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Chen PB, Chen R, LaPierre N, Chen Z, Mefford J, Marcus E, Heffel MG, Soto DC, Ernst J, Luo C, Flint J. Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior. CELL GENOMICS 2024; 4:100545. [PMID: 38697120 PMCID: PMC11099346 DOI: 10.1016/j.xgen.2024.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/23/2024] [Accepted: 04/04/2024] [Indexed: 05/04/2024]
Abstract
Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.
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Affiliation(s)
- Patrick B Chen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Rachel Chen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Chen
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joel Mefford
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Emilie Marcus
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Matthew G Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniela C Soto
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason Ernst
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Fisher SA, Patrick K, Hoang T, Marcq E, Behrouzfar K, Young S, Miller TJ, Robinson BWS, Bueno R, Nowak AK, Lesterhuis WJ, Morahan G, Lake RA. The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop. FRONTIERS IN TOXICOLOGY 2024; 6:1373003. [PMID: 38694815 PMCID: PMC11061428 DOI: 10.3389/ftox.2024.1373003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Objectives: This study combines two innovative mouse models in a major gene discovery project to assess the influence of host genetics on asbestos related disease (ARD). Conventional genetics studies provided evidence that some susceptibility to mesothelioma is genetic. However, the identification of host modifier genes, the roles they may play, and whether they contribute to disease susceptibility remain unknown. Here we report a study designed to rapidly identify genes associated with mesothelioma susceptibility by combining the Collaborative Cross (CC) resource with the well-characterised MexTAg mesothelioma mouse model. Methods: The CC is a powerful mouse resource that harnesses over 90% of common genetic variation in the mouse species, allowing rapid identification of genes mediating complex traits. MexTAg mice rapidly, uniformly, and predictably develop mesothelioma, but only after asbestos exposure. To assess the influence of host genetics on ARD, we crossed 72 genetically distinct CC mouse strains with MexTAg mice and exposed the resulting CC-MexTAg (CCMT) progeny to asbestos and monitored them for traits including overall survival, the time to ARD onset (latency), the time between ARD onset and euthanasia (disease progression) and ascites volume. We identified phenotype-specific modifier genes associated with these traits and we validated the role of human orthologues in asbestos-induced carcinogenesis using human mesothelioma datasets. Results: We generated 72 genetically distinct CCMT strains and exposed their progeny (2,562 in total) to asbestos. Reflecting the genetic diversity of the CC, there was considerable variation in overall survival and disease latency. Surprisingly, however, there was no variation in disease progression, demonstrating that host genetic factors do have a significant influence during disease latency but have a limited role once disease is established. Quantitative trait loci (QTL) affecting ARD survival/latency were identified on chromosomes 6, 12 and X. Of the 97-protein coding candidate modifier genes that spanned these QTL, eight genes (CPED1, ORS1, NDUFA1, HS1BP3, IL13RA1, LSM8, TES and TSPAN12) were found to significantly affect outcome in both CCMT and human mesothelioma datasets. Conclusion: Host genetic factors affect susceptibility to development of asbestos associated disease. However, following mesothelioma establishment, genetic variation in molecular or immunological mechanisms did not affect disease progression. Identification of multiple candidate modifier genes and their human homologues with known associations in other advanced stage or metastatic cancers highlights the complexity of ARD and may provide a pathway to identify novel therapeutic targets.
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Affiliation(s)
- Scott A. Fisher
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- School of Biomedical Sciences, The University of Western Australia, Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
| | - Kimberley Patrick
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- School of Biomedical Sciences, The University of Western Australia, Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
| | - Tracy Hoang
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
| | - Elly Marcq
- Center for Oncological Research (CORE), University of Antwerp, Antwerp, Belgium
- Lab of Dendritic Cell Biology and Cancer Immunotherapy, VIB Center for Inflammation Research, Brussels, Belgium
- Brussels Center for Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Kiarash Behrouzfar
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- School of Biomedical Sciences, The University of Western Australia, Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
| | - Sylvia Young
- Centre for Diabetes Research, Harry Perkins Institute of Medical Research, Perth, WA, Australia
| | - Timothy J. Miller
- Medical School, The University of Western Australia, Perth, WA, Australia
| | - Bruce W. S. Robinson
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
- Medical School, The University of Western Australia, Perth, WA, Australia
| | - Raphael Bueno
- Division of Thoracic Surgery, The Lung Center and the International Mesothelioma Program, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Anna K. Nowak
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
- Medical School, The University of Western Australia, Perth, WA, Australia
| | | | - Grant Morahan
- Centre for Diabetes Research, Harry Perkins Institute of Medical Research, Perth, WA, Australia
| | - Richard A. Lake
- National Centre for Asbestos Related Diseases (NCARD), Perth, WA, Australia
- Institute for Respiratory Health, University of Western Australia, Perth, WA, Australia
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3
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Yosief RHS, Lone IM, Nachshon A, Himmelbauer H, Gat‐Viks I, Iraqi FA. Identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA-Seq approach. Animal Model Exp Med 2024; 7:36-47. [PMID: 38356021 PMCID: PMC10961901 DOI: 10.1002/ame2.12386] [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: 08/23/2023] [Accepted: 12/15/2023] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Aspergillus fumigatus (Af) is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic background. The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus (Af) using an RNAseq approach in CC lines and hepatic gene expression. METHODS We studied 31 male mice from 25 CC lines at 8 weeks old; the mice were infected with Af. Liver tissues were extracted from these mice 5 days post-infection, and next-generation RNA-sequencing (RNAseq) was performed. The GENE-E analysis platform was used to generate a clustered heat map matrix. RESULTS Significant variation in body weight changes between CC lines was observed. Hepatic gene expression revealed 12 top prioritized candidate genes differentially expressed in resistant versus susceptible mice based on body weight changes. Interestingly, three candidate genes are located within genomic intervals of the previously mapped quantitative trait loci (QTL), including Gm16270 and Stox1 on chromosome 10 and Gm11033 on chromosome 8. CONCLUSIONS Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af. As a next step, eQTL analysis will be performed for our RNA-Seq data. Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.
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Affiliation(s)
- Roa'a H. S. Yosief
- Department of Clinical Microbiology and Immunology, Sackler Faculty of MedicineTel‐Aviv UniversityTel‐AvivIsrael
| | - Iqbal M. Lone
- Department of Clinical Microbiology and Immunology, Sackler Faculty of MedicineTel‐Aviv UniversityTel‐AvivIsrael
| | - Aharon Nachshon
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life SciencesTel Aviv UniversityTel AvivIsrael
| | - Heinz Himmelbauer
- Institute of Computational Biology, Department of Biotechnology, University of Natural Resources and Life Sciences, Muthgasse 181190 ViennaAustria
| | - Irit Gat‐Viks
- School of Molecular Cell Biology and Biotechnology, George S. Wise Faculty of Life SciencesTel Aviv UniversityTel AvivIsrael
| | - Fuad A. Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of MedicineTel‐Aviv UniversityTel‐AvivIsrael
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Chen PB, Chen R, LaPierre N, Chen Z, Mefford J, Marcus E, Heffel MG, Soto DC, Ernst J, Luo C, Flint J. Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574060. [PMID: 38260483 PMCID: PMC10802323 DOI: 10.1101/2024.01.03.574060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Knowing the genes involved in quantitative traits provides a critical entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.
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5
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Odell SG, Hudson AI, Praud S, Dubreuil P, Tixier MH, Ross-Ibarra J, Runcie DE. Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci. G3 (BETHESDA, MD.) 2022; 12:6509518. [PMID: 35100382 PMCID: PMC8895984 DOI: 10.1093/g3journal/jkac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022]
Abstract
The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.
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Affiliation(s)
- Sarah G Odell
- Department of Plant Sciences, University of California, Davis, CA 95616, USA.,Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA
| | - Sébastien Praud
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | - Pierre Dubreuil
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | | | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA.,Genome Center, University of California, Davis, CA 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
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Follistatin mediates learning and synaptic plasticity via regulation of Asic4 expression in the hippocampus. Proc Natl Acad Sci U S A 2021; 118:2109040118. [PMID: 34544873 PMCID: PMC8488609 DOI: 10.1073/pnas.2109040118] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2021] [Indexed: 11/18/2022] Open
Abstract
Adult neurogenesis, which is known to be a heritable trait, is thought to be involved in learning, stress-related anxiety, and antidepressant action. In this study, we map genes influencing adult neurogenesis and identify a candidate gene, follistatin (Fst) for further study. By utilizing a brain-specific knockout and viral vector-mediated gene transfer, we reveal the importance of hippocampal FST in neurogenesis, learning, and synaptic plasticity. From RNA sequencing and chromatin immunoprecipitation experiments, we identify Asic4 as a critical downstream target gene regulated by FST. Our work demonstrates that FST functions in the hippocampus at least in part through regulating Asic4 expression. Overall, we illustrate the role of hippocampal Fst in learning and synaptic plasticity. The biological mechanisms underpinning learning are unclear. Mounting evidence has suggested that adult hippocampal neurogenesis is involved although a causal relationship has not been well defined. Here, using high-resolution genetic mapping of adult neurogenesis, combined with sequencing information, we identify follistatin (Fst) and demonstrate its involvement in learning and adult neurogenesis. We confirmed that brain-specific Fst knockout (KO) mice exhibited decreased hippocampal neurogenesis and demonstrated that FST is critical for learning. Fst KO mice exhibit deficits in spatial learning, working memory, and long-term potentiation (LTP). In contrast, hippocampal overexpression of Fst in KO mice reversed these impairments. By utilizing RNA sequencing and chromatin immunoprecipitation, we identified Asic4 as a target gene regulated by FST and show that Asic4 plays a critical role in learning deficits caused by Fst deletion. Long-term overexpression of hippocampal Fst in C57BL/6 wild-type mice alleviates age-related decline in cognition, neurogenesis, and LTP. Collectively, our study reveals the functions for FST in adult neurogenesis and learning behaviors.
