Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns

Brommage, Robert; Powell, David R.; Vogel, Peter

doi:10.1242/dmm.038224

Cited by 19 publications

(27 citation statements)

References 234 publications

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“…Lexicon Pharmaceuticals' Genome5000 TM effort examining the druggable genome confirmed known bone phenotypes for 23 genes and identified 11 genes, including Notum (86), for which bone phenotypes were not previously characterized (87). Importantly, skeletal phenotypes were described for Fam20c (non-lethal Raine syndrome), Lrrk1 (osteosclerotic metaphyseal dysplasia), Pappa2 (short stature), Sfrp4 (Pyle's disease), and Slc10a7 (skeletal dysplasia) prior to knowledge of the human skeletal dysplasias when mutated in humans (84). For the 439 mouse genes discussed in this review, 149 genes have been examined by the IMPC, yielding 63 viable adult homozygous mouse mutants.…”

Section: Mouse Modelsmentioning

confidence: 87%

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High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders

Brommage

Ohlsson

2020

Front. Endocrinol.

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The 2019 International Skeletal Dysplasia Society nosology update lists 441 genes for which mutations result in rare human skeletal disorders. These genes code for enzymes (33%), scaffolding proteins (18%), signal transduction proteins (16%), transcription factors (14%), cilia proteins (8%), extracellular matrix proteins (5%), and membrane transporters (4%). Skeletal disorders include aggrecanopathies, channelopathies, ciliopathies, cohesinopathies, laminopathies, linkeropathies, lysosomal storage diseases, protein-folding and RNA splicing defects, and ribosomopathies. With the goal of evaluating the ability of mouse models to mimic these human genetic skeletal disorders, a PubMed literature search identified 260 genes for which mutant mice were examined for skeletal phenotypes. These mouse models included spontaneous and ENU-induced mutants, global and conditional gene knockouts, and transgenic mice with gene over-expression or specific base-pair substitutions. The human X-linked gene ARSE and small nuclear RNA U4ATAC, a component of the minor spliceosome, do not have mouse homologs. Mouse skeletal phenotypes mimicking human skeletal disorders were observed in 249 of the 260 genes (96%) for which comparisons are possible. A supplemental table in spreadsheet format provides PubMed weblinks to representative publications of mutant mouse skeletal phenotypes. Mutations in 11 mouse genes (Ccn6,

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Section: Mouse Modelsmentioning

confidence: 87%

“…Two high-throughput mouse reverse genetics gene knockout phenotyping campaigns have been undertaken (84). The International Mouse Phenotyping Consortium (IMPC, www.mousephenotype.org) aims to characterize knockout mouse phenotypes for all 20,000 genes (74,85).…”

Section: Mouse Modelsmentioning

confidence: 99%

High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders

Brommage

Ohlsson

2020

Front. Endocrinol.

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“…Unfortunately, MGI data are not readily accessible via an application program interface (API), like the IMPC data, making it more difficult to incorporate into an automated pipeline. Furthermore, there may be bias introduced when incorporating evidence obtained from data generated in disease-specific studies as many of these focus on confirming reports in humans and may only test for specific traits reflecting the implicated human disease [ 51 , 52 ]. It is expected that as the IMPC database expands to include more genes, the automated gene prioritization pipeline we developed will also improve.…”

Section: Discussionmentioning

confidence: 99%

An Automated Functional Annotation Pipeline That Rapidly Prioritizes Clinically Relevant Genes for Autism Spectrum Disorder

Veatch

Butler

Elsea

et al. 2020

IJMS

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Human genetic studies have implicated more than a hundred genes in Autism Spectrum Disorder (ASD). Understanding how variation in implicated genes influence expression of co-occurring conditions and drug response can inform more effective, personalized approaches for treatment of individuals with ASD. Rapidly translating this information into the clinic requires efficient algorithms to sort through the myriad of genes implicated by rare gene-damaging single nucleotide and copy number variants, and common variation detected in genome-wide association studies (GWAS). To pinpoint genes that are more likely to have clinically relevant variants, we developed a functional annotation pipeline. We defined clinical relevance in this project as any ASD associated gene with evidence indicating a patient may have a complex, co-occurring condition that requires direct intervention (e.g., sleep and gastrointestinal disturbances, attention deficit hyperactivity, anxiety, seizures, depression), or is relevant to drug development and/or approaches to maximizing efficacy and minimizing adverse events (i.e., pharmacogenomics). Starting with a list of all candidate genes implicated in all manifestations of ASD (i.e., idiopathic and syndromic), this pipeline uses databases that represent multiple lines of evidence to identify genes: (1) expressed in the human brain, (2) involved in ASD-relevant biological processes and resulting in analogous phenotypes in mice, (3) whose products are targeted by approved pharmaceutical compounds or possessing pharmacogenetic variation and (4) whose products directly interact with those of genes with variants recommended to be tested for by the American College of Medical Genetics (ACMG). Compared with 1000 gene sets, each with a random selection of human protein coding genes, more genes in the ASD set were annotated for each category evaluated (p ≤ 1.99 × 10−2). Of the 956 ASD-implicated genes in the full set, 18 were flagged based on evidence in all categories. Fewer genes from randomly drawn sets were annotated in all categories (x = 8.02, sd = 2.56, p = 7.75 × 10−4). Notably, none of the prioritized genes are represented among the 59 genes compiled by the ACMG, and 78% had a pathogenic or likely pathogenic variant in ClinVar. Results from this work should rapidly prioritize potentially actionable results from genetic studies and, in turn, inform future work toward clinical decision support for personalized care based on genetic testing.

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“…Occasionally, the two physicians interact to gather more data before a final diagnosis can be made. Additional experiments are sometimes required to validate novel gene and/or mutation functions, although this is being ameliorated by large-scale phenotyping efforts 11 . The bottleneck, however, is in the training and certification of these multi-disciplinary teams.…”

Section: Rare Diseases For the Short Termmentioning

confidence: 99%

Rare versus common diseases: a false dichotomy in precision medicine

2021

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Precision medicine initiatives are being launched worldwide, each with the capacity to sequence many thousands to millions of human genomes. At the strategic planning level, all are debating the extent to which these resources will be directed towards rare diseases (and cancers) versus common diseases. However, these are not mutually exclusive choices. The organizational and governmental infrastructure created for rare diseases is extensible to common diseases. As we will explain, the underlying technology can also be used to identify drug targets for common diseases with a strategy focused on naturally occurring human knockouts. This flips on its head the prevailing modus operandi of studying people with diseases of interest, shifting the onus to defining traits worth emulating by pharmaceuticals, and searching phenotypically for people with these traits. This also shifts the question of what is rare or common from the many underlying causes to the possibility of a common final pathway.

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Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns

Cited by 19 publications

References 234 publications

High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders

High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders

An Automated Functional Annotation Pipeline That Rapidly Prioritizes Clinically Relevant Genes for Autism Spectrum Disorder

Rare versus common diseases: a false dichotomy in precision medicine

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