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Hays M. Genetic conflicts in budding yeast: The 2μ plasmid as a model selfish element. Semin Cell Dev Biol 2024; 161-162:31-41. [PMID: 38598944 DOI: 10.1016/j.semcdb.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Abstract
Antagonistic coevolution, arising from genetic conflict, can drive rapid evolution and biological innovation. Conflict can arise both between organisms and within genomes. This review focuses on budding yeasts as a model system for exploring intra- and inter-genomic genetic conflict, highlighting in particular the 2-micron (2μ) plasmid as a model selfish element. The 2μ is found widely in laboratory strains and industrial isolates of Saccharomyces cerevisiae and has long been known to cause host fitness defects. Nevertheless, the plasmid is frequently ignored in the context of genetic, fitness, and evolution studies. Here, I make a case for further exploring the evolutionary impact of the 2μ plasmid as well as other selfish elements of budding yeasts, discuss recent advances, and, finally, future directions for the field.
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Affiliation(s)
- Michelle Hays
- Department of Genetics, Stanford University, Stanford, CA, United States.
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2
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Allayee H, Farber CR, Seldin MM, Williams EG, James DE, Lusis AJ. Systems genetics approaches for understanding complex traits with relevance for human disease. eLife 2023; 12:e91004. [PMID: 37962168 PMCID: PMC10645424 DOI: 10.7554/elife.91004] [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: 07/14/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
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Affiliation(s)
- Hooman Allayee
- Departments of Population & Public Health Sciences, University of Southern CaliforniaLos AngelesUnited States
- Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Charles R Farber
- Center for Public Health Genomics, University of Virginia School of MedicineCharlottesvilleUnited States
- Departments of Biochemistry & Molecular Genetics, University of Virginia School of MedicineCharlottesvilleUnited States
- Public Health Sciences, University of Virginia School of MedicineCharlottesvilleUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, University of California, IrvineIrvineUnited States
| | - Evan Graehl Williams
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourgLuxembourg
| | - David E James
- School of Life and Environmental Sciences, University of SydneyCamperdownAustralia
- Faculty of Medicine and Health, University of SydneyCamperdownAustralia
- Charles Perkins Centre, University of SydneyCamperdownAustralia
| | - Aldons J Lusis
- Departments of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Medicine, University of California, Los AngelesLos AngelesUnited States
- Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLALos AngelesUnited States
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Schubert OT, Bloom JS, Sadhu MJ, Kruglyak L. Genome-wide base editor screen identifies regulators of protein abundance in yeast. eLife 2022; 11:e79525. [PMID: 36326816 PMCID: PMC9633064 DOI: 10.7554/elife.79525] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
Proteins are key molecular players in a cell, and their abundance is extensively regulated not just at the level of gene expression but also post-transcriptionally. Here, we describe a genetic screen in yeast that enables systematic characterization of how protein abundance regulation is encoded in the genome. The screen combines a CRISPR/Cas9 base editor to introduce point mutations with fluorescent tagging of endogenous proteins to facilitate a flow-cytometric readout. We first benchmarked base editor performance in yeast with individual gRNAs as well as in positive and negative selection screens. We then examined the effects of 16,452 genetic perturbations on the abundance of eleven proteins representing a variety of cellular functions. We uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Overall, our novel screening approach provides unprecedented insights into the components, scale and connectedness of the protein regulatory network.
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Affiliation(s)
- Olga T Schubert
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
- Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH)ZürichSwitzerland
- Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag)DübendorfSwitzerland
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
| | - Meru J Sadhu
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical Institute, University of California, Los AngelesLos AngelesUnited States
- Institute for Quantitative and Computational Biology, University of California, Los AngelesLos AngelesUnited States
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Parker CC, Philip VM, Gatti DM, Kasparek S, Kreuzman AM, Kuffler L, Mansky B, Masneuf S, Sharif K, Sluys E, Taterra D, Taylor WM, Thomas M, Polesskaya O, Palmer AA, Holmes A, Chesler EJ. Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population. Alcohol Clin Exp Res 2022; 46:941-960. [PMID: 35383961 DOI: 10.1111/acer.14825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/04/2022] [Accepted: 03/30/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. METHODS We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes. RESULTS We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance. CONCLUSIONS Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.
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Affiliation(s)
- Clarissa C Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Vivek M Philip
- Center for Computational Sciences, The Jackson Laboratory, Bar Harbor, Maine, USA
| | - Daniel M Gatti
- Center for Computational Sciences, The Jackson Laboratory, Bar Harbor, Maine, USA
| | - Steven Kasparek
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Andrew M Kreuzman
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Lauren Kuffler
- Center for Mammalian Genetics, The Jackson Laboratory, Bar Harbor, Maine, USA
| | - Benjamin Mansky
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Sophie Masneuf
- Laboratory of Behavioral and Genomic Neuroscience, NIAAA, NIH, Rockville, MD, USA
| | - Kayvon Sharif
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Erica Sluys
- Laboratory of Behavioral and Genomic Neuroscience, NIAAA, NIH, Rockville, MD, USA
| | - Dominik Taterra
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Walter M Taylor
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Mary Thomas
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Oksana Polesskaya
- 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
| | - 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
| | - Andrew Holmes
- Laboratory of Behavioral and Genomic Neuroscience, NIAAA, NIH, Rockville, MD, USA
| | - Elissa J Chesler
- Center for Mammalian Genetics, The Jackson Laboratory, Bar Harbor, Maine, USA
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Huminiecki Ł. Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science. ENTROPY (BASEL, SWITZERLAND) 2021; 24:17. [PMID: 35052043 PMCID: PMC8774939 DOI: 10.3390/e24010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.
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Affiliation(s)
- Łukasz Huminiecki
- Evolutionary, Computational, and Statistical Genetics, Department of Molecula Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, Postępu 36A, Jastrzębiec, 05-552 Warsaw, Poland
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6
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Small ML. On Mobilization. PERSONAL NETWORKS 2021:573-595. [DOI: 10.1017/9781108878296.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Aiderus A, Newberg JY, Guzman-Rojas L, Contreras-Sandoval AM, Meshey AL, Jones DJ, Amaya-Manzanares F, Rangel R, Ward JM, Lee SC, Ban KHK, Rogers K, Rogers SM, Selvanesan L, McNoe LA, Copeland NG, Jenkins NA, Tsai KY, Black MA, Mann KM, Mann MB. Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression. PLoS Genet 2021; 17:e1009094. [PMID: 34398873 PMCID: PMC8389471 DOI: 10.1371/journal.pgen.1009094] [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: 09/06/2020] [Revised: 08/26/2021] [Accepted: 07/14/2021] [Indexed: 12/01/2022] Open
Abstract
The systematic identification of genetic events driving cellular transformation and tumor progression in the absence of a highly recurrent oncogenic driver mutation is a challenge in cutaneous oncology. In cutaneous squamous cell carcinoma (cuSCC), the high UV-induced mutational burden poses a hurdle to achieve a complete molecular landscape of this disease. Here, we utilized the Sleeping Beauty transposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progression in vivo in the absence of UV-IR, and identified both known tumor suppressor genes and novel oncogenic drivers of cuSCC. Functional analysis confirms an oncogenic role for the ZMIZ genes, and tumor suppressive roles for KMT2C, CREBBP and NCOA2, in the initiation or progression of human cuSCC. Taken together, our in vivo screen demonstrates an extremely heterogeneous genetic landscape of cuSCC initiation and progression, which can be harnessed to better understand skin oncogenic etiology and prioritize therapeutic candidates. Non-melanoma skin cancers, the most common cancers in the US, are caused by UV skin exposure. Nearly 1 million cases of cutaneous squamous cell carcinoma (cuSCC) are diagnosed in the US each year. While most cuSCCs are highly treatable, more than twice as many individuals die from this disease as from melanoma. The high burden of UV-induced DNA damage in human skin poses a challenge for identifying initiating and cooperating mutations that promote cuSCC development and for defining potential therapeutic targets. Here, we describe a genetic screen in mice using a DNA transposon system to mutagenize the genome of keratinocytes and drive squamous cell carcinoma in the absence of UV. By sequencing where the transposons selectively integrated in the genomes of normal skin, skin with pre-cancerous lesions and skin with fully developed cuSCCs from our mouse model, we were able to identify frequently mutated genes likely important for this disease. Our analysis also defined cooperation between sets of genes not previously appreciated in cuSCC. Our mouse model and ensuing data provide a framework for understanding the genetics of cuSCC and for defining the molecular changes that may lead to the future therapies for patients.
