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Butana is one of the local dairy cattle breeds of Sudan commonly kept by smallholder producers. This breed has been strongly promoted to advance the dairy production sector in the country. The main problem, however, is the lack of a systematic breeding program that involves smallholder producers. The aim of the current study was to identify the most promising design for a breeding program to improve the milk yield performance of Butana cattle under smallholder production conditions. In total, 3 breeding scenarios, including (1) the use of farm bulls, (2) the use of village bulls, and (3) the rotational use of village bulls within village groups, were simulated using a stochastic simulation approach. For each breeding scenario, 3 selection methods for bulls were considered, namely random mating, phenotypic selection, and selection based on estimated breeding value (EBV). The results showed that no genetic gain was realized with random mating in all breeding scenarios. In the farm bull breeding scenario, annual genetic gain (standard deviation units) ranged from 0.01 to 0.19 (phenotypic selection) and from 0.01 to 0.39 (selection based on EBV). In the village bull breeding scenarios, the annual genetic gain ranged from 0.01 to 0.21 (phenotypic selection) and 0.01 to 0.45 (selection based on EBV). The lowest genetic gain was realized for the rotational use of village bulls among villages within groups. Through the rotational use of village bulls, however, a higher genetic variance was maintained than in the farm and village bull breeding scenarios. We concluded that a village bull breeding program with selection based on EBV of young bulls was the most promising breeding design for achieving the breeding goal. Further studies are needed to assess the organizational feasibility of such a breeding program to ensure the participation of smallholder producers and its sustainability.

Intramuscular fat (IMF) content and fatty acids (FA) profile determine important sensory and technological characteristics of the meat, being interesting traits to be included in breeding programs for Iberian pig. In a previous study, we analyzed the longissimus dorsi transcriptome of Iberian pigs with divergent breeding values for IMF identifying 63 and 12 functional candidate genes and long non-coding RNA being differentially expressed. The first objective of this work was to use the RNA-seq data generated in that study in order to design a low-density panel with 50 single-nucleotide polymorphisms (SNP). The second aim was to evaluate the effects of this SNP panel on IMF content and backfat FA profile in a closed commercial population of Iberian pigs. The IMF content and backfat FA composition were determined in 940 Iberian pigs. A total of 837 SNPs were identified in the selection of genes and lncRNA. Out of the 50 selected SNP, 11 were monomorphic in the population and 2 failed in their genotyping. Association analyses were carried out for the 37 SNP segregating in the population (minor allele frequency, MAF > 0.05). Regarding IMF content, a significant additive effect (q-value G SNP, in which the G allele was linked to an increase of 7.59% (additive effect expressed as a percentage of the mean of the trait). For the backfat FA composition, we observed 24 SNP with significant associations. The strongest effects were detected for the ELOVL6:g.112186423A > G and FASN_rs331694510G > A SNP. The A allele of both SNPs were associated to a lower percentage of palmitic (C16:0) and palmitoleic (C16:1) acids and an increase in oleic acid (C18:1). In addition, the AMPD3:g.49043886C > G SNP had significant effects on C16:0, stearic (C18:0), C18:1 and on the sum of saturated and monounsaturated fatty acids. Finally, significant effects of the EGR2:66286345G > T SNP on C18:0 and C18:1 and other minor FA were observed. Our findings reveal ADIPOQ:g.124646194T > G, ELOVL6:g.112186423A > G, and FASN_rs331694510G > A may be useful in breeding programs for Iberian pigs including FA profile and IMF content as selection goals, provided that their potential effects on other traits of interest are controlled., This work was funded by contracts (CON15-078, CON17-125, CON19-281) between the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria and the company Sánchez Romero Carvajal Jabugo SA., in addition to the IDI-20171141 grant from the Centro para el Desarrollo Tecnológico Industrial (CDTI). R

Conventional breeding can be complemented by different strategies that increase the efficiency of the methodologies and the current rate of increase in yields in order to meet demand. The use of molecular markers with the aim of developing linkage maps of the species, the use of Blup (Best Linear Unbiased Prediction) for an efficient selection of progenitors to hybridize, the use of in vitro culture to artificially increase the number of F1 plants or the use of digital phenotyping for efficient digital characterization that can be performed during the periodic and routine regeneration of accessions in germplasm collections. Key words: Molecular markers, Blup, in vitro culture, digital phenotyping.

