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Background: Maize lethal necrosis (MLN) disease is a significant constraint for maize producers in sub-Saharan Africa (SSA). The disease decimates the maize crop, in some cases, causing total crop failure with far-reaching impacts on regional food security., Results: In this review, we analyze the impacts of MLN in Africa, finding that resource-poor farmers and consumers are the most vulnerable populations. We examine the molecular mechanism of MLN virus transmission, role of vectors and host plant resistance identifying a range of potential opportunities for genetic and phytosanitary interventions to control MLN. We discuss the likely exacerbating effects of climate change on the MLN menace and describe a sobering example of negative genetic association between tolerance to heat/drought and susceptibility to viral infection. We also review role of microRNAs in host plant response to MLN causing viruses as well as heat/drought stress that can be carefully engineered to develop resistant varieties using novel molecular techniques., Conclusions: With the dual drivers of increased crop loss due to MLN and increased demand of maize for food, the development and deployment of simple and safe technologies, like resistant cultivars developed through accelerated breeding or emerging gene editing technologies, will have substantial positive impact on livelihoods in the region. We have summarized the available genetic resources and identified a few large-effect QTLs that can be further exploited to accelerate conversion of existing farmer-preferred varieties into resistant cultivars., (© 2022. The Author(s).)

Through the local selection of landraces, humans have guided the adaptation of crops to a vast range of climatic and ecological conditions. This is particularly true of maize, which was domesticated in a restricted area of Mexico but now displays one of the broadest cultivated ranges worldwide. Here, we sequenced 67 genomes with an average sequencing depth of 18x to document routes of introduction, admixture and selective history of European maize and its American counterparts. To avoid the confounding effects of recent breeding, we targeted germplasm (lines) directly derived from landraces. Among our lines, we discovered 22,294,769 SNPs and between 0.9% to 4.1% residual heterozygosity. Using a segmentation method, we identified 6,978 segments of unexpectedly high rate of heterozygosity. These segments point to genes potentially involved in inbreeding depression, and to a lesser extent to the presence of structural variants. Genetic structuring and inferences of historical splits revealed 5 genetic groups and two independent European introductions, with modest bottleneck signatures. Our results further revealed admixtures between distinct sources that have contributed to the establishment of 3 groups at intermediate latitudes in North America and Europe. We combined differentiation- and diversity-based statistics to identify both genes and gene networks displaying strong signals of selection. These include genes/gene networks involved in flowering time, drought and cold tolerance, plant defense and starch properties. Overall, our results provide novel insights into the evolutionary history of European maize and highlight a major role of admixture in environmental adaptation, paralleling recent findings in humans.

This paper describes AlphaSim, a software package for simulating plant and animal breeding programs. AlphaSim enables the simulation of multiple aspects of breeding programs with a high degree of flexibility. AlphaSim simulates breeding programs in a series of steps: (i) simulate haplotype sequences and pedigree; (ii) drop haplotypes into the base generation of the pedigree and select single-nucleotide polymorphism (SNP) and quantitative trait nucleotide (QTN); (iii) assign QTN effects, calculate genetic values, and simulate phenotypes; (iv) drop haplotypes into the burn-in generations; and (v) perform selection and simulate new generations. The program is flexible in terms of historical population structure and diversity, recent pedigree structure, trait architecture, and selection strategy. It integrates biotechnologies such as doubled-haploids (DHs) and gene editing and allows the user to simulate multiple traits and multiple environments, specify recombination hot spots and cold spots, specify gene jungles and deserts, perform genomic predictions, and apply optimal contribution selection. AlphaSim also includes restart functionalities, which increase its flexibility by allowing the simulation process to be paused so that the parameters can be changed or to import an externally created pedigree, trial design, or results of an analysis of previously simulated data. By combining the options, a user can simulate simple or complex breeding programs with several generations, variable population structures and variable breeding decisions over time. In conclusion, AlphaSim is a flexible and computationally efficient software package to simulate biotechnology enhanced breeding programs with the aim of performing rapid, low-cost, and objective in silico comparison of breeding technologies., (Copyright © 2016 Crop Science Society of America.)