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Affiliation(s)
- Aziz Aiderus
- Department of Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Justin Y. Newberg
- Department of Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
| | - Liliana Guzman-Rojas
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
| | - Ana M. Contreras-Sandoval
- Department of Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Amanda L. Meshey
- Department of Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Devin J. Jones
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
| | - Felipe Amaya-Manzanares
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
| | - Roberto Rangel
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
| | - Jerrold M. Ward
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Song-Choon Lee
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Kenneth Hon-Kim Ban
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Keith Rogers
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Susan M. Rogers
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Luxmanan Selvanesan
- Centre for Translational Cancer Research, Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Leslie A. McNoe
- Centre for Translational Cancer Research, Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Neal G. Copeland
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Nancy A. Jenkins
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
| | - Kenneth Y. Tsai
- Departments of Anatomic Pathology & Tumor Biology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Donald A. Adam Melanoma and Skin Cancer Research Center of Excellence, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America
| | - Michael A. Black
- Centre for Translational Cancer Research, Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Karen M. Mann
- Department of Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America
- Departments of Gastrointestinal Oncology & Malignant Hematology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Cancer Biology and Evolution Program, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Michael B. Mann
- Department of Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, United States of America
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Biopolis, Singapore, Republic of Singapore
- Donald A. Adam Melanoma and Skin Cancer Research Center of Excellence, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America
- Cancer Biology and Evolution Program, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Department of Cutaneous Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- * E-mail:
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Lovisari F, Simonato M. Gene networks and microRNAs: Promises and challenges for treating epilepsies and their comorbidities. Epilepsy Behav 2021; 121:106488. [PMID: 31494060 DOI: 10.1016/j.yebeh.2019.106488] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/06/2019] [Accepted: 08/08/2019] [Indexed: 02/04/2023]
Abstract
Neurobiology research has used an essentially reductionist approach for many years, dissecting out the brain in more simple elements. Recent technical advances, like systems biology, have made now possible to embrace a more holistic vision and try to tackle the complexity of the system. In this short review, we describe how these approaches, in particular analyses or gene networks and of microRNAs, may be useful for epilepsy research. We will describe and discuss recent studies that illustrate how these research approaches can lead to the identification of therapeutic targets and pharmacological strategies to prevent or treat some forms of epilepsy. We aim to show that studying epilepsy and its comorbidities within a complex system framework is a promising integration to the traditional reductionist approaches, and that it will become more and more important in the future for developing new therapies. This article is part of the Special Issue "NEWroscience 2018."
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Affiliation(s)
- Francesca Lovisari
- Section of Pharmacology, Department of Medical Sciences, University of Ferrara, Italy
| | - Michele Simonato
- Section of Pharmacology, Department of Medical Sciences, University of Ferrara, Italy; School of Medicine, University Vita-Salute San Raffaele, Milan, Italy.
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9
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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: 25] [Impact Index Per Article: 8.3] [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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10
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Bastiaanse H, Henry IM, Tsai H, Lieberman M, Canning C, Comai L, Groover A. A systems genetics approach to deciphering the effect of dosage variation on leaf morphology in Populus. THE PLANT CELL 2021; 33:940-960. [PMID: 33793772 PMCID: PMC8226299 DOI: 10.1093/plcell/koaa016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/30/2020] [Indexed: 05/05/2023]
Abstract
Gene copy number variation is frequent in plant genomes of various species, but the impact of such gene dosage variation on morphological traits is poorly understood. We used a large population of Populus carrying genomically characterized insertions and deletions across the genome to systematically assay the effect of gene dosage variation on a suite of leaf morphology traits. A systems genetics approach was used to integrate insertion and deletion locations, leaf morphology phenotypes, gene expression, and transcriptional network data, to provide an overview of how gene dosage influences morphology. Dosage-sensitive genomic regions were identified that influenced individual or pleiotropic morphological traits. We also identified cis-expression quantitative trait loci (QTL) within these dosage QTL regions, a subset of which modulated trans-expression QTL as well. Integration of data types within a gene co-expression framework identified co-expressed gene modules that are dosage sensitive, enriched for dosage expression QTL, and associated with morphological traits. Functional description of these modules linked dosage-sensitive morphological variation to specific cellular processes, as well as candidate regulatory genes. Together, these results show that gene dosage variation can influence morphological variation through complex changes in gene expression, and suggest that frequently occurring gene dosage variation has the potential to likewise influence quantitative traits in nature.
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Affiliation(s)
- Héloïse Bastiaanse
- Present address: VIB Center for Plant Systems Biology, Ghent University, 9052 Ghent, Belgium
| | - Isabelle M Henry
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
| | - Helen Tsai
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
| | - Meric Lieberman
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
| | - Courtney Canning
- Pacific Southwest Research Station, US Forest Service, Davis, California 95618
| | - Luca Comai
- Genome Center, University of California Davis, Davis 95616
- Department of Plant Biology, University of California Davis, Davis 95616
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Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions. Methods 2020; 189:22-33. [PMID: 33096239 DOI: 10.1016/j.ymeth.2020.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/29/2020] [Accepted: 10/18/2020] [Indexed: 02/01/2023] Open
Abstract
Identifying genes and non-coding genetic variants that are genetically associated with complex diseases and the underlying mechanisms is one of the most important questions in functional genomics. Due to the limited statistical power and the lack of mechanistic modeling, traditional genome-wide association studies (GWAS) is restricted to fully address this question. Based on multi-omics data integration, cell-type specific regulatory networks can be built to improve GWAS analysis. In this study, we developed a new computational infrastructure, APRIL, to incorporate 3D chromatin interactions into regulatory network construction, which can extend the networks to include long-range cis-regulatory links between non-coding GWAS SNPs and target genes. Combinatorial transcription factors that co-regulate groups of genes are also inferred to further expand the networks with trans-regulation. A suite of machine learning predictions and statistical tests are incorporated in APRIL to predict novel disease-associated genes based on the expanded regulatory networks. Important features of non-coding regulatory elements and genetic variants are prioritized in network-based predictions, providing systems-level insights on the mechanisms of transcriptional dysregulation associated with complex diseases.
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12
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A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize. Genome Res 2020; 30:1593-1604. [PMID: 33060172 PMCID: PMC7605251 DOI: 10.1101/gr.255224.119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 09/24/2020] [Indexed: 12/21/2022]
Abstract
The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms makes the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype–phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein coexpression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified 15 loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein coexpression network, and a reprogramming of the genetic control of the abundance of many proteins, including those involved in stress response. Colocalizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the coexpression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.