Genomic selection uses genome-wide molecular marker data to predict an animal's genetic value in the breeding program. This study's objective was to present heritability estimates and accuracy of genomic prediction using different methods for meat quality traits in Pekin duck. There were two kinds of ducks in the genomic selection training population: 639 fat-type ducks and 540 lean-type ducks. A single-trait animal model was used to estimate heritability and adjust the phenotype. GBLUP and BayesR methods were performed to estimate the SNP effects. The accuracy of genomic prediction was calculated using 5-fold cross-validation. The accuracy varied from 0.235 to 0.501 with the lowest accuracy estimated for traits associated with abdominal fat weight in the combined population and the most remarkable accuracy observed for abdominal fat percentage traits in the lean-type duck population. Overall, BayesR can achieve the highest prediction accuracy, while the combined population strategy could be used to increase the accuracy of prediction only when the two populations have the same breeding aim for a certain trait.

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m−2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM–WGP) demonstrated that CGM–WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Background In recent years, farmers have complained that the only way to obtain seeds is to select plants that show good performance under local climate conditions in the region. This study aimed to investigate the diversity of rice accessions grown in the region to build a breeding program. Methods and results A total of 62 accessions of rice from farmers and research stations were collected from the Kurdistan region, including short-grain and long-grain types, for molecular genetics and diversity analysis. In this study, 37 polymorphic simple sequence repeat (SSR) markers were selected with several molecular genetics software programs. The results show that these SSR markers are very effective for this investigation, generating a total of 152 observed alleles (Na), 75.166 Effective number of alleles (Ne) and an average of 4.1 and 2.03 alleles per locus, respectively. The average polymorphic information content (PIC) per locus was recorded as 0.404. The research presented here confirms two subpopulations, japonica (C1 and C2) and indica (C3), based on molecular genetics data analysis. Analysis of molecular variance revealed that the 72% variance was due to the variation among populations and 28% within the population. Conclusions Altogether, these results indicate that there is very low gene flow. These results show the importance of the study of genetic diversity and relationships for starting breeding and improvement programs for rice in the Kurdistan region. Supplementary Information The online version contains supplementary material available at 10.1007/s11033-021-06920-x.

Soybean [Glycine max (L.) Merr.] is widely planted in the world, and provides most of the plant protein and oil for humans. Fresh pod and seed are the basic harvested organs in vegetable soybean production, and are important characteristics in vegetable variety registration and product export. To dissect the genetic basis of fresh pod and seed, one RIL population was analyzed for eight related traits under four environments. Eight QTL clusters were detected on seven chromosomes with phenotypic variation explained (PVE) 5.28–17.30% under multiple environments or conferring diverse traits, and six were demonstrated by BLUP values. One pleiotropism QTL on chromosome 20 with PVEs 8.92–17.30% was linked with fresh pod weight, fresh seed weight, length and area under multiple environments, and was verified by BLUP values with PVEs 12.07–16.81%. Moreover, significant phenotype differences between favorable and unfavorable alleles demonstrated these linkage QTLs. The significant correlations of QTL markers for fresh pod length and large-pod phenotype (pod length ≥ 4.5 cm, the criterion for vegetable variety registration and product export) were identified, which provided selection markers for large-pod identification. Further analysis showed that the large-pod RIL parent “C813” possessed six favorable alleles of linked QTLs, and the elite line “L-63” possessed all of the eight favorable alleles, which presented the best phenotypes in the population. Based on these, the candidate genes were identified to present different expressions between RIL parents at different pod developmental-stages. Thus, these QTLs and candidate genes provided novel insights into the genetic basis for vegetable soybean fresh pod and seed in breeding program.

Key message A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Abstract Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package “selectiongain” from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith–Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package “selectiongain” with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

Identification and characterization of leaf and neck blast resistant varieties in terms of number of resistance genes and their allelic relationship is a pre-requisite for any breeding program to breed resistant cultivars. Genetics of pathotype specific field resistance was studied in six resistant genotypes against leaf and neck blast pathogen (Pyricularia oryzae). Crosses were attempted in half diallel fashion among themselves and with susceptible parents during Kharif 2017 and 2018. Parents, F1 and F2 generations were evaluated for leaf and neck blast reaction under artificial epiphytotic conditions. Based on the disease reaction of F1 and segregation pattern in F2 populations in Resistant (R) × Susceptible (S) and Resistant (R) × Resistant (R) crosses, it was observed that resistance in genotypes HPR 2880, HPR 3001, HPR 3007 and Kasturi were governed by two independently inherited dominant resistance genes whereas, three resistance genes conferred resistance in genotypes HPR 1156 and HPR 3010 for both leaf and neck blast. Segregation pattern in F2 populations in R × R crosses suggested that resistance genes for leaf and neck blast have at least one common allele in genotypes, HPR 2880, HPR 3001, HPR 3007 and Kasturi which are different from the genes identified in genotypes, HPR 1156 and HPR 3010.

