Polymorphism information generated by next-generation sequencing (NGS) technologies has enabled applications of genome-wide markers assisted breeding. However, handling such large-scale data remains a challenge for experimental researchers and breeders, calling for the urgent development of a flexible and straightforward analysis tool for NGS data. We developed "IonBreeders" as bioinformatics plugins that implement general analysis steps from genotyping to genomic prediction. IonBreeders comprises three plugins, "ABH", "IMPUTATION", and "GENOMIC PREDICTION", for format conversion of genotyping data, preprocessing and imputation of genotyping data, and genomic prediction, respectively. "ABH" converts genotyping data derived from NGS into the ABH format, which is acceptable for our further plugins and with other breeding software tools, R/qtl, MapMaker, and AntMap. "IMPUTATION" filters out non-informative markers and imputes missing marker genotypes. In "GENOMIC PREDICTION", users can use four statistical methods based on their target trait, quantitative trait locus effect, and number of markers, and construct a prediction model for genomic selection. IonBreeders is operated in Torrent Suite, but can also handle genotype data in standard formats, e.g., Variant Call Format (VCF), by format conversion using free software or our provided scripts.

Tomato (Solanum lycopersicum) is considered a model plant species for a group of economically important crops, such as potato, pepper, eggplant, since it exhibits a reduced genomic size (950 Mb), a short generation time, and routine transformation technologies. Moreover, it shares with the other Solanaceous plants the same haploid chromosome number and a high level of conserved genomic organization. Finally, many genomic and genetic resources are actually available for tomato, and the sequencing of its genome is in progress. These features make tomato an ideal species for theoretical studies and practical applications in the genomics field. The present review describes how structural genomics assist the selection of new varieties resistant to pathogens that cause damage to this crop. Many molecular markers highly linked to resistance genes and cloned resistance genes are available and could be used for a high-throughput screening of multiresistant varieties. Moreover, a new genomics-assisted breeding approach for improving fruit quality is presented and discussed. It relies on the identification of genetic mechanisms controlling the trait of interest through functional genomics tools. Following this approach, polymorphisms in major gene sequences responsible for variability in the expression of the trait under study are then exploited for tracking simultaneously favourable allele combinations in breeding programs using high-throughput genomic technologies. This aims at pyramiding in the genetic background of commercial cultivars alleles that increase their performances. In conclusion, tomato breeding strategies supported by advanced technologies are expected to target increased productivity and lower costs of improved genotypes even for complex traits.

Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole-genome markers to predict future hybrids. Predicted superior hybrids are then field evaluated and released as new hybrid cultivars after their superior performances are confirmed. This will increase the opportunity of selecting true superior hybrids with minimum costs. Here, we used genomic best linear unbiased prediction to perform hybrid performance prediction using an existing rice population of 1495 hybrids. Replicated 10-fold cross-validations showed that the prediction abilities on ten agronomic traits ranged from 0.35 to 0.92. Using the 1495 rice hybrids as a training sample, we predicted six agronomic traits of 100 hybrids derived from half diallel crosses involving 21 parents that are different from the parents of the hybrids in the training sample. The prediction abilities were relatively high, varying from 0.54 (yield) to 0.92 (grain length). We concluded that the current population of 1495 hybrids can be used to predict hybrids from seemingly unrelated parents. Eventually, we used this training population to predict all potential hybrids of cytoplasm male sterile lines from 3000 rice varieties from the 3K Rice Genome Project. Using a breeding index combining 10 traits, we identified the top and bottom 200 predicted hybrids. SNP genotypes of the training population and parameters estimated from this training population are available for general uses and further validation in genomic hybrid prediction of all potential hybrids generated from all varieties of rice.

The article concentrates on studying tolerance to soil salinization, water flooding, and blast in Russian and Asian rice varieties, as well as hybrids of the second and third generations from their crossing in order to obtain sustainable paddy crops based on domestic varieties using DNA markers. Samples IR 52713-2B-8-2B-1-2, IR 74099-3R-3-3, and NSIC Rc 106 were used as donors of the SalTol tolerance gene. Varieties with the Sub1A locus were used as donors of the flood resistance gene: Br-11, CR-1009, Inbara-3, TDK-1, and Khan Dan. The lines C101-A-51 (Pi-2), C101-Lac (Pi-1, Pi-33), IR-58 (Pi-ta), and Moroberekan (Pi-b) were used to transfer blast resistance genes. Hybridization of the stress-sensitive domestic varieties Novator, Flagman, Virazh, and Boyarin with donor lines of the genes of interest was carried out. As a result of the studies carried out using molecular marking based on PCR in combination with traditional breeding, early-maturing rice lines with genes for resistance to salinity (SalTol) and flooding (Sub1A), suitable for cultivation in southern Russia, were obtained. Introgression and pyramiding of the blast resistance genes Pi-1, Pi-2, Pi-33, Pi-ta, and Pi-b into the genotypes of domestic rice varieties were carried out. DNA marker analysis revealed disease-resistant rice samples carrying 5 target genes in a homozygous state. The created rice varieties that carry the genes for blast resistance (Pentagen, Magnate, Pirouette, Argamac, Kapitan, and Lenaris) were submitted for state variety testing. The introduction of such varieties into production will allow us to avoid epiphytotic development of the disease, preserving the biological productivity of rice and obtaining environmentally friendly agricultural products.