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Mosedale M, Cai Y, Eaddy JS, Kirby PJ, Wolenski FS, Dragan Y, Valdar W. Human-relevant mechanisms and risk factors for TAK-875-Induced liver injury identified via a gene pathway-based approach in Collaborative Cross mice. Toxicology 2021; 461:152902. [PMID: 34418498 PMCID: PMC8936092 DOI: 10.1016/j.tox.2021.152902] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/05/2021] [Accepted: 08/16/2021] [Indexed: 10/20/2022]
Abstract
Development of TAK-875 was discontinued when a small number of serious drug-induced liver injury (DILI) cases were observed in Phase 3 clinical trials. Subsequent studies have identified hepatocellular oxidative stress, mitochondrial dysfunction, altered bile acid homeostasis, and immune response as mechanisms of TAK-875 DILI and the contribution of genetic risk factors in oxidative response and mitochondrial pathways to the toxicity susceptibility observed in patients. We tested the hypothesis that a novel preclinical approach based on gene pathway analysis in the livers of Collaborative Cross mice could be used to identify human-relevant mechanisms of toxicity and genetic risk factors at the level of the hepatocyte as reported in a human genome-wide association study. Eight (8) male mice (4 matched pairs) from each of 45 Collaborative Cross lines were treated with a single oral (gavage) dose of either vehicle or 600 mg/kg TAK-875. As expected, liver injury was not detected histologically and few changes in plasma biomarkers of hepatotoxicity were observed. However, gene expression profiling in the liver identified hundreds of transcripts responsive to TAK-875 treatment across all strains reflecting alterations in immune response and bile acid homeostasis and the interaction of treatment and strain reflecting oxidative stress and mitochondrial dysfunction. Fold-change expression values were then used to develop pathway-based phenotypes for genetic mapping which identified candidate risk factor genes for TAK-875 toxicity susceptibility at the level of the hepatocyte. Taken together, these findings support our hypothesis that a gene pathway-based approach using Collaborative Cross mice could inform sensitive strains, human-relevant mechanisms of toxicity, and genetic risk factors for TAK-875 DILI. This novel preclinical approach may be helpful in understanding, predicting, and ultimately preventing clinical DILI for other drugs.
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Affiliation(s)
- Merrie Mosedale
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, NC, 27599, United States.
| | - Yanwei Cai
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States.
| | - J Scott Eaddy
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, NC, 27599, United States.
| | - Patrick J Kirby
- Takeda Pharmaceuticals International Co., Cambridge, MA, 02139, United States.
| | - Francis S Wolenski
- Takeda Pharmaceuticals International Co., Cambridge, MA, 02139, United States.
| | - Yvonne Dragan
- Takeda Pharmaceuticals International Co., Cambridge, MA, 02139, United States.
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States.
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Dorman A, Binenbaum I, Abu-Toamih Atamni HJ, Chatziioannou A, Tomlinson I, Mott R, Iraqi FA. Genetic mapping of novel modifiers for Apc Min induced intestinal polyps' development using the genetic architecture power of the collaborative cross mice. BMC Genomics 2021; 22:566. [PMID: 34294033 PMCID: PMC8299641 DOI: 10.1186/s12864-021-07890-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 07/14/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Familial adenomatous polyposis is an inherited genetic disease, characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli (Apc) gene. Mice carrying a nonsense mutation in the Apc gene at R850, which is designated ApcMin/+ (Multiple intestinal neoplasia), develop intestinal adenomas. Several genetic modifier loci of Min (Mom) were previously mapped, but so far, most of the underlying genes have not been identified. To identify novel modifier loci associated with ApcMin/+, we performed quantitative trait loci (QTL) analysis for polyp development using 49 F1 crosses between different Collaborative Cross (CC) lines and C57BL/6 J-ApcMin/+mice. The CC population is a genetic reference panel of recombinant inbred lines, each line independently descended from eight genetically diverse founder strains. C57BL/6 J-ApcMin/+ males were mated with females from 49 CC lines. F1 offspring were terminated at 23 weeks and polyp counts from three sub-regions (SB1-3) of small intestinal and colon were recorded. RESULTS The number of polyps in all these sub-regions and colon varied significantly between the different CC lines. At 95% genome-wide significance, we mapped nine novel QTL for variation in polyp number, with distinct QTL associated with each intestinal sub-region. QTL confidence intervals varied in width between 2.63-17.79 Mb. We extracted all genes in the mapped QTL at 90 and 95% CI levels using the BioInfoMiner online platform to extract, significantly enriched pathways and key linker genes, that act as regulatory and orchestrators of the phenotypic landscape associated with the ApcMin/+ mutation. CONCLUSIONS Genomic structure of the CC lines has allowed us to identify novel modifiers and confirmed some of the previously mapped modifiers. Key genes involved mainly in metabolic and immunological processes were identified. Future steps in this analysis will be to identify regulatory elements - and possible epistatic effects - located in the mapped QTL.
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Affiliation(s)
- Alexandra Dorman
- Department of Clinical Microbiology & Immunology, Sackler Faculty of Medicine, Ramat Aviv, 69978 Tel-Aviv, Israel
| | - Ilona Binenbaum
- Department of Biology, University of Patras, Patras, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Hanifa J. Abu-Toamih Atamni
- Department of Clinical Microbiology & Immunology, Sackler Faculty of Medicine, Ramat Aviv, 69978 Tel-Aviv, Israel
| | | | - Ian Tomlinson
- Cancer Research UK Edinburgh Centre, Charles and Ethel Barr Chair of Cancer Research, University of Edinburgh, Edinburgh, UK
| | - Richard Mott
- Department of Genetics, University Collage of London, London, UK
| | - Fuad A. Iraqi
- Department of Clinical Microbiology & Immunology, Sackler Faculty of Medicine, Ramat Aviv, 69978 Tel-Aviv, Israel
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Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength. Nat Commun 2021; 12:3408. [PMID: 34099702 PMCID: PMC8184749 DOI: 10.1038/s41467-021-23649-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 05/10/2021] [Indexed: 12/14/2022] Open
Abstract
Genome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes SERTAD4 and GLT8D2. We also perform GWAS in the DO for a wide-range of bone traits and identify Qsox1 as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics. Osteoporosis GWAS faces two challenges, causal gene discovery and a lack of phenotypic diversity. Here, the authors use the Diversity Outbred mouse population to inform human GWAS using networks and map genetic loci for 55 bone traits, identifying new potential bone strength genes.
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Puglisi D, Delbono S, Visioni A, Ozkan H, Kara İ, Casas AM, Igartua E, Valè G, Piero ARL, Cattivelli L, Tondelli A, Fricano A. Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction. FRONTIERS IN PLANT SCIENCE 2021; 12:664148. [PMID: 34108982 PMCID: PMC8183822 DOI: 10.3389/fpls.2021.664148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/26/2021] [Indexed: 06/12/2023]
Abstract
Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.
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Affiliation(s)
- Damiano Puglisi
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Stefano Delbono
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Andrea Visioni
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas, Avenue Hafiane Cherkaoui, Rabat, Morocco
| | - Hakan Ozkan
- Department of Field Crops, Faculty of Agriculture, University of Cukurova, Adana, Turkey
| | - İbrahim Kara
- Bahri Dagdas International Agricultural Research Institute, Konya, Turkey
| | - Ana M. Casas
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Ernesto Igartua
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Giampiero Valè
- DiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Vercelli, Italy
| | - Angela Roberta Lo Piero
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Alessandro Tondelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Agostino Fricano
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
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11
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Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, Clark M, Dell'Acqua M, Dixon LE, Filippi CV, Fradgley N, Gardner KA, Mackay IJ, O'Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh RK, Thudi M, Varshney RK, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb) 2020; 125:396-416. [PMID: 32616877 PMCID: PMC7784848 DOI: 10.1038/s41437-020-0336-6] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.
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Affiliation(s)
| | | | - Samer Amer
- University of Reading, Reading, RG6 6AH, UK
- Faculty of Agriculture, Alexandria University, Alexandria, 23714, Egypt
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jay Biernaskie
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Scott A Boden
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | | | | | - Laura E Dixon
- Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Carla V Filippi
- Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Nick Fradgley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- SRUC, West Mains Road, Kings Buildings, Edinburgh, EH9 3JG, UK
| | | | | | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rakesh Kumar Singh
- International Center for Biosaline Agriculture, Academic City, Dubai, United Arab Emirates
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Alex Whan
- CSIRO, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - James Cockram
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Mott
- UCL Genetics Institute, Gower Street, London, WC1E 6BT, UK
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12
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Crouse WL, Kelada SNP, Valdar W. Inferring the Allelic Series at QTL in Multiparental Populations. Genetics 2020; 216:957-983. [PMID: 33082282 PMCID: PMC7768242 DOI: 10.1534/genetics.120.303393] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 10/12/2020] [Indexed: 12/25/2022] Open
Abstract
Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.
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Affiliation(s)
- Wesley L Crouse
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Samir N P Kelada
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina 27599
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
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13
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Parker CC, Lusk R, Saba LM. Alcohol Sensitivity as an Endophenotype of Alcohol Use Disorder: Exploring Its Translational Utility between Rodents and Humans. Brain Sci 2020; 10:E725. [PMID: 33066036 PMCID: PMC7600833 DOI: 10.3390/brainsci10100725] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 12/21/2022] Open
Abstract
Alcohol use disorder (AUD) is a complex, chronic, relapsing disorder with multiple interacting genetic and environmental influences. Numerous studies have verified the influence of genetics on AUD, yet the underlying biological pathways remain unknown. One strategy to interrogate complex diseases is the use of endophenotypes, which deconstruct current diagnostic categories into component traits that may be more amenable to genetic research. In this review, we explore how an endophenotype such as sensitivity to alcohol can be used in conjunction with rodent models to provide mechanistic insights into AUD. We evaluate three alcohol sensitivity endophenotypes (stimulation, intoxication, and aversion) for their translatability across human and rodent research by examining the underlying neurobiology and its relationship to consumption and AUD. We show examples in which results gleaned from rodents are successfully integrated with information from human studies to gain insight in the genetic underpinnings of AUD and AUD-related endophenotypes. Finally, we identify areas for future translational research that could greatly expand our knowledge of the biological and molecular aspects of the transition to AUD with the broad hope of finding better ways to treat this devastating disorder.
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Affiliation(s)
- Clarissa C. Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT 05753, USA
| | - Ryan Lusk
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Laura M. Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
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14
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Chitre AS, Polesskaya O, Holl K, Gao J, Cheng R, Bimschleger H, Garcia Martinez A, George T, Gileta AF, Han W, Horvath A, Hughson A, Ishiwari K, King CP, Lamparelli A, Versaggi CL, Martin C, St Pierre CL, Tripi JA, Wang T, Chen H, Flagel SB, Meyer P, Richards J, Robinson TE, Palmer AA, Solberg Woods LC. Genome-Wide Association Study in 3,173 Outbred Rats Identifies Multiple Loci for Body Weight, Adiposity, and Fasting Glucose. Obesity (Silver Spring) 2020; 28:1964-1973. [PMID: 32860487 PMCID: PMC7511439 DOI: 10.1002/oby.22927] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/03/2020] [Accepted: 05/04/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Obesity is influenced by genetic and environmental factors. Despite the success of human genome-wide association studies, the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify the genetic loci underlying obesity and related morphometric and metabolic traits. METHODS This study measured obesity-relevant traits, including body weight, body length, BMI, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat genome-wide association study to date. Genetic loci were identified using a linear mixed model to account for the complex family relationships of the HS and using covariates to account for differences among the three phenotyping centers. RESULTS This study identified 32 independent loci, several of which contained only a single gene (e.g., Epha5, Nrg1, Klhl14) or obvious candidate genes (e.g., Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and there was extensive pleiotropy at individual loci. CONCLUSIONS This study demonstrates the utility of HS rats for investigating the genetics of obesity-related traits across institutions and identify several candidate genes for future functional testing.
