The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease (original) (raw)

Journal Article

,

European Bioinformatics Institute, European Molecular Biology Laboratory

, Welcome Genome Campus, Hinxton CB10 1SD,

UK

To whom correspondence should be addressed. Tel: +44 1223 49 44 44; Email: tudor@ebi.ac.uk

Search for other works by this author on:

,

European Bioinformatics Institute, European Molecular Biology Laboratory

, Welcome Genome Campus, Hinxton CB10 1SD,

UK

Search for other works by this author on:

,

European Bioinformatics Institute, European Molecular Biology Laboratory

, Welcome Genome Campus, Hinxton CB10 1SD,

UK

Search for other works by this author on:

,

European Bioinformatics Institute, European Molecular Biology Laboratory

, Welcome Genome Campus, Hinxton CB10 1SD,

UK

Search for other works by this author on:

,

European Bioinformatics Institute, European Molecular Biology Laboratory

, Welcome Genome Campus, Hinxton CB10 1SD,

UK

Search for other works by this author on:

,

European Bioinformatics Institute, European Molecular Biology Laboratory

, Welcome Genome Campus, Hinxton CB10 1SD,

UK

Search for other works by this author on:

,

William Harvey Research Institute, Queen Mary University of London

, London EC1M 6BQ,

UK

Search for other works by this author on:

,

Mary Lyon Centre at MRC Harwell

, Harwell Campus OX11 7UE,

UK

Search for other works by this author on:

,

Mary Lyon Centre at MRC Harwell

, Harwell Campus OX11 7UE,

UK

Search for other works by this author on:

,

Mary Lyon Centre at MRC Harwell

, Harwell Campus OX11 7UE,

UK

Search for other works by this author on:

... Show more

Received:

09 September 2022

Revision received:

05 October 2022

Accepted:

14 October 2022

Published:

28 October 2022

• PDF
• Split View
• • Article contents
  • Figures & tables
  • Video
  • Audio
  • Supplementary Data
• Cite

Cite

Tudor Groza, Federico Lopez Gomez, Hamed Haseli Mashhadi, Violeta Muñoz-Fuentes, Osman Gunes, Robert Wilson, Pilar Cacheiro, Anthony Frost, Piia Keskivali-Bond, Bora Vardal, Aaron McCoy, Tsz Kwan Cheng, Luis Santos, Sara Wells, Damian Smedley, Ann-Marie Mallon, Helen Parkinson, The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D1038–D1045, https://doi.org/10.1093/nar/gkac972
Close

Navbar Search Filter Mobile Enter search term Search

Abstract

The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal makes available curated, integrated and analysed knockout mouse phenotyping data generated by the IMPC project consisting of 85M data points and over 95,000 statistically significant phenotype hits mapped to human diseases. The IMPC portal delivers a substantial reference dataset that supports the enrichment of various domain-specific projects and databases, as well as the wider research and clinical community, where the IMPC genotype–phenotype knowledge contributes to the molecular diagnosis of patients affected by rare disorders. Data from 9,000 mouse lines and 750 000 images provides vital resources enabling the interpretation of the ignorome, and advancing our knowledge on mammalian gene function and the mechanisms underlying phenotypes associated with human diseases. The resource is widely integrated and the lines have been used in over 4,600 publications indicating the value of the data and the materials.

INTRODUCTION

The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) produces whole gene knock out (KO) mouse lines for community use and delivers broad comprehensive phenotyping data and the KO lines as an international resource enabling functional analyses with genome-wide coverage as the eventual goal (1). The lines and the data are vital resources for elucidating both the genetics and the mechanisms underlying phenotypes associated with human diseases and they inform our understanding of the ‘ignorome’—the portion of the mammalian coding genome which is poorly functionally characterised and which we seek to illuminate.

Since its inception in 2011, IMPC has published 17 data releases describing over 8000 genes and their associations to 90 000 significant phenotypes, in addition to 750 000 images and over 1000 human disease models. Standardisation and use of established ontologies underpin the integration and publishing of the resource. The International Mouse Phenotyping Resource of Standardised Screens (IMPReSS) database drives the validation of the raw data, while the Mammalian Phenotype Ontology (MP) (2) is used to capture mammalian phenotypes and to enable correlation to human phenotypes defined by the Human Phenotype Ontology (3). The knowledge externalised by the resource supports the identification of new mouse models of rare and common human diseases, new gene functions and the development of novel methodological approaches that form the basis of new gene–disease associations. Moreover, it underpins successful experiments effectively uncovering pleiotropy (4–6), of particular importance when elucidating the genetic causes of syndromic disorders, as well as wide-ranging sexual dimorphism (7).