Barley is one of the major cereal crops, which can provide a significant source of genes for stress tolerance due to its high diversity and adaptability. Metabolite traits are considered to be significant for adaptation of barley to heat stress. In the present study, genetic relationships between 120 barley genotypes were determined with 50 simple sequence repeat (SSR) and 26 inter simple sequence repeat (ISSR) markers under heat stress and non-stress conditions. Moreover, genetic diversity of barley accessions was investigated using the studied markers covering 7 chromosomes of barley. In general, 153 and 85 polymorphic alleles were detected for SSR and ISSR and number of the observed polymorphic allele varied between 2–9 and 2–6, with an average of 3.26 and 3.26 alleles per locus, respectively. Markers of Bmag0223, GBMS180/180, HVM7, ISSR22, ISSR25, and ISSR48 were the most informative due to their high polymorphism information content value demonstrating that putative techniques utilized in this research can be powerful and valuable tools in breeding program of barley. Association analysis was performed between 9 important traits and SSR and ISSR markers using four statistical models. The results revealed that the model containing both population structure (Q) and general similarity in genetic background arising from shared kinship (K) factors reduced false positive associations between markers and phenotypes. According to the results, some of markers related to more than one trait under normal conditions (ISSR31-2, HVM62, and GBMS180/180) and heat stress conditions (ISSR20-5, EBmac635, HVM14, and ISSR37-3) were determined, which can be considered to be the most interesting candidates for further studies and simultaneously will provide a useful target for the future breeding programs, such as marker-assisted selection (MAS).

Objective: The objectives of the study were to evaluate reproductive performance and selection response through genetic trend of community-based breeding programs (CBBPs) of Bonga sheep.Methods: Reproduction traits data were collected between 2012 and 2018 from Bonga sheep CBBPs. Phenotypic performance was analyzed using the general linear model procedures of Statistical Analysis System. Genetic parameters were estimated by univariate animal model for age at first lambing (AFL) and repeatability models for lambing interval (LI), litter size (LS), and annual reproductive rate (ARR) traits using restricted maximum likelihood method of WOMBAT. For correlations bivariate animal model was used. Best model was chosen based on likelihood ratio test. The genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth/ lambing.Results: The overall least squares mean±standard error of AFL, LI, LS, and ARR were 375± 12.5, 284±9.9, 1.45±0.010, and 2.31±0.050, respectively. Direct heritability estimates for AFL, LI, LS, and ARR were 0.07±0.190, 0.06±0.120, 0.18±0.070, and 0.25±0.203, respectively. The low heritability for both AFL and LI showed that these traits respond little to selection programs but rather highly depend on animal management options. The annual genetic gains were –0.0281 days, –0.016 days, –0.0002 lambs and 0.0003 lambs for AFL, LI, LS, and ARR, respectively.Conclusion: Implications of the result to future improvement programs were improving management of animals, conservation of prolific flocks and out scaling the CBBP to get better results.

The oil palm breeding program for the species Elaeis guineensis and the backcross Elaeis oleifera is running slowly because oil palm is an annual plant. Therefore, it is necessary to have an alternative approach that can accelerate the oil palm breeding program. The SNP (single nucleotide polymorphism) genome-wide approach was then used to study the association between 18 phenotypes of bunch component in oil palm germplasm of E. oleifera from Suriname and Brazil Coari, some interspecific hybrids and some elite progeny of E. guineensis. The genotyping by sequencing (GBS) analysis produced a total of 459 million or approximately 798 thousand reads per sample and 3,252 SNPs were eligible for 456 genotypes. Using various association models, eleven normalized phenotypic data showed significant associations with 29 SNPs. Based on the annotations, 17 SNPs were related to genes wtih certain biological functions. Three SNPs were found to be at the exon of a gene, namely SNP4416, SNP349 and SNP3865, while the other 15 SNPs were at the intragenic to a gene. Four SNPs are common SNPs in phenotypes C16:0 and C18:1 as weel as in C20 0 and C20:1. This research shows the potential of SNPs that can be used as an alternative approach to E. oleifera backcross breeding, although further research is needed for validation purposes.