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Affiliation(s)
- Apurva S Chitre
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Katie Holl
- Human and Molecular Genetic Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Riyan Cheng
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Hannah Bimschleger
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Angel Garcia Martinez
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Tony George
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, New York, USA
| | - Alexander F Gileta
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - Wenyan Han
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Aidan Horvath
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Alesa Hughson
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Keita Ishiwari
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, New York, USA
| | | | | | | | - Connor Martin
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, New York, USA
| | | | - Jordan A Tripi
- Department of Psychology, University at Buffalo, Buffalo, New York, USA
| | - Tengfei Wang
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Hao Chen
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Shelly B Flagel
- Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - Paul Meyer
- Department of Psychology, University at Buffalo, Buffalo, New York, USA
| | - Jerry Richards
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, New York, USA
| | - Terry E Robinson
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, California, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
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15
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Mosedale M, Cai Y, Eaddy JS, Corty RW, Nautiyal M, Watkins PB, Valdar W. Identification of Candidate Risk Factor Genes for Human Idelalisib Toxicity Using a Collaborative Cross Approach. Toxicol Sci 2020; 172:265-278. [PMID: 31501888 DOI: 10.1093/toxsci/kfz199] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Idelalisib is a phosphatidylinositol 3-kinase inhibitor highly selective for the delta isoform that has shown good efficacy in treating chronic lymphocytic leukemia and follicular lymphoma. In clinical trials, however, idelalisib was associated with rare, but potentially serious liver and lung toxicities. In this study, we used the Collaborative Cross (CC) mouse population to identify genetic factors associated with the drug response that may inform risk management strategies for idelalisib in humans. Eight male mice (4 matched pairs) from 50 CC lines were treated once daily for 14 days by oral gavage with either vehicle or idelalisib at a dose selected to achieve clinically relevant peak plasma concentrations (150 mg/kg/day). The drug was well tolerated across all CC lines, and there were no observations of overt liver injury. Differences across CC lines were seen in drug concentration in plasma samples collected at the approximate Tmax on study Days 1, 7, and 14. There were also small but statistically significant treatment-induced alterations in plasma total bile acids and microRNA-122, and these may indicate early hepatocellular stress required for immune-mediated hepatotoxicity in humans. Idelalisib treatment further induced significant elevations in the total cell count of terminal bronchoalveolar lavage fluid, which may be analogous to pneumonitis observed in the clinic. Genetic mapping identified loci associated with interim plasma idelalisib concentration and the other 3 treatment-related endpoints. Thirteen priority candidate quantitative trait genes identified in CC mice may now guide interrogation of risk factors for adverse drug responses associated with idelalisib in humans.
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Affiliation(s)
- Merrie Mosedale
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| | - Yanwei Cai
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Department of Genetics
| | - John Scott Eaddy
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709
| | | | - Manisha Nautiyal
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709
| | - Paul B Watkins
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| | - William Valdar
- Department of Genetics.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
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16
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Katz DC, Aponte JD, Liu W, Green RM, Mayeux JM, Pollard KM, Pomp D, Munger SC, Murray SA, Roseman CC, Percival CJ, Cheverud J, Marcucio RS, Hallgrímsson B. Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes. PLoS One 2020; 15:e0233377. [PMID: 32502155 PMCID: PMC7274373 DOI: 10.1371/journal.pone.0233377] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 05/04/2020] [Indexed: 02/06/2023] Open
Abstract
The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.
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Affiliation(s)
- David C. Katz
- Department of Cell Biology & Anatomy, Alberta Children’s Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, AB, Canada
| | - J. David Aponte
- Department of Cell Biology & Anatomy, Alberta Children’s Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, AB, Canada
| | - Wei Liu
- Department of Cell Biology & Anatomy, Alberta Children’s Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, AB, Canada
| | - Rebecca M. Green
- Department of Cell Biology & Anatomy, Alberta Children’s Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, AB, Canada
| | - Jessica M. Mayeux
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States of America
| | - K. Michael Pollard
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Daniel Pomp
- Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC, United States of America
| | - Steven C. Munger
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | | | - Charles C. Roseman
- Department of Evolution, Ecology, and Behavior, University of Illinois Urbana Champaign, Urbana, IL, United States of America
| | - Christopher J. Percival
- Department of Anthropology, Stony Brook University, Stony Brook, NY, United States of America
| | - James Cheverud
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
| | - Ralph S. Marcucio
- Department of Orthopaedic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, United States of America
| | - Benedikt Hallgrímsson
- Department of Cell Biology & Anatomy, Alberta Children’s Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, AB, Canada
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17
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Levy R, Levet C, Cohen K, Freeman M, Mott R, Iraqi F, Gabet Y. A genome-wide association study in mice reveals a role for Rhbdf2 in skeletal homeostasis. Sci Rep 2020; 10:3286. [PMID: 32094386 PMCID: PMC7039944 DOI: 10.1038/s41598-020-60146-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 02/06/2020] [Indexed: 12/16/2022] Open
Abstract
Low bone mass and an increased risk of fracture are predictors of osteoporosis. Individuals who share the same bone-mineral density (BMD) vary in their fracture risk, suggesting that microstructural architecture is an important determinant of skeletal strength. Here, we utilized the rich diversity of the Collaborative Cross mice to identify putative causal genes that contribute to the risk of fractures. Using microcomputed tomography, we examined key structural features that pertain to bone quality in the femoral cortical and trabecular compartments of male and female mice. We estimated the broad-sense heritability to be 50–60% for all examined traits, and we identified five quantitative trait loci (QTL) significantly associated with six traits. We refined each QTL by combining information inferred from the ancestry of the mice, ranging from RNA-Seq data and published literature to shortlist candidate genes. We found strong evidence for new candidate genes, particularly Rhbdf2, whose close association with the trabecular bone volume fraction and number was strongly suggested by our analyses. We confirmed our findings with mRNA expression assays of Rhbdf2 in extreme-phenotype mice, and by phenotyping bones of Rhbdf2 knockout mice. Our results indicate that Rhbdf2 plays a decisive role in bone mass accrual and microarchitecture.
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Affiliation(s)
- Roei Levy
- Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 69978, Israel.
| | - Clemence Levet
- Dunn School of Pathology, South Parks Road, Oxford, OX1 3RE, UK
| | - Keren Cohen
- Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Matthew Freeman
- Dunn School of Pathology, South Parks Road, Oxford, OX1 3RE, UK
| | - Richard Mott
- UCL Genetics Institute, University College London, Gower St., London, WC1E 6BT, UK
| | - Fuad Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yankel Gabet
- Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 69978, Israel
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18
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Keele GR, Quach BC, Israel JW, Chappell GA, Lewis L, Safi A, Simon JM, Cotney P, Crawford GE, Valdar W, Rusyn I, Furey TS. Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation. PLoS Genet 2020; 16:e1008537. [PMID: 31961859 PMCID: PMC7010298 DOI: 10.1371/journal.pgen.1008537] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 02/10/2020] [Accepted: 11/23/2019] [Indexed: 01/08/2023] Open
Abstract
Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues-liver, lung, and kidney-in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.
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Affiliation(s)
- Gregory R. Keele
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Bryan C. Quach
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Center for Omics Discovery and Epidemiology, Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, United States of America
| | - Jennifer W. Israel
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Grace A. Chappell
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Lauren Lewis
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Alexias Safi
- Department of Pediatrics, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Jeremy M. Simon
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Paul Cotney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Gregory E. Crawford
- Department of Pediatrics, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Terrence S. Furey
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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19
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Exploring genetic architecture of grain yield and quality traits in a 16-way indica by japonica rice MAGIC global population. Sci Rep 2019; 9:19605. [PMID: 31862941 PMCID: PMC6925145 DOI: 10.1038/s41598-019-55357-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 11/18/2019] [Indexed: 12/27/2022] Open
Abstract
Identification of Quantitative Trait Loci (QTL) has been a challenge for complex traits due to the use of populations with narrow genetic base. Most of QTL mapping studies were carried out from crosses made within the subspecies, either indica × indica or japonica × japonica. In this study we report advantages of using Multi-parent Advanced Generation Inter-Crosses global population, derived from a combination of eight indica and eight japonica elite parents, in QTL discovery for yield and grain quality traits. Genome-wide association study and interval mapping identified 38 and 34 QTLs whereas Bayesian networking detected 60 QTLs with 22 marker-marker associations, 32 trait-trait associations and 65 marker-trait associations. Notably, nine known QTLs/genes qPH1/OsGA20ox2, qDF3/OsMADS50, PL, QDg1, qGW-5b, grb7-2, qGL3/GS3, Amy6/Wx gene and OsNAS3 were consistently identified by all approaches for nine traits whereas qDF3/OsMADS50 was co-located for both yield and days-to-flowering traits on chromosome 3. Moreover, we identified a number of candidate QTLs in either one or two analyses but further validations will be needed. The results indicate that this new population has enabled identifications of significant QTLs and interactions for 16 traits through multiple approaches. Pyramided recombinant inbred lines provide a valuable source for integration into future breeding programs.
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20
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Abstract
In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS.
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Abstract
The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses from extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.
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22
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Noll KE, Ferris MT, Heise MT. The Collaborative Cross: A Systems Genetics Resource for Studying Host-Pathogen Interactions. Cell Host Microbe 2019; 25:484-498. [PMID: 30974083 PMCID: PMC6494101 DOI: 10.1016/j.chom.2019.03.009] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Host genetic variation has a major impact on infectious disease susceptibility. The study of pathogen resistance genes, largely aided by mouse models, has significantly advanced our understanding of infectious disease pathogenesis. The Collaborative Cross (CC), a newly developed multi-parental mouse genetic reference population, serves as a tractable model system to study how pathogens interact with genetically diverse populations. In this review, we summarize progress utilizing the CC as a platform to develop improved models of pathogen-induced disease and to map polymorphic host response loci associated with variation in susceptibility to pathogens.
