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Open Access Highly Accessed Methodology article

Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems

Rongling Wu1*, Jiguo Cao2, Zhongwen Huang3, Zhong Wang4, Junyi Gai5* and Eduardo Vallejos6

Author Affiliations

1 Center for Computational Biology, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Beijing Forestry University, Beijing 100083, China

2 Department of Statistics & Actuarial Science, Simon Fraser University, Burnaby, B.C. Canada V5A 1S6

3 Department of Agronomy, Henan Institute of Science and Technology, Xinxiang 453003, China

4 Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA

5 National Center for Soybean Improvement, National Key Laboratory for Crop Genetics and Germplasm Enhancement, Soybean Research Institute, Nanjing Agricultural University, Nanjing 210095, China

6 Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA

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BMC Systems Biology 2011, 5:84  doi:10.1186/1752-0509-5-84

Published: 27 May 2011

Abstract

Background

Every phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate and engineer functionally relevant components to produce a desirable system phenotype.

Results

We describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traits by treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function through genes, and provides a quantitative and testable platform for assessing the interplay between gene action and development. We applied systems mapping to analyze biomass growth data in a mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomass partitioning to leaves, stem, and roots.

Conclusions

We show that systems mapping implemented by design principles of biological systems is quite versatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropic relationships underlying plant physiology and development. Systems mapping should enable geneticists to shed light on the genetic complexity of any biological system in plants and other organisms and predict its physiological and pathological states.