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Baliga NS, Björkegren JLM, Boeke JD, Boutros M, Crawford NPS, Dudley AM, Farber CR, Jones A, Levey AI, Lusis AJ, Mak HC, Nadeau JH, Noyes MB, Petretto E, Seyfried NT, Steinmetz LM, Vonesch SC. The State of Systems Genetics in 2017. Cell Syst 2019; 4:7-15. [PMID: 28125793 DOI: 10.1016/j.cels.2017.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Cell Systems invited 16 experts to share their views on the field of systems genetics. In questions repeated in the headings, we asked them to define systems genetics, highlight its relevance to researchers outside the field, discuss what makes a strong systems genetics paper, and paint a picture of where the field is heading in the coming years. Their responses, ordered by the journal but otherwise unedited, make it clear that deciphering genotype to phenotype relationships is a central challenge of systems genetics and will require understanding how networks and higher-order properties of biological systems underlie complex traits. In addition, our experts illuminate the applications and relevance of systems genetics to human disease, the gut microbiome, development of tools that connect the global research community, sustainability, drug discovery, patient-specific disease and network models, and personalized treatments. Finally, a table of suggested reading provides a sample of influential work in the field.
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Affiliation(s)
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029-6574, USA; Department of Medical Biochemistry and Biophysics, Vascular Biology Unit, Karolinska Institutet, Stockholm, Sweden; Department of Physiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Estonia
| | - Jef D Boeke
- Institute for Systems Genetics, NYU Langone Medical Center, New York, NY, USA
| | - Michael Boutros
- German Cancer Research Center (DKFZ) and Heidelberg University, Germany
| | | | - Aimée M Dudley
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Charles R Farber
- Departments of Public Health Sciences and Biochemistry and Molecular Genetics, Center for Public Health Genomics, University of Virginia, Charlottesville VA, 22908, USA
| | - Allan Jones
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Aldons J Lusis
- Department of Medicine; Department of Microbiology, Immunology and Molecular Genetics; Department of Human Genetics, University of California Los Angeles, California, USA
| | - H Craig Mak
- Cell Systems, Cell Press, Cambridge, MA, USA.
| | - Joseph H Nadeau
- Pacific Northwest Research Institute, Seattle, Washington, USA
| | - Marcus B Noyes
- Institute for Systems Genetics, NYU Langone Medical Center, New York, NY, USA
| | - Enrico Petretto
- Cardiovascular & Metabolic Disorders Program and Centre for Computational Biology, Duke-National University of Singapore (NUS) Medical School, Singapore
| | - Nicholas T Seyfried
- Department of Biochemistry and Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Lars M Steinmetz
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany; Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sibylle C Vonesch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
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14
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Al-Barghouthi BM, Farber CR. Dissecting the Genetics of Osteoporosis using Systems Approaches. Trends Genet 2018; 35:55-67. [PMID: 30470485 DOI: 10.1016/j.tig.2018.10.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 10/01/2018] [Accepted: 10/22/2018] [Indexed: 02/06/2023]
Abstract
Osteoporosis is a condition characterized by low bone mineral density (BMD) and an increased risk of fracture. Traits contributing to osteoporotic fracture are highly heritable, indicating that a comprehensive understanding of bone requires a thorough understanding of the genetic basis of bone traits. Towards this goal, genome-wide association studies (GWASs) have identified over 500 loci associated with bone traits. However, few of the responsible genes have been identified, and little is known of how these genes work together to influence systems-level bone function. In this review, we describe how systems genetics approaches can be used to fill these knowledge gaps.
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Affiliation(s)
- Basel M Al-Barghouthi
- Center for Public Health Genomics, Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Charles R Farber
- Center for Public Health Genomics, Departments of Public Health Sciences and Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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15
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Isidoro-García M, Sánchez-Martín A, García-Sánchez A, Sanz C, García-Berrocal B, Dávila I. Pharmacogenetics and the treatment of asthma. Pharmacogenomics 2017; 18:1271-1280. [PMID: 28776467 DOI: 10.2217/pgs-2017-0024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Heterogeneity defines both the natural history of asthma as well as patient's response to treatment. Pharmacogenomics contribute to understand the genetic basis of drug response and thus to define new therapeutic targets or molecular biomarkers to evaluate treatment effectiveness. This review is initially focused on different genes so far involved in the pharmacological response to asthma treatment. Specific considerations regarding allergic asthma, the pharmacogenetics aspects of polypharmacy and the application of pharmacogenomics in new drugs in asthma will also be addressed. Finally, future perspectives related to epigenetic regulatory elements and the potential impact of systems biology in pharmacogenetics of asthma will be considered.
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Affiliation(s)
- María Isidoro-García
- Department of Clinical Biochemistry, Pharmacogenetics Unit, University Hospital of Salamanca, Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Allergy Department, Salamanca, Spain.,Department of Medicine, Faculty of Medicine, University of Salamanca, Salamanca, Spain
| | - Almudena Sánchez-Martín
- Institute for Biomedical Research of Salamanca (IBSAL), Allergy Department, Salamanca, Spain.,Department of Pharmacy, Faculty of Medicine, University Hospital of Salamanca, Salamanca, Spain
| | - Asunción García-Sánchez
- Institute for Biomedical Research of Salamanca (IBSAL), Allergy Department, Salamanca, Spain.,Department of Biomedical & Diagnostic Sciences, Faculty of Medicine, University of Salamanca, Spain
| | - Catalina Sanz
- Institute for Biomedical Research of Salamanca (IBSAL), Allergy Department, Salamanca, Spain.,Department of Microbiology & Genetics, Faculty of Biology, University of Salamanca, Salamanca, Spain
| | - Belén García-Berrocal
- Department of Clinical Biochemistry, Pharmacogenetics Unit, University Hospital of Salamanca, Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Allergy Department, Salamanca, Spain
| | - Ignacio Dávila
- Institute for Biomedical Research of Salamanca (IBSAL), Allergy Department, Salamanca, Spain.,Department of Biomedical & Diagnostic Sciences, Faculty of Medicine, University of Salamanca, Spain.,Department of Allergy, Faculty of Medicine, University Hospital of Salamanca, Salmanaca, Spain
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16
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Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice. Genetics 2017; 206:621-639. [PMID: 28592500 PMCID: PMC5499176 DOI: 10.1534/genetics.116.198051] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 04/03/2017] [Indexed: 12/20/2022] Open
Abstract
In this study, Tyler et al. analyzed the complex genetic architecture of metabolic disease-related traits using the Diversity Outbred mouse population Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.
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17
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Attie AD, Churchill GA, Nadeau JH. How mice are indispensable for understanding obesity and diabetes genetics. Curr Opin Endocrinol Diabetes Obes 2017; 24:83-91. [PMID: 28107248 PMCID: PMC5837807 DOI: 10.1097/med.0000000000000321] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE OF REVIEW The task of cataloging human genetic variation and its relation to disease is rapidly approaching completion. The new challenge is to discover the function of disease-associated genes and to understand the pathways that lead to human disease. We propose that achieving this new level of understanding will increasingly rely on the use of model organisms. We discuss the advantages of the mouse as a model organism to our understanding of human disease. RECENT FINDINGS The collection of available mouse strains represents as much genetic and phenotypic variation as is found in the human population. However, unlike humans, mice can be subjected to experimental breeding protocols and the availability of tissues allows for a far greater and deeper level of phenotyping. New methods for gene editing make it relatively easy to create mouse models of known human mutations. The distinction between genetic and epigenetic inheritance can be studied in great detail. Various experimental protocols enable the exploration of the role of the microbiome in physiology and disease. SUMMARY We propose that there will be an interdependence between human and model organism research. Technological advances and new genetic screening platforms in the mouse have greatly improved the path to gene discovery and mechanistic studies of gene function.
