Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping.

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.

Phenomic prediction of maize hybrids.

Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging.

Targeted Sequencing Reveals Large-Scale Sequence Polymorphism in Maize Candidate Genes for Biomass Production and Composition.

A major goal of maize genomic research is to identify sequence polymorphisms responsible for phenotypic variation in traits of economic importance. Large-scale detection of sequence variation is critical for linking genes, or genomic regions, to phenotypes. However, due to its size and complexity, it remains expensive to generate whole genome sequences of sufficient coverage for divergent maize lines, even with access to next generation sequencing (NGS) technology. Because methods involving reduction of genome complexity, such as genotyping-by-sequencing (GBS), assess only a limited fraction of sequence variation, targeted sequencing of selected genomic loci offers an attractive alternative. We therefore designed a sequence capture assay to target 29 Mb genomic regions and surveyed a total of 4,648 genes possibly affecting biomass production in 21 diverse inbred maize lines (7 flints, 14 dents). Captured and enriched genomic DNA was sequenced using the 454 NGS platform to 19.6-fold average depth coverage, and a broad evaluation of read alignment and variant calling methods was performed to select optimal procedures for variant discovery. Sequence alignment with the B73 reference and de novo assembly identified 383,145 putative single nucleotide polymorphisms (SNPs), of which 42,685 were non-synonymous alterations and 7,139 caused frameshifts. Presence/absence variation (PAV) of genes was also detected. We found that substantial sequence variation exists among genomic regions targeted in this study, which was particularly evident within coding regions. This diversification has the potential to broaden functional diversity and generate phenotypic variation that may lead to new adaptations and the modification of important agronomic traits. Further, annotated SNPs identified here will serve as useful genetic tools and as candidates in searches for phenotype-altering DNA variation. In summary, we demonstrated that sequencing of captured DNA is a powerful approach for variant discovery in maize genes.

Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems.

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.

Genetic structure and diversity of wild sorghum populations (Sorghum spp.) from different eco-geographical regions of Kenya.

Wild sorghums are extremely diverse phenotypically, genetically and geographically. However, there is an apparent lack of knowledge on the genetic structure and diversity of wild sorghum populations within and between various eco-geographical regions. This is a major obstacle to both their effective conservation and potential use in breeding programs. The objective of this study was to assess the genetic diversity and structure of wild sorghum populations across a range of eco-geographical conditions in Kenya. Sixty-two wild sorghum populations collected from the 4 main sorghum growing regions in Kenya were genotyped using 18 simple sequence repeat markers. The study showed that wild sorghum is highly variable with the Coast region displaying the highest diversity. Analysis of molecular variance showed a significant variance component within and among wild sorghum populations within regions. The genetic structure of wild sorghum populations indicated that gene flow is not restricted to populations within the same geographic region. A weak regional differentiation was found among populations, reflecting human intervention in shaping wild sorghum genetic structure through seed-mediated gene flow. The sympatric occurrence of wild and cultivated sorghums coupled with extensive seed-mediated gene flow, suggests a potential crop-to-wild gene flow and vice versa across the regions. Wild sorghum displayed a mixed mating system. The wide range of estimated outcrossing rates indicate that some environmental conditions may exist where self-fertilisation is favoured while others cross-pollination is more advantageous.

Wild sorghum from different eco-geographic regions of Kenya display a mixed mating system.

Knowledge of mating systems is required in order to understand the genetic composition and evolutionary potential of plant populations. Outcrossing in a population may co-vary with the ecological and historical factors influencing it. However, literature on the outcrossing rate is limited in terms of wild sorghum species coverage and eco-geographic reference. This study investigated the outcrossing rates in wild sorghum populations from different ecological conditions of Kenya. Twelve wild sorghum populations were collected in four sorghum growing regions. Twenty-four individuals per population were genotyped using six polymorphic simple sequence repeat (SSR) markers to compute their indirect equilibrium estimates of outcrossing rate as well as population structure. In addition, the 12 populations were planted in a field in a randomised block design with five replications. Their progeny (250 individuals per population) were genotyped with the six SSR markers to estimate multi-locus outcrossing rates. Equilibrium estimates of outcrossing rates ranged from 7.0 to 75.0%, while multi-locus outcrossing rates (t (m)) ranged from 8.9 to 70.0% with a mean of 49.7%, indicating that wild sorghum exhibits a mixed mating system. The wide range of estimated outcrossing rates in wild sorghum populations indicate that environmental conditions may exist under which fitness is favoured by outcrossing and others under which selfing is more advantageous. The genetic structure of the populations studied is concordant with that expected for a species displaying mixed mating system.