Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications.
Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase-specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non-invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome-wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker-trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non-parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage-specific growth affecting genes to elucidate important processes operating at different developmental phases.
Corresponding author e-mail: e_mutegi.1@yahoo.com 2 3 AbstractUnderstanding the extent and partitioning of diversity within and among crop landraces and their wild/ weedy relatives constitutes the first step in conserving and unlocking their genetic potential. This study aimed to characterize the genetic structure and relationships within and between cultivated and wild sorghum at country scale in Kenya, and to elucidate some of the underlying evolutionary mechanisms. We analyzed a total of 439 individuals comprising 329 cultivated and 110 wild sorghums using 24 microsatellite markers. We observed a total of 295 alleles across all loci and individuals, with 257 different alleles being detected in the cultivated sorghum gene pool and 238 alleles in the wild sorghum gene pool.We found that the wild sorghum gene pool harboured significantly more genetic diversity than its domesticated counterpart, a reflection that domestication of sorghum was accompanied by a genetic bottleneck. Overall, our study found close genetic proximity between cultivated sorghum and its wild progenitor, with the extent of crop-wild divergence varying among cultivation regions. The observed genetic proximity may have arisen primarily due to historical and/or contemporary gene flow between the two congeners, with differences in farmers' practices explaining inter-regional gene flow differences. This suggests that deployment of transgenic sorghum in Kenya may lead to escape of transgenes into wildweedy sorghum relatives. In both cultivated and wild sorghum, genetic diversity was found to be structured more along geographical level than agro-climatic level. This indicated that gene flow and genetic drift contributed to shaping the contemporary genetic structure in the two congeners. Spatial autocorrelation analysis revealed a strong spatial genetic structure in both cultivated and wild sorghums at the country scale, which could be explained by medium-to long-distance seed movement.
The potential gene flow between a crop and its wild relatives is largely determined by the overlaps in their ecological and geographical distributions. Ecogeographical databases are therefore indispensable tools for the sustainable management of genetic resources. In order to expand our knowledge of Sorghum bicolor distribution in Kenya, we conducted in situ collections of wild, weedy and cultivated sorghum. Qualitative and quantitative morphological traits were measured for each sampled wild sorghum plant. Farmers' knowledge relating to the management of sorghum varieties and autecology of wild sorghum was also obtained. Cluster analysis supports the existence of several wild sorghum morphotypes that might correspond to at least three of the five ecotypes recognized in Africa. Intermediate forms between wild and cultivated sorghum belonging to the S. bicolor ssp. drummondii are frequently found in predominantly sorghum growing areas. Crop-wild gene flow in sorghum is likely to occur in many agroecosystems of Kenya.
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