J Biomet Biostat ISSN:2155-6180 JBMBS, an open access journal Research Article Open Access Editorial Open Access Biometrics & Biostatistics Keywords: Genetic mapping; Complex trait; Genetic architecture; Regulatory network; Dynamic system Genetic Dissection of Complex Traits Most quantitative traits of signifcant importance to agriculture, biology and medicine are determined by multiple genes of unknown number, each being operational to diferent degrees [1]. Te culmination of these genes produces a network of actions and interactions, forming a complex network of genetic architecture. Tis complexity can be graphically imagined by taking the elements (nodes) of the network to depict main efects of individual genes and the connections (edges) between elements as the efects of genetic interactions (also called epistasis). Te concept of genetic architecture can be understood from many diferent perspectives, but its composite picture can be described by the following factors: • Te number of genes • Te chromosomal distribution of genes • Te main genetic efects of each gene • Te interaction between allelic efects at diferent genes (epistasis) • Te pleiotropic efects of genes on diferent traits • Te expression of alleles conditional on the physical or biological environment • Te molecular basis of allelic variation • Te regulatory or coding region of causal variants • Te parent-of-origin efects of alleles or genetic imprinting Te current theory of complex trait genetics is based on the hypothesis that genetic variants in the genetic code, such as single- nucleotide polymorphisms (SNPs), insertions or deletions (indels), and copy number variants, act in concert to determine the phenotypic value of a trait through functional alterations in the activity, expression level, stability, and splicing of the RNA and proteins they encode. Genetic mapping that attributes a phenotypic trait to its underlying quantitative trait loci (QTLs) using polymorphic markers is powerful for mapping the locations of QTLs on the genome and estimating their efects of genetic actions and interactions [2]. As a routine technique of genetic analysis, QTL mapping has been instrumental for studying the genetic architecture of complex traits [3]. Developmental Dissection of Complex Traits Development includes a broad spectrum of processes. For example in plants, these processes include the formation of a complete embryo from a zygote, seed germination, the elaboration of a mature vegetative plant from the embryo, the formation of fowers, fruits, and seeds, and many of the plant’s responses to its environment. Each of these processes is fundamental to determine the size, shape and production of all higher plants. For this reason, knowledge of the genetic basis of the variation in each process is important for understanding adaptive evolution and deriving elite domestic crop varieties. While traditional approaches for mapping QTLs with phenotypes measured at particular times fail to capture the dynamic structure and pattern of the process, two new statistical methods, called functional mapping (incorporated in a package of sofware FunMap [4,5]) and systems mapping, integrates biological mechanisms and dynamic processes of the trait into the genetic mapping framework through mathematical and computational models [6-11]. Functional mapping unifes the strengths of statistics, genetics, and developmental biology, thus facilitating the test of the interplay between genetic action and development. Te principle of functional mapping can be expanded to map ontogenetic QTLs that govern all developmental events in a plant’s lifetime [12]. Previous work for functional mapping focused on the identifcation of QTLs for a particular phase of development using a mathematical model for growth trajectories during this specifc phase. Tus, identifed QTLs from this approach cannot be inferred to afect the landscape of ontogenetic growth and development. In plants ontogenetic QTL mapping, three major issues remain to be resolved: *Corresponding author: Rongling Wu, Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA, E-mail: rwu@phs.psu.edu, rwu@bjfu.edu.cn Received May 09, 2012; Accepted June 01, 2012; Published June 02, 2012 Citation: Wu R (2012) Predicting the Genotype-Phenotype Map of Complex Traits. J Biomet Biostat 3:e109. doi:10.4172/2155-6180.1000e109 Copyright: © 2012 Wu R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Predicting the Genotype-Phenotype Map of Complex Traits Rongling Wu* Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA Abstract How to predict phenotypic development in a changing environment from the genotype of complex organisms is one of the most important and challenging questions we face in modern biology. This challenge can be addressed through establishing a framework that identifes and maps the mechanistic basis of the processes from genotype to phenotype. The central rationale of this framework is based on the genetic, developmental and regulatory dissection of phenotypic changes in response to different environments. First, a phenotype is genetically complex because of the involvement of many genes that display pervasive interactions with other genes and with environmental factors. Second, the formation of any phenotype involves a series of developmental events and biological alterations that entail cell growth, differentiation and morphogenesis. Third, DNA polymorphisms affect variation in a phenotype by perturbing transcripts, metabolites and proteins in transcriptional and regulatory networks. In this editorial, I attempt to provide a big picture of each of these three aspects on phenotypic dissection. The genotype-phenotype prediction can be enabled by integrating mathematical models for developmental processes from morphogenesis to pattern formation as well as for transcript, protein and metabolite abundance affecting high-order phenotypes through a series of biochemical steps. Wu, J Biomet Biostat 2012, 3:4 http://dx.doi.org/10.4172/2155-6180.1000e109 Volume 3 • Issue 4 • 1000e109