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Affiliation(s)
- Alan D Attie
- aDepartment of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin bThe Jackson Laboratory, Bar Harbor, Maine cPacific Northwest Research Institute, Seattle, Washington, USA
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18
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Walter NAR, Denmark DL, Kozell LB, Buck KJ. A Systems Approach Implicates a Brain Mitochondrial Oxidative Homeostasis Co-expression Network in Genetic Vulnerability to Alcohol Withdrawal. Front Genet 2017; 7:218. [PMID: 28096806 PMCID: PMC5206817 DOI: 10.3389/fgene.2016.00218] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 12/08/2016] [Indexed: 12/31/2022] Open
Abstract
Genetic factors significantly affect vulnerability to alcohol dependence (alcoholism). We previously identified quantitative trait loci on distal mouse chromosome 1 with large effects on predisposition to alcohol physiological dependence and associated withdrawal following both chronic and acute alcohol exposure in mice (Alcdp1 and Alcw1, respectively). We fine-mapped these loci to a 1.1–1.7 Mb interval syntenic with human 1q23.2-23.3. Alcw1/Alcdp1 interval genes show remarkable genetic variation among mice derived from the C57BL/6J and DBA/2J strains, the two most widely studied genetic animal models for alcohol-related traits. Here, we report the creation of a novel recombinant Alcw1/Alcdp1 congenic model (R2) in which the Alcw1/Alcdp1 interval from a donor C57BL/6J strain is introgressed onto a uniform, inbred DBA/2J genetic background. As expected, R2 mice demonstrate significantly less severe alcohol withdrawal compared to wild-type littermates. Additionally, comparing R2 and background strain animals, as well as reciprocal congenic (R8) and appropriate background strain animals, we assessed Alcw1/Alcdp1 dependent brain gene expression using microarray and quantitative PCR analyses. To our knowledge this includes the first Weighted Gene Co-expression Network Analysis using reciprocal congenic models. Importantly, this allows detection of co-expression patterns limited to one or common to both genetic backgrounds with high or low predisposition to alcohol withdrawal severity. The gene expression patterns (modules) in common contain genes related to oxidative phosphorylation, building upon human and animal model studies that implicate involvement of oxidative phosphorylation in alcohol use disorders (AUDs). Finally, we demonstrate that administration of N-acetylcysteine, an FDA-approved antioxidant, significantly reduces symptoms of alcohol withdrawal (convulsions) in mice, thus validating a phenotypic role for this network. Taken together, these studies support the importance of mitochondrial oxidative homeostasis in alcohol withdrawal and identify this network as a valuable therapeutic target in human AUDs.
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Affiliation(s)
- Nicole A R Walter
- Research and Development, Portland Veterans Affairs Medical Center, PortlandOR, USA; Department of Behavioral Neuroscience, School of Medicine, Oregon Health and Science University, PortlandOR, USA
| | - DeAunne L Denmark
- Research and Development, Portland Veterans Affairs Medical Center, PortlandOR, USA; Department of Behavioral Neuroscience, School of Medicine, Oregon Health and Science University, PortlandOR, USA
| | - Laura B Kozell
- Research and Development, Portland Veterans Affairs Medical Center, PortlandOR, USA; Department of Behavioral Neuroscience, School of Medicine, Oregon Health and Science University, PortlandOR, USA
| | - Kari J Buck
- Research and Development, Portland Veterans Affairs Medical Center, PortlandOR, USA; Department of Behavioral Neuroscience, School of Medicine, Oregon Health and Science University, PortlandOR, USA
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19
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Abstract
One of the central goals in biology is to understand how and how much of the phenotype of an organism is encoded in its genome. Although many genes that are crucial for organismal processes have been identified, much less is known about the genetic bases underlying quantitative phenotypic differences in natural populations. We discuss the fundamental gap between the large body of knowledge generated over the past decades by experimental genetics in the laboratory and what is needed to understand the genotype-to-phenotype problem on a broader scale. We argue that systems genetics, a combination of systems biology and the study of natural variation using quantitative genetics, will help to address this problem. We present major advances in these two mostly disconnected areas that have increased our understanding of the developmental processes of flowering time control and root growth. We conclude by illustrating and discussing the efforts that have been made toward systems genetics specifically in plants.
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Affiliation(s)
- Takehiko Ogura
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), 1030 Vienna, Austria;
| | - Wolfgang Busch
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), 1030 Vienna, Austria;
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20
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Network Analysis Implicates Alpha-Synuclein (Snca) in the Regulation of Ovariectomy-Induced Bone Loss. Sci Rep 2016; 6:29475. [PMID: 27378017 PMCID: PMC4932518 DOI: 10.1038/srep29475] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 06/20/2016] [Indexed: 12/21/2022] Open
Abstract
The postmenopausal period in women is associated with decreased circulating estrogen levels, which accelerate bone loss and increase the risk of fracture. Here, we gained novel insight into the molecular mechanisms mediating bone loss in ovariectomized (OVX) mice, a model of human menopause, using co-expression network analysis. Specifically, we generated a co-expression network consisting of 53 gene modules using expression profiles from intact and OVX mice from a panel of inbred strains. The expression of four modules was altered by OVX, including module 23 whose expression was decreased by OVX across all strains. Module 23 was enriched for genes involved in the response to oxidative stress, a process known to be involved in OVX-induced bone loss. Additionally, module 23 homologs were co-expressed in human bone marrow. Alpha synuclein (Snca) was one of the most highly connected “hub” genes in module 23. We characterized mice deficient in Snca and observed a 40% reduction in OVX-induced bone loss. Furthermore, protection was associated with the altered expression of specific network modules, including module 23. In summary, the results of this study suggest that Snca regulates bone network homeostasis and ovariectomy-induced bone loss.
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21
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Culminskaya I, Kulminski AM, Yashin AI. Coordinated Action of Biological Processes during Embryogenesis Can Cause Genome-Wide Linkage Disequilibrium in the Human Genome and Influence Age-Related Phenotypes. ANNALS OF GERONTOLOGY AND GERIATRIC RESEARCH 2016; 3:1035. [PMID: 28357417 PMCID: PMC5367637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
A role of non-Mendelian inheritance in genetics of complex, age-related traits is becoming increasingly recognized. Recently, we reported on two inheritable clusters of SNPs in extensive genome-wide linkage disequilibrium (LD) in the Framingham Heart Study (FHS), which were associated with the phenotype of premature death. Here we address biologically-related properties of these two clusters. These clusters have been unlikely selected randomly because they are functionally and structurally different from matched sets of randomly selected SNPs. For example, SNPs in LD from each cluster are highly significantly enriched in genes (p=7.1×10-22 and p=5.8×10-18), in general, and in short genes (p=1.4×10-47 and p=4.6×10-7), in particular. Mapping of SNPs in LD to genes resulted in two, partly overlapping, networks of 1764 and 4806 genes. Both these networks were gene enriched in developmental processes and in biological processes tightly linked with development including biological adhesion, cellular component organization, locomotion, localization, signaling, (p<10-4, q<10-4 for each category). Thorough analysis suggests connections of these genetic networks with different stages of embryogenesis and highlights biological interlink of specific processes enriched for genes from these networks. The results suggest that coordinated action of biological processes during embryogenesis may generate genome-wide networks of genetic variants, which may influence complex age-related phenotypes characterizing health span and lifespan.
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Affiliation(s)
| | - Alexander M. Kulminski
- Corresponding author: Alexander Kulminski, Biodemography of Aging Research Unit, Duke University, Box 90420, Durham, NC, 27708, USA, Tel: 919-684-4962; Fax: 919-684-3861;
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Mizrachi E, Myburg AA. Systems genetics of wood formation. CURRENT OPINION IN PLANT BIOLOGY 2016; 30:94-100. [PMID: 26943939 DOI: 10.1016/j.pbi.2016.02.007] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 02/12/2016] [Accepted: 02/15/2016] [Indexed: 05/02/2023]
Abstract
In woody plants, xylogenesis is an exceptionally strong carbon sink requiring robust transcriptional control and dynamic coordination of cellular and metabolic processes directing carbon allocation and partitioning into secondary cell wall biosynthesis. As a biological process, wood formation is an excellent candidate for systems modeling due to the strong correlation patterns and interconnectedness observed for transcriptional and metabolic component traits contributing to complex phenotypes such as cell wall chemistry and ultrastructure. Genetic variation in undomesticated tree populations provides abundant perturbation of systems components, adding another dimension to plant systems biology (besides spatial and temporal variation). High-throughput analysis of molecular component traits in adult trees has provided the first insights into the systems genetics of wood, an important renewable feedstock for biomaterials and bioenergy.
