| Chapter title |
The genotype-phenotype maps of systems biology and quantitative genetics: distinct and complementary.
|
|---|---|
| Chapter number | 17 |
| Book title |
Evolutionary Systems Biology
|
| Published in |
Advances in experimental medicine and biology, June 2012
|
| DOI | 10.1007/978-1-4614-3567-9_17 |
| Pubmed ID | |
| Book ISBNs |
978-1-4614-3566-2, 978-1-4614-3567-9
|
| Authors |
Landry CR, Rifkin SA, Christian R. Landry, Scott A. Rifkin |
| Editors |
Orkun S. Soyer |
| Abstract |
The processes by which genetic variation in complex traits is generated and maintained in populations has for a long time been treated in abstract and statistical terms. As a consequence, quantitative genetics has provided limited insights into our understanding of the molecular bases of quantitative trait variation. With the developing technological and conceptual tools of systems biology, cellular and molecular processes are being described in greater detail. While we have a good description of how signaling and other molecular networks are organized in the cell, we still do not know how genetic variation affects these pathways, because systems and molecular biology usually ignore the type and extent of genetic variation found in natural populations. Here we discuss the quantitative genetics and systems biology approaches for the study of complex trait architecture and discuss why these two disciplines would synergize with each other to answer questions that neither of the two could answer alone. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 22% |
| United Kingdom | 1 | 11% |
| Montenegro | 1 | 11% |
| Unknown | 5 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 56% |
| Scientists | 4 | 44% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 2% |
| Malaysia | 1 | 2% |
| Austria | 1 | 2% |
| Australia | 1 | 2% |
| Slovenia | 1 | 2% |
| United States | 1 | 2% |
| Poland | 1 | 2% |
| Unknown | 42 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 33% |
| Researcher | 11 | 22% |
| Student > Master | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Professor > Associate Professor | 4 | 8% |
| Other | 7 | 14% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 57% |
| Biochemistry, Genetics and Molecular Biology | 9 | 18% |
| Engineering | 4 | 8% |
| Computer Science | 3 | 6% |
| Immunology and Microbiology | 2 | 4% |
| Other | 1 | 2% |
| Unknown | 2 | 4% |