Plant breeding has been very successful in developing improved varieties using conventional tools and methodologies. Nowadays, the availability of genomic tools and resources is leading to a new revolution of plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. Next Generation Sequencing (NGS) technologies are allowing the mass sequencing of genomes and transcriptomes, which is producing a vast array of genomic information. The analysis of NGS data by means of bioinformatics developments allows discovering new genes and regulatory sequences and their positions, and makes available large collections of molecular markers. Genome-wide expression studies provide breeders with an understanding of the molecular basis of complex traits. Genomic approaches include TILLING and EcoTILLING, which make possible to screen mutant and germplasm collections for allelic variants in target genes. Re-sequencing of genomes is very useful for the genome-wide discovery of markers amenable for high-throughput genotyping platforms, like SSRs and SNPs, or the construction of high density genetic maps. All these tools and resources facilitate studying the genetic diversity, which is important for germplasm management, enhancement and use. Also, they allow the identification of markers linked to genes and QTLs, using a diversity of techniques like bulked segregant analysis (BSA), fine genetic mapping, or association mapping. These new markers are used for marker assisted selection, including marker assisted backcross selection, 'breeding by design', or new strategies, like genomic selection. In conclusion, advances in genomics are providing breeders with new tools and methodologies that allow a great leap forward in plant breeding, including the 'superdomestication' of crops and the genetic dissection and breeding for complex traits.

The cultivated tomato, Lycopersicon esculentum, is the second most consumed vegetable worldwide and a well-studied crop species in terms of genetics, genomics, and breeding. It is one of the earliest crop plants for which a genetic linkage map was constructed, and currently there are several molecular maps based on crosses between the cultivated and various wild species of tomato. The high-density molecular map, developed based on an L. esculentum x L. pennellii cross, includes more than 2200 markers with an average marker distance of less than 1 cM and an average of 750 kbp per cM. Different types of molecular markers such as RFLPs, AFLPs, SSRs, CAPS, RGAs, ESTs, and COSs have been developed and mapped onto the 12 tomato chromosomes. Markers have been used extensively for identification and mapping of genes and QTLs for many biologically and agriculturally important traits and occasionally for germplasm screening, fingerprinting, and marker-assisted breeding. The utility of MAS in tomato breeding has been restricted largely due to limited marker polymorphism within the cultivated species and economical reasons. Also, when used, MAS has been employed mainly for improving simply-inherited traits and not much for improving complex traits. The latter has been due to unavailability of reliable PCR-based markers and problems with linkage drag. Efforts are being made to develop high-throughput markers with greater resolution, including SNPs. The expanding tomato EST database, which currently includes approximately 214 000 sequences, the new microarray DNA chips, and the ongoing sequencing project are expected to aid development of more practical markers. Several BAC libraries have been developed that facilitate map-based cloning of genes and QTLs. Sequencing of the euchromatic portions of the tomato genome is paving the way for comparative and functional analysis of important genes and QTLs.

Cassava ( Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Among the legume family, mungbean (Vigna radiata) has become one of the important crops in Asia, showing a steady increase in global production. It provides a good source of protein and contains most notably folate and iron. Beyond the nutritional value of mungbean, certain features make it a well-suited model organism among legume plants because of its small genome size, short life-cycle, self-pollinating, and close genetic relationship to other legumes. In the past, there have been several efforts to develop molecular markers and linkage maps associated with agronomic traits for the genetic improvement of mungbean and, ultimately, breeding for cultivar development to increase the average yields of mungbean. The recent release of a reference genome of the cultivated mungbean (V. radiata var. radiata VC1973A) and an additional de novo sequencing of a wild relative mungbean (V. radiata var. sublobata) has provided a framework for mungbean genetic and genome research, that can further be used for genome-wide association and functional studies to identify genes related to specific agronomic traits. Moreover, the diverse gene pool of wild mungbean comprises valuable genetic resources of beneficial genes that may be helpful in widening the genetic diversity of cultivated mungbean. This review paper covers the research progress on molecular and genomics approaches and the current status of breeding programs that have developed to move toward the ultimate goal of mungbean improvement.