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Affiliation(s)
- Eshchar Mizrachi
- Department of Genetics, Forestry and Agricultural Biotechnology Institute (FABI), Genomics Research Institute (GRI), University of Pretoria, Private Bag X20, Pretoria 0028, South Africa.
| | - Alexander A Myburg
- Department of Genetics, Forestry and Agricultural Biotechnology Institute (FABI), Genomics Research Institute (GRI), University of Pretoria, Private Bag X20, Pretoria 0028, South Africa.
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23
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Crawford NPS. Deciphering the Dark Matter of Complex Genetic Inheritance. Cell Syst 2016; 2:144-6. [PMID: 27135361 DOI: 10.1016/j.cels.2016.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
A multifaceted "systems genetics" strategy illuminates the relationship between genotype and coronary artery disease.
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Affiliation(s)
- Nigel P S Crawford
- Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda MD, 20892, USA.
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24
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Slovak R, Ogura T, Satbhai SB, Ristova D, Busch W. Genetic control of root growth: from genes to networks. ANNALS OF BOTANY 2016; 117:9-24. [PMID: 26558398 PMCID: PMC4701154 DOI: 10.1093/aob/mcv160] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 08/28/2015] [Indexed: 05/08/2023]
Abstract
BACKGROUND Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. SCOPE This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. CONCLUSIONS While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics.
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Affiliation(s)
- Radka Slovak
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria
| | - Takehiko Ogura
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria
| | - Santosh B Satbhai
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria
| | - Daniela Ristova
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria
| | - Wolfgang Busch
- Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria
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Obeidat M, Hao K, Bossé Y, Nickle DC, Nie Y, Postma DS, Laviolette M, Sandford AJ, Daley DD, Hogg JC, Elliott WM, Fishbane N, Timens W, Hysi PG, Kaprio J, Wilson JF, Hui J, Rawal R, Schulz H, Stubbe B, Hayward C, Polasek O, Järvelin MR, Zhao JH, Jarvis D, Kähönen M, Franceschini N, North KE, Loth DW, Brusselle GG, Smith AV, Gudnason V, Bartz TM, Wilk JB, O’Connor GT, Cassano PA, Tang W, Wain LV, Artigas MS, Gharib SA, Strachan DP, Sin DD, Tobin MD, London SJ, Hall IP, Paré PD. Molecular mechanisms underlying variations in lung function: a systems genetics analysis. THE LANCET. RESPIRATORY MEDICINE 2015; 3:782-95. [PMID: 26404118 PMCID: PMC5021067 DOI: 10.1016/s2213-2600(15)00380-x] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 08/06/2015] [Accepted: 08/12/2015] [Indexed: 02/02/2023]
Abstract
BACKGROUND Lung function measures reflect the physiological state of the lung, and are essential to the diagnosis of chronic obstructive pulmonary disease (COPD). The SpiroMeta-CHARGE consortium undertook the largest genome-wide association study (GWAS) so far (n=48,201) for forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in the general population. The lung expression quantitative trait loci (eQTLs) study mapped the genetic architecture of gene expression in lung tissue from 1111 individuals. We used a systems genetics approach to identify single nucleotide polymorphisms (SNPs) associated with lung function that act as eQTLs and change the level of expression of their target genes in lung tissue; termed eSNPs. METHODS The SpiroMeta-CHARGE GWAS results were integrated with lung eQTLs to map eSNPs and the genes and pathways underlying the associations in lung tissue. For comparison, a similar analysis was done in peripheral blood. The lung mRNA expression levels of the eSNP-regulated genes were tested for associations with lung function measures in 727 individuals. Additional analyses identified the pleiotropic effects of eSNPs from the published GWAS catalogue, and mapped enrichment in regulatory regions from the ENCODE project. Finally, the Connectivity Map database was used to identify potential therapeutics in silico that could reverse the COPD lung tissue gene signature. FINDINGS SNPs associated with lung function measures were more likely to be eQTLs and vice versa. The integration mapped the specific genes underlying the GWAS signals in lung tissue. The eSNP-regulated genes were enriched for developmental and inflammatory pathways; by comparison, SNPs associated with lung function that were eQTLs in blood, but not in lung, were only involved in inflammatory pathways. Lung function eSNPs were enriched for regulatory elements and were over-represented among genes showing differential expression during fetal lung development. An mRNA gene expression signature for COPD was identified in lung tissue and compared with the Connectivity Map. This in-silico drug repurposing approach suggested several compounds that reverse the COPD gene expression signature, including a nicotine receptor antagonist. These findings represent novel therapeutic pathways for COPD. INTERPRETATION The system genetics approach identified lung tissue genes driving the variation in lung function and susceptibility to COPD. The identification of these genes and the pathways in which they are enriched is essential to understand the pathophysiology of airway obstruction and to identify novel therapeutic targets and biomarkers for COPD, including drugs that reverse the COPD gene signature in silico. FUNDING The research reported in this article was not specifically funded by any agency. See Acknowledgments for a full list of funders of the lung eQTL study and the Spiro-Meta CHARGE GWAS.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Peter D Paré
- University of British Columbia Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada; Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
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Sima C, Cheng Q, Rautava J, Levesque C, Sherman P, Glogauer M. Identification of quantitative trait loci influencing inflammation-mediated alveolar bone loss: insights into polygenic inheritance of host-biofilm disequilibria in periodontitis. J Periodontal Res 2015; 51:237-49. [PMID: 26126603 DOI: 10.1111/jre.12303] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2015] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND OBJECTIVE The relative contribution of genetic and environmental factors to the onset and progression of periodontitis is inconclusive. Despite the high prevalence, phenotypic heterogeneity and significant local and systemic implications of this disease, early detection and individualized therapy are problematic. Using a murine model of periodontitis in a panel of 17 recombinant inbred mice, the current study addressed the heritability of, and oral dysbiosis associated with, inflammation-mediated alveolar bone loss (iABL), the hallmark of periodontitis. MATERIAL AND METHODS Quantitative trait locus (QTL) genomics and quantitative PCR for over 99% of known murine oral microbiota were used. RESULTS It was found that iABL is a polygenic trait with 32.7% heritability. One suggestive QTL, nicknamed inflammation-mediated alveolar bone loss locus (iABLL), was identified on chromosome 2. Eleven genes involved in innate immune responses and bone metabolism, particularly related to macrophage and osteoblast function, namely Etl4, Pdss1, Cobll1, 9330158F14Rik, Xirp2, Stk39, Mettl5, Metapl1, Itga6, Pdk1 and Sp3, were found in the iABLL using cis expression QTL and nonsynonymous single nucleotide polymorphism analyses. Specific oral microbiome shifts in saliva and tongue mucosa are associated with disease in this model. CONCLUSION Our results indicate that complex host-biofilm interactions generate pathogenic states that extend beyond subgingival biofilms and periodontal tissues. Although no temporal relationship between the onset of iABL and microbiome changes were established, our findings suggest that host factors may be responsible for pathogenic shifts in subgingival biofilms when persistent and undisturbed.
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Affiliation(s)
- C Sima
- Department of Applied Oral Sciences, The Forsyth Institute, Cambridge, MA, USA.,Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, USA.,Matrix Dynamics Group, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
| | - Q Cheng
- Matrix Dynamics Group, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
| | - J Rautava
- Department of Oral Pathology and Oral Radiology, Institute of Dentistry, University of Turku, Turku, Finland.,Cell Biology Program, Research Institute, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - C Levesque
- Department of Oral Microbiology, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
| | - P Sherman
- Cell Biology Program, Research Institute, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - M Glogauer
- Matrix Dynamics Group, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada
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Buchner DA, Nadeau JH. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res 2015; 25:775-91. [PMID: 25953951 PMCID: PMC4448675 DOI: 10.1101/gr.187450.114] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 03/31/2015] [Indexed: 01/14/2023]
Abstract
Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.