Scarlet eggplant (Solanum aethiopicum gr. gilo) is a part of African indigenous vegetables and acknowledged as a source of variations in the breeding of Brinjal. Since its genetic diversity is still largely unexplored, therefore genetic diversity and population structure of this plant were investigated in this study. Scarlet eggplant germplasm made of fifty-two accessions originated from two districts of Rwanda was assessed by employing the iPBS-retrotransposon markers system. Twelve most polymorphic primers were employed for molecular characterization and they yielded 329 total bands whereupon 85.03% were polymorphic. The recorded mean polymorphism information content was 0.363 and other diversity indices such as; mean the effective number of alleles, mean Shannon’s information index and gene diversity with the following values; 1.298, 0.300 and 0.187 respectively. A superior level of diversity was noticed among accessions from Musanze district. The model-based structure, neighbor-joining, and principal coordinate analysis (PCoA) gathered scarlet germplasm in a divergence manner to their collection district. Analysis of molecular variance (AMOVA) displayed that the utmost variations (81%) in scarlet eggplant germplasm are resulting in differences within populations. The extensive diversity of scarlet eggplant in Rwanda might be used to form the base and genetic resource of an exhaustive breeding program of this economically important African indigenous vegetable. For instance, accessions MZE53 and GKE11 might be proposed as parent candidates due to their high relative genetic distance (0.6781).

AhRt1 controlling red testa color in peanut was fine-mapped to an interval of 580 kb on chromosome A03, and one gene encoding bHLH transcriptional factor was identified as the putative candidate gene. Peanut with red testa has higher nutritional and economic value than the traditional pink testa varieties. Identification of genes controlling red testa color will accelerate the breeding program and facilitate uncovering the genetic mechanism. In this study, in order to identify gene underlying the red testa color in peanut, a F2 population derived from a cross between a pink testa peanut variety “Fuhua 8” and a red testa variety “Quanhonghua 1” was constructed. The genetic analysis for the F2 population revealed that the red testa color was controlled by one single dominant locus. This locus, named as AhRt1 (Arachis hypogaea Red Testa 1), was preliminary identified in chromosome A03 by BSA-sequencing analysis. Using a segregation mapping population, AhRt1 was fine-mapped to a 580-kb genomic region by substitution mapping strategy. Gene candidate analysis suggested that one predicted gene encoding bHLH transcriptional factor may be the possible candidate gene for AhRt1. A diagnostic marker closely linked to candidate gene has been developed for validating the fine-mapping result in different populations and peanut germplasm. Our findings will benefit the breeding program for developing new varieties with red testa color and laid foundation for map-based cloning gene responsible for red testa in peanut.

Key message Despite phenotyping the training set under unfavorable conditions on smallholder farms in Madagascar, we were able to successfully apply genomic prediction to select donors among gene bank accessions. Abstract Poor soil fertility and low fertilizer application rates are main reasons for the large yield gap observed for rice produced in sub-Saharan Africa. Traditional varieties that are preserved in gene banks were shown to possess traits and alleles that would improve the performance of modern variety under such low-input conditions. How to accelerate the utilization of gene bank resources in crop improvement is an unresolved question and here our objective was to test whether genomic prediction could aid in the selection of promising donors. A subset of the 3,024 sequenced accessions from the IRRI rice gene bank was phenotyped for yield and agronomic traits for two years in unfertilized farmers’ fields in Madagascar, and based on these data, a genomic prediction model was developed. This model was applied to predict the performance of the entire set of 3024 accessions, and the top predicted performers were sent to Madagascar for confirmatory trials. The prediction accuracies ranged from 0.10 to 0.30 for grain yield, from 0.25 to 0.63 for straw biomass, to 0.71 for heading date. Two accessions have subsequently been utilized as donors in rice breeding programs in Madagascar. Despite having conducted phenotypic evaluations under challenging conditions on smallholder farms, our results are encouraging as the prediction accuracy realized in on-farm experiments was in the range of accuracies achieved in on-station studies. Thus, we could provide clear empirical evidence on the value of genomic selection in identifying suitable genetic resources for crop improvement, if genotypic data are available.

Background. Stem rust of wheat, caused by the biotrophic fungus Puccinia graminis Pers. f. sp. tritici Eriks. et Henn., is a dangerous disease that afflicts serious economic damage to the cultivation of durum wheat.Materials and methods. Cultivars and promising materials developed at the Spring Durum Wheat Breeding Laboratory, Omsk Agrarian Scientific Center (OASC), lines obtained under the CIMMYT and KASIB programs, and accessions from the VIR collection were the objects of research. Field experiments, phenological observations, and assessment of stem rust resistance were carried out from 1990 through 2019 in the OASC experimental fields using conventional methods. Resistance to the Ug99 race was evaluated under natural infection pressure at the Kenya Agricultural and Livestock Research Organization (KALRO).Results. Promising source material was identified and included in the Laboratory’s breeding program. The ways were shown to develop stable cultivars of spring durum wheat for the environments of Western Siberia. A strategy was proposed for the selection of genotypes in hybrid combinations of spring durum wheat: it would include early selection (starting from F2 ) for disease resistance with simultaneous screening for quantitative traits and pasta-making qualities. The description and advantages of cv. ‘Omsky korall’, submitted to the State Variety Trials in 2018, are presented. This cultivar combines high yield, adaptability, resistance to the local population and the Ug99 race of the stem rust pathogen, and excellent pasta-making qualities.Conclusion. As a result of these studies under heavy stem rust pressure in Western Siberia, all breeding nurseries obtained source material resistant to the Omsk population of P. graminis. The released cultivars ‘Omskaya yantarnaya’ and ‘Omsky izumrud’ demonstrate resistance to the Omsk population of the stem rust causative agent.

