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Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease.
Moreno-Moral, Aida; Pesce, Francesco; Behmoaras, Jacques; Petretto, Enrico.
Affiliation
  • Moreno-Moral A; Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
  • Pesce F; National Heart and Lung Institute, Faculty of Medicine, Imperial College London, Hammersmith Campus, Imperial Centre for Translational and Experimental Medicine, London, UK.
  • Behmoaras J; Centre for Complement and Inflammation Research, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK.
  • Petretto E; Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore. enrico.petretto@duke-nus.edu.sg.
Methods Mol Biol ; 1488: 337-362, 2017.
Article in En | MEDLINE | ID: mdl-27933533
Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.
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Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Regulation / Multifactorial Inheritance / Systems Biology / Gene Regulatory Networks / Genetic Association Studies / Genetics, Population Type of study: Prognostic_studies Limits: Humans Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2017 Document type: Article Affiliation country: Singapore Country of publication: United States
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Collection: 01-internacional Database: MEDLINE Main subject: Gene Expression Regulation / Multifactorial Inheritance / Systems Biology / Gene Regulatory Networks / Genetic Association Studies / Genetics, Population Type of study: Prognostic_studies Limits: Humans Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2017 Document type: Article Affiliation country: Singapore Country of publication: United States