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Ashbrook DG, Arends D, Prins P, Mulligan MK, Roy S, Williams EG, Lutz CM, Valenzuela A, Bohl CJ, Ingels JF, McCarty MS, Centeno AG, Hager R, Auwerx J, Lu L, Williams RW. A platform for experimental precision medicine: The extended BXD mouse family. Cell Syst 2021; 12:235-247.e9. [PMID: 33472028 PMCID: PMC7979527 DOI: 10.1016/j.cels.2020.12.002] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/29/2020] [Accepted: 12/21/2020] [Indexed: 12/17/2022]
Abstract
The challenge of precision medicine is to model complex interactions among DNA variants, phenotypes, development, environments, and treatments. We address this challenge by expanding the BXD family of mice to 140 fully isogenic strains, creating a uniquely powerful model for precision medicine. This family segregates for 6 million common DNA variants-a level that exceeds many human populations. Because each member can be replicated, heritable traits can be mapped with high power and precision. Current BXD phenomes are unsurpassed in coverage and include much omics data and thousands of quantitative traits. BXDs can be extended by a single-generation cross to as many as 19,460 isogenic F1 progeny, and this extended BXD family is an effective platform for testing causal modeling and for predictive validation. BXDs are a unique core resource for the field of experimental precision medicine.
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Affiliation(s)
- David G Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Danny Arends
- Lebenswissenschaftliche Fakultät, Albrecht Daniel Thaer-Institut, Humboldt-Universität zu Berlin, Invalidenstraße 42, 10115 Berlin, Germany
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Megan K Mulligan
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan G Williams
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg
| | - Cathleen M Lutz
- Mouse Repository and the Rare and Orphan Disease Center, the Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Alicia Valenzuela
- Mouse Repository and the Rare and Orphan Disease Center, the Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Casey J Bohl
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Jesse F Ingels
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Melinda S McCarty
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Arthur G Centeno
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Reinmar Hager
- Division of Evolution & Genomic Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
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Mulligan MK, Abreo T, Neuner SM, Parks C, Watkins CE, Houseal MT, Shapaker TM, Hook M, Tan H, Wang X, Ingels J, Peng J, Lu L, Kaczorowski CC, Bryant CD, Homanics GE, Williams RW. Identification of a Functional Non-coding Variant in the GABA A Receptor α2 Subunit of the C57BL/6J Mouse Reference Genome: Major Implications for Neuroscience Research. Front Genet 2019; 10:188. [PMID: 30984232 PMCID: PMC6449455 DOI: 10.3389/fgene.2019.00188] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 02/21/2019] [Indexed: 12/16/2022] Open
Abstract
GABA type-A (GABA-A) receptors containing the α2 subunit (GABRA2) are expressed in most brain regions and are critical in modulating inhibitory synaptic function. Genetic variation at the GABRA2 locus has been implicated in epilepsy, affective and psychiatric disorders, alcoholism and drug abuse. Gabra2 expression varies as a function of genotype and is modulated by sequence variants in several brain structures and populations, including F2 crosses originating from C57BL/6J (B6J) and the BXD recombinant inbred family derived from B6J and DBA/2J. Here we demonstrate a global reduction of GABRA2 brain protein and mRNA in the B6J strain relative to other inbred strains, and identify and validate the causal mutation in B6J. The mutation is a single base pair deletion located in an intron adjacent to a splice acceptor site that only occurs in the B6J reference genome. The deletion became fixed in B6J between 1976 and 1991 and is now pervasive in many engineered lines, BXD strains generated after 1991, the Collaborative Cross, and the majority of consomic lines. Repair of the deletion using CRISPR-Cas9-mediated gene editing on a B6J genetic background completely restored brain levels of GABRA2 protein and mRNA. Comparison of transcript expression in hippocampus, cortex, and striatum between B6J and repaired genotypes revealed alterations in GABA-A receptor subunit expression, especially in striatum. These results suggest that naturally occurring variation in GABRA2 levels between B6J and other substrains or inbred strains may also explain strain differences in anxiety-like or alcohol and drug response traits related to striatal function. Characterization of the B6J private mutation in the Gabra2 gene is of critical importance to molecular genetic studies in neurobiological research because this strain is widely used to generate genetically engineered mice and murine genetic populations, and is the most widely utilized strain for evaluation of anxiety-like, depression-like, pain, epilepsy, and drug response traits that may be partly modulated by GABRA2 function.
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Affiliation(s)
- Megan K Mulligan
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Timothy Abreo
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Sarah M Neuner
- Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, United States.,The Jackson Laboratory, Bar Harbor, ME, United States
| | - Cory Parks
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Christine E Watkins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - M Trevor Houseal
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Thomas M Shapaker
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Michael Hook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Haiyan Tan
- Departments of Structural Biology and Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Xusheng Wang
- Departments of Structural Biology and Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Jesse Ingels
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
| | | | - Camron D Bryant
- Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry, Boston University School of Medicine, Boston, MA, United States
| | - Gregg E Homanics
- Departments of Anesthesiology and Perioperative Medicine, Neurobiology, and Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, United States
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Ashbrook DG, Mulligan MK, Williams RW. Post-genomic behavioral genetics: From revolution to routine. GENES, BRAIN, AND BEHAVIOR 2018; 17:e12441. [PMID: 29193773 PMCID: PMC5876106 DOI: 10.1111/gbb.12441] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 11/02/2017] [Accepted: 11/20/2017] [Indexed: 12/16/2022]
Abstract
What was once expensive and revolutionary-full-genome sequence-is now affordable and routine. Costs will continue to drop, opening up new frontiers in behavioral genetics. This shift in costs from the genome to the phenome is most notable in large clinical studies of behavior and associated diseases in cohorts that exceed hundreds of thousands of subjects. Examples include the Women's Health Initiative (www.whi.org), the Million Veterans Program (www. RESEARCH va.gov/MVP), the 100 000 Genomes Project (genomicsengland.co.uk) and commercial efforts such as those by deCode (www.decode.com) and 23andme (www.23andme.com). The same transition is happening in experimental neuro- and behavioral genetics, and sample sizes of many hundreds of cases are becoming routine (www.genenetwork.org, www.mousephenotyping.org). There are two major consequences of this new affordability of massive omics datasets: (1) it is now far more practical to explore genetic modulation of behavioral differences and the key role of gene-by-environment interactions. Researchers are already doing the hard part-the quantitative analysis of behavior. Adding the omics component can provide powerful links to molecules, cells, circuits and even better treatment. (2) There is an acute need to highlight and train behavioral scientists in how best to exploit new omics approaches. This review addresses this second issue and highlights several new trends and opportunities that will be of interest to experts in animal and human behaviors.