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Affiliation(s)
- Kelsey E Noll
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Martin T Ferris
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Mark T Heise
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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23
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Abu‐Toamih Atamni HJ, Botzman M, Mott R, Gat‐Viks I, Iraqi FA. Mapping novel genetic loci associated with female liver weight variations using Collaborative Cross mice. Animal Model Exp Med 2018; 1:212-220. [PMID: 30891567 PMCID: PMC6388055 DOI: 10.1002/ame2.12036] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 09/03/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology. METHODS Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high-density single nucleotide polymorphic markers. RESULTS Statistical analysis revealed a significant (P < 0.05) variation of liver weight between the CC lines, with broad sense heritability (H 2) of 0.32 and genetic coefficient of variation (CVG) of 0.28. Subsequently, quantitative trait locus (QTL) mapping was performed, and results showed a significant QTL only for females on chromosome 8 at genomic interval 88.61-93.38 Mb (4.77 Mb). Three suggestive QTL were mapped at chromosomes 4, 12 and 13. The four QTL were designated as LWL1-LWL4 referring to liver weight loci 1-4 on chromosomes 8, 4, 12 and 13, respectively. CONCLUSION To our knowledge, this report presents, for the first time, the utilization of the CC for mapping QTL associated with baseline liver weight in mice. Our findings demonstrate that liver weight is a complex trait controlled by multiple genetic factors that differ significantly between sexes.
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Affiliation(s)
| | - Maya Botzman
- Faculty of Life SciencesTel‐Aviv UniversityTel‐AvivIsrael
| | - Richard Mott
- Department of GeneticsUniversity College of LondonLondonUK
| | - Irit Gat‐Viks
- Faculty of Life SciencesTel‐Aviv UniversityTel‐AvivIsrael
| | - Fuad A. Iraqi
- Sackler Faculty of MedicineTel‐Aviv UniversityTel‐AvivIsrael
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24
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Zhang J, Malo D, Mott R, Panthier JJ, Montagutelli X, Jaubert J. Identification of new loci involved in the host susceptibility to Salmonella Typhimurium in collaborative cross mice. BMC Genomics 2018; 19:303. [PMID: 29703142 PMCID: PMC5923191 DOI: 10.1186/s12864-018-4667-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 04/12/2018] [Indexed: 12/31/2022] Open
Abstract
Background Salmonella is a Gram-negative bacterium causing a wide range of clinical syndromes ranging from typhoid fever to diarrheic disease. Non-typhoidal Salmonella (NTS) serovars infect humans and animals, causing important health burden in the world. Susceptibility to salmonellosis varies between individuals under the control of host genes, as demonstrated by the identification of over 20 genetic loci in various mouse crosses. We have investigated the host response to S. Typhimurium infection in 35 Collaborative Cross (CC) strains, a genetic population which involves wild-derived strains that had not been previously assessed. Results One hundred and forty-eight mice from 35 CC strains were challenged intravenously with 1000 colony-forming units (CFUs) of S. Typhimurium. Bacterial load was measured in spleen and liver at day 4 post-infection. CC strains differed significantly (P < 0.0001) in spleen and liver bacterial loads, while sex and age had no effect. Two significant quantitative trait loci (QTLs) on chromosomes 8 and 10 and one suggestive QTL on chromosome 1 were found for spleen bacterial load, while two suggestive QTLs on chromosomes 6 and 17 were found for liver bacterial load. These QTLs are caused by distinct allelic patterns, principally involving alleles originating from the wild-derived founders. Using sequence variations between the eight CC founder strains combined with database mining for expression in target organs and known immune phenotypes, we were able to refine the QTLs intervals and establish a list of the most promising candidate genes. Furthermore, we identified one strain, CC042/GeniUnc (CC042), as highly susceptible to S. Typhimurium infection. Conclusions By exploring a broader genetic variation, the Collaborative Cross population has revealed novel loci of resistance to Salmonella Typhimurium. It also led to the identification of CC042 as an extremely susceptible strain. Electronic supplementary material The online version of this article (10.1186/s12864-018-4667-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jing Zhang
- Institut Pasteur, Department of Development & Stem Cell Biology, Mouse Functional Genetics, F-75015, Paris, France.,Centre National de la Recherche Scientifique, CNRS UMR 3738, F-75015, Paris, France
| | - Danielle Malo
- McGill University Research Centre on Complex Traits, Montreal, QC, Canada
| | - Richard Mott
- University College London, UCL Genetics Institute, London, UK
| | - Jean-Jacques Panthier
- Institut Pasteur, Department of Development & Stem Cell Biology, Mouse Functional Genetics, F-75015, Paris, France.,Centre National de la Recherche Scientifique, CNRS UMR 3738, F-75015, Paris, France
| | - Xavier Montagutelli
- Institut Pasteur, Department of Development & Stem Cell Biology, Mouse Functional Genetics, F-75015, Paris, France.,Centre National de la Recherche Scientifique, CNRS UMR 3738, F-75015, Paris, France
| | - Jean Jaubert
- Institut Pasteur, Department of Development & Stem Cell Biology, Mouse Functional Genetics, F-75015, Paris, France. .,Centre National de la Recherche Scientifique, CNRS UMR 3738, F-75015, Paris, France.
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25
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Mosedale M, Kim Y, Brock WJ, Roth SE, Wiltshire T, Eaddy JS, Keele GR, Corty RW, Xie Y, Valdar W, Watkins PB. Editor's Highlight: Candidate Risk Factors and Mechanisms for Tolvaptan-Induced Liver Injury Are Identified Using a Collaborative Cross Approach. Toxicol Sci 2018; 156:438-454. [PMID: 28115652 DOI: 10.1093/toxsci/kfw269] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Clinical trials of tolvaptan showed it to be a promising candidate for the treatment of Autosomal Dominant Polycystic Kidney Disease (ADPKD) but also revealed potential for idiosyncratic drug-induced liver injury (DILI) in this patient population. To identify risk factors and mechanisms underlying tolvaptan DILI, 8 mice in each of 45 strains of the genetically diverse Collaborative Cross (CC) mouse population were treated with a single oral dose of either tolvaptan or vehicle. Significant elevations in plasma alanine aminotransferase (ALT) were observed in tolvaptan-treated animals in 3 of the 45 strains. Genetic mapping coupled with transcriptomic analysis in the liver was used to identify several candidate susceptibility genes including epoxide hydrolase 2, interferon regulatory factor 3, and mitochondrial fission factor. Gene pathway analysis revealed that oxidative stress and immune response pathways were activated in response to tolvaptan treatment across all strains, but genes involved in regulation of bile acid homeostasis were most associated with tolvaptan-induced elevations in ALT. Secretory leukocyte peptidase inhibitor (Slpi) mRNA was also induced in the susceptible strains and was associated with increased plasma levels of Slpi protein, suggesting a potential serum marker for DILI susceptibility. In summary, tolvaptan induced signs of oxidative stress, mitochondrial dysfunction, and innate immune response in all strains, but variation in bile acid homeostasis was most associated with susceptibility to the liver response. This CC study has indicated potential mechanisms underlying tolvaptan DILI and biomarkers of susceptibility that may be useful in managing the risk of DILI in ADPKD patients.
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Affiliation(s)
- Merrie Mosedale
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| | - Yunjung Kim
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - William J Brock
- Otsuka Pharmaceutical Development and Commercialization, Inc., Rockville, Maryland 20850.,Brock Scientific Consulting, Montgomery Village, Maryland 20886
| | - Sharin E Roth
- Otsuka Pharmaceutical Development and Commercialization, Inc., Rockville, Maryland 20850
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599.,Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - J Scott Eaddy
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| | - Gregory R Keele
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - Robert W Corty
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - Yuying Xie
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - William Valdar
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599.,Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina 27599
| | - Paul B Watkins
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
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Abstract
Heterogeneous Stock (HS) populations allow for fine-resolution genetic mapping of a variety of complex traits. HS mice and rats were created from breeding together eight inbred strains, followed by maintaining the colony in a manner that minimizes inbreeding. After 50 or more generations of breeding, the resulting animals' chromosomes represent a genetic mosaic of the founders' haplotypes, with the average distance between recombination events in the centiMorgan range. This allows for genetic mapping to only a few Mb, a much smaller region than what can be identified using traditional F2 intercross or backcross mapping strategies. HS animals have been used to fine-map a variety of complex traits including anxiety and fear behaviors, diabetes, asthma, and heart disease, among others. Once a quantitative trait locus (QTL) has been identified, founder sequence and expression analysis can be used to identify underlying causal genes. In the following review, we provide an overview of how HS rats and mice have been used to identify genetic loci, and in some cases the causal genes, underlying complex traits. We discuss the creation and breeding strategies for both HS rats and mice. We then discuss the statistical analyses used to identify genetic loci, as well as strategies to identify causal genes underlying these loci. We end the chapter by discussing limitations faced when using HS populations, including several statistical challenges that have not been fully resolved.
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Affiliation(s)
- Leah C Solberg Woods
- Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53130, USA.
| | - Richard Mott
- UCL Genetics Institute, University College London, Gower St., London, WC1E 6BT, UK
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27
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Nashef A, Qabaja R, Salaymeh Y, Botzman M, Munz M, Dommisch H, Krone B, Hoffmann P, Wellmann J, Laudes M, Berger K, Kocher T, Loos B, van der Velde N, Uitterlinden AG, de Groot LCPGM, Franke A, Offenbacher S, Lieb W, Divaris K, Mott R, Gat-Viks I, Wiess E, Schaefer A, Iraqi FA, Haddad YH. Integration of Murine and Human Studies for Mapping Periodontitis Susceptibility. J Dent Res 2018; 97:537-546. [PMID: 29294296 DOI: 10.1177/0022034517744189] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Periodontitis is one of the most common inflammatory human diseases with a strong genetic component. Due to the limited sample size of available periodontitis cohorts and the underlying trait heterogeneity, genome-wide association studies (GWASs) of chronic periodontitis (CP) have largely been unsuccessful in identifying common susceptibility factors. A combination of quantitative trait loci (QTL) mapping in mice with association studies in humans has the potential to discover novel risk loci. To this end, we assessed alveolar bone loss in response to experimental periodontal infection in 25 lines (286 mice) from the Collaborative Cross (CC) mouse population using micro-computed tomography (µCT) analysis. The orthologous human chromosomal regions of the significant QTL were analyzed for association using imputed genotype data (OmniExpress BeadChip arrays) derived from case-control samples of aggressive periodontitis (AgP; 896 cases, 7,104 controls) and chronic periodontitis (CP; 2,746 cases, 1,864 controls) of northwest European and European American descent, respectively. In the mouse genome, QTL mapping revealed 2 significant loci (-log P = 5.3; false discovery rate = 0.06) on chromosomes 1 ( Perio3) and 14 ( Perio4). The mapping resolution ranged from ~1.5 to 3 Mb. Perio3 overlaps with a previously reported QTL associated with residual bone volume in F2 cross and includes the murine gene Ccdc121. Its human orthologue showed previously a nominal significant association with CP in humans. Use of variation data from the genomes of the CC founder strains further refined the QTL and suggested 7 candidate genes ( CAPN8, DUSP23, PCDH17, SNORA17, PCDH9, LECT1, and LECT2). We found no evidence of association of these candidates with the human orthologues. In conclusion, the CC populations enabled mapping of confined QTL that confer susceptibility to alveolar bone loss in mice and larger human phenotype-genotype samples and additional expression data from gingival tissues are likely required to identify true positive signals.