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Affiliation(s)
- David A Buchner
- Department of Genetics and Genome Sciences, Department of Biochemistry, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Joseph H Nadeau
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122, USA
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Hofker MH, Fu J, Wijmenga C. The genome revolution and its role in understanding complex diseases. Biochim Biophys Acta Mol Basis Dis 2014; 1842:1889-1895. [DOI: 10.1016/j.bbadis.2014.05.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Revised: 04/30/2014] [Accepted: 05/06/2014] [Indexed: 12/26/2022]
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Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix. Heredity (Edinb) 2014; 113:526-32. [PMID: 24984606 DOI: 10.1038/hdy.2014.57] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 04/15/2014] [Accepted: 04/22/2014] [Indexed: 02/02/2023] Open
Abstract
Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.
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Feltus FA. Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2014; 223:45-8. [PMID: 24767114 DOI: 10.1016/j.plantsci.2014.03.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2013] [Revised: 02/18/2014] [Accepted: 03/02/2014] [Indexed: 05/02/2023]
Abstract
Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits.
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Affiliation(s)
- F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA.
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van der Sijde MR, Ng A, Fu J. Systems genetics: From GWAS to disease pathways. Biochim Biophys Acta Mol Basis Dis 2014; 1842:1903-1909. [PMID: 24798234 DOI: 10.1016/j.bbadis.2014.04.025] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 03/21/2014] [Accepted: 04/27/2014] [Indexed: 01/01/2023]
Abstract
Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.
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Affiliation(s)
- Marijke R van der Sijde
- University of Groningen, University Medical Centre Groningen, Department of Genetics, The Netherlands.
| | - Aylwin Ng
- Centre for Computational and Integrative Biology and Gastrointestinal Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Jingyuan Fu
- University of Groningen, University Medical Centre Groningen, Department of Genetics, The Netherlands.
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Mesner LD, Ray B, Hsu YH, Manichaikul A, Lum E, Bryda EC, Rich SS, Rosen CJ, Criqui MH, Allison M, Budoff MJ, Clemens TL, Farber CR. Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density. J Clin Invest 2014; 124:2736-49. [PMID: 24789909 DOI: 10.1172/jci73072] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels.
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Kogelman LJA, Kadarmideen HN. Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 2:S5. [PMID: 25032480 PMCID: PMC4101698 DOI: 10.1186/1752-0509-8-s2-s5] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Background High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits. Results We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight. Conclusions We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.
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Wang Z, Wang Y, Wang N, Wang J, Wang Z, Vallejos CE, Wu R. Towards a comprehensive picture of the genetic landscape of complex traits. Brief Bioinform 2014; 15:30-42. [PMID: 22930650 PMCID: PMC3896925 DOI: 10.1093/bib/bbs049] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Accepted: 07/09/2012] [Indexed: 12/11/2022] Open
Abstract
The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.
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Affiliation(s)
- Zhong Wang
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA.
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35
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Lehoczky JA, Thomas PE, Patrie KM, Owens KM, Villarreal LM, Galbraith K, Washburn J, Johnson CN, Gavino B, Borowsky AD, Millen KJ, Wakenight P, Law W, Van Keuren ML, Gavrilina G, Hughes ED, Saunders TL, Brihn L, Nadeau JH, Innis JW. A novel intergenic ETnII-β insertion mutation causes multiple malformations in polypodia mice. PLoS Genet 2013; 9:e1003967. [PMID: 24339789 PMCID: PMC3854779 DOI: 10.1371/journal.pgen.1003967] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Accepted: 10/04/2013] [Indexed: 11/28/2022] Open
Abstract
Mouse early transposon insertions are responsible for ∼10% of spontaneous mutant phenotypes. We previously reported the phenotypes and genetic mapping of Polypodia, (Ppd), a spontaneous, X-linked dominant mutation with profound effects on body plan morphogenesis. Our new data shows that mutant mice are not born in expected Mendelian ratios secondary to loss after E9.5. In addition, we refined the Ppd genetic interval and discovered a novel ETnII-β early transposon insertion between the genes for Dusp9 and Pnck. The ETn inserted 1.6 kb downstream and antisense to Dusp9 and does not disrupt polyadenylation or splicing of either gene. Knock-in mice engineered to carry the ETn display Ppd characteristic ectopic caudal limb phenotypes, showing that the ETn insertion is the Ppd molecular lesion. Early transposons are actively expressed in the early blastocyst. To explore the consequences of the ETn on the genomic landscape at an early stage of development, we compared interval gene expression between wild-type and mutant ES cells. Mutant ES cell expression analysis revealed marked upregulation of Dusp9 mRNA and protein expression. Evaluation of the 5′ LTR CpG methylation state in adult mice revealed no correlation with the occurrence or severity of Ppd phenotypes at birth. Thus, the broad range of phenotypes observed in this mutant is secondary to a novel intergenic ETn insertion whose effects include dysregulation of nearby interval gene expression at early stages of development. Mobile genetic elements, particularly early transposons (ETn), cause malformations by inserting within genes leading to disruption of exons, splicing or polyadenylation. Few mutagenic early transposon insertions have been found outside genes and the effects of such insertions on surrounding gene regulation is poorly understood. We discovered a novel intergenic ETnII-β insertion in the mouse mutant Polypodia (Ppd). We reproduced the mutant phenotype after engineering the mutation in wild-type cells with homologous recombination, proving that this early transposon insertion is Ppd. Mutant mice are not born in expected Mendelian ratios secondary to loss after E9.5. Embryonic stem cells from mutant mice show upregulated transcription of an adjacent gene, Dusp9. Thus, at an early and critical stage of development, dysregulated gene transcription is one consequence of the insertion mutation. DNA methylation of the ETn 5′ LTR is not correlated with phenotypic outcome in mutant mice. Polypodia is an example of an intergenic mobile element insertion in mice causing dramatic morphogenetic defects and fetal death.
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Affiliation(s)
- Jessica A. Lehoczky
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Peedikayil E. Thomas
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
- Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kevin M. Patrie
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kailey M. Owens
- Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lisa M. Villarreal
- Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kenneth Galbraith
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Joe Washburn
- Biomedical Research Core Facilities, DNA Sequencing Core Lab, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Craig N. Johnson
- Biomedical Research Core Facilities, DNA Sequencing Core Lab, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Bryant Gavino
- Murine Molecular Constructs Laboratory-MMCL Mouse Biology Program, University of California, Davis, California, United States of America
| | - Alexander D. Borowsky
- University of California, Davis, Center for Comparative Medicine and Comprehensive Cancer Center, Department of Pathology and Laboratory Medicine, Davis, California, United States of America
| | - Kathleen J. Millen
- Division of Genetic Medicine, Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington, United States of America
| | - Paul Wakenight
- Division of Genetic Medicine, Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington, United States of America
| | - William Law
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Margaret L. Van Keuren
- Transgenic Animal Model Core Lab, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Galina Gavrilina
- Transgenic Animal Model Core Lab, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Elizabeth D. Hughes
- Transgenic Animal Model Core Lab, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Thomas L. Saunders
- Transgenic Animal Model Core Lab, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lesil Brihn
- Department of Genetics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Joseph H. Nadeau
- Pacific Northwest Research Institute, Seattle, Washington, United States of America
| | - Jeffrey W. Innis
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
- Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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Yang R, Li H, Fu L, Liu Y. An efficient approach to large-scale genotype-phenotype association analyses. Brief Bioinform 2013; 15:814-22. [PMID: 23990269 DOI: 10.1093/bib/bbt061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Modern molecular biotechnology generates a great deal of intermediate information, such as transcriptional and metabolic products in bridging DNA and complex traits. In genome-wide linkage analysis and genome-wide association study, regression analysis for large-scale correlated phenotypes is applied to map genes for those by-products that are regarded as quantitative traits. For a single trait, least absolute shrinkage and selection operator with coordinate descent step can be employed to efficiently shrink sparse non-zero genetic effects of quantitative trait loci (QTLs). However, regression analyses in a trait-by-trait basis do not take account of the correlations among the analyzed traits. In this study, conditional phenotype of each trait is defined, given other traits. Large-scale genotype-phenotype association analyses are therefore transformed to separate genotype-conditional phenotype ones. Meanwhile, the correlation architecture between each trait and other traits can also be provided by shrinkage estimation for each conditional phenotype. Simulation demonstrates that the proposed conditional mapping method is generally identical to joint mapping method based on multivariate analysis in terms of statistical detection power and parameter estimation. Application of the method is provided to locate eQTL in yeast.
