No abstract

The logistic or S-shaped curve of growth is one of the few universal laws in biology. It is certain that there exist specific genes affecting growth curves, but, due to a lack of statistical models, it is unclear how these genes cause phenotypic differentiation in growth and developmental trajectories. In this paper we present a statistical model for detecting major genes responsible for growth trajectories. This model is incorporated with pervasive logistic growth curves under the maximum likelihood framework and, thus, is expected to improve over previous models in both parameter estimation and inference. The power of this model is demonstrated by an example using forest tree data, in which evidence of major genes affecting stem growth processes is successfully detected. The implications for this model and its extensions are discussed.

The capacity to root from cuttings is a key factor for the mass deployment of superior genotypes in clonal forestry. We studied the genetic basis of rooting capacity by mapping quantitative trait loci (QTLs) that control growth rate and form of root traits in a full-sib family of 93 hybrids derived from an interspecific cross between two Populus species, P. deltoides and P. euramericana. The hybrid family was typed for different marker systems (including SSRs, AFLPs, RAPDs, ISSRs, and SNPs), leading to the construction of two linkage maps based on the female P. deltoides (D map) and male P. euramericana (E map) with a pseudotestcross mapping strategy. The two maps were scanned by functional mapping to detect QTLs that control early growth trajectories of two rooting traits, maximal single-root length and the total number of roots per cutting, measured at five time points in water culture. Of the six QTLs detected for these two growth traits, only one is segregating in P. deltoides with poor rooting capacity, while the other five are segregating in P. euramericana showing good rooting capacity. Tests with functional mapping suggest different developmental patterns of the genetic effects of these root QTLs in time course. Five QTLs were detected to change their effects on root growth trajectories with time, whereas one detected to affect root growth consistently in time course. Knowledge about the genetic and developmental control mechanisms of root QTLs will have important implications for the genetic improvement of vegetative propagation traits in Populus.

The database of poplar transcription factors (DPTF) is a plant transcription factor (TF) database containing 2576 putative poplar TFs distributed in 64 families. These TFs were identified from both computational prediction and manual curation. We have provided extensive annotations including sequence features, functional domains, GO assignment and expression evidence for all TFs. In addition, DPTF contains cross-links to the Arabidopsis and rice transcription factor databases making it a unique resource for genome-scale comparative studies of transcriptional regulation in model plants. Availiability: DPTF is available at http://dptf.cbi.pku.edu.cn.