Relevance. F1 hybrids breeding programs of onion are based on the use of nuclear cytoplasmic male sterility (NCMS). Breeding and seed production based on NCMS suggest a development of a female sterile maternal line, a maintainer line and a paternal component. The development of an isogenic pair of sterile line – maintainer line is one of the most labor-, time-consuming and intellectually-intensive stages of a breeding program, the implementation of which becomes more complicated with an increase in the number of traits / genes for which selection is carried out.Methods and results. This paper presents a genetic scheme for the development of a Downy mildewresistant (pathogen P. destructor) maintainer line of onion using marker-assisted selection (MAS). F2 populations were obtained from the cross of the female maintainer inbred line, Bn1- (13) (genotype – cytN msms pdpd) and male inbred line resistant to Downy mildew, No. 136 (genotype – cytT MsMs PdPd). DNA marker DMR1 were used for Downy mildew resistance gene Pd, jnurf13 – for maintainer gene ms and marker system 5`cob: orfA501 – for N, S and T cytoplasms. It was shown that the MAS application allow to develop a stable Downy mildew resistant maintainer line cytN msms PdPd in two generations.

This paper reviews and analyzes key features from cassava breeding at the International Center for Tropical Agriculture (CIAT) over 50 years and draws lessons for public breeding efforts broadly. The breeding team, jointly with national program partners and the private processing sector, defined breeding objectives and guiding business plans. These have evolved through the decades and currently focus on four global product profiles. The recurrent selection method also evolved and included innovations such as estimation of phenotypic breeding values, increasing the number of locations in the first stage of agronomic evaluations, gradual reduction of the duration of breeding cycles (including rapid cycling for high-heritability traits), the development of protocols for the induction of flowering, and the introduction of genome-wide predictions. The impact of cassava breeding depends significantly on the type of target markets. When roots are used for large processing facilities for starch, animal feeding or ethanol production (such as in SE Asia), the adoption of improved varieties is nearly universal and productivity at the regional scale increases significantly. When markets and relevant infrastructure are weak or considerable proportion of the production goes for local artisanal processing and on-farm consumption, the impact has been lower. The potential of novel breeding tools needs to be properly assessed for the most effective allocation of resources. Finally, a brief summary of challenges and opportunities for the future of cassava breeding is presented. The paper describes multiple ways that public and private sector breeding programs can learn from each other to optimize success.

Key message We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018. Abstract Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.

Key message Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Abstract Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

We identified cryptic early blight resistance introgressions in tomato breeding lines and demonstrated efficient genotypic selection for resistance in the context of a tomato breeding program. Early blight is a widespread and problematic disease affecting tomatoes (Solanum lycopersicum). Caused by the fungal pathogen Alternaria linariae (syn. A. tomatophila), symptoms include lesions on tomato stems, fruit, and foliage, often resulting in yield losses. Breeding tomatoes with genetic resistance would enhance production sustainability. Using cross-market breeding populations, we identified several quantitative trait loci (QTL) associated with early blight resistance. Early blight resistance putatively derived from ‘Campbell 1943′ was confirmed in modern fresh market tomato breeding lines. This resistance offered substantial protection against early blight stem lesions (collar rot) and moderate protection from defoliation. A distinctive and potentially novel form of early blight foliar resistance was discovered in a processing tomato breeding line and is probably derived from S. pimpinellifolium via ‘Hawaii 7998’. Additional field trials validated the three most promising large-effect QTL, EB-1.2, EB-5, and EB-9. Resistance effects for EB-5 and EB-9 were consistent across breeding populations and environments, while EB-1.2’s effect was population specific. Using genome-wide marker-assisted backcrossing, we developed fresh market tomato lines that were near-isogenic for early blight QTL. Resistance in these lines was largely mediated by just two QTL, EB-5 and EB-9, that together captured 49.0 and 68.7% of the defoliation and stem lesion variance, respectively. Our work showcases the value of mining cryptic introgressions in tomato lines, and across market classes, for use as additional sources of disease resistance.

