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

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.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.Predicting the phenotype from the genotype of complex organisms is one of the most important and challenging questions we face in modern biology and medicine [1]. Genetic mapping, dissecting a phenotypic trait to its underlying quantitative trait loci (QTLs) through the use of molecular markers, has proven powerful for establishing genotype-phenotype relationships and predicting phenotypes of individual organisms based on their QTL genotypes responsible for the trait [2]. The success of this prediction depends on how well we can map the underlying QTLs and characterize complex interactions of these QTLs with each other and with environmental factors. Powerful statistical models have been developed in the past two decades to detect QTLs and study their biological function in a diverse array of phenotypic traits [3-9]. Worldwide,