points to potentially important testicular roles for some of them. We further characterized six candidate genes at the protein level in the mouse. We conclude that conserved genes induced in testis tend to show similar mRNA/protein expression patterns across species. Specifically, our results suggest roles during embryogenesis and adult spermatogenesis for Foxr1 and Sox30 and during spermiogenesis and fertility for Fam71b, 1700019N19Rik, Hmgb4, and Zfp597.

High-throughput screening of mouse gene knockouts identifies established and novel skeletal phenotypes

Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening (HTS) data for 3 762 distinct global gene knockout (KO) mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type (WT) cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass: 23 previously reported genes (Calcr, Cebpb, Crtap, Dcstamp, Dkk1, Duoxa2, Enpp1, Fgf23, Kiss1/Kiss1r, Kl (Klotho), Lrp5, Mstn, Neo1, Npr2, Ostm1, Postn, Sfrp4, Slc30a5, Slc39a13, Sost, Sumf1, Src, Wnt10b), five novel genes extensively characterized (Cldn18, Fam20c, Lrrk1, Sgpl1, Wnt16), five novel genes with preliminary characterization (Agpat2, Rassf5, Slc10a7, Slc26a7, Slc30a10) and three novel undisclosed genes coding for potential osteoporosis drug targets.

Joint genetic analysis of hippocampal size in mouse and human identifies a novel gene linked to neurodegenerative disease

BACKGROUND

Variation in hippocampal volume has been linked to significant differences in memory, behavior, and cognition among individuals. To identify genetic variants underlying such differences and associated disease phenotypes, multinational consortia such as ENIGMA have used large magnetic resonance imaging (MRI) data sets in human GWAS studies. In addition, mapping studies in mouse model systems have identified genetic variants for brain structure variation with great power. A key challenge is to understand how genetically based differences in brain structure lead to the propensity to develop specific neurological disorders.

RESULTS

We combine the largest human GWAS of brain structure with the largest mammalian model system, the BXD recombinant inbred mouse population, to identify novel genetic targets influencing brain structure variation that are linked to increased risk for neurological disorders. We first use a novel cross-species, comparative analysis using mouse and human genetic data to identify a candidate gene, MGST3, associated with adult hippocampus size in both systems. We then establish the coregulation and function of this gene in a comprehensive systems-analysis.

CONCLUSIONS

We find that MGST3 is associated with hippocampus size and is linked to a group of neurodegenerative disorders, such as Alzheimer's.

Using association rule mining to determine promising secondary phenotyping hypotheses

MOTIVATION

Large-scale phenotyping projects such as the Sanger Mouse Genetics project are ongoing efforts to help identify the influences of genes and their modification on phenotypes. Gene-phenotype relations are crucial to the improvement of our understanding of human heritable diseases as well as the development of drugs. However, given that there are ∼: 20 000 genes in higher vertebrate genomes and the experimental verification of gene-phenotype relations requires a lot of resources, methods are needed that determine good candidates for testing.

RESULTS

In this study, we applied an association rule mining approach to the identification of promising secondary phenotype candidates. The predictions rely on a large gene-phenotype annotation set that is used to find occurrence patterns of phenotypes. Applying an association rule mining approach, we could identify 1967 secondary phenotype hypotheses that cover 244 genes and 136 phenotypes. Using two automated and one manual evaluation strategies, we demonstrate that the secondary phenotype candidates possess biological relevance to the genes they are predicted for. From the results we conclude that the predicted secondary phenotypes constitute good candidates to be experimentally tested and confirmed.

AVAILABILITY

The secondary phenotype candidates can be browsed through at http://www.sanger.ac.uk/resources/databases/phenodigm/gene/secondaryphenotype/list.

