Natural variation and QTL mapping in understanding plant development

Abstract

Understanding the genetic and molecular basis of a developmental mechanism remains a challenge. Mendelian approaches in model organisms such as Caenorhabditis elegans, Drosophila, zebrafish, mouse and Arabidopsis thaliana have greatly enhanced our understanding of discrete developmental processes such as organ identity and pattern formation. However, many traits like phase changes during development are quantitative in nature. The continuous nature of the quantitative phenotypes and the underlying genetic complexity greatly diminish the power of Mendelian genetic approaches. In a parallel approach natural isolates of a species that often show genetically controlled phenotypic variation and experimental populations derived from these isolates can be used for quantitative trait locus (QTL) mapping that can explain this phenotypic variation. Powerful computational and statistical methods developed in the last decade coupled with the advent of molecular markers have revolutionized our ability to detect the QTLs. At least for some of the major effect QTLs cloning of the quantitative trait gene(s) (QTG) underlying the QTL has been possible. Understanding the genetic architecture of naturally occurring phenotypic variation in developmental traits will provide clues not only to the molecular basis of quantitative traits, but also to how environment interacts with the physiology of the plant and presumably explain the adaptive significance of such genetic variation. In this article, I review how the natural variation and QTL mapping has helped our understanding of plant development using examples mainly from maize, rice, Arabidopsis and tomato. Specifically, I will address how exploiting natural variation has advanced our knowledge of the genetic basis of plant architecture, helped us identify novel genetic pathways and genes, and even elucidate unknown functions of known genes. At the end, I discuss the potential of natural variation and QTL mapping to understand complex environmental effects that influence plant development