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Ficklin SP, Feltus FA. A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa. PLoS One 2013; 8:e68551. [PMID: 23874666 PMCID: PMC3713027 DOI: 10.1371/journal.pone.0068551] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Accepted: 05/30/2013] [Indexed: 12/13/2022] Open
Abstract
Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.
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Affiliation(s)
- Stephen P Ficklin
- Plant and Environmental Sciences, Clemson University, Clemson, South Carolina, United States of America
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Dahlin A, Tantisira KG. Integrative systems biology approaches in asthma pharmacogenomics. Pharmacogenomics 2013; 13:1387-404. [PMID: 22966888 DOI: 10.2217/pgs.12.126] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
In order to improve therapeutic outcomes, there is a tremendous need to identify patients who are likely to respond to a given asthma treatment. Pharmacogenomic studies have explained a portion of the variability in drug response and provided an increasing list of candidate genes and SNPs. However, as phenotypic variation arises from a network of complex interactions among genetic and environmental factors, rather than individual genes or SNPs, a multidisciplinary, systems-level approach is required in order to understand the inter-relationships among these factors. Systems biology, which seeks to capture interactions between genetic factors and other variables, offers a promising approach to improved therapeutic outcomes in asthma. This aritcle will review and update progress in the pharmacogenomics of asthma and then discuss the application of systems biology approaches to asthma pharmacogenomics.
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Affiliation(s)
- Amber Dahlin
- Channing Laboratory, Brigham & Women's Hospital & Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
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Wang Y, Vik JO, Omholt SW, Gjuvsland AB. Effect of regulatory architecture on broad versus narrow sense heritability. PLoS Comput Biol 2013; 9:e1003053. [PMID: 23671414 PMCID: PMC3649986 DOI: 10.1371/journal.pcbi.1003053] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Accepted: 03/23/2013] [Indexed: 11/18/2022] Open
Abstract
Additive genetic variance (VA ) and total genetic variance (VG ) are core concepts in biomedical, evolutionary and production-biology genetics. What determines the large variation in reported VA /VG ratios from line-cross experiments is not well understood. Here we report how the VA /VG ratio, and thus the ratio between narrow and broad sense heritability (h(2) /H(2) ), varies as a function of the regulatory architecture underlying genotype-to-phenotype (GP) maps. We studied five dynamic models (of the cAMP pathway, the glycolysis, the circadian rhythms, the cell cycle, and heart cell dynamics). We assumed genetic variation to be reflected in model parameters and extracted phenotypes summarizing the system dynamics. Even when imposing purely linear genotype to parameter maps and no environmental variation, we observed quite low VA /VG ratios. In particular, systems with positive feedback and cyclic dynamics gave more non-monotone genotype-phenotype maps and much lower VA /VG ratios than those without. The results show that some regulatory architectures consistently maintain a transparent genotype-to-phenotype relationship, whereas other architectures generate more subtle patterns. Our approach can be used to elucidate these relationships across a whole range of biological systems in a systematic fashion.
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Affiliation(s)
- Yunpeng Wang
- Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway
| | - Jon Olav Vik
- Centre for Integrative Genetics (CIGENE), Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Stig W. Omholt
- Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway
- NTNU Norwegian University of Science and Technology, Department of Biology, Centre for Biodiversity Dynamics, Realfagsbygget, NO-7491 Trondheim, Norway
| | - Arne B. Gjuvsland
- Centre for Integrative Genetics (CIGENE), Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
- * E-mail:
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Abstract
Vaccines are the most cost effective public health measure for preventing viral infection and limiting epidemic spread within susceptible populations. However, the efficacy of current protective vaccines is highly variable, particularly in aging populations. In addition, there have been a number of challenges in the development of new vaccines due to a lack of detailed understanding of the immune correlates of protection. To identify the mechanisms underlying the variability of the immune response to vaccines, system-level tools need to be developed that will further our understanding of virus-host interactions and correlates of vaccine efficacy. This will provide critical information for rational vaccine design and allow the development of an analog to the "precision medicine" framework (already acknowledged as a powerful approach in medicine and therapeutics) to be applied to vaccinology.
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Affiliation(s)
- Michael Mooney
- Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Oregon, United States
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41
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Dauncey MJ. Genomic and epigenomic insights into nutrition and brain disorders. Nutrients 2013; 5:887-914. [PMID: 23503168 PMCID: PMC3705325 DOI: 10.3390/nu5030887] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 02/28/2013] [Accepted: 03/08/2013] [Indexed: 12/22/2022] Open
Abstract
Considerable evidence links many neuropsychiatric, neurodevelopmental and neurodegenerative disorders with multiple complex interactions between genetics and environmental factors such as nutrition. Mental health problems, autism, eating disorders, Alzheimer's disease, schizophrenia, Parkinson's disease and brain tumours are related to individual variability in numerous protein-coding and non-coding regions of the genome. However, genotype does not necessarily determine neurological phenotype because the epigenome modulates gene expression in response to endogenous and exogenous regulators, throughout the life-cycle. Studies using both genome-wide analysis of multiple genes and comprehensive analysis of specific genes are providing new insights into genetic and epigenetic mechanisms underlying nutrition and neuroscience. This review provides a critical evaluation of the following related areas: (1) recent advances in genomic and epigenomic technologies, and their relevance to brain disorders; (2) the emerging role of non-coding RNAs as key regulators of transcription, epigenetic processes and gene silencing; (3) novel approaches to nutrition, epigenetics and neuroscience; (4) gene-environment interactions, especially in the serotonergic system, as a paradigm of the multiple signalling pathways affected in neuropsychiatric and neurological disorders. Current and future advances in these four areas should contribute significantly to the prevention, amelioration and treatment of multiple devastating brain disorders.
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42
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Calabrese G, Bennett BJ, Orozco L, Kang HM, Eskin E, Dombret C, De Backer O, Lusis AJ, Farber CR. Systems genetic analysis of osteoblast-lineage cells. PLoS Genet 2012; 8:e1003150. [PMID: 23300464 PMCID: PMC3531492 DOI: 10.1371/journal.pgen.1003150] [Citation(s) in RCA: 43] [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: 05/12/2012] [Accepted: 10/23/2012] [Indexed: 12/20/2022] Open
Abstract
The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected “hub” genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types. The osteoblast-lineage consists of a range of cells from osteogenic precursors that mature into bone-forming osteoblasts to osteocytes that are entombed in bone. Each cell in the lineage serves a number of distinct and critical roles in the growth and maintenance of the skeleton, as well as many extra-skeletal functions. Over the last decade, many of the major regulatory pathways governing the differentiation and activity of these cells have been discovered. In contrast, little is known regarding the composition or function of gene networks within the lineage. The goal of this study was to increase our understanding of how genes are organized into networks in osteoblasts. Towards this goal, we used microarray gene expression profiles from bone to identify a group of genes that formed a network specific to the osteoblast-lineage. We used the knowledge of this network to identify novel genes that are important for regulating various aspects of osteoblast function. These data improve our understanding of the gene networks operative in cells of the osteoblast-lineage.