The IMPC data exists in a complex ecosystem of projects and databases that are evolving to deliver systematic analyses of cellular, organism level and population analyses. All of these contribute to our understanding of function and will extend beyond coding regions to deliver a map of genotype–phenotype understanding. IMPC delivers data for integration with resources such as the Mouse Genome Database (8) or the Monarch Initiative (9), and clinical resources enabling the analysis of rare disease patient genomes (10).

Here, we provide an overview of the latest data release and of the IMPC Web Portal, we discuss a series of use cases and before concluding, we present a brief description of the complex IMPC data flow.

THE IMPC RESOURCE

Data Releases (DR) provide a stable and versioned reference set of analysed IMPC data covering all data generated by the consortium until the release date - or alternatively, all data incorporated in a previous data release augmented with the increment generated since that release. These timestamped releases are critical for reproducible analyses and enable a clear provenance-based integration into external resources. Newly generated data between releases (by the production centres) is collected, stringently QCed and run through an Extract-Transform-Load, statistical and the disease association pipelines before being integrated with the current release and packaged into a new release.

IMPC publishes three data releases per year. Figure 1 depicts the evolution of the number of knockouts and number of data points across the 11 years of IMPC existence. FAIRness is ensured by using standard identifiers—MGI IDs—and community ontologies—for example, MP and HPO. Table 1 lists the numbers associated with the latest release—DR17 from 19 July 2022. DR17 covers a total of 85 911 461 data points on 8267 genes and 97 294 significant phenotypes aligned to 1321 human diseases. In addition, it also includes 786 081 images with a total size of ∼1.6TB, generated from 21 procedures spanning all life stages. For example, gene Cib2 (https://www.mousephenotype.org/data/genes/MGI:1929293) is associated with 12 significant phenotypes, inferred from 241 measurements and 424 data points, and enriched with 162 images (79 echo, 65 X-rays, 8 LacZ embryo, 7 LacZ adult and 3 ABR waveforms).

Table 1.

IMPC Data Release 17 (DR17; 19 July 2022) statistics covering genes, associated phenotypes and the procedures supporting the data collection and QC

Table 1.

IMPC Data Release 17 (DR17; 19 July 2022) statistics covering genes, associated phenotypes and the procedures supporting the data collection and QC

Figure 1.

IMPC data releases evolution over the last 11 years.

To ensure data consistency, integrity and shareability, the IMPC phenotyping protocols are standardised and are available on IMPReSS. DR17 was supported by an IMPReSS version containing 80 procedures with 5084 parameters. Of these parameters 1164 record metadata, 2019 are analysed for phenotypes and 66 are media parameters (more details are provided in the Data Flow section).

Finally, Figure 2 depicts the number of knockouts split across phenotype categories, with the top being behavioural phenotypes and phenotypes of the hematopoietic system.

Figure 2.

IMPC Data release 17 (DR17; 19 July 2022) phenotype categories.

All data collected by the IMPC is freely available and several access channels are provided, based on the individual needs of the users - ranging from individual items to complete data releases. Programmatic access to the entire range of up-to-date IMPC data is available through an Application Programming Interface (API), documented at https://www.mousephenotype.org/help/programmatic-data-access/. Snapshots of the entire dataset captured at the time of each data release are also available for bulk download via FTP (ftp://ftp.ebi.ac.uk/pub/databases/impc/).

THE IMPC WEB PORTAL

The IMPC Web Portal enables users to access and explore the resource in a simplified format. In 2019, after engaging various user groups, including clinicians and biomedical researchers, the Web Portal underwent a major redesign, which led to a new data access and browsing paradigm focusing on common user journeys and simpler entry points for new users. To improve findability, the top-level menu provides immediate access to both data as well as impact, via publications showcasing the use of IMPC data (Figure 3). The ‘Data’ section, in addition to advanced visualisation tools and options for data download, also includes bespoke data collections. These organise data into biological themes to provide easy entry points for users searching for cardiovascular, embryo development, histopathology, essential genes and late adult data. Similarly, the ‘Publications’ section enables users to immediately access literature demonstrating the impact of and knowledge generated with IMPC data.

Figure 3.

The IMPC Web portal. The main page provides immediate access to gene and phenotype-based queries, domain-specific data collections, data supporting IMPC publications and help and training materials

The home page of the Web Portal allows users to explore the IMPC knowledge using names or identifiers for phenotypes and genes - as shown in Figure 3. The key information in IMPC is encoded using widely-adopted nomenclatures and ontologies. This strategy assists users searching for phenotypes using a synonym or for genes using MGI IDs, mouse or human symbols.