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Affiliation(s)
- A Nashef
- 1 Department of Prosthodontics, Hadassah Faculty of Dental Medicine, Hebrew University, Jerusalem, Israel
| | - R Qabaja
- 1 Department of Prosthodontics, Hadassah Faculty of Dental Medicine, Hebrew University, Jerusalem, Israel
| | - Y Salaymeh
- 2 Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - M Botzman
- 3 Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - M Munz
- 4 Department of Periodontology and Synoptic Medicine, Institute for Dental and Craniofacial Sciences, Charité-University Medicine Berlin, Berlin, Germany
- 5 Institute for Integrative and Experimental Genomics, University Medical Center Schleswig-Holstein-Campus, Lübeck, Germany
| | - H Dommisch
- 4 Department of Periodontology and Synoptic Medicine, Institute for Dental and Craniofacial Sciences, Charité-University Medicine Berlin, Berlin, Germany
| | - B Krone
- 6 Institute of Medical Informatics, Biometry and Epidemiology, University Clinic Essen, Essen, Germany
| | - P Hoffmann
- 7 Institute of Human Genetics, University of Bonn, Bonn, Germany
- 8 Germany und Human Genomics Research Group, Department of Biomedicine, University Hospital of Basel, Basel, Switzerland
| | - J Wellmann
- 9 Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - M Laudes
- 10 Clinic of Internal Medicine, University Clinic Schleswig-Holstein, Kiel, Germany
| | - K Berger
- 9 Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - T Kocher
- 11 Unit of Periodontology, Department of Restorative Dentistry, Periodontology, Endodontology, Preventive Dentistry and Pedodontics, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - B Loos
- 12 Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, the Netherlands
| | - N van der Velde
- 13 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
- 14 Department of Internal Medicine Section of Geriatrics, Amsterdam Medical Center, Amsterdam, the Netherlands
| | - A G Uitterlinden
- 13 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - L C P G M de Groot
- 15 Department of Epidemiology and the EMGO Institute of Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
| | - A Franke
- 16 Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
| | - S Offenbacher
- 17 School of Dentistry, Department of Periodontology, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - W Lieb
- 18 Institute of Epidemiology, Biobank popgen, Christian-Albrechts-University, Kiel, Germany
| | - K Divaris
- 19 Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- 20 School of Dentistry, Department of Pediatric Dentistry, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - R Mott
- 21 Genetics Institute, University Collage of London, London, UK
| | - I Gat-Viks
- 3 Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - E Wiess
- 22 Maurice and Gabriella Goldschleger School of Dental Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - A Schaefer
- 4 Department of Periodontology and Synoptic Medicine, Institute for Dental and Craniofacial Sciences, Charité-University Medicine Berlin, Berlin, Germany
| | - F A Iraqi
- 2 Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Y H Haddad
- 1 Department of Prosthodontics, Hadassah Faculty of Dental Medicine, Hebrew University, Jerusalem, Israel
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28
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Zhang K, Liu S, Li W, Liu S, Li X, Fang Y, Zhang J, Wang Y, Xu S, Zhang J, Song J, Qi Z, Tian X, Tian Z, Li WX, Ning H. Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies. FRONTIERS IN PLANT SCIENCE 2018; 9:1690. [PMID: 30519252 PMCID: PMC6258895 DOI: 10.3389/fpls.2018.01690] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 10/31/2018] [Indexed: 05/19/2023]
Abstract
Protein content (PC), an important trait in soybean (Glycine max) breeding, is controlled by multiple genes with relatively small effects. To identify the quantitative trait nucleotides (QTNs) controlling PC, we conducted a multi-locus genome-wide association study (GWAS) for PC in 144 four-way recombinant inbred lines (FW-RILs). All the FW-RILs were phenotyped for PC in 20 environments, including four locations over 4 years with different experimental treatments. Meanwhile, all the FW-RILs were genotyped using SoySNP660k BeadChip, producing genotype data for 109,676 non-redundant single-nucleotide polymorphisms. A total of 129 significant QTNs were identified by five multi-locus GWAS methods. Based on the 22 common QTNs detected by multiple GWAS methods or in multiple environments, pathway analysis identified 8 potential candidate genes that are likely to be involved in protein synthesis and metabolism in soybean seeds. Using superior allele information for 22 common QTNs in 22 elite and 7 inferior lines, we found higher superior allele percentages in the elite lines and lower percentages in the inferior lines. These findings will contribute to the discovery of the polygenic networks controlling PC in soybean, increase our understanding of the genetic foundation and regulation of PC, and be useful for molecular breeding of high-protein soybean varieties.
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Affiliation(s)
- Kaixin Zhang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shiping Liu
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xiyu Li
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yanlong Fang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jun Zhang
- College of Agronomy, Jilin Agricultural University, Changchun, China
| | - Yue Wang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shichao Xu
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jianan Zhang
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jie Song
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhongying Qi
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xiaocui Tian
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- *Correspondence: Wen-Xia Li, Hailong Ning,
| | - Hailong Ning
- Key Laboratory of Soybean Biology in the Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- Northeastern Key Laboratory of Soybean Biology and Breeding/Genetics in the Chinese Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- *Correspondence: Wen-Xia Li, Hailong Ning,
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29
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Oreper D, Cai Y, Tarantino LM, de Villena FPM, Valdar W. Inbred Strain Variant Database (ISVdb): A Repository for Probabilistically Informed Sequence Differences Among the Collaborative Cross Strains and Their Founders. G3 (BETHESDA, MD.) 2017; 7:1623-1630. [PMID: 28592645 PMCID: PMC5473744 DOI: 10.1534/g3.117.041491] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 03/20/2017] [Indexed: 02/07/2023]
Abstract
The Collaborative Cross (CC) is a panel of recently established multiparental recombinant inbred mouse strains. For the CC, as for any multiparental population (MPP), effective experimental design and analysis benefit from detailed knowledge of the genetic differences between strains. Such differences can be directly determined by sequencing, but until now whole-genome sequencing was not publicly available for individual CC strains. An alternative and complementary approach is to infer genetic differences by combining two pieces of information: probabilistic estimates of the CC haplotype mosaic from a custom genotyping array, and probabilistic variant calls from sequencing of the CC founders. The computation for this inference, especially when performed genome-wide, can be intricate and time-consuming, requiring the researcher to generate nontrivial and potentially error-prone scripts. To provide standardized, easy-to-access CC sequence information, we have developed the Inbred Strain Variant Database (ISVdb). The ISVdb provides, for all the exonic variants from the Sanger Institute mouse sequencing dataset, direct sequence information for CC founders and, critically, the imputed sequence information for CC strains. Notably, the ISVdb also: (1) provides predicted variant consequence metadata; (2) allows rapid simulation of F1 populations; and (3) preserves imputation uncertainty, which will allow imputed data to be refined in the future as additional sequencing and genotyping data are collected. The ISVdb information is housed in an SQL database and is easily accessible through a custom online interface (http://isvdb.unc.edu), reducing the analytic burden on any researcher using the CC.
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Affiliation(s)
- Daniel Oreper
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
| | - Yanwei Cai
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
| | - Lisa M Tarantino
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy University of North Carolina, Chapel Hill, North Carolina 27599-7265
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Lineberger Comprehensive Cancer Center
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Lineberger Comprehensive Cancer Center
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30
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Xiao Y, Liu H, Wu L, Warburton M, Yan J. Genome-wide Association Studies in Maize: Praise and Stargaze. MOLECULAR PLANT 2017; 10:359-374. [PMID: 28039028 DOI: 10.1016/j.molp.2016.12.008] [Citation(s) in RCA: 215] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2016] [Revised: 12/02/2016] [Accepted: 12/20/2016] [Indexed: 05/18/2023]
Abstract
Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype-phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.
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Affiliation(s)
- Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Haijun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Liuji Wu
- Synergetic Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China
| | - Marilyn Warburton
- United States of Department of Agriculture, Agricultural Research Service, Corn Host Plant Resistance Research Unit, Box 9555, MS 39762, Mississippi, USA
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
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Abstract
A key characteristic of systems genetics is its reliance on populations that vary to a greater or lesser degree in genetic complexity-from highly admixed populations such as the Collaborative Cross and Diversity Outcross to relatively simple crosses such as sets of consomic strains and reduced complexity crosses. This protocol is intended to help investigators make more informed decisions about choices of resources given different types of questions. We consider factors such as costs, availability, and ease of breeding for common scenarios. In general, we recommend using complementary resources and minimizing depth of resampling of any given genome or strain.
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Affiliation(s)
- Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, 77 S. Manassas Street, Memphis, TN, 38163, USA.
| | - Evan G Williams
- Department of Biology, Institute for Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
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Abstract
Infection is one of the leading causes of human mortality and morbidity. Exposure to microbial agents is obviously required. However, also non-microbial environmental and host factors play a key role in the onset, development and outcome of infectious disease, resulting in large of clinical variability between individuals in a population infected with the same microbe. Controlled and standardized investigations of the genetics of susceptibility to infectious disease are almost impossible to perform in humans whereas mouse models allow application of powerful genomic techniques to identify and validate causative genes underlying human diseases with complex etiologies. Most of current animal models used in complex traits diseases genetic mapping have limited genetic diversity. This limitation impedes the ability to create incorporated network using genetic interactions, epigenetics, environmental factors, microbiota, and other phenotypes. A novel mouse genetic reference population for high-resolution mapping and subsequently identifying genes underlying the QTL, namely the Collaborative Cross (CC) mouse genetic reference population (GRP) was recently developed. In this chapter, we discuss a variety of approaches using CC mice for mapping genes underlying quantitative trait loci (QTL) to dissect the host response to polygenic traits, including infectious disease caused by bacterial agents and its toxins.
