Chapter title |
Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease
|
---|---|
Chapter number | 16 |
Book title |
Systems Genetics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6427-7_16 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6425-3, 978-1-4939-6427-7
|
Authors |
Aida Moreno-Moral, Francesco Pesce, Jacques Behmoaras, Enrico Petretto, Moreno-Moral, Aida, Pesce, Francesco, Behmoaras, Jacques, Petretto, Enrico |
Editors |
Klaus Schughart, Robert W. Williams |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 75% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 5 | 36% |
Student > Ph. D. Student | 3 | 21% |
Student > Master | 2 | 14% |
Other | 1 | 7% |
Student > Bachelor | 1 | 7% |
Other | 1 | 7% |
Unknown | 1 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 7 | 50% |
Biochemistry, Genetics and Molecular Biology | 4 | 29% |
Social Sciences | 1 | 7% |
Medicine and Dentistry | 1 | 7% |
Unknown | 1 | 7% |