Development of a QTL-environment-based predictive model for node addition rate in common bean

To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day− 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50–90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions

Phytochromes A and B Mediate Red-Light-Induced Positive Phototropism in Roots

The interaction of tropisms is important in determining the final growth form of the plant body. In roots, gravitropism is the predominant tropistic response, but phototropism also plays a role in the oriented growth of roots in flowering plants. In blue or white light, roots exhibit negative phototropism that is mediated by the phototropin family of photoreceptors. In contrast, red light induces a positive phototropism in Arabidopsis roots. Because this red-light-induced response is weak relative to both gravitropism and negative phototropism, we used a novel device to study phototropism without the complications of a counteracting gravitational stimulus. This device is based on a computer-controlled system using real-time image analysis of root growth and a feedback-regulated rotatable stage. Our data show that this system is useful to study root phototropism in response to red light, because in wild-type roots, the maximal curvature detected with this apparatus is 30° to 40°, compared with 5° to 10° without the feedback system. In positive root phototropism, sensing of red light occurs in the root itself and is not dependent on shoot-derived signals resulting from light perception. Phytochrome (Phy)A and phyB were severely impaired in red-light-induced phototropism, whereas the phyD and phyE mutants were normal in this response. Thus, PHYA and PHYB play a key role in mediating red-light-dependent positive phototropism in roots. Although phytochrome has been shown to mediate phototropism in some lower plant groups, this is one of the few reports indicating a phytochrome-dependent phototropism in flowering plants

A dynamic model with QTL covariables for predicting flowering time of common bean (Phaseolus vulgaris) genotypes

Multi-genotype multi-environment trials, associated with characterization of the environment, marker in-formation for the genotypes and measurements of the phenotypic traits of interest can potentially provide thebasis for models to predict the behavior of untested genotypes in new environments. However, there is as yet noclear indication of the best form of such models, nor how to parameterize them. The purpose of this study was topropose and test an approach to crop-QTL modeling, applied to prediction of time to flowering in common bean (Phaseolus vulgaris), which avoids the pitfall of estimating separately the parameters for each genotype. Theenvironmental model is a dynamic model with development rates that depend on daily temperature and daylength. Three of the model parameters are expressed as linear functions of the QTLs for time to flowering,resulting in a model that combines environmental variables and QTLs. An innovative approach to parameterestimation is proposed, based on least squares, which makes it quite easy to estimate all the parameters of thismodel simultaneously, using all the data. The parameterized model explains most of the genotypic and en-vironmental variability in the data, and 47% of the genotype by environment (GxE) interaction. Cross validationshows that the model extrapolates well to new genotypes in the same environments as those of the data, and alsoto new environments if they are similar in terms of temperature and photoperiod to those in the training data

A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables

The common bean is a tropical facultative short day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G x E interactions. Phenotypic data were collected from a bi-parental [Andean x Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G x E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTLs. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 days. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in-silico QTL allele combinations that could yield desired phenotypes under different climatic conditions

Intronic ATTTC repeat expansions in STARD7 in familial adult myoclonic epilepsy linked to chromosome 2

Familial Adult Myoclonic Epilepsy (FAME) is characterised by cortical myoclonic tremor usually from the second decade of life and overt myoclonic or generalised tonic-clonic seizures. Four independent loci have been implicated in FAME on chromosomes (chr) 2, 3, 5 and 8. Using whole genome sequencing and repeat primed PCR, we provide evidence that chr2-linked FAME (FAME2) is caused by an expansion of an ATTTC pentamer within the first intron of STARD7. The ATTTC expansions segregate in 158/158 individuals typically affected by FAME from 22 pedigrees including 16 previously reported families recruited worldwide. RNA sequencing from patient derived fibroblasts shows no accumulation of the AUUUU or AUUUC repeat sequences and STARD7 gene expression is not affected. These data, in combination with other genes bearing similar mutations that have been implicated in FAME, suggest ATTTC expansions may cause this disorder, irrespective of the genomic locus involved