CONTACT

ao5@sanger.ac.uk or ds5@sanger.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic disruption of the sh3pxd2a gene reveals an essential role in mouse development and the existence of a novel isoform of tks5

Tks5 is a scaffold protein and Src substrate involved in cell migration and matrix degradation through its essential role in invadosome formation and function. We have previously described that Tks5 is fundamental for zebrafish neural crest cell migration in vivo. In the present study, we sought to investigate the function of Tks5 in mammalian development by analyzing mice mutant for sh3pxd2a, the gene encoding Tks5. Homozygous disruption of the sh3pxd2a gene by gene-trapping in mouse resulted in neonatal death and the presence of a complete cleft of the secondary palate. Interestingly, embryonic fibroblasts from homozygous gene-trap sh3pxd2a mice lacked only the highest molecular weight band of the characteristic Tks5 triplet observed in protein extracts, leaving the lower molecular weight bands unaffected. This finding, together with the existence of two human Expressed Sequence Tags lacking the first 5 exons of SH3PXD2A, made us hypothesize about the presence of a second alternative transcription start site located in intron V. We performed 5′RACE on mouse fibroblasts and isolated a new transcript of the sh3pxd2a gene encoding a novel Tks5 isoform, that we named Tks5β. This novel isoform diverges from the long form of Tks5 in that it lacks the PX-domain, which confers affinity for phosphatidylinositol-3,4-bisphosphate. Instead, Tks5β has a short unique amino terminal sequence encoded by the newly discovered exon 6β; this exon includes a start codon located 29 bp from the 5'-end of exon 6. Tks5β mRNA is expressed in MEFs and all mouse adult tissues analyzed. Tks5β is a substrate for the Src tyrosine kinase and its expression is regulated through the proteasome degradation pathway. Together, these findings indicate the essentiality of the larger Tks5 isoform for correct mammalian development and the transcriptional complexity of the sh3pxd2a gene.

Phenotype ontologies and cross-species analysis for translational research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.

Linking tissues to phenotypes using gene expression profiles

Despite great biological and computational efforts to determine the genetic causes underlying human heritable diseases, approximately half (3500) of these diseases are still without an identified genetic cause. Model organism studies allow the targeted modification of the genome and can help with the identification of genetic causes for human diseases. Targeted modifications have led to a vast amount of model organism data. However, these data are scattered across different databases, preventing an integrated view and missing out on contextual information. Once we are able to combine all the existing resources, will we be able to fully understand the causes underlying a disease and how species differ. Here, we present an integrated data resource combining tissue expression with phenotypes in mouse lines and bringing us one step closer to consequence chains from a molecular level to a resulting phenotype. Mutations in genes often manifest in phenotypes in the same tissue that the gene is expressed in. However, in other cases, a systems level approach is required to understand how perturbations to gene-networks connecting multiple tissues lead to a phenotype. Automated evaluation of the predicted tissue–phenotype associations reveals that 72–76% of the phenotypes are associated with disruption of genes expressed in the affected tissue. However, 55–64% of the individual phenotype-tissue associations show spatially separated gene expression and phenotype manifestation. For example, we see a correlation between ‘total body fat’ abnormalities and genes expressed in the ‘brain’, which fits recent discoveries linking genes expressed in the hypothalamus to obesity. Finally, we demonstrate that the use of our predicted tissue–phenotype associations can improve the detection of a known disease–gene association when combined with a disease gene candidate prediction tool. For example, JAK2, the known gene associated with Familial Erythrocytosis 1, rises from the seventh best candidate to the top hit when the associated tissues are taken into consideration. Database URL:http://www.sanger.ac.uk/resources/databases/phenodigm/phenotype/list

