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Kenny J, Mullin BH, Tomlinson W, Robertson B, Yuan J, Chen W, Zhao J, Pavlos NJ, Walsh JP, Wilson SG, Tickner J, Morahan G, Xu J. Age-dependent genetic regulation of osteoarthritis: independent effects of immune system genes. Arthritis Res Ther 2023; 25:232. [PMID: 38041181 PMCID: PMC10691153 DOI: 10.1186/s13075-023-03216-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/22/2023] [Indexed: 12/03/2023] Open
Abstract
OBJECTIVES Osteoarthritis (OA) is a joint disease with a heritable component. Genetic loci identified via genome-wide association studies (GWAS) account for an estimated 26.3% of the disease trait variance in humans. Currently, there is no method for predicting the onset or progression of OA. We describe the first use of the Collaborative Cross (CC), a powerful genetic resource, to investigate knee OA in mice, with follow-up targeted multi-omics analysis of homologous regions of the human genome. METHODS We histologically screened 275 mice for knee OA and conducted quantitative trait locus (QTL) mapping in the complete cohort (> 8 months) and the younger onset sub-cohort (8-12 months). Multi-omic analysis of human genetic datasets was conducted to investigate significant loci. RESULTS We observed a range of OA phenotypes. QTL mapping identified a genome-wide significant locus on mouse chromosome 19 containing Glis3, the human equivalent of which has been identified as associated with OA in recent GWAS. Mapping the younger onset sub-cohort identified a genome-wide significant locus on chromosome 17. Multi-omic analysis of the homologous region of the human genome (6p21.32) indicated the presence of pleiotropic effects on the expression of the HLA - DPB2 gene and knee OA development risk, potentially mediated through the effects on DNA methylation. CONCLUSIONS The significant associations at the 6p21.32 locus in human datasets highlight the value of the CC model of spontaneous OA that we have developed and lend support for an immune role in the disease. Our results in mice also add to the accumulating evidence of a role for Glis3 in OA.
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Affiliation(s)
- Jacob Kenny
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia.
| | - Benjamin H Mullin
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - William Tomlinson
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
| | - Brett Robertson
- Australian Institute of Robotic Orthopaedics, Crawley, WA, Australia
| | - Jinbo Yuan
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
| | - Weiwei Chen
- Research Centre for Regenerative Medicine, and Guangxi Key Laboratory of Regenerative Medicine, Guangxi Medical University, Guangxi, China
| | - Jinmin Zhao
- Research Centre for Regenerative Medicine, and Guangxi Key Laboratory of Regenerative Medicine, Guangxi Medical University, Guangxi, China
| | - Nathan J Pavlos
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
| | - John P Walsh
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
- Medical School, University of Western Australia, Crawley, WA, Australia
| | - Scott G Wilson
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
- Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jennifer Tickner
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
| | - Grant Morahan
- Centre for Diabetes Research, Harry Perkins Institute for Medical Research, Nedlands, WA, Australia.
| | - Jiake Xu
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, 6009, Australia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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Meier MJ, Beal MA, Schoenrock A, Yauk CL, Marchetti F. Whole Genome Sequencing of the Mutamouse Model Reveals Strain- and Colony-Level Variation, and Genomic Features of the Transgene Integration Site. Sci Rep 2019; 9:13775. [PMID: 31551502 PMCID: PMC6760142 DOI: 10.1038/s41598-019-50302-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 09/05/2019] [Indexed: 12/30/2022] Open
Abstract
The MutaMouse transgenic rodent model is widely used for assessing in vivo mutagenicity. Here, we report the characterization of MutaMouse's whole genome sequence and its genetic variants compared to the C57BL/6 reference genome. High coverage (>50X) next-generation sequencing (NGS) of whole genomes from multiple MutaMouse animals from the Health Canada (HC) colony showed ~5 million SNVs per genome, ~20% of which are putatively novel. Sequencing of two animals from a geographically separated colony at Covance indicated that, over the course of 23 years, each colony accumulated 47,847 (HC) and 17,677 (Covance) non-parental homozygous single nucleotide variants. We found no novel nonsense or missense mutations that impair the MutaMouse response to genotoxic agents. Pairing sequencing data with array comparative genomic hybridization (aCGH) improved the accuracy and resolution of copy number variants (CNVs) calls and identified 300 genomic regions with CNVs. We also used long-read sequence technology (PacBio) to show that the transgene integration site involved a large deletion event with multiple inversions and rearrangements near a retrotransposon. The MutaMouse genome gives important genetic context to studies using this model, offers insight on the mechanisms of structural variant formation, and contributes a framework to analyze aCGH results alongside NGS data.
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Affiliation(s)
- Matthew J Meier
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.,Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Marc A Beal
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.,Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, Canada
| | - Andrew Schoenrock
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Carole L Yauk
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada
| | - Francesco Marchetti
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada.
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Abstract
The Collaborative Cross (CC) was designed to facilitate rapid gene mapping and consists of hundreds of recombinant inbred lines descended from eight diverse inbred founder strains. A decade in production, it can now be applied to mapping projects. Here, we provide a proof of principle for rapid identification of major-effect genes using the CC. To do so, we chose coat color traits since the location and identity of many relevant genes are known. We ascertained in 110 CC lines six different coat phenotypes: albino, agouti, black, cinnamon, and chocolate coat colors and the white-belly trait. We developed a pipeline employing modifications of existing mapping tools suitable for analyzing the complex genetic architecture of the CC. Together with analysis of the founders’ genome sequences, mapping was successfully achieved with sufficient resolution to identify the causative genes for five traits. Anticipating the application of the CC to complex traits, we also developed strategies to detect interacting genes, testing joint effects of three loci. Our results illustrate the power of the CC and provide confidence that this resource can be applied to complex traits for detection of both qualitative and quantitative trait loci.
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