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Affiliation(s)
- Gina Calabrese
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Brian J. Bennett
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Luz Orozco
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
| | - Hyun M. Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Eleazar Eskin
- Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America
| | - Carlos Dombret
- Unité de Recherche en Physiologie Moléculaire (URPHYM), Namur Research Institute for Life Sciences (NARILIS), FUNDP School of Medicine, University of Namur, Namur, Belgium
| | - Olivier De Backer
- Unité de Recherche en Physiologie Moléculaire (URPHYM), Namur Research Institute for Life Sciences (NARILIS), FUNDP School of Medicine, University of Namur, Namur, Belgium
| | - Aldons J. Lusis
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Charles R. Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Cardiovascular Medicine, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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43
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Feltus FA, Vandenbrink JP. Bioenergy grass feedstock: current options and prospects for trait improvement using emerging genetic, genomic, and systems biology toolkits. BIOTECHNOLOGY FOR BIOFUELS 2012; 5:80. [PMID: 23122416 PMCID: PMC3502489 DOI: 10.1186/1754-6834-5-80] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Accepted: 10/05/2012] [Indexed: 05/19/2023]
Abstract
For lignocellulosic bioenergy to become a viable alternative to traditional energy production methods, rapid increases in conversion efficiency and biomass yield must be achieved. Increased productivity in bioenergy production can be achieved through concomitant gains in processing efficiency as well as genetic improvement of feedstock that have the potential for bioenergy production at an industrial scale. The purpose of this review is to explore the genetic and genomic resource landscape for the improvement of a specific bioenergy feedstock group, the C4 bioenergy grasses. First, bioenergy grass feedstock traits relevant to biochemical conversion are examined. Then we outline genetic resources available bioenergy grasses for mapping bioenergy traits to DNA markers and genes. This is followed by a discussion of genomic tools and how they can be applied to understanding bioenergy grass feedstock trait genetic mechanisms leading to further improvement opportunities.
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Affiliation(s)
- Frank Alex Feltus
- Department of Genetics & Biochemistry, Clemson University, 105 Collings Street. BRC #302C, Clemson, SC, 29634, USA
| | - Joshua P Vandenbrink
- Department of Genetics & Biochemistry, Clemson University, 105 Collings Street. BRC #302C, Clemson, SC, 29634, USA
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44
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Genetics of coronary artery disease: Genome-wide association studies and beyond. Atherosclerosis 2012; 225:1-10. [DOI: 10.1016/j.atherosclerosis.2012.05.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Revised: 05/15/2012] [Accepted: 05/16/2012] [Indexed: 12/14/2022]
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45
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Gini B, Hager R. Recombinant inbred systems can advance research in behavioral ecology. Front Genet 2012; 3:198. [PMID: 23060902 PMCID: PMC3463890 DOI: 10.3389/fgene.2012.00198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2012] [Accepted: 09/14/2012] [Indexed: 01/22/2023] Open
Abstract
Recombinant inbred (RI) systems such as the BXD mouse family represent a population with defined genetic architecture and variation that approximates those of natural populations. With the development of novel RI lines and sophisticated methods that conjointly analyze phenotype, gene sequence, and expression data, RI systems such as BXD are a timely and powerful tool to advance the field of behavioral ecology. The latter traditionally focused on functional questions such as the adaptive value of behavior but largely ignored underlying genetics and mechanisms. In this perspective, we argue that using RI systems to address questions in behavioral ecology and evolutionary biology has great potential to advance research in these fields. We outline key questions and how they can be tackled using RI systems and BXD in particular. The unique opportunity to analyze genetic and phenotypic data from studies conducted in different laboratories and at different times is a key benefit of RI systems and may lead the way to a better understanding of how adaptive phenotypes arise from genetic and environmental factors.
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Affiliation(s)
- Beatrice Gini
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester Manchester, UK
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46
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Abstract
From the early 1990s to the middle of the last decade, the search for genes influencing osteoporosis proved difficult with few successes. However, over the last 5 years this has begun to change with the introduction of genome-wide association (GWA) studies. In this short period of time, GWA studies have significantly accelerated the pace of gene discovery, leading to the identification of nearly 100 independent associations for osteoporosis-related traits. However, GWA does not specifically pinpoint causal genes or provide functional context for associations. Thus, there is a need for approaches that provide systems-level insight on how associated variants influence cellular function, downstream gene networks, and ultimately disease. In this review we discuss the emerging field of "systems genetics" and how it is being used in combination with and independent of GWA to improve our understanding of the molecular mechanisms involved in bone fragility.
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Affiliation(s)
- Charles R Farber
- Department of Medicine and Biochemistry & Molecular Genetics, Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
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47
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Silverman EK, Loscalzo J. Network medicine approaches to the genetics of complex diseases. DISCOVERY MEDICINE 2012; 14:143-152. [PMID: 22935211 PMCID: PMC3545396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.
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Affiliation(s)
- Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital
- Department of Medicine, Brigham and Women’s Hospital
- Harvard Medical School
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Brigham and Women’s Hospital
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital
- Department of Medicine, Brigham and Women’s Hospital
- Harvard Medical School
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48
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49
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Lusis AJ. Genetics of atherosclerosis. Trends Genet 2012; 28:267-75. [PMID: 22480919 DOI: 10.1016/j.tig.2012.03.001] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2011] [Revised: 02/29/2012] [Accepted: 03/01/2012] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies (GWAS) from the past several years have provided the first unbiased evidence of the genes contributing to common cardiovascular disease traits in European and some Asian populations. The results not only confirmed the importance of prior knowledge, such as the central role of lipoproteins, but also revealed that there is still much to learn about the underlying mechanisms of this disease, as most of the associated genes do not appear to be involved in pathways previously connected to atherosclerosis. In this review, I focus on the common forms of the disease and look at both human and animal model studies. I summarize what was known before GWAS, highlight how the field has been changed by GWAS, and discuss future considerations, such as the limitations of GWAS and strategies that may lead to a more complete, mechanistic understanding of atherosclerosis.
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Affiliation(s)
- Aldons J Lusis
- University of California, Los Angeles, Department of Medicine/Division of Cardiology, Los Angeles, CA 90095-1679, USA.
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50
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Mizrachi E, Mansfield SD, Myburg AA. Cellulose factories: advancing bioenergy production from forest trees. THE NEW PHYTOLOGIST 2012; 194:54-62. [PMID: 22474687 DOI: 10.1111/j.1469-8137.2011.03971.x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Fast-growing, short-rotation forest trees, such as Populus and Eucalyptus, produce large amounts of cellulose-rich biomass that could be utilized for bioenergy and biopolymer production. Major obstacles need to be overcome before the deployment of these genera as energy crops, including the effective removal of lignin and the subsequent liberation of carbohydrate constituents from wood cell walls. However, significant opportunities exist to both select for and engineer the structure and interaction of cell wall biopolymers, which could afford a means to improve processing and product development. The molecular underpinnings and regulation of cell wall carbohydrate biosynthesis are rapidly being elucidated, and are providing tools to strategically develop and guide the targeted modification required to adapt forest trees for the emerging bioeconomy. Much insight has already been gained from the perturbation of individual genes and pathways, but it is not known to what extent the natural variation in the sequence and expression of these same genes underlies the inherent variation in wood properties of field-grown trees. The integration of data from next-generation genomic technologies applied in natural and experimental populations will enable a systems genetics approach to study cell wall carbohydrate production in trees, and should advance the development of future woody bioenergy and biopolymer crops.
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Affiliation(s)
- Eshchar Mizrachi
- Department of Genetics, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria 0002, South Africa
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