Functional mapping of quantitative trait loci associated with rice tillering

Abstract

Several biologically significant parameters that are related to rice tillering are closely associated with rice grain yield. Although identification of the genes that control rice tillering and therefore influence crop yield would be valuable for rice production management and genetic improvement, these genes remain largely unidentified. In this study, we carried out functional mapping of quantitative trait loci (QTLs) for rice tillering in 129 doubled haploid lines, which were derived from a cross between IR64 and Azucena. We measured the average number of tillers in each plot at seven developmental stages and fit the growth trajectory of rice tillering with the Wang-Lan-Ding mathematical model. Four biologically meaningful parameters in this model--the potential maximum for tiller number (K), the optimum tiller time (t(0)), and the increased rate (r), or the reduced rate (c) at the time of deviation from t(0)--were our defined variables for multi-marker joint analysis under the framework of penalized maximum likelihood, as well as composite interval mapping. We detected a total of 27 QTLs that accounted for 2.49-8.54% of the total phenotypic variance. Nine common QTLs across multi-marker joint analysis and composite interval mapping showed high stability, while one QTL was environment-specific and three were epistatic. We also identified several genomic segments that are associated with multiple traits. Our results describe the genetic basis of rice tiller development, enable further marker-assisted selection in rice cultivar development, and provide useful information for rice production management.

Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data

“…Several two-stage approaches have been presented [20,[34][35][36]. They first fit a linear or a logistic growth curve at an individual level to phenotypic measurements observed over time, and then treat the estimated curve parameters for all the individuals as the latent trait values in QTL mapping, to be analyzed by any QTL mapping tool.…”

Unconditional and conditional analysis of epistasis between tillering QTLs based on single segment substitution lines in rice

“…Using molecular marker maps and QTL mapping technology, mapping of QTLs for tiller number has been extensively conducted 6 – 9 . Two common methods were used for developmental traits, one being by analyzing the performance of a trait observed at a fixed stage of ontogenesis to estimate QTL accumulated effects from the beginning to the investigation stage, and the other being analyzing successively the observations at various developmental stages to reveal QTL expression dynamics 8 .…”

Integrating Evolutionary Game Theory into Mechanistic Genotype–Phenotype Mapping

“…To gain more insight into the genetic control of trait formation, a dynamic model called functional mapping has been developed [10][11][12][13][14]. By integrating the physiological and developmental pathways underlying phenotype formation using mathematical equations, functional mapping is equipped with a capacity to identify genes involved in rate-limiting processes and to quantify the dynamic effect pattern of these genes across time and space scales [15][16][17].…”

“…Recent intense use of genome-wide association studies (GWASs) has led to the identification of a myriad of QTLs that affect various complex traits in plants, animals, and humans [5][6][7], but together these loci can account for only a small fraction of the total genetic variation for each trait in the population [8]. The causes for this so-called missing heritability have been extensively explored, although no consensus conclusion has been reached yet [4,9].To gain more insight into the genetic control of trait formation, a dynamic model called functional mapping has been developed [10][11][12][13][14]. By integrating the physiological and developmental pathways underlying phenotype formation using mathematical equations, functional mapping is equipped with a capacity to identify genes involved in rate-limiting processes and to quantify the dynamic effect pattern of these genes across time and space scales [15][16][17].…”