Spot blotch, an important foliar fungal disease caused by Bipolaris sorokiniana (Sacc.) Shoemaker [stat. anam.], is the influential production restraint over the global wheat-growing regions. A set of eight diverse wheat genotypes including a few breeding lines from Uttar Banga Krishi Viswavidyalaya and commonly accepted susceptible cultivars were screened for spot blotch disease and grain yield across five different locations in West Bengal, India, under environments for natural disease infestation in post-rainy seasons of 2018–2019 and 2019–2020. The present investigation revealed significant variation among the genotypes for spot blotch resistance in different environments as a result of genotype main effect and genotype environment interaction. This study also recorded two genotypes as resistant against spot blotch disease over the five locations. Identified resistant genotypes increased grain yield as a consequence of resistance offered against spot blotch disease suggesting the possibility of considering disease resistance as a must-have trait in all the wheat cultivars that could be identified for cultivation in the North-Eastern Plain Zone of India. Two genotypes resistant to spot blotch recorded 1.26–1.46 times higher grain yield over control under disease pressure that can be recommended for identification and exploited in a resistance breeding program for wheat spot blotch disease-prone region.

Key message Utilizing a high-density integrated genetic linkage map of hexaploid sweetpotato, we discovered a major dominant QTL for root-knot nematode (RKN) resistance and modeled its effects. This discovery is useful for development of a modern sweetpotato breeding program that utilizes marker-assisted selection and genomic selection approaches for faster genetic gain of RKN resistance. Abstract The root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) causes significant storage root quality reduction and yields losses in cultivated sweetpotato [Ipomoea batatas (L.) Lam.]. In this study, resistance to RKN was examined in a mapping population consisting of 244 progenies derived from a cross (TB) between ‘Tanzania,’ a predominant African landrace cultivar with resistance to RKN, and ‘Beauregard,’ an RKN susceptible major cultivar in the USA. We performed quantitative trait loci (QTL) analysis using a random-effect QTL mapping model on the TB genetic map. An RKN bioassay incorporating potted cuttings of each genotype was conducted in the greenhouse and replicated five times over a period of 10 weeks. For each replication, each genotype was inoculated with ca. 20,000 RKN eggs, and root-knot galls were counted ~62 days after inoculation. Resistance to RKN in the progeny was highly skewed toward the resistant parent, exhibiting medium to high levels of resistance. We identified one major QTL on linkage group 7, dominant in nature, which explained 58.3% of the phenotypic variation in RKN counts. This work represents a significant step forward in our understanding of the genetic architecture of RKN resistance and sets the stage for future utilization of genomics-assisted breeding in sweetpotato breeding programs.

The use of chia (Salvia hispanica L.) seeds as functional food is increasing worldwide and research on plant traits is important as the crop spreads. The seedling stage has been identified as crucial for establishment, survival, and competition with weeds but information on chia at this stage is scanty, especially regarding root traits. In this study, 8 genotypes of chia: 4 populations (Australia, Mexico, Peru, Basilicata) and 4 breeding lines (G3, G8, G17, WS) were evaluated under controlled conditions at two early stages (six leaves, S-6; ten leaves, S-10) with the aim to assess growth characteristics and variability in root morphological traits. The shoot height and dry matter were measured together with the following root traits: dry matter and architectural parameters characterized through image analysis of root scans: length, surface, average diameter, tips, forks and crossings. Plants showed a greater phenotypic variation at S-6 than at S-10 (average CV = 40% and 22%, respectively). A high variability was recorded for root biomass, root volume, number of forks and crossings (average CV = 54, 40, 44 and 48%, respectively), while root diameter showed lower variability (average CV = 10%). Four principal components explained more than 97% of the total variance and root length, surface area, volume and average diameter contributed mainly to the variability. Cluster analysis showed that genotypes were very different for morphological root traits. Australia and G8 had the most developed root system with longer and coarser roots. Our results could be used in a breeding program for new cultivars with a more vigorous seedling.