In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches combining omic technologies (genomics, transcriptomics, proteomics, metabolomics and phenomics) using bioinformatic tools will facilitate the identification of target genes and markers for complex traits and will give a better insight into the mechanisms behind the traits.

Pandemic (H1N1) 2009 virus was detected in breeding turkeys on 2 farms in Valparaiso, Chile. Infection was associated with measurable declines in egg production and shell quality. Although the source of infection is not yet known, the outbreak was controlled, and the virus was eliminated from the birds.

Plenty of molecular markers have been developed by contemporary sequencing technologies, whereas few of them are successfully applied in breeding, thus we present a review on how sequencing can facilitate marker-assisted selection in plant breeding. The growing global population and shrinking arable land area require efficient plant breeding. Novel strategies assisted by certain markers have proven effective for genetic gains. Fortunately, cutting-edge sequencing technologies bring us a deluge of genomes and genetic variations, enlightening the potential of marker development. However, a large gap still exists between the potential of molecular markers and actual plant breeding practices. In this review, we discuss marker-assisted breeding from a historical perspective, describe the road from crop sequencing to breeding, and highlight how sequencing facilitates the application of markers in breeding practice.

This paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software.

Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study can be applied to optimize genomic prediction for other diploid and polyploid species.

Conservation breeding programs such as zoos play a major role in preventing extinction, but their sustainability may be impeded by neutral and adaptive population genetic change. These changes are difficult to detect for a single species or context, and impact global conservation efforts. We analyse pedigree data from 15 vertebrate species - over 30,000 individuals - to examine offspring survival over generations of captive breeding. Even accounting for inbreeding, we find that the impacts of increasing generations in captivity are highly variable across species, with some showing substantial increases or decreases in offspring survival over generations. We find further differences between dam and sire effects in first- versus multi-generational analysis. Crucially, our multispecies analysis reveals that responses to captivity could not be predicted from species' evolutionary (phylogenetic) relationships. Even under best-practice captive management, generational fitness changes that cannot be explained by known processes (such as inbreeding depression), are occurring.

Predicting the consequences of climate change is a major challenge in ecology and wildlife management. While the impact of changes in climatic conditions on distribution ranges has been documented for many organisms, the consequences of changes in resource dynamics for species' overall performance have seldom been investigated. This study addresses this gap by identifying the factors shaping the reproductive synchrony of ungulates. In temporally-variable environments, reproductive phenology of individuals is a key determinant of fitness, with the timing of reproduction affecting their reproductive output and future performance. We used a satellite-based index of resource availability to explore how the level of seasonality and inter-annual variability in resource dynamics affect birth season length of ungulate populations. Contrary to what was previously thought, we found that both the degree of seasonal fluctuation in resource dynamics and inter-annual changes in resource availability influence the degree of birth synchrony within wild ungulate populations. Our results highlight how conclusions from previous interspecific analyses, which did not consider the existence of shared life-history among species, should be treated with caution. They also support the existence of a multi-faceted link between temporal variation in resource availability and breeding synchrony in terrestrial mammals, and increase our understanding of the mechanisms shaping reproductive synchrony in large herbivores, thus enhancing our ability to predict the potential impacts of climate change on biodiversity.

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here, we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots Uria aalge breeding on Skomer Island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high-quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions.

Dopamine is part of the reward system triggering the social decision-making network in the brain. It has hence great potential importance in the regulation of social behaviour, but its significance in the control of behaviour in highly social animals is currently limited. We studied the role of the dopaminergic system in social decision-making in the cooperatively breeding cichlid fish, Neolamprologus pulcher, by blocking or stimulating the dopaminergic D1-like and D2-like receptors. We first tested the effects of different dosages and timing of administration on subordinate group members' social behaviour within the group in an unchallenging environment. In a second experiment we pharmacologically manipulated D1-like and D2-like receptors while experimentally challenging N. pulcher groups by presenting an egg predator, and by increasing the need for territory maintenance through digging out sand from the shelter. Our results show that the D1-like and D2-like receptor pathways are differently involved in the modulation of aggressive, submissive and affiliative behaviours. Interestingly, the environmental context seems particularly crucial regarding the role of the D2-like receptors in behavioural regulation of social encounters among group members, indicating a potential pathway in agonistic and cooperative interactions in a pay-to-stay scenario. We discuss the importance of environmental information in mediating the role of dopamine for the modulation of social behaviour.