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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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McCarter C, Howrylak J, Kim S. Learning gene networks underlying clinical phenotypes using SNP perturbation. PLoS Comput Biol 2020; 16:e1007940. [PMID: 33095769 PMCID: PMC7584257 DOI: 10.1371/journal.pcbi.1007940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 05/11/2020] [Indexed: 11/18/2022] Open
Abstract
Availability of genome sequence, molecular, and clinical phenotype data for large patient cohorts generated by recent technological advances provides an opportunity to dissect the genetic architecture of complex diseases at system level. However, previous analyses of such data have largely focused on the co-localization of SNPs associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus mapping. Thus, their description of the molecular mechanisms behind the SNPs influencing clinical phenotypes was limited to the single gene linked to the co-localized SNP. Here we introduce PerturbNet, a statistical framework for learning gene networks that modulate the influence of genetic variants on phenotypes, using genetic variants as naturally occurring perturbation of a biological system. PerturbNet uses a probabilistic graphical model to directly model the cascade of perturbation from genetic variants to the gene network to the phenotype network along with the networks at each layer of the biological system. PerturbNet learns the entire model by solving a single optimization problem with an efficient algorithm that can analyze human genome-wide data within a few hours. PerturbNet inference procedures extract a detailed description of how the gene network modulates the genetic effects on phenotypes. Using simulated and asthma data, we demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and identifies gene networks and network modules mediating the SNP effects on traits, providing deeper insights into the underlying molecular mechanisms.
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Affiliation(s)
- Calvin McCarter
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Judie Howrylak
- Pulmonary, Allergy and Critical Care Division, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Seyoung Kim
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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Badsha MB, Fu AQ. Learning Causal Biological Networks With the Principle of Mendelian Randomization. Front Genet 2019; 10:460. [PMID: 31164902 PMCID: PMC6536645 DOI: 10.3389/fgene.2019.00460] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 04/30/2019] [Indexed: 01/09/2023] Open
Abstract
Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i. e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when multiple phenotypes are involved. We extend the interpretation of the Principle of Mendelian randomization (PMR) and present MRPC, a novel machine learning algorithm that incorporates the PMR in the PC algorithm, a classical algorithm for learning causal graphs in computer science. MRPC learns a causal biological network efficiently and robustly from integrating individual-level genotype and molecular phenotype data, in which directed edges indicate causal directions. We demonstrate through simulation that MRPC outperforms several popular general-purpose network inference methods and PMR-based methods. We apply MRPC to distinguish direct and indirect targets among multiple genes associated with expression quantitative trait loci. Our method is implemented in the R package MRPC, available on CRAN (https://cran.r-project.org/web/packages/MRPC/index.html).
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Affiliation(s)
- Md. Bahadur Badsha
- Department of Statistical Science, Center for Modeling Complex Interactions, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, United States
| | - Audrey Qiuyan Fu
- Department of Statistical Science, Center for Modeling Complex Interactions, Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, United States
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