Pluripotent stem cell-derived hepatocyte-like cells

Liver disease is an important clinical problem, impacting over 30 million Americans and over 600 million people worldwide. It is the 12th leading cause of death in the United States and the 16th worldwide. Due to a paucity of donor organs, several thousand Americans die yearly while waiting for liver transplantation. Unfortunately, alternative tissue sources such as fetal hepatocytes and hepatic cell lines are unreliable, difficult to reproduce, and do not fully recapitulate hepatocyte phenotype and functions. As a consequence, alternative cell sources that do not have these limitations have been sought. Human embryonic stem (hES) cell- and induced pluripotent stem (iPS) cell-derived hepatocyte-like cells may enable cell based therapeutics, the study of the mechanisms of human disease and human development, and provide a platform for screening the efficacy and toxicity of pharmaceuticals. iPS cells can be differentiated in a step-wise fashion with high efficiency and reproducibility into hepatocyte-like cells that exhibit morphologic and phenotypic characteristics of hepatocytes. In addition, iPS-derived hepatocyte-like cells (iHLCs) possess some functional hepatic activity as they secrete urea, alpha-1-antitrypsin, and albumin. However, the combined phenotypic and functional traits exhibited by iHLCs resemble a relatively immature hepatic phenotype that more closely resembles that of fetal hepatocytes rather than adult hepatocytes. Specifically, iHLCs express fetal markers such as alpha-fetoprotein and lack key mature hepatocyte functions, as reflected by drastically reduced activity (~0.1%) of important detoxification enzymes (i.e. CYP2A6, CYP3A4). These key differences between iHLCs and primary adult human hepatocytes have limited the use of stem cells as a renewable source of functional adult hepatocytes for in vitro and in vivo applications. Unfortunately, the developmental pathways that control hepatocyte maturation from a fetal into an adult hepatocyte are poorly understood, which has hampered the field in its efforts to induce further maturation of iPS-derived hepatic lineage cells. This review analyzes recent developments in the derivation of hepatocyte-like cells, and proposes important points to consider and assays to perform during their characterization. In the future, we envision that iHLCs will be used as in vitro models of human disease, and in the longer term, provide an alternative cell source for drug testing and clinical therapy.

In vivo metabolite profiling as a means to identify uncharacterized lipase function: recent success stories within the alpha beta hydrolase domain (ABHD) enzyme family

Genome sequencing efforts have identified many uncharacterized lipase/esterase enzymes that have potential to be drug targets for metabolic diseases such as obesity, diabetes, and atherosclerosis. However, sequence information and associated structural predictions provide only a loose framework for linking enzyme function to disease risk. We are now confronted with the challenge of functionally annotating a large number of uncharacterized lipases, with the goal of generating new therapies for metabolic diseases. This daunting challenge involves gathering not only sequence-driven predictions, but also more importantly structural, biochemical (substrates and products), and physiological data. At the center of such drug discovery efforts are accurately identifying physiologically relevant substrates and products of individual lipases, and determining whether newly identified substrates/products can modulate disease in appropriate preclinical animal model systems. This review describes the importance of coupling in vivo metabolite profiling to in vitro enzymology as a powerful means to assign lipase function in disease specific contexts using animal models. In particular, we highlight recent examples using this multidisciplinary approach to functionally annotate genes within the α/β hydrolase fold domain (ABHD) family of enzymes. These new discoveries within the ABHD enzyme family serve as powerful examples of linking novel lipase function to human disease. This article is part of a Special Issue entitled Tools to study lipid functions.

Rationally optimized cryopreservation of multiple mouse embryonic stem cell lines: I--Comparative fundamental cryobiology of multiple mouse embryonic stem cell lines and the implications for embryonic stem cell cryopreservation protocols

The post-thaw recovery of mouse embryonic stem cells (mESCs) is often assumed to be adequate with current methods. However as this publication will show, this recovery of viable cells actually varies significantly by genetic background. Therefore there is a need to improve the efficiency and reduce the variability of current mESC cryopreservation methods. To address this need, we employed the principles of fundamental cryobiology to improve the cryopreservation protocol of four mESC lines from different genetic backgrounds (BALB/c, CBA, FVB, and 129R1 mESCs) through a comparative study characterizing the membrane permeability characteristics and membrane integrity osmotic tolerance limits of each cell line. In the companion paper, these values were used to predict optimal cryoprotectants, cooling rates, warming rates, and plunge temperatures, and then these predicted optimal protocols were validated against standard freezing protocols.

Mining skeletal phenotype descriptions from scientific literature

Phenotype descriptions are important for our understanding of genetics, as they enable the computation and analysis of a varied range of issues related to the genetic and developmental bases of correlated characters. The literature contains a wealth of such phenotype descriptions, usually reported as free-text entries, similar to typical clinical summaries. In this paper, we focus on creating and making available an annotated corpus of skeletal phenotype descriptions. In addition, we present and evaluate a hybrid Machine Learning approach for mining phenotype descriptions from free text. Our hybrid approach uses an ensemble of four classifiers and experiments with several aggregation techniques. The best scoring technique achieves an F-1 score of 71.52%, which is close to the state-of-the-art in other domains, where training data exists in abundance. Finally, we discuss the influence of the features chosen for the model on the overall performance of the method.