The level of genetic diversity among the available breeding materials determines the potential success of a breeding program. In this study, 92 maize inbred lines were genotyped with 3047 single-nucleotide polymorphism (SNP) markers using a Kompetitive allele-specific polymerase chain reaction (KASP™) genotyping protocol. The objectives were to determine the level and pattern of genetic diversity and define potential heterotic groups of maize inbred lines developed by the Agricultural Research Council maize program of South Africa. More than 91% of the SNPs used were polymorphic with a mean polymorphic information content of 0.36. Gene diversity ranged from 0.35 to 0.37, with a mean of 0.36. Cluster analysis revealed the presence of three distinct subpopulations. Analysis of molecular variance revealed low but highly significant (P

Genetic diversity and differences among durum-wheat cultivars evolved in various regions of the world are important for sustainable production in the current climate change scenario. Information regarding genetic differences was also important for the correct choice of parental material for the selection of high quality cultivars. Two elite and six obsolete cultivars of durum-wheat were characterized with 25-simple sequence repeats (SSR) markers. All accessions were evaluated for 2-agronomic-traits (Yield (Y) and Thousand-Kernel-Weight (TKW)) and 11 grain quality-traits (grain protein content (GPC), grain moisture contents (H), carotene content (CT), sedimentation test (SDS), gluten content (GC), gluten index (GI), semolina color index (L*, a*, b*) and alveographic parameters (W and P/L)) under randomized complete block design with three replication for two crop seasons (2015-2017). Genetic characterization through SSR markers revealed 126 alleles with an average of 5.04 alleles locus-1 and had average 0.79 polymorphism information content (PIC). The comparisons revealed that elite accessions were more productive in terms of grain yield and TKW, whereas obsolete accessions showed high GPC and end-use quality-traits. The generated dendrogram based on SSR markers, agronomic, seed quality-traits clearly differentiate the genotypes in two main groups obsolete and elite accessions. Analysis of correlation revealed a significant association between the traits TKW, Y, b*, a*, GPC, GC, SDS and H. High genetic diversity found between elite and obsolete cultivars for parameters such as yield, end-use quality and their correlation with SSR markers could help breeders for an eventual breeding program on durum-wheat.

Beany flavor of soybean (Glycine max (L.) Merr.) is caused by oxidation of polyunsaturated fatty acids by the action of three lipoxygenases (LOX1, LOX2 and LOX3) present in mature seeds. The unpleasant flavor restricts human consumption of soybean products. This problem could be solved through genetic elimination of alleles that code these enzymes. Parental cultivars and two hybrid population were selected and analyzed using genetic markers for alleles locus, encoding Lox1, Lox2 and Lox3 free. The SSR marker Satt 212 confirmed the presence of the homozygous null-allele Lx3 in the cultivar BRS 213, which were used for hybridization with BR 36. Heterozygote F1 hybrid plants and homozygous Lx3 lines in F2 segregating populations were successfully identified. The SSR markers Sat090 and Sat417 were the most effective diagnostic markers among all SSR markers tested. Satt090 and Satt417 confirmed the presence of the homozygous Lx2 null-allele in the parental cultivar BRS 213 by flanking Lx2 loci at 3,00 and 2,77 cM, respectively. The presence of Lx2 null allele in the F2 segregating populations between BRS 213 and BRS 155 was successfully identified with a selection efficiency of 98% and have great potential for further application in the Brazilian breeding program aimed at improving soybean seed quality.

Bacterial blight (BB), caused by Xanthomonas citri pv. malvacearum (Xcm), is a destructive disease to cotton production in many countries. In the U.S., Xcm race 18 is the most virulent and widespread race and can cause serious yield losses. Planting BB-resistant cotton cultivars is the most effective method of controlling this disease. In this study, 335 U.S. Upland cotton accessions were evaluated for resistance to race 18 using artificial inoculations by scratching cotyledons on an individual plant basis in a greenhouse. The analysis of variance detected significant genotypic variation in disease incidence, and 50 accessions were resistant including 38 lines with no symptoms on either cotyledons or true leaves. Many of the resistant lines were developed in the MAR (multi-adversity resistance) breeding program at Texas A&M University, whereas others were developed before race 18 was first reported in the U.S. in 1973, suggesting a broad base of resistance to race 18. A genome-wide association study (GWAS) based on 26,301 single nucleotide polymorphic (SNP) markers detected 11 quantitative trait loci (QTL) anchored by 79 SNPs, including three QTL on each of the three chromosomes A01, A05 and D02, and one QTL on each of D08 and D10. This study has identified a set of obsolete Upland germplasm with resistance to race 18 and specific chromosomal regions delineated by SNPs for resistance. The results will assist in breeding cotton for BB resistance and facilitate further genomic studies in fine mapping resistance genes to enhance the understanding of the genetic basis of BB resistance in cotton.