Gene pages are the central component of the IMPC Web Portal, displaying the corresponding KO lines, physiological systems affected (e.g. cardiovascular, nervous, hematopoietic, etc.—see Figure 3), the significant phenotypes detected (e.g. abnormal bone structure, increased grip strength, etc), images available, publications using these lines and relationships to human disease (Figure 4, panel A). Moreover, they provide an entry point into accessing all the data and statistical analyses underpinning the phenotype calls represented as chart pages, which display advanced visualisations over the data (Figure 4, panel B), comprehensive descriptions of both the assays and the statistical analyses performed and a complete provenance trail. Finally, links to external sources, such as, MGI or the Monarch Initiative are provided in appropriate contexts.

Figure 4.

The IMPC gene and phenotype pages exemplified using the Cib2 gene and specific details on the phenotypes increased circulating cholesterol level and abnormal startle reflex. (A) Gene pages display data and information using a block-oriented approach, with each block focusing on one aspect—for example, significant phenotypes, images, publications. (B) Phenotype pages provide in-depth visual analyses and comprehensive provenance information.

The Documentation (Help) section of the Web Portal covers always the most up-to-date IMPC content and is iteratively updated to reflect changes in the way we present results or in our statistical analysis practices. In addition, the ‘Getting Started with IMPC Data’ section links to a comprehensive series of training materials and to information on gaining access and using the resource.

USE CASES

IMPC focuses on delivering a reference dataset that enables functional analyses using the mammalian genome and hence supporting the enrichment of various domain-specific downstream projects and databases. These range from the mouse genome database, where IMPC contributes with mouse alleles and phenotypes, to the wider research and clinical community, where IMPC genotype–phenotype knowledge contributes to the genetic diagnosis of patients as well as various integration projects.

The resource adds value to the data through processes that efficiently annotate, integrate and disseminate results in context and via different modalities helpful to our stakeholders listed in Table 2. These efforts, primarily communicated via the Web Portal and scientific papers, provide the research community with new mouse models for further investigation (e.g. deeper characterization of a particular physiological system) and thousands of gene function predictions at scale. These allow the generation of new hypotheses that are increasing our understanding of the mammalian genome, forming the basis of new research, and identifying disease associations. Table 2 showcases the impact of the resource via advances and contributions to the generation of new knowledge that has been possible with the emergence and analysis of IMPC data.

Table 2.

IMPC data stakeholders, associated use cases and impact

Table 2.

IMPC data stakeholders, associated use cases and impact

Notable examples demonstrating immediate clinical utility include the analyses on Tanc2 or Tmem63b. The former provided support for diagnosis of 20 patients with a neurodevelopmental disorder syndrome with rare de novo and inherited disruptive mutations in TANC2 (11). While a subset of patients displayed hyperactivity, the challenge was the lack of a detailed understanding of the pleiotropic effects of the mutations. The Tanc2 IMPC mouse knockouts (homozygous viable) also displayed hyperactivity, in addition to a reduced body size and adipose tissue and an altered metabolic rate. More importantly, their phenotype included liver abnormal cellular morphology and dysfunction (abnormal metabolite levels), which in humans are easily accessible via biomarkers of liver dysfunction. This enabled the detection of an abnormal liver function, which may lead to systemic disease (diabetes/arteriosclerosis/cardiovascular disease/stroke). Similarly, hyperactivity and limb-grasping phenotypes recorded on IMPC Tmem63b knockouts (homozygous lethal phenotype, heterozygous viable) were used to connect five patients with three variants (one overlapping in three patients) (12).

A major use case of the IMPC data are human disease models. These serve a dual purpose: (i) to evaluate the recapitulation of human phenotypes for known gene–disease associations in the mouse knockout and (ii) to uncover potential novel gene–phenotype associations by identifying mouse knockouts mimicking clinical features observed in patients. These phenotypic similarity computations between mouse knockouts and known human disorders are performed by the PhenoDigm algorithm (34), which relies on the availability of standardised phenotypic annotations (2,3). The former objective is illustrated in Table 3, that summarises the number of IMPC mouse knockouts with a one-to-one human orthologue associated with OMIM /Orphanet disorders (41,42) and the corresponding PhenoDigm results according to DR17. With regard to predicted gene–disease associations for genes not known to be associated with Mendelian disorders, the evidence provided by these similarity computations successfully contributed to identifying novel developmental disorder genes (22). Human disorders associated with a gene by orthology (i) and human disorders predicted to be associated with a gene based on PhenoDigm scores (ii) are available in each Gene Page.

Table 3.

Mouse orthologues of human Mendelian disease-associated genes that entered the IMPC phenotyping pipeline according to DR17.0 and potential models for these disorders

Table 3.