Decomposing phenotype descriptions for the human skeletal phenome

Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome. We use this approach to showcase the usefulness of the generic concept of phenotype decomposition by performing an experimental study on all skeletal phenotype concepts defined in the Human Phenotype Ontology.

Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research

Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species. We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases. This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.

Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research

Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species.

We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases.

This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.

Bioinformatic perspectives in the neuronal ceroid lipofuscinoses

The neuronal ceroid lipofuscinoses (NCLs) are a group of rare genetic diseases characterised clinically by the progressive deterioration of mental, motor and visual functions and histopathologically by the intracellular accumulation of autofluorescent lipopigment - ceroid - in affected tissues. The NCLs are clinically and genetically heterogeneous and more than 14 genetically distinct NCL subtypes have been described to date (CLN1-CLN14) (Haltia and Goebel, 2012 [1]). In this review we will chronologically summarise work which has led over the years to identification of NCL genes, and outline the potential of novel genomic techniques and related bioinformatic approaches for further genetic dissection and diagnosis of NCLs. This article is part of a Special Issue entitled: The Neuronal Ceroid Lipofuscinoses or Batten Disease.

The facial evolution: looking backward and moving forward

Three-dimensional (3D) facial analysis is ideal for high-resolution, nonionizing, noninvasive objective, high-throughput phenotypic, and phenomic studies. It is a natural complement to (epi)genetic technologies to facilitate advances in the understanding of rare and common diseases. The face is uniquely reflective of the primordial tissues, and there is evidence supporting the application of 3D facial analysis to the investigation of variation and disease including studies showing that the face can reflect systemic health, provides diagnostic clues to disorders, and that facial variation reflects biological pathways. In addition, facial variation has been related to evolutionary factors. The purpose of this review is to look backward to suggest that knowledge of human evolution supports, and may instruct, the application and interpretation of studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease. Furthermore, in the context of advances of deep phenotyping and data integration, to look forward to suggest approaches to scalable implementation of facial analysis, and to suggest avenues for future research and clinical application of this technology.

Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish

Numerous disease syndromes are associated with regions of copy number variation (CNV) in the human genome and, in most cases, the pathogenicity of the CNV is thought to be related to altered dosage of the genes contained within the affected segment. However, establishing the contribution of individual genes to the overall pathogenicity of CNV syndromes is difficult and often relies on the identification of potential candidates through manual searches of the literature and online resources. We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders. There are currently more than 5000 human genes about which nothing is known phenotypically but for which detailed phenotypic information for the mouse and/or zebrafish orthologs is available. Here, we present an ontology-based approach to identify similarities between human disease manifestations and the mutational phenotypes in characterized model organism genes; this approach can therefore be used even in cases where there is little or no information about the function of the human genes. We applied this algorithm to detect candidate genes for 27 recurrent CNV disorders and identified 802 gene-phenotype associations, approximately half of which involved genes that were previously reported to be associated with individual phenotypic features and half of which were novel candidates. A total of 431 associations were made solely on the basis of model organism phenotype data. Additionally, we observed a striking, statistically significant tendency for individual disease phenotypes to be associated with multiple genes located within a single CNV region, a phenomenon that we denote as pheno-clustering. Many of the clusters also display statistically significant similarities in protein function or vicinity within the protein-protein interaction network. Our results provide a basis for understanding previously un-interpretable genotype-phenotype correlations in pathogenic CNVs and for mobilizing the large amount of model organism phenotype data to provide insights into human genetic disorders.

Supervised segmentation of phenotype descriptions for the human skeletal phenome using hybrid methods

Background

Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. In order to fully capture the intrinsic value and knowledge expressed within them, we need to take advantage of their inner structure, which implicitly combines qualities and anatomical entities. The first step in this process is the segmentation of the phenotype descriptions into their atomic elements.

