Shovelomics root traits assessed on the EURoot maize panel are highly heritable across environments but show low genotype-by-nitrogen interaction

Abstract The need for sustainable intensification of agriculture in the coming decades requires a reduction in nitrogen (N) fertilization. One opportunity to reduce N application rates without major losses in yield is breeding for nutrient efficient crops. A key parameter that influences nutrient uptake efficiency is the root system architecture (RSA). To explore the impact of N availability on RSA and to investigate the impact of the growth environment, a diverse set of 36 inbred dent maize lines crossed to the inbred flint line UH007 as a tester was evaluated for N-response over 2 years on three different sites. RSA was investigated by excavating and imaging of the root crowns followed by image analysis with REST software. Despite strong site and year effects, trait heritability was generally high. Root traits showing the greatest heritability (> 0.7) were the width of the root stock, indicative of the horizontal expansion, and the fill factor, a measure of the density of the root system. Heritabilities were in a similar range under high or low N application. Under N deficiency the root stock size decreased, the horizontal expansion decreased and the root stock became less dense. However, there was little differential response of the genotypes to low N availability. Thus, the assessed root traits were more constitutively expressed rather than showing genotype-specific plasticity to low N. In contrast, strong differences were observed for 'stay green' and silage yield, indicating that these highly heritable traits are good indicators for responsiveness to low N

Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

BACKGROUND: The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program. RESULTS: The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives. CONCLUSIONS: Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users

Shovelomics root traits assessed on the EURoot maize panel are highly heritable across environments but show low genotype-by-nitrogen interaction

The need for sustainable intensification of agriculture in the coming decades requires a reduction in nitrogen (N) fertilization. One opportunity to reduce N application rates without major losses in yield is breeding for nutrient efficient crops. A key parameter that influences nutrient uptake efficiency is the root system architecture (RSA). To explore the impact of N availability on RSA and to investigate the impact of the growth environment, a diverse set of 36 inbred dent maize lines crossed to the inbred flint line UH007 as a tester was evaluated for N-response over 2 years on three different sites. RSA was investigated by excavating and imaging of the root crowns followed by image analysis with REST software. Despite strong site and year effects, trait heritability was generally high. Root traits showing the greatest heritability (> 0.7) were the width of the root stock, indicative of the horizontal expansion, and the fill factor, a measure of the density of the root system. Heritabilities were in a similar range under high or low N application. Under N deficiency the root stock size decreased, the horizontal expansion decreased and the root stock became less dense. However, there was little differential response of the genotypes to low N availability. Thus, the assessed root traits were more constitutively expressed rather than showing genotype-specific plasticity to low N. In contrast, strong differences were observed for ‘stay green’ and silage yield, indicating that these highly heritable traits are good indicators for responsiveness to low N.ISSN:0014-2336ISSN:1573-506

Genomic prediction and landscape genomics in a large maize landraces collection using high-throughput pool genotyping identifies promising sources of diversity for prebreeding

Maize landraces are a valuable source of genetic diversity for facing climate change due to their local adaptation. High-throughput pool genotyping (HPG) is a cost-effective approach to genotype maize landraces and identify promising sources of alleles for tolerance to abiotic stress. We applied this approach on a large world-wide collection of maize landraces to i) characterize its genetic structuration; ii) identify genomic regions involved in adaptation through environmental association studies; iii) perform genomic prediction (GP) of both adaptive and agronomic traits. Landraces were structured according to their history and environmental conditions. GP yielded high accuracy, allowing to identify promising landraces. We identified genomic regions associated with bioclimatic variables that could be putatively involved in adaptation to abiotic stress. Combining eco-genetic and genomic prediction opens an avenue for using these genetic resources for prebreeding