Mouse orthologues of human Mendelian disease-associated genes that entered the IMPC phenotyping pipeline according to DR17.0 and potential models for these disorders

THE IMPC DATA FLOW

The IMPC strives to generate and deliver open, quality controlled, robust and reproducible data to the scientific and user community. Figure 5 depicts the flow of the data within the overall IMPC ecosystem from mouse production to curated data made available via packaged releases or the Web Portal. The starting point is the coordination between the mouse production facilities.

Figure 5.

The IMPC data flow. Data generated by the phenotyping centres via the systematic phenotyping pipeline is aggregated, validated and undergoes stringent QC procedures before being served as input to the Extract-Transform-Load and statistical analysis pipeline. The outcome—a curated genotype–phenotype resource - is made available via data releases and the IMPC Web Portal.

Tracking the status of mouse production is a critical activity that underpins the fundamental aim of the program to maximise the number of genes knocked out and minimise the number of animals. A tracking system—GenTaR (https://www.gentar.org/tracker/#/) is used to minimise overlap, enable QC of data and to provide progress updates to external researchers who registered their interest on specific genes.

Raw data produced by the centres is crawled on a daily basis and validated against the definitions captured by IMPReSS. This ensures data is consistent across all centres. As the consortium’s and the wider scientific community’s data analysis efforts have matured, the role of high-quality data has been key to ensure reproducibility and an appropriate foundation for hypothesis testing. IMPReSS is designed in collaboration with the phenotyping centres and the scientific community and specifies (in human and machine readable formats) both how the procedures are to be performed and how and what data is to be submitted by the phenotyping centres.

The procedures are organised in pipelines, which define the order and age at which the procedures are to be performed. The core IMPC Early Adult (EA) in vivo pipeline starts at 9 weeks running to 16 weeks with behavioural, morphological, metabolic and cardiovascular assessments (Figure 6). Viability, fertility and embryo procedures are also carried out. Additional centre specific pipelines allow inclusion of procedures not part of the core pipeline. With the extension of the project to later adult phenotyping, Late Adult (LA) and Interval pipelines are also defined on IMPReSS. The LA pipelines are based on the core EA pipeline with some procedures removed due to biological changes in the baseline animals, such as deafness, which would not result in meaningful results. Each procedure defines the parameters to collect data for, including the metadata parameters, and the specifications for them.

Figure 6.

The IMPC phenotyping pipeline.

All the data goes through a stringent QC process. The QC checks include identifying high, low and impossible values, inconsistencies between values for different parameters, data shifts and drifts and date QC. As an example, all data for parameters in Open Field are assessed for high and low values and data drifts and shifts over time. As the parameters record data for different sections of the experimentation arena, in a number of time periods and the totals, the consistency of these relationships is also assessed; for example the sum of periphery and centre distance travelled should match the total distance. The date of experiment will be checked against the specimen date of birth to confirm the procedure was performed at the right age.

Data that passes these quality checks, in addition to the associated imagery, is provided as input to an Extract, Transform and Load (ETL) pipeline that prepares it for packaging and publishing via versioned data releases. The ETL pipeline consolidates the integration of cross-reference data from internal and external sources—e.g., GenTaR, the Mouse Genome Database (8) and various ontologies - and standardises the production of datasets for ingestion by the statistical and the disease association pipelines.

To identify pheno-deviant lines, data generated from knockout mice are subjected to statistical analysis where the parameters measured during the assay are compared with the same parameters measured in parallel from control wild-type mice from an identical background strain. When a test is considered statistically significant, ontology terms from the Mammalian Phenotype Ontology (MP) (2) are automatically associated to the individual genotypes based on association specified in IMPReSS for every analysed parameter. Subsequently, the disease association pipeline—developed in collaboration with the Monarch Initiative (9)—generates a score for how phenotypically similar an IMPC line is to a particular human disease. This is achieved based on the similarity of terms in the Human Phenotype Ontology (3) used to annotate rare diseases in humans and MP terms used to annotate the IMPC procedures.

The final step of the data flow is the packaging of the data into versioned bundles and making it available for users on the Web Portal, programmatically via APIs and as downloadable archives on FTP.

CONCLUSION

The IMPC focuses on delivering a reference dataset consisting of comprehensive and standardised phenotyping data that enables functional analyses using the mammalian genome. We will continue to deliver a robust, high quality and reproducible dataset, maximally integrated within the wider knowledge ecosystem focused on advances to human health.

For the future, we aim to bring more depth to the resource by integrating additional, relevant knowledge sources to enable novel analyses. For example, as complex disease analyses move from association studies to causative or ‘effector’ gene analyses, the functional data provided by IMPC and others is critical to understand the phenotypic consequences of variants, to understand which genes are essential and which contribute to diseases enabling downstream functional follow up.