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Glucose tolerance female-specific QTL mapped in collaborative cross mice. Mamm Genome 2016; 28:20-30. [PMID: 27807798 DOI: 10.1007/s00335-016-9667-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 10/12/2016] [Indexed: 12/11/2022]
Abstract
Type-2 diabetes (T2D) is a complex metabolic disease characterized by impaired glucose tolerance. Despite environmental high risk factors, host genetic background is a strong component of T2D development. Herein, novel highly genetically diverse strains of collaborative cross (CC) lines from mice were assessed to map quantitative trait loci (QTL) associated with variations of glucose-tolerance response. In total, 501 mice of 58 CC lines were maintained on high-fat (42 % fat) diet for 12 weeks. Thereafter, an intraperitoneal glucose tolerance test (IPGTT) was performed for 180 min. Subsequently, the values of Area under curve for the glucose at zero and 180 min (AUC0-180), were measured, and used for QTL mapping. Heritability and coefficient of variations in glucose tolerance (CVg) were calculated. One-way analysis of variation was significant (P < 0.001) for AUC0-180 between the CC lines as well between both sexes. Despite Significant variations for both sexes, QTL analysis was significant, only for females, reporting a significant female-sex-dependent QTL (~2.5 Mbp) associated with IPGTT AUC0-180 trait, located on Chromosome 8 (32-34.5 Mbp, containing 51 genes). Gene browse revealed QTL for body weight/size, genes involved in immune system, and two main protein-coding genes involved in the Glucose homeostasis, Mboat4 and Leprotl1. Heritability and coefficient of genetic variance (CVg) were 0.49 and 0.31 for females, while for males, these values 0.34 and 0.22, respectively. Our findings demonstrate the roles of genetic factors controlling glucose tolerance, which significantly differ between sexes requiring independent studies for females and males toward T2D prevention and therapy.
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Adams DJ, Rowe DW, Ackert-Bicknell CL. Genetics of aging bone. Mamm Genome 2016; 27:367-80. [PMID: 27272104 DOI: 10.1007/s00335-016-9650-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 05/24/2016] [Indexed: 01/08/2023]
Abstract
With aging, the skeleton experiences a number of changes, which include reductions in mass and changes in matrix composition, leading to fragility and ultimately an increase of fracture risk. A number of aspects of bone physiology are controlled by genetic factors, including peak bone mass, bone shape, and composition; however, forward genetic studies in humans have largely concentrated on clinically available measures such as bone mineral density (BMD). Forward genetic studies in rodents have also heavily focused on BMD; however, investigations of direct measures of bone strength, size, and shape have also been conducted. Overwhelmingly, these studies of the genetics of bone strength have identified loci that modulate strength via influencing bone size, and may not impact the matrix material properties of bone. Many of the rodent forward genetic studies lacked sufficient mapping resolution for candidate gene identification; however, newer studies using genetic mapping populations such as Advanced Intercrosses and the Collaborative Cross appear to have overcome this issue and show promise for future studies. The majority of the genetic mapping studies conducted to date have focused on younger animals and thus an understanding of the genetic control of age-related bone loss represents a key gap in knowledge.
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Affiliation(s)
- Douglas J Adams
- Department of Orthopaedic Surgery, University of Connecticut Musculoskeletal Institute, University of Connecticut Health, Farmington, CT, 06030, USA
| | - David W Rowe
- Center for Regenerative Medicine and Skeletal Development, Department of Reconstructive Sciences, Biomaterials and Skeletal Development, University of Connecticut Health, Farmington, CT, USA
| | - Cheryl L Ackert-Bicknell
- Center for Musculoskeletal Research, Department of Orthopaedics and Rehabilitation, School of Medicine and Dentistry, University of Rochester, 601 Elmwood Ave, Box 665, Rochester, NY, 14624, USA.
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Reilly KM. Using the Collaborative Cross to Study the Role of Genetic Diversity in Cancer-Related Phenotypes. Cold Spring Harb Protoc 2016; 2016:pdb.prot079178. [PMID: 26933242 DOI: 10.1101/pdb.prot079178] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Human populations are genetically diverse and often a single mouse model can only represent a small subset of the human population. Studying genetic diversity directly can improve the predictive value of mouse models of cancer biology and research on the effects of carcinogens and therapeutics in humans. The collaborative cross is a panel of inbred mouse lines that captures 90% of the genetic diversity of the Mus musculus strain and can help identify regions of the genome that are responsible for variation in cancer phenotypes across the population. The appropriate procedure will depend on the mouse model used; here, three mouse cross designs are described as examples.
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Affiliation(s)
- Karlyne M Reilly
- Mouse Cancer Genetics Program, National Cancer Institute, Frederick, Maryland 21702
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Dorman A, Baer D, Tomlinson I, Mott R, Iraqi FA. Genetic analysis of intestinal polyp development in Collaborative Cross mice carrying the Apc (Min/+) mutation. BMC Genet 2016; 17:46. [PMID: 26896154 PMCID: PMC4761170 DOI: 10.1186/s12863-016-0349-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 02/05/2016] [Indexed: 11/28/2022] Open
Abstract
Background Colorectal cancer is an abnormal tissue development in the colon or rectum. Most of CRCs develop due to somatic mutations, while only a small proportion is caused by inherited mutations. Familial adenomatous polyposis is an inherited genetic disease, which is characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli gene. Mice carrying and non-sense mutation in Adenomatous polyposis coli gene at site R850, which designated ApcR850X/+ (Min), develop intestinal adenomas, while the bulk of the disease is in the small intestine. A number of genetic modifier loci of Min have been mapped, but so far most of the underlying genes have not been identified. In our previous studies, we have shown that Collaborative Cross mice are a powerful tool for mapping loci responsible for phenotypic variation. As a first step towards identification of novel modifiers of Min, we assessed the phenotypic variation between 27 F1 crosses between different Collaborative cross mice and C57BL/6-Min lines. Results Here, C57BL/6-Min male mice were mated with females from 27 Collaborative cross lines. F1 offspring were terminated at 23 weeks old and multiple phenotypes were collected: polyp counts, intestine length, intestine weight, packed cell volume and spleen weight. Additionally, in eight selected F1 Collaborative cross-C57BL/6-Min lines, body weight was monitored and compared to control mice carry wildtype Adenomatous polyposis coli gene. We found significant (p < 0.05) phenotypic variation between the 27 F1 Collaborative cross-C57BL/6-Min lines for all the tested phenotypes, and sex differences with traits; Colon, body weight and intestine length phenotypes, only. Heritability calculation showed that these phenotypes are mainly controlled by genetic factors. Conclusions Variation in polyp development is controlled, an appreciable extent, by genetic factors segregating in the Collaborative cross population and suggests that it is suited for identifying modifier genes associated with ApcMin/+ mutation, after assessing sufficient number of lines for quantitative trait loci analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0349-6) contains supplementary material, which is available to authorized users.
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Atamni HJAT, Mott R, Soller M, Iraqi FA. High-fat-diet induced development of increased fasting glucose levels and impaired response to intraperitoneal glucose challenge in the collaborative cross mouse genetic reference population. BMC Genet 2016; 17:10. [PMID: 26728312 PMCID: PMC4700737 DOI: 10.1186/s12863-015-0321-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 12/20/2015] [Indexed: 12/17/2022] Open
Abstract
Background The prevalence of Type 2 Diabetes (T2D) mellitus in the past decades, has reached epidemic proportions. Several lines of evidence support the role of genetic variation in the pathogenesis of T2D and insulin resistance. Elucidating these factors could contribute to developing new medical treatments and tools to identify those most at risk. The aim of this study was to characterize the phenotypic response of the Collaborative Cross (CC) mouse genetic resource population to high-fat diet (HFD) induced T2D-like disease to evluate its suitability for this purpose. Results We studied 683 mice of 21 different lines of the CC population. Of these, 265 mice (149 males and 116 females) were challenged by HFD (42 % fat); and 384 mice (239 males and145 females) of 17 of the 21 lines were reared as control group on standard Chow diet (18 % fat). Briefly, 8 week old mice were maintained on HFD until 20 weeks of age, and subsequently assessed by intraperitoneal glucose tolerance test (IPGTT). Biweekly body weight (BW), body length (BL), waist circumstance (WC), and body mass index (BMI) were measured. On statistical analysis, trait measurements taken at 20 weeks of age showed significant sex by diet interaction across the different lines and traits. Consequently, males and females were analyzed, separately. Differences among lines were analyzed by ANOVA and shown to be significant (P <0.05), for BW, WC, BMI, fasting blood glucose, and IPGTT-AUC. We use these data to infer broad sense heritability adjusted for number of mice tested in each line; coefficient of genetic variation; genetic correlations between the same trait in the two sexes, and phenotypic correlations between different traits in the same sex. Conclusions These results are consistent with the hypothesis that host susceptibility to HFD-induced T2D is a complex trait and controlled by multiple genetic factors and sex, and that the CC population can be a powerful tool for genetic dissection of this trait. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0321-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hanifa J Abu-Toamih Atamni
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, 69978, Israel.
| | | | | | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, 69978, Israel.