Keywords: genomic selection; breeding; plant breeding; animal breeding; dairy breeding; selection gain EN genomic selection breeding plant breeding animal breeding dairy breeding selection gain 1 6 6 11/24/21 20211119 NES 211119 Introduction Within the last 20 years, after the landmark paper by Meuwissen et al. ([26]), genomic selection (GS) has been widely incorporated in plant and animal breeding (Crossa et al., [12]; Hickey et al., [20]). Conclusion Dairy Cattle Breeding Compared to Plant Breeding Genomic selection was adopted in dairy cattle breeding almost instantly after genotyping costs dropped below the anticipated break-even point, presumably because the routine use of pedigree-based predictions, and a culture of centrally processing data of fragmented production units, had already been established (Schaeffer, [30]; Wiggans et al., [33]). Animal breeding, genomic selection, breeding, plant breeding, dairy breeding, selection gain Genomic Selection in Dairy Cattle Breeding Dairy cattle breeding provided good conditions for the introduction of GS. [Extracted from the article]

Another opinion contribution was provided by Gholami et al. who compared the adoption of GS across different breeding institutions, in more detail dairy cattle breeding and public and private plant breeding programs. Keywords: genomic selection (GS); plant breeding; selection gain; breeding schemes; genotype-by-environment interaction EN genomic selection (GS) plant breeding selection gain breeding schemes genotype-by-environment interaction 1 3 3 05/31/22 20220527 NES 220527 Genomic selection (GS) has been one of the most prominent Research Topics in breeding science in the last two decades after the milestone paper by Meuwissen et al. ([1]). Plant breeding, selection gain, genotype-by-environment interaction, genomic selection (GS), breeding schemes. [Extracted from the article]

Conservation and exploitation of crop wild relative species is an important component in ensuring food security and improving current agricultural output. By identifying agriculturally important characteristics that express favorable response to both biotic and abiotic stress currently unused by breeders, the incorporation of this new genetic material into genetic background stocks may help mitigate problems imposed by climate change, land degradation, and population pressure. This is particularly important in countries that will be more severely affected by the threat of reduced yields. The ability to effectively manage genetic resources col- lections and integrate unique and diverse data types is crucial in exploring, understanding, and exploiting the diversity contained within gene-banks. Providing a common interface through which experimental and background data can be disseminated to both researchers and breeders will bring focus and facilitate community building into research communities. We have taken wild barley (Hordeum spp.) and potato (Solanum spp.) collections along with wheat (Triticum spp.) and maize (Zea mays subsp. mays) and their wild relatives and incorporated this data into web-based information resources built using the Germinate platform (https://ics.hutton.ac.uk/get-germinate, accessed 4 Apr. 2017). We have tailored these to better meet the demands of researchers by developing both new data visualization tools and integration with current software such as Helium, Flapjack, and CurlyWhirly (https://ics.hutton.ac.uk/software, accessed 4 Apr. 2017) and presented the data in a common platform. While the underlying species differ, the approach taken ensures that tools are compatible across all database instances. We will describe these database instances and show that Germinate offers a common platform that will aid in the exploration and wider use of these species. [ABSTRACT FROM AUTHOR]

Quality control (QC) of germplasm identity and purity is a critical component of breeding and conservation activities. SNP genotyping technologies and increased availability of markers provide the opportunity to employ genotyping as a low-cost and robust component of this QC. In the public sector available low-cost SNP QC genotyping methods have been developed from a very limited panel of markers of 1,000 to 1,500 markers without broad selection of the most informative SNPs. Selection of optimal SNPs and definition of appropriate germplasm sampling in addition to platform section impact on logistical and resource-use considerations for breeding and conservation applications when mainstreaming QC. In order to address these issues, we evaluated the selection and use of SNPs for QC applications from large DArTSeq data sets generated from CIMMYT maize inbred lines (CMLs). Two QC genotyping strategies were developed, the first is a “rapid QC”, employing a small number of SNPs to identify potential mislabeling of seed packages or plots, the second is a “broad QC”, employing a larger number of SNP, used to identify each germplasm entry and to measure heterogeneity. The optimal marker selection strategies combined the selection of markers with high minor allele frequency, sampling of clustered SNP in proportion to marker cluster distance and selecting markers that maintain a uniform genomic distribution. The rapid and broad QC SNP panels selected using this approach were further validated using blind test assessments of related re-generation samples. The influence of sampling within each line was evaluated. Sampling 192 individuals would result in close to 100% possibility of detecting a 5% contamination in the entry, and approximately a 98% probability to detect a 2% contamination of the line. These results provide a framework for the establishment of QC genotyping. A comparison of financial and time costs for use of these approaches across different platforms is discussed providing a framework for institutions involved in maize conservation and breeding to assess the resource use effectiveness of QC genotyping. Application of these research findings, in combination with existing QC approaches, will ensure the regeneration, distribution and use in breeding of true to type inbred germplasm. These findings also provide an effective approach to optimize SNP selection for QC genotyping in other species. [ABSTRACT FROM AUTHOR]