The IMPC phenotype descriptions listed in the context of genes in the Ensembl genome browser, MGI or the OpenTargets platform are examples of a successful outgoing data integration. A similar, untapped resource is the UCSC Genome Browser. While a phenotype and literature track exist in the context of the human genome (covering data sources such as OMIM), this is absent in the context of the mouse genome. We aim to fill this gap and enable IMPC to be the premier mouse phenotype data provider for the UCSC Genome Browser.

Finally, advanced visualisation capabilities will be added to the Web Portal to improve the user experience and the exploratory discovery and interpretation of the data. Heatmaps have been implemented on the ‘Late adult data’ page to enable users to inspect the genes that have been phenotyped for the ageing procedures. We will develop similar widgets on all other Data collection pages, including the automated body system pages. We will also add gene clustering options based on phenotype (MP) and Gene Ontology (GO) component classes to enable an understanding of the relationship between the impacted biological processes and the expressed phenotype.

DATA AVAILABILITY

• IMPC Web Portal: https://www.mousephenotype.org/
• IMPC FTP data releases: http://ftp.ebi.ac.uk/pub/databases/impc/
• The Mammalian Phenotype Ontology: https://github.com/mgijax/mammalian-phenotype-ontology
• The Human Phenotype Ontology and its resources: http://www.human-phenotype-ontology.org
• UPheno: https://github.com/obophenotype/upheno

ACKNOWLEDGEMENTS

The authors thank the members of the IMPC consortium, the EMBL-EBI Industry Programme, Staff of MRC Harwell and the NIH KOMP2 program. They are especially grateful to the users who have contacted them through their user support form or have participated in our usability testing sessions to improve the portal. They also thank the IMPC Steering and Scientific Committees for feedback on the interfaces.

FUNDING

National Institutes of Health (NIH) [2UM1HG006370-11]; EMBL-EBI Core Funding (to H.P., T.G.). Funding for open access charge: NIH.

Conflict of interest statement. None declared.

REFERENCES

Brown

S.D.M.

,

Holmes

C.C.

,

Mallon

A.-M.

,

Meehan

T.F.

,

Smedley

D.

,

Wells

S.

High-throughput mouse phenomics for characterizing mammalian gene function

.

Nat. Rev. Genet.

2018

;

19

:

357

–

370

.

Smith

C.L.

,

Eppig

J.T.

The mammalian phenotype ontology: enabling robust annotation and comparative analysis

.

Wiley Interdiscip. Rev. Syst. Biol. Med.

2009

;

1

:

390

–

399

.

Köhler

S.

,

Carmody

L.

,

asilevsky

N.

,

Jacobsen

J.O.B.

,

Danis

D.

,

Gourdine

J.-P.

,

Gargano

M.

,

Harris

N.L.

,

Matentzoglu

N.

,

McMurr

J.A.

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

.

Nucleic Acids Res.

2019

;

47

:

D1018

–

D1027

.

White

J.K.

,

Gerdin

A.-K.

,

Karp

N.A.

,

Ryder

E.

,

Buljan

M.

,

Bussell

J.N.

,

Salisbury

J.

,

Clare

S.

,

Ingham

N.J.

,

Podrini

C.

et al. .

Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes

.

Cell

.

2013

;

154

:

452

–

464

.

de Angelis

M.H.

,

Nicholson

G.

,

Selloum

M.

,

White

J.

,

Morgan

H.

,

Ramirez-Solis

R.

,

Sorg

T.

,

Wells

S.

,

Fuchs

H.

,

Fray

M.

et al. .

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

.

Nat. Genet.

2015

;

47

:

969

–

978

.

Muñoz-Fuentes

V.

,

Haselimashhadi

H.

,

Santos

L.

,

Westerberg

H.

,

Parkinson

H.

,

Mason

J.

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

.

Mamm. Genome

.

2022

;

33

:

135

–

142

.

Karp

N.A.

,

Mason

J.

,

Beaudet

A.L.

,

Benjamini

Y.

,

Bower

L.

,

Braun

R.E.

,

Brown

S.D.M.

,

Chesler

E.J.

,

Dickinson

M.E.

,

Flenniken

A.M.

et al. .

Prevalence of sexual dimorphism in mammalian phenotypic traits

.

Nat. Commun.

2017

;

8

:

15475

.

Bult

C.J.

,

Blake

J.A.

,

Smith

C.L.

,

Kadin

J.A.

,

Richardson

J.E.

Mouse Genome Database Group

Mouse Genome Database (MGD) 2019

.

Nucleic Acids Res.

2019

;

47

:

D801

–

D806

.

Shefchek

K.A.

,

Harris

N.L.

