Towards a comprehensive picture of the genetic landscape of complex traits

Abstract

The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.

Keywords: DNA polymorphism; complex traits; differential equations; network mappin; systems biology.

Figure 1:

Traditional genetic mapping that links DNA variation (AT versus CG) in a region of the plant genome to phenotypic variation in static plant traits, such as leaf display, through a statistical test. This approach simply considers the biochemical pathways from DNA to the phenotype as a black box, unable to address the biological complexity of phenotypic formation and development.

Figure 2:

Biological networks that are involved in trait formation and development. Genetic mapping is moving from a direct genotype–phenotype correspondence (Figure 1) to regulatory networks that underlies the formation of a phenotypic trait. Functional mapping associates DNA variants with the developmental process (early, middle and late) of a trait, such as plant height, facilitating the elucidation of the developmental and genetic basis of trait variation. Systems mapping extends the dynamic idea of functional mapping to dissect the phenotype into its interrelating components based on design principles and then map QTLs that determine each of the components and component–component interactions and coordination. In this example, systems mapping dissects plant height into its leaf and root components over a time scale and study how leaf and root traits coordinate and compete with each other to affect height growth. Now, network mapping combines the advantages of functional mapping and systems mapping to identify eQTLs (for gene expression), pQTLs (for protein expression) and mQTLs (for metabolic expression) and their genetic interactions that control regulatory networks that cause the final phenotype. Network mapping entails the integrative and innovative use of different tools from diverse disciplines, such as genetics, mathematics, development, statistics, computer science and engineering.

Figure 3:

Dynamic changes of gene and protein expression in a time course, with the pattern and behavior depending on different QTL genotypes (A, B and C). In (D), a phase plane analysis is conducted for the three genotypes, showing their different patterns of gene–protein expression dynamics.