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Levy R, Mott RF, Iraqi FA, Gabet Y. Collaborative cross mice in a genetic association study reveal new candidate genes for bone microarchitecture. BMC Genomics 2015; 16:1013. [PMID: 26611327 PMCID: PMC4661944 DOI: 10.1186/s12864-015-2213-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Accepted: 11/13/2015] [Indexed: 12/26/2022] Open
Abstract
Background The microstructure of trabecular bone is a composite trait governed by a complex interaction of multiple genetic determinants. Identifying these genetic factors should significantly improve our ability to predict of osteoporosis and its associated risks. Genetic mapping using collaborative cross mice (CC), a genetically diverse recombinant inbred mouse reference panel, offers a powerful tool to identify causal loci at a resolution under one mega base-pairs, with a relatively small cohort size. Here, we utilized 31 CC lines (160 mice of both sexes in total) to perform genome-wide haplotype mapping across 77,808 single-nucleotide polymorphisms (SNPs). Haplotype scans were refined by imputation with the catalogue of sequence variation segregating in the CC to suggest potential candidate genes. Trabecular traits were obtained following microtomographic analysis, performed on 10-μm resolution scans of the femoral distal metaphysis. We measured the trabecular bone volume fraction (BV/TV), number (Tb.N), thickness (Tb.Th), and connectivity density (Conn.D). Results Heritability of these traits ranged from 0.6 to 0.7. In addition there was a significant (P < 0.01) sex effect in all traits except Tb.Th. Our haplotype scans yielded six quantitative trait loci (QTL) at 1 % false discovery rate; BV/TV and Tb.Th produced two proximal loci each, on chromosome 2 and 7, respectively, and Tb.N and Conn.D yielded one locus on chromosomes 8 and 14, respectively. We identified candidate genes with previously-reported functions in bone biology, and implicated unexpected genes whose function in bone biology has yet to be assigned. Based on the literature, among the genes that ranked particularly high in our analyses (P < 10-6) and which have a validated causal role in skeletal biology, are Avp, Oxt, B2m (associated with BV/TV), Cnot7 (with Tb.N), Pcsk6, Rgma (with Tb.Th), Rb1, and Cpb2 (with Conn.D). Other candidate genes strongly suggested by our analyses are Sgcz, Fgf20 (associated with Tb.N), and Chd2 (with Tb.Th). Conclusion We have demonstrated for the first time genome-wide significant association between several genetic loci and trabecular microstructural parameters for genes with previously reported experimental observations, as well as proposing a role for new candidate genes with no previously characterized skeletal function. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2213-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Roei Levy
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Richard F Mott
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yankel Gabet
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Abstract
The genetic basis of type 2 diabetes remains incompletely defined despite the use of multiple genetic strategies. Multiparental populations such as heterogeneous stocks (HS) facilitate gene discovery by allowing fine mapping to only a few megabases, significantly decreasing the number of potential candidate genes compared to traditional mapping strategies. In the present work, we employed expression and sequence analysis in HS rats (Rattus norvegicus) to identify Tpcn2 as a likely causal gene underlying a 3.1-Mb locus for glucose and insulin levels. Global gene expression analysis on liver identified Tpcn2 as the only gene in the region that is differentially expressed between HS rats with glucose intolerance and those with normal glucose regulation. Tpcn2 also maps as a cis-regulating expression QTL and is negatively correlated with fasting glucose levels. We used founder sequence to identify variants within this region and assessed association between 18 variants and diabetic traits by conducting a mixed-model analysis, accounting for the complex family structure of the HS. We found that two variants were significantly associated with fasting glucose levels, including a nonsynonymous coding variant within Tpcn2. Studies in Tpcn2 knockout mice demonstrated a significant decrease in fasting glucose levels and insulin response to a glucose challenge relative to those in wild-type mice. Finally, we identified variants within Tpcn2 that are associated with fasting insulin in humans. These studies indicate that Tpcn2 is a likely causal gene that may play a role in human diabetes and demonstrate the utility of multiparental populations for positionally cloning genes within complex loci.
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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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Abstract
A general Bayesian model, Diploffect, is described for estimating the effects of founder haplotypes at quantitative trait loci (QTL) detected in multiparental genetic populations; such populations include the Collaborative Cross (CC), Heterogeneous Socks (HS), and many others for which local genetic variation is well described by an underlying, usually probabilistically inferred, haplotype mosaic. Our aim is to provide a framework for coherent estimation of haplotype and diplotype (haplotype pair) effects that takes into account the following: uncertainty in haplotype composition for each individual; uncertainty arising from small sample sizes and infrequently observed haplotype combinations; possible effects of dominance (for noninbred subjects); genetic background; and that provides a means to incorporate data that may be incomplete or has a hierarchical structure. Using the results of a probabilistic haplotype reconstruction as prior information, we obtain posterior distributions at the QTL for both haplotype effects and haplotype composition. Two alternative computational approaches are supplied: a Markov chain Monte Carlo sampler and a procedure based on importance sampling of integrated nested Laplace approximations. Using simulations of QTL in the incipient CC (pre-CC) and Northport HS populations, we compare the accuracy of Diploffect, approximations to it, and more commonly used approaches based on Haley–Knott regression, describing trade-offs between these methods. We also estimate effects for three QTL previously identified in those populations, obtaining posterior intervals that describe how the phenotype might be affected by diplotype substitutions at the modeled locus.
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Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P, Leung H, Varshney RK, Cavanagh CR. MAGIC populations in crops: current status and future prospects. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2015; 128:999-1017. [PMID: 25855139 DOI: 10.1007/s00122-015-2506-0] [Citation(s) in RCA: 129] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Accepted: 03/20/2015] [Indexed: 05/20/2023]
Abstract
MAGIC populations present novel challenges and opportunities in crops due to their complex pedigree structure. They offer great potential both for dissecting genomic structure and for improving breeding populations. The past decade has seen the rise of multiparental populations as a study design offering great advantages for genetic studies in plants. The genetic diversity of multiple parents, recombined over several generations, generates a genetic resource population with large phenotypic diversity suitable for high-resolution trait mapping. While there are many variations on the general design, this review focuses on populations where the parents have all been inter-mated, typically termed Multi-parent Advanced Generation Intercrosses (MAGIC). Such populations have already been created in model animals and plants, and are emerging in many crop species. However, there has been little consideration of the full range of factors which create novel challenges for design and analysis in these populations. We will present brief descriptions of large MAGIC crop studies currently in progress to motivate discussion of population construction, efficient experimental design, and genetic analysis in these populations. In addition, we will highlight some recent achievements and discuss the opportunities and advantages to exploit the unique structure of these resources post-QTL analysis for gene discovery.
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Affiliation(s)
- B Emma Huang
- Digital Productivity and Agriculture Flagships, CSIRO, Dutton Park, QLD, 4102, Australia,
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Pascual L, Desplat N, Huang BE, Desgroux A, Bruguier L, Bouchet JP, Le QH, Chauchard B, Verschave P, Causse M. Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era. PLANT BIOTECHNOLOGY JOURNAL 2015; 13:565-77. [PMID: 25382275 DOI: 10.1111/pbi.12282] [Citation(s) in RCA: 102] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 09/19/2014] [Accepted: 09/24/2014] [Indexed: 05/22/2023]
Abstract
Identification of the polymorphisms controlling quantitative traits remains a challenge for plant geneticists. Multiparent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage or association mapping populations by increasing the precision of quantitative trait loci (QTL) mapping. Here, we present the first tomato MAGIC population and highlight its potential for the valorization of intraspecific variation, QTL mapping and causal polymorphism identification. The population was developed by crossing eight founder lines, selected to include a wide range of genetic diversity, whose genomes have been previously resequenced. We selected 1536 SNPs among the 4 million available to enhance haplotype prediction and recombination detection in the population. The linkage map obtained showed an 87% increase in recombination frequencies compared to biparental populations. The prediction of the haplotype origin was possible for 89% of the MAGIC line genomes, allowing QTL detection at the haplotype level. We grew the population in two greenhouse trials and detected QTLs for fruit weight. We mapped three stable QTLs and six specific of a location. Finally, we showed the potential of the MAGIC population when coupled with whole genome sequencing of founder lines to detect candidate SNPs underlying the QTLs. For a previously cloned QTL on chromosome 3, we used the predicted allelic effect of each founder and their genome sequences to select putative causal polymorphisms in the supporting interval. The number of candidate polymorphisms was reduced from 12 284 (in 800 genes) to 96 (in 54 genes), including the actual causal polymorphism. This population represents a new permanent resource for the tomato genetics community.
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Affiliation(s)
- Laura Pascual
- INRA, UR1052, Génétique et Amélioration des Fruits et Légumes, Montfavet, France
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Treusch S, Albert FW, Bloom JS, Kotenko IE, Kruglyak L. Genetic mapping of MAPK-mediated complex traits Across S. cerevisiae. PLoS Genet 2015; 11:e1004913. [PMID: 25569670 PMCID: PMC4287466 DOI: 10.1371/journal.pgen.1004913] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Accepted: 11/21/2014] [Indexed: 01/22/2023] Open
Abstract
Signaling pathways enable cells to sense and respond to their environment. Many cellular signaling strategies are conserved from fungi to humans, yet their activity and phenotypic consequences can vary extensively among individuals within a species. A systematic assessment of the impact of naturally occurring genetic variation on signaling pathways remains to be conducted. In S. cerevisiae, both response and resistance to stressors that activate signaling pathways differ between diverse isolates. Here, we present a quantitative trait locus (QTL) mapping approach that enables us to identify genetic variants underlying such phenotypic differences across the genetic and phenotypic diversity of S. cerevisiae. Using a Round-robin cross between twelve diverse strains, we identified QTL that influence phenotypes critically dependent on MAPK signaling cascades. Genetic variants under these QTL fall within MAPK signaling networks themselves as well as other interconnected signaling pathways. Finally, we demonstrate how the mapping results from multiple strain background can be leveraged to narrow the search space of causal genetic variants. Wild yeast strains differ in phenotypes that are controlled by highly conserved signaling pathways. Yet it remains unknown how naturally occurring genetic variants influence signaling pathways in yeast. We have developed an approach to facilitate the mapping of genetic variants that underlie these phenotypic differences in a set of wild strain. Our mapping approach requires minimal strain engineering and enables the rapid isolation of mapping populations from any strain background. In particular, we have mapped genetic variants in twelve highly diverse yeast strains. Further, we demonstrate how the mapping results from these twelve strains can be used jointly to narrow the number of genetic variants identified to a set of putative causal variants. We identify genetic variants in genes with various roles in cell signaling. Our results illustrate the interplay of different signaling pathways and which signaling genes are more likely to contain variants of large phenotypic impact.
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Affiliation(s)
- Sebastian Treusch
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Frank W. Albert
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Joshua S. Bloom
- Howard Hughes Medical Institute, Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Iulia E. Kotenko
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Leonid Kruglyak
- Howard Hughes Medical Institute, Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
- Howard Hughes Medical Institute, Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, California, United States of America
- * E-mail:
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Vered K, Durrant C, Mott R, Iraqi FA. Susceptibility to Klebsiella pneumonaie infection in collaborative cross mice is a complex trait controlled by at least three loci acting at different time points. BMC Genomics 2014; 15:865. [PMID: 25283706 PMCID: PMC4201739 DOI: 10.1186/1471-2164-15-865] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Accepted: 09/24/2014] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Klebsiella pneumoniae (Kp) is a bacterium causing severe pneumonia in immunocompromised hosts and is often associated with sepsis. With the rise of antibiotic resistant bacteria, there is a need for new effective and affordable control methods; understanding the genetic architecture of susceptibility to Kp will help in their development. We performed the first quantitative trait locus (QTL) mapping study of host susceptibility to Kp infection in immunocompetent Collaborative Cross mice (CC). We challenged 328 mice from 73 CC lines intraperitoneally with 104 colony forming units of Kp strain K2. Survival and body weight were monitored for 15 days post challenge. 48 of the CC lines were genotyped with 170,000 SNPs, with which we mapped QTLs. RESULTS CC lines differed significantly (P < 0.05) in mean survival time, between 1 to 15 days post infection, and broad sense heritability was 0.45. Distinct QTL were mapped at specific time points during the challenge. A QTL on chromosome 4 was found only on day 2 post infection, and QTL on chromosomes 8 and 18, only on day 8. By using the sequence variations of the eight inbred strain founders of the CC to refine QTL localization we identify several candidate genes. CONCLUSION Host susceptibility to Kp is a complex trait, controlled by multiple genetic factors that act sequentially during the course of infection.