,

Gargano

M.

,

Matentzoglu

N.

,

Unni

D.

,

Brush

M.

,

Keith

D.

,

Conlin

T.

,

Vasilevsky

N.

,

Zhang

X.A.

et al. .

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

.

Nucleic Acids Res.

2020

;

48

:

D704

–

D715

.

100,000 Genomes Project Pilot Investigators

Smedley

D.

,

Smith

K.R.

et al. .

100,000 genomes pilot on rare-disease diagnosis in health care - preliminary report

.

N. Engl. J. Med.

2021

;

385

:

1868

–

1880

.

Garrett

L.

,

Da Silva-Buttkus

P.

,

Rathkolb

B.

,

Gerlini

R.

,

Becker

L.

,

Sanz-Moreno

A.

,

Seisenberger

C.

,

Zimprich

A.

,

Aguilar-Pimentel

A.

,

Amarie

O.V.

et al. .

Post-synaptic scaffold protein TANC2 in psychiatric and somatic disease risk

.

Dis. Model. Mech.

2022

;

15

:

dmm049205

.

Cacheiro

P.

,

Muñoz-Fuentes

V.

,

Murray

S.A.

,

Dickinson

M.E.

,

Bucan

M.

,

Nutter

L.M.J.

,

Peterson

K.A.

,

Haselimashhadi

H.

,

Flenniken

A.M.

,

Morgan

H.

et al. .

Human and mouse essentiality screens as a resource for disease gene discovery

.

Nat. Commun.

2020

;

11

:

655

.

Dickinson

M.E.

,

Flenniken

A.M.

,

Ji

X.

,

Teboul

L.

,

Wong

M.D.

,

White

J.K.

,

Meehan

T.F.

,

Weninger

W.J.

,

Westerberg

H.

,

Adissu

H.

et al. .

High-throughput discovery of novel developmental phenotypes

.

Nature

.

2016

;

537

:

508

–

514

.

Bowl

M.R.

,

Simon

M.M.

,

Ingham

N.J.

,

Greenaway

S.

,

Santos

L.

,

Cater

H.

,

Taylor

S.

,

Mason

J.

,

Kurbatova

N.

,

Pearson

S.

et al. .

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

.

Nat. Commun.

2017

;

8

:

886

.

Rozman

J.

,

Rathkolb

B.

,

Oestereicher

M.A.

,

Schütt

C.

,

Aakash Ravindranath

C.

,

Leuchtenberger

S.

,

Sharma

S.

,

Kistler

M.

,

Willershäuser

M.

,

Brommage

R.

et al. .

Identification of genetic elements in metabolism by high-throughput mouse phenotyping

.

Nat. Commun.

2018

;

9

:

288

.

Zhang

T.

,

Xie

P.

,

Dong

Y.

,

Liu

Z.

,

Zhou

F.

,

Pan

D.

,

Huang

Z.

,

Zhai

Q.

,

Gu

Y.

,

Wu

Q.

et al. .

High-throughput discovery of genetic determinants of circadian misalignment

.

PLoS Genet.

2020

;

16

:

e1008577

.

Moore

B.A.

,

Leonard

B.C.

,

Sebbag

L.

,

Edwards

S.G.

,

Cooper

A.

,

Imai

D.M.

,

Straiton

E.

,

Santos

L.

,

Reilly

C.

,

Griffey

S.M.

et al. .

Identification of genes required for eye development by high-throughput screening of mouse knockouts

.

Commun. Biol.

2018

;

1

:

236

.

Swan

A.L.

,

Schütt

C.

,

Rozman

J.

,

Moreno

M.D.M.M.

,

Brandmaier

S.

,

Simon

M.

,

Leuchtenberger

S.

,

Griffiths

M.

,

Brommage

R.

,

Keskivali-Bond

P.

et al. .

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density

.

PLoS Genet.

2020

;

16

:

e1009190

.

Spielmann

N.

,

Miller

G.

,

Oprea

T.I.

,

Chih-Hsu

W.

,

Fobo

G.

,

Frishman

G.

,

Montrone

C.

,

Hamed Mashhadi

H.

,

Mason

J.

,

Violeta Fuentes

M.

et al. .

Extensive identification of genes involved in congenital and structural heart disorders and cardiomyopathy

.

Nat. Cardiovasc. Res.

2022

;

1

:

157

–

173

.

Wotton

J.M.

,

Peterson

E.

,

Flenniken

A.M.

,

Bains

R.S.

,

Veeraragavan

S.

,

Bower

L.R.

,

Bubier

J.A.

,

Parisien

M.

,

Bezginov

A.

,

Haselimashhadi

H.

et al. .