Results

We present a two-phase hybrid segmentation method that combines a series individual classifiers using different aggregation schemes (set operations and simple majority voting). The approach is tested on a corpus comprised of skeletal phenotype descriptions emerged from the Human Phenotype Ontology. Experimental results show that the best hybrid method achieves an F-Score of 97.05% in the first phase and F-Scores of 97.16% / 94.50% in the second phase.

Conclusions

The performance of the initial segmentation of anatomical entities and qualities (phase I) is not affected by the presence / absence of external resources, such as domain dictionaries. From a generic perspective, hybrid methods may not always improve the segmentation accuracy as they are heavily dependent on the goal and data characteristics.

The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data

The Mammalian Phenotype Ontology (MP) is a structured vocabulary for describing mammalian phenotypes and serves as a critical tool for efficient annotation and comprehensive retrieval of phenotype data. Importantly, the ontology contains broad and specific terms, facilitating annotation of data from initial observations or screens and detailed data from subsequent experimental research. Using the ontology structure, data are retrieved inclusively, i.e., data annotated to chosen terms and to terms subordinate in the hierarchy. Thus, searching for "abnormal craniofacial morphology" also returns annotations to "megacephaly" and "microcephaly," more specific terms in the hierarchy path. The development and refinement of the MP is ongoing, with new terms and modifications to its organization undergoing continuous assessment as users and expert reviewers propose expansions and revisions. A wealth of phenotype data on mouse mutations and variants annotated to the MP already exists in the Mouse Genome Informatics database. These data, along with data curated to the MP by many mouse mutagenesis programs and mouse repositories, provide a platform for comparative analyses and correlative discoveries. The MP provides a standard underpinning to mouse phenotype descriptions for existing and future experimental and large-scale phenotyping projects. In this review we describe the MP as it presently exists, its application to phenotype annotations, the relationship of the MP to other ontologies, and the integration of the MP within large-scale phenotyping projects. Finally we discuss future application of the MP in providing standard descriptors of the phenotype pipeline test results from the International Mouse Phenotype Consortium projects.

Deep phenotyping for precision medicine

In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.

Computational tools for comparative phenomics: the role and promise of ontologies

A major aim of the biological sciences is to gain an understanding of human physiology and disease. One important step towards such a goal is the discovery of the function of genes that will lead to a better understanding of the physiology and pathophysiology of organisms, which will ultimately lead to better diagnosis and therapy. Our increasing ability to phenotypically characterise genetic variants of model organisms coupled with systematic and hypothesis-driven mutagenesis is resulting in a wealth of information that could potentially provide insight into the functions of all genes in an organism. The challenge we are now facing is to develop computational methods that can integrate and analyse such data. The introduction of formal ontologies that make their semantics explicit and accessible to automated reasoning provides the tantalizing possibility of standardizing biomedical knowledge allowing for novel, powerful queries that bridge multiple domains, disciplines, species, and levels of granularity. We review recent computational approaches that facilitate the integration of experimental data from model organisms with clinical observations in humans. These methods foster novel cross-species analysis approaches, thereby enabling comparative phenomics and leading to the potential of translating basic discoveries from the model systems into diagnostic and therapeutic advances at the clinical level.

The need for mouse models in osteoporosis genetics research

Osteoporosis, the progressive loss of bone mass resulting in fragility fractures, affects ∼75 million people in the United States, Europe and Japan. Bone mineral density (BMD) correlates with fracture risk and is widely used in clinical settings to predict fracture. Numerous studies have demonstrated that peak bone mass is highly heritable and consequently a number of genome-wide association studies (GWASs) have been conducted to identify the genes that regulate BMD. Traditional intercross mapping in the mouse has met with limited successes in the field of skeletal biology. With the advent of human GWAS, questions have arisen about the continued need for mouse models in genetics research. However, significant advances have been made in the field of mouse genetics, including new genetics resource populations and loci mapping techniques, which enable gene-level mapping resolution. In this review, we discuss the need for mouse models to help understand the skeletal biology underlying novel human GWAS findings, how loci discovered in the mouse can be used to complement GWAS analysis and highlight the recent advances made in the field of skeletal biology from the use of these new and developing resources. We conclude this paper with a discussion of the need for systems-level approaches in the skeletal biology field, with an emphasis on the need for pathway and network analyses.