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Affiliation(s)
| | | | | | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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The Collaborative Cross – A next generation mouse genetic resource population for high resolution genomic analysis of complex traits. Livest Sci 2014. [DOI: 10.1016/j.livsci.2014.05.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Keane TM, Wong K, Adams DJ, Flint J, Reymond A, Yalcin B. Identification of structural variation in mouse genomes. Front Genet 2014; 5:192. [PMID: 25071822 PMCID: PMC4079067 DOI: 10.3389/fgene.2014.00192] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 06/12/2014] [Indexed: 01/25/2023] Open
Abstract
Structural variation is variation in structure of DNA regions affecting DNA sequence length and/or orientation. It generally includes deletions, insertions, copy-number gains, inversions, and transposable elements. Traditionally, the identification of structural variation in genomes has been challenging. However, with the recent advances in high-throughput DNA sequencing and paired-end mapping (PEM) methods, the ability to identify structural variation and their respective association to human diseases has improved considerably. In this review, we describe our current knowledge of structural variation in the mouse, one of the prime model systems for studying human diseases and mammalian biology. We further present the evolutionary implications of structural variation on transposable elements. We conclude with future directions on the study of structural variation in mouse genomes that will increase our understanding of molecular architecture and functional consequences of structural variation.
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Affiliation(s)
| | - Kim Wong
- Wellcome Trust Sanger Institute Hinxton, Cambridge, UK
| | - David J Adams
- Wellcome Trust Sanger Institute Hinxton, Cambridge, UK
| | | | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne Lausanne, Switzerland
| | - Binnaz Yalcin
- Center for Integrative Genomics, University of Lausanne Lausanne, Switzerland ; Institute of Genetics and Molecular and Cellular Biology Illkirch, France
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Alam I, Koller DL, Cañete T, Blázquez G, López-Aumatell R, Martínez-Membrives E, Díaz-Morán S, Tobeña A, Fernández-Teruel A, Stridh P, Diez M, Olsson T, Johannesson M, Baud A, Econs MJ, Foroud T. High-resolution genome screen for bone mineral density in heterogeneous stock rat. J Bone Miner Res 2014; 29:1619-26. [PMID: 24643965 PMCID: PMC4074219 DOI: 10.1002/jbmr.2195] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2013] [Revised: 01/25/2014] [Accepted: 02/03/2014] [Indexed: 01/09/2023]
Abstract
We previously demonstrated that skeletal mass, structure, and biomechanical properties vary considerably in heterogeneous stock (HS) rat strains. In addition, we observed strong heritability for several of these skeletal phenotypes in the HS rat model, suggesting that it represents a unique genetic resource for dissecting the complex genetics underlying bone fragility. The purpose of this study was to identify and localize genes associated with bone mineral density in HS rats. We measured bone phenotypes from 1524 adult male and female HS rats between 17 and 20 weeks of age. Phenotypes included dual-energy X-ray absorptiometry (DXA) measurements for bone mineral content and areal bone mineral density (aBMD) for femur and lumbar spine (L3-L5), and volumetric BMD measurements by CT for the midshaft and distal femur, femur neck, and fifth lumbar vertebra (L5). A total of 70,000 polymorphic single-nucleotide polymorphisms (SNPs) distributed throughout the genome were selected from genotypes obtained from the Affymetrix rat custom SNPs array for the HS rat population. These SNPs spanned the HS rat genome with a mean linkage disequilibrium coefficient between neighboring SNPs of 0.95. Haplotypes were estimated across the entire genome for each rat using a multipoint haplotype reconstruction method, which calculates the probability of descent for each genotyped locus from each of the eight founder HS strains. The haplotypes were tested for association with each bone density phenotype via a mixed model with covariate adjustment. We identified quantitative trait loci (QTLs) for BMD phenotypes on chromosomes 2, 9, 10, and 13 meeting a conservative genomewide empiric significance threshold (false discovery rate [FDR] = 5%; p < 3 × 10(-6)). Importantly, most QTLs were localized to very small genomic regions (1-3 megabases [Mb]), allowing us to identify a narrow set of potential candidate genes including both novel genes and genes previously shown to have roles in skeletal development and homeostasis.
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Affiliation(s)
- Imranul Alam
- Medicine, Indiana University School of Medicine, IN, USA
| | - Daniel L. Koller
- Medical and Molecular Genetics, Indiana University School of Medicine, IN, USA
| | - Toni Cañete
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, School of Medicine, Universitat Autònoma deBarcelona, 08193-Bellaterra, Barcelona, Spain
| | - Gloria Blázquez
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, School of Medicine, Universitat Autònoma deBarcelona, 08193-Bellaterra, Barcelona, Spain
| | | | - Esther Martínez-Membrives
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, School of Medicine, Universitat Autònoma deBarcelona, 08193-Bellaterra, Barcelona, Spain
| | - Sira Díaz-Morán
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, School of Medicine, Universitat Autònoma deBarcelona, 08193-Bellaterra, Barcelona, Spain
| | - Adolf Tobeña
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, School of Medicine, Universitat Autònoma deBarcelona, 08193-Bellaterra, Barcelona, Spain
| | - Alberto Fernández-Teruel
- Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, School of Medicine, Universitat Autònoma deBarcelona, 08193-Bellaterra, Barcelona, Spain
| | - Pernilla Stridh
- Clinical Neuroscience, Center for Molecular Medicine, Neuroimmunolgy Unit, Karolinska Institutet, S171 76 Stockholm, Sweden
| | - Margarita Diez
- Clinical Neuroscience, Center for Molecular Medicine, Neuroimmunolgy Unit, Karolinska Institutet, S171 76 Stockholm, Sweden
| | - Tomas Olsson
- Clinical Neuroscience, Center for Molecular Medicine, Neuroimmunolgy Unit, Karolinska Institutet, S171 76 Stockholm, Sweden
| | - Martina Johannesson
- Clinical Neuroscience, Center for Molecular Medicine, Neuroimmunolgy Unit, Karolinska Institutet, S171 76 Stockholm, Sweden
| | - Amelie Baud
- Wellcome Trust Center for Human Genetics, Oxford OX3 7BN, United Kingdom
| | - Michael J. Econs
- Medicine, Indiana University School of Medicine, IN, USA
- Medical and Molecular Genetics, Indiana University School of Medicine, IN, USA
| | - Tatiana Foroud
- Medicine, Indiana University School of Medicine, IN, USA
- Medical and Molecular Genetics, Indiana University School of Medicine, IN, USA
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49
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Genomes and phenomes of a population of outbred rats and its progenitors. Sci Data 2014; 1:140011. [PMID: 25977769 PMCID: PMC4381735 DOI: 10.1038/sdata.2014.11] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 05/12/2014] [Indexed: 01/31/2023] Open
Abstract
Finding genetic variants that contribute to phenotypic variation is one of the main challenges of modern genetics. We used an outbred population of rats (Heterogeneous Stock, HS) in a combined sequence-based and genetic mapping analysis to identify sequence variants and genes contributing to complex traits of biomedical relevance. Here we describe the sequences of the eight inbred progenitors of the HS and the variants that segregate between them. We report the genotyping of 1,407 HS rats, and the collection from 2,006 rats of 195 phenotypic measures that are relevant to models of anxiety, type 2 diabetes, hypertension and osteoporosis. We make available haplotype dosages for the 1,407 genotyped rats, since genetic mapping in the HS is best carried out by reconstructing each HS chromosome as a mosaic of the progenitor genomes. Finally, we have deposited an R object that makes it easy to incorporate our sequence data into any genetic study of HS rats. Our genetic data are available for both Rnor3.4 and Rnor5.0 rat assemblies.
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King EG, Sanderson BJ, McNeil CL, Long AD, Macdonald SJ. Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genet 2014; 10:e1004322. [PMID: 24810915 PMCID: PMC4014434 DOI: 10.1371/journal.pgen.1004322] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2013] [Accepted: 03/10/2014] [Indexed: 12/01/2022] Open
Abstract
Modern genetic mapping is plagued by the “missing heritability” problem, which refers to the discordance between the estimated heritabilities of quantitative traits and the variance accounted for by mapped causative variants. One major potential explanation for the missing heritability is allelic heterogeneity, in which there are multiple causative variants at each causative gene with only a fraction having been identified. The majority of genome-wide association studies (GWAS) implicitly assume that a single SNP can explain all the variance for a causative locus. However, if allelic heterogeneity is prevalent, a substantial amount of genetic variance will remain unexplained. In this paper, we take a haplotype-based mapping approach and quantify the number of alleles segregating at each locus using a large set of 7922 eQTL contributing to regulatory variation in the Drosophila melanogaster female head. Not only does this study provide a comprehensive eQTL map for a major community genetic resource, the Drosophila Synthetic Population Resource, but it also provides a direct test of the allelic heterogeneity hypothesis. We find that 95% of cis-eQTLs and 78% of trans-eQTLs are due to multiple alleles, demonstrating that allelic heterogeneity is widespread in Drosophila eQTL. Allelic heterogeneity likely contributes significantly to the missing heritability problem common in GWAS studies. For traits with complex genetic inheritance it has generally proven very difficult to identify the majority of the specific causative variants involved. A range of hypotheses have been put forward to explain this so-called “missing heritability”. One idea—allelic heterogeneity, where genes each harbor multiple different causative variants—has received little attention, because it is difficult to detect with most genetic mapping designs. Here we make use of a panel of Drosophila melanogaster lines derived from multiple founders, allowing us to directly test for the presence of multiple alleles at a large set of genetic loci influencing gene expression. We find that the vast majority of loci harbor more than two functional alleles, demonstrating extensive allelic heterogeneity at the level of gene expression and suggesting that such heterogeneity is an important factor determining the genetic basis of complex trait variation in general.
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Affiliation(s)
- Elizabeth G. King
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, United States of America
- * E-mail:
| | - Brian J. Sanderson
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, United States of America
| | - Casey L. McNeil
- Department of Biology, Newman University, Wichita, Kansas, United States of America
| | - Anthony D. Long
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, United States of America
| | - Stuart J. Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, United States of America
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