Identifying genetic determinants of inflammatory pain in mice using a large-scale gene-targeted screen

.

Pain

.

2022

;

163

:

1139

–

1157

.

Meehan

T.F.

,

Conte

N.

,

West

D.B.

,

Jacobsen

J.O.

,

Mason

J.

,

Warren

J.

,

Chen

C.-K.

,

Tudose

I.

,

Relac

M.

,

Matthews

P.

et al. .

Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium

.

Nat. Genet.

2017

;

49

:

1231

–

1238

.

Cacheiro

P.

,

Haendel

M.A.

,

Smedley

D.

International Mouse Phenotyping Consortium and the Monarch Initiative

New models for human disease from the International Mouse Phenotyping Consortium

.

Mamm. Genome

.

2019

;

30

:

143

–

150

.

Muñoz-Fuentes

V.

,

Cacheiro

P.

,

Meehan

T.F.

,

Aguilar-Pimentel

J.A.

,

Brown

S.D.M.

,

Flenniken

A.M.

,

Flicek

P.

,

Galli

A.

,

Mashhadi

H.H.

,

de Angelis

M.H.

The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation

.

Conserv. Genet.

2018

;

19

:

995

–

1005

.

Brommage

R.

,

Liu

J.

,

Hansen

G.M.

,

Kirkpatrick

L.L.

,

Potter

D.G.

,

Sands

A.T.

,

Zambrowicz

B.

,

Powell

D.R.

,

Vogel

P.

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

.

Bone Res.

2014

;

2

:

14034

.

Pereira

M.

,

Ko

J.-H.

,

Logan

J.

,

Protheroe

H.

,

Kim

K.-B.

,

Tan

A.L.M.

,

Croucher

P.I.

,

Park

K.-S.

,

Rotival

M.

,

Petretto

E.

et al. .

A trans-eQTL network regulates osteoclast multinucleation and bone mass

.

Elife

.

2020

;

9

:

e55549

.

Rowe

D.W.

,

Hong

S.-H.

,

Zhang

C.

,

Shin

D.-G.

,

Adams

D.J.

,

Youngstrom

D.W.

,

Chen

L.

,

Wu

Z.

,

Zhou

Y.

,

Maye

P.

Skeletal screening IMPC/KOMP using μCT and computer automated cryohistology: Application to the Efna4 KO mouse line

.

Bone

.

2021

;

144

:

115688

.

Abeler-Dörner

L.

,

Laing

A.G.

,

Lorenc

A.

,

Ushakov

D.S.

,

Clare

S.

,

Speak

A.O.

,

Duque-Correa

M.A.

,

White

J.K.

,

Ramirez-Solis

R.

,

Saran

N.

et al. .

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation

.

Nat. Immunol.

2020

;

21

:

86

–

100

.

Collins

S.C.

,

Mikhaleva

A.

,

Vrcelj

K.

,

Vancollie

V.E.

,

Wagner

C.

,

Demeure

N.

,

Whitley

H.

,

Kannan

M.

,

Balz

R.

,

Anthony

L.F.E.

et al. .

Large-scale neuroanatomical study uncovers 198 gene associations in mouse brain morphogenesis

.

Nat. Commun.

2019

;

10

:

3465

.

Ingham

N.J.

,

Pearson

S.A.

,

Vancollie

V.E.

,

Rook

V.

,

Lewis

M.A.

,

Chen

J.

,

Buniello

A.

,

Martelletti

E.

,

Preite

L.

,

Lam

C.C.

et al. .

Mouse screen reveals multiple new genes underlying mouse and human hearing loss

.

PLoS Biol.

2019

;

17

:

e3000194

.

van der Weyden

L.

,

Swiatkowska

A.

,

Iyer

V.

,

Speak

A.O.

,

Adams

D.J.

A genome-wide screen in mice to identify cell-extrinsic regulators of pulmonary metastatic colonisation

.

G3 (Bethesda)

.

2020

;

10

:

1869

–

1877

.

Wotton

J.M.

,

Peterson

E.

,

Anderson

L.

,

Murray

S.A.

,

Braun

R.E.

,

Chesler

E.J.

,

White

J.K.

,

Kumar

V.

Machine learning-based automated phenotyping of inflammatory nocifensive behavior in mice

.

Mol. Pain

.

2020

;

16

:

1744806920958596

.

Horner

N.R.

,

Venkataraman

S.

,

Armit

C.

,

Casero

R.

,

Brown

J.M.

,

Wong

M.D.

,

van Eede

M.C.

,

Henkelman

R.M.

,

Johnson

S.

,

Teboul

L.

et al. .

LAMA: automated image analysis for the developmental phenotyping of mouse embryos

.

Development

.