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Affiliation(s)
- D G Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, College of Medicine, Memphis, Tennessee
| | - M K Mulligan
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, College of Medicine, Memphis, Tennessee
| | - R W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, College of Medicine, Memphis, Tennessee
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Hillar C, Onnis G, Rhea D, Tecott L. Active State Organization of Spontaneous Behavioral Patterns. Sci Rep 2018; 8:1064. [PMID: 29348406 PMCID: PMC5773533 DOI: 10.1038/s41598-017-18276-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Accepted: 12/06/2017] [Indexed: 11/16/2022] Open
Abstract
We report the development and validation of a principled analytical approach to reveal the manner in which diverse mouse home cage behaviors are organized. We define and automate detection of two mutually-exclusive low-dimensional spatiotemporal units of behavior: “Active” and “Inactive” States. Analyses of these features using a large multimodal 16-strain behavioral dataset provide a series of novel insights into how feeding, drinking, and movement behaviors are coordinately expressed in Mus Musculus. Moreover, we find that patterns of Active State expression are exquisitely sensitive to strain, and classical supervised machine learning incorporating these features provides 99% cross-validated accuracy in genotyping animals using behavioral data alone. Altogether, these findings advance understanding of the organization of spontaneous behavior and provide a high-throughput phenotyping strategy with wide applicability to behavioral neuroscience and animal models of disease.
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Affiliation(s)
- C Hillar
- University of California, San Francisco Department of Psychiatry, 1550 4th Street, San Francisco, CA, 94158, USA
| | - G Onnis
- University of California, San Francisco Department of Psychiatry, 1550 4th Street, San Francisco, CA, 94158, USA
| | - D Rhea
- University of California, San Francisco Department of Psychiatry, 1550 4th Street, San Francisco, CA, 94158, USA
| | - L Tecott
- University of California, San Francisco Department of Psychiatry, 1550 4th Street, San Francisco, CA, 94158, USA.
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Genetic control of oromotor phenotypes: A survey of licking and ingestive behaviors in highly diverse strains of mice. Physiol Behav 2017; 177:34-43. [PMID: 28411104 DOI: 10.1016/j.physbeh.2017.04.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 04/03/2017] [Accepted: 04/10/2017] [Indexed: 02/08/2023]
Abstract
In order to examine genetic influences on fluid ingestion, 20-min intake of either water or 0.1M sucrose was measured in a lickometer in 18 isogenic strains of mice, including 15 inbred strains and 3 F1 hybrid crosses. Intake and licking data were examined at a number of levels, including lick rate as defined by mean or median interlick interval, as well as several microstructural parameters (i.e. burst-pause structure). In general, strain variation for ingestive phenotypes were correlated across water and sucrose in all strains, indicating fundamental, rather than stimulus-specific, mechanisms of intake. Strain variation was substantial and robust, with heritabilities for phenotypes ranging from 0.22 to 0.73. For mean interlick interval (MPI; a measure of lick rate) strains varied continuously from 94.3 to 127.0ms, a range consistent with previous studies. Furthermore, variation among strains for microstructural traits such as burst size and number suggested that strains possess different overall ingestive strategies, with some favoring more short bursts, and others favoring fewer, long bursts. Strains also varied in cumulative intake functions, exhibiting both linear and decelerated rates of intake across the session.
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Schughart K, Williams RW. The Collaborative Cross Resource for Systems Genetics Research of Infectious Diseases. Methods Mol Biol 2017; 1488:579-596. [PMID: 27933545 PMCID: PMC7120135 DOI: 10.1007/978-1-4939-6427-7_28] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
An increasing body of evidence highlights the role of host genetic variation in driving susceptibility to severe disease following pathogen infection. In order to fully appreciate the importance of host genetics on infection susceptibility and resulting disease, genetically variable experimental model systems should be employed. These systems allow for the identification, characterization, and mechanistic dissection of genetic variants that cause differential disease responses. Herein we discuss application of the Collaborative Cross (CC) panel of recombinant inbred strains to study viral pathogenesis, focusing on practical considerations for experimental design, assessment and analysis of disease responses within the CC, as well as some of the resources developed for the CC. Although the focus of this chapter is on viral pathogenesis, many of the methods presented within are applicable to studies of other pathogens, as well as to case-control designs in genetically diverse populations.
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Affiliation(s)
- Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Research & University of Veterinary Medicine Hannover, Braunschweig, Niedersachsen Germany
| | - Robert W. Williams
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee USA
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Abstract
The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.
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