2021

;

148

:

dev192955

.

Haselimashhadi

H.

,

Mason

J.C.

,

Munoz-Fuentes

V.

,

López-Gómez

F.

,

Babalola

K.

,

Acar

E.F.

,

Kumar

V.

,

White

J.

,

Flenniken

A.M.

,

King

R.

et al. .

Soft windowing application to improve analysis of high-throughput phenotyping data

.

Bioinformatics

.

2020

;

36

:

1492

–

1500

.

Smedley

D.

,

Oellrich

A.

,

Köhler

S.

,

Ruef

B.

,

Project

S.M.G.

,

Westerfield

M.

,

Robinson

P.

,

Lewis

S.

,

Mungall

C.

PhenoDigm: analyzing curated annotations to associate animal models with human diseases

.

Database (Oxford)

.

2013

;

2013

:

bat025

.

Sheils

T.

,

Mathias

S.L.

,

Siramshetty

V.B.

,

Bocci

G.

,

Bologa

C.G.

,

Yang

J.J.

,

Waller

A.

,

Southall

N.

,

Nguyen

D.-T.

,

Tudor

I.

How to illuminate the Druggable Genome using Pharos

.

Curr. Protoc. Bioinform.

2020

;

69

:

e92

.

Ochoa

D.

,

Hercules

A.

,

Carmona

M.

,

Suveges

D.

,

Gonzalez-Uriarte

A.

,

Malangone

C.

,

Miranda

A.

,

Fumis

L.

,

Carvalho-Silva

D.

,

Spitzer

M.

et al. .

Open Targets Platform: supporting systematic drug-target identification and prioritisation

.

Nucleic Acids Res.

2021

;

49

:

D1302

–

D1310

.

Jung

C.J.

,

Zhang

J.

,

Trenchard

E.

,

Lloyd

K.C.

,

West

D.B.

,

Rosen

B.

,

de Jong

P.J.

Efficient gene targeting in mouse zygotes mediated by CRISPR/Cas9-protein

.

Transgenic Res.

2017

;

26

:

263

–

277

.

Birling

M.-C.

,

Yoshiki

A.

,

Adams

D.J.

,

Ayabe

S.

,

Beaudet

A.L.

,

Bottomley

J.

,

Bradley

A.

,

Brown

S.D.M.

,

Antje Bürger

,

Bushell

W.

et al. .

A resource of targeted mutant mouse lines for 5,061 genes

.

Nat. Genet.

2021

;

53

:

416

–

419

.

Peterson

K.A.

,

Khalouei

S.

,

Wood

J.A.

,

Lanza

D.G.

,

Lintott

L.G.

,

Willis

B.J.

,

Seavitt

J.R.

,

Hanafi

N.

,

Braun

R.E.

,

Dickinson

M.E.

et al. .

Whole genome analysis for 163 guide RNAs in Cas9 edited mice reveals minimal off-target activity

.

2021

;

bioRxiv doi:

11 August 2021, preprint: not peer reviewed

https://doi.org/10.1101/2021.08.11.455876.

Elrick

H.

,

Peterson

K.A.

,

Wood

J.A.

,

Lanza

D.G.

,

Acar

E.F.

,

Ryder

E.J.

,

Ayabe

S.

,

Birling

M.-C.

,

Caulder

A.

,

Chiani

F.

et al. .

The production of 4,182 mouse lines identifies experimental and biological variables impacting Cas9-mediated mutant mouse line production

.

2021

;

bioRxiv doi:

06 October 2021, preprint: not peer reviewed

https://doi.org/10.1101/2021.10.06.463037.

Amberger

J.S.

,

Bocchini

C.A.

,

Scott

A.F.

,

Hamosh

A.

OMIM.org: leveraging knowledge across phenotype-gene relationships

.

Nucleic Acids Res.

2019

;

47

:

D1038

–

D1043

.

Pavan

S.

,

Rommel

K.

,

Marquina

M.E.M.

,

Höhn

S.

,

Lanneau

V.

,

Rath

A.

Clinical practice guidelines for rare diseases: the Orphanet Database

.

PLoS One

.

2017

;

12

:

e0170365

.

© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

I agree to the terms and conditions. You must accept the terms and conditions.

Submit a comment

Name

Affiliations

Comment title

Comment

You have entered an invalid code

Thank you for submitting a comment on this article. Your comment will be reviewed and published at the journal's discretion. Please check for further notifications by email.

Citations

Views

Altmetric

Metrics

Total Views 5,855

4,482 Pageviews

1,373 PDF Downloads

Since 10/1/2022

Citations

120 Web of Science

×

Email alerts

Citing articles via

• Latest

• Most Read

• Most Cited

More from Oxford Academic