Most orphan crops have not been fully sequenced, hence we rely on genome sequences of related species to align markers to different chromosomes. This hinders their utilisation in plant population improvement programs. Utilising the advances in the science of sequencing technologies, the population structure, relatedness, and genetic diversity among accessions can be assessed quickly for better exploitation in forage breeding programs. Using DArTseq technology, we studied the genetic and structural variation in 65 Lablab purpureus (L.) Sweet conserved gene-bank accessions using 9320 DArTseq-based SNPs and 15,719 SilicoDart markers. These markers had a low discriminating ability with mean polymorphic information content (P.I.C.) of 0.14 with DArTseq-based SNPs and 0.13 with SilicoDart markers. However, the markers had a high mean call rate of 73% with DArTseq-based SNPs and 97% with SilicoDart markers. Analysis of molecular variance revealed a high within populations variance (99.4%), indicating a high gene exchange or low genetic differentiation (PhiPT = 0.0057) among the populations. Structure analysis showed three allelic pools in variable clusters of ΔK = 3 and 6. Phylogenetic tree of lablab accessions showed three main groups with variable membership coefficients. Most pairs of accessions (40.3%) had genetic distances between 0.10 and 0.15 for SilicoDart markers, while for DArTseq-based SNPs, (46.5%) had genetic distances between 0.20 and 0.25. Phylogenetic clustering and minimum spanning analysis divided the 65 accessions into three groups, irrespective of their origin. For the first time, this study produced high-density markers with good genom coverage. The utilisation of these accessions in a forage program will base on the information from molecular-based grouping. The outcomes uncovered the presence of noteworthy measure of variety in Uganda, CIAT and ILRI accessions, thus demonstrating an opportunity for further marker-trait-association studies.

PPARGC1A gene plays an important role in the activation of various important hormone receptors and transcriptional factors involved in the regulation of adaptive thermogenesis, gluconeogenesis, fiber-type switching in skeletal muscle, mitochondrial biogenesis, and adipogenesis, regulating the reproduction and proposed as a candidate gene for milk-related traits in cattle. This study identified polymorphisms in the PPARGC1A gene in Italian Mediterranean buffaloes and their associations to milk production and quality traits (lactation length, peak milk yield, fat and protein yield, and percentage). As a result, a total of seven SNPs (g.-78A>G, g.224651G>C, g.286986G>A, g.304050G>A, g.325647G>A, g.325817T>C, and g.325997G>A) were identified by DNA pooled sequencing. Analysis of productivity traits within the genotyped animals revealed that the g.286986G>A located at intron 4 was associated with milk production traits, but the g.325817T>C had no association with milk production. Polymorphisms in g.-78A>G was associated with peak milk yield and milk yield, while g.304050G>A and g.325997 G>A were associated with both milk yield and protein percentage. Our findings suggest that polymorphisms in the buffalo PPARGC1A gene are associated with milk production traits and can be used as a candidate gene for milk traits and marker-assisted selection in the buffalo breeding program.

Artículo de investigación The global production of sheep milk is growing, and the main industrial use of sheep milk is cheese making. The Spanish Churra sheep breed is one of the most important native dairy breeds in Spain. The present study aimed to estimate genetic parameters for a wide range of traits influencing the cheese-making ability of Churra sheep milk. Using a total of 1,049 Churra ewes, we studied the following cheese-making traits: 4 traits related to milk coagulation properties (rennet coagulation time, curd-firming time, and curd firmness at 30 and 60 min after addition of rennet), 2 traits related to cheese yield (individual laboratory cheese yield and individual laboratory dried curd yield), and 3 traits measuring curd firmness over time (maximum curd firmness, time to attain maximum curd firmness, and syneresis). In addition, a list of milk traits, including the native pH of the milk and several milk production and composition traits (milk yield; the fat, protein, and dried extract percentages; and the somatic cell count), were also analyzed for the studied animals. After discarding the noncoagulating samples (only 3.7%), data of 1,010 ewes were analyzed with multiple-trait animal models by using the restricted maximum likelihood method to estimate (co)variance components, heritabilities, and genetic correlations. In general, the heritability estimates were low to moderate, ranging from 0.08 (for the individual laboratory dried curd yield trait) to 0.42 (for the fat percentage trait). High genetic correlations were found within pairs of related traits (i.e., 0.93 between fat and dried extract percentages, −0.93 between the log of the curd-firming time and curd firmness at 30 min, 0.70 between individual laboratory cheese yield and individual laboratory dried curd yield, and −0.94 between time to attain maximum curd firmness and syneresis). Considering all the information provided here, we suggest that in addition to the current consideration of the protein percentage trait for improving cheese yield traits, the inclusion of the pH of milk as a measured trait in the Churra dairy breeding program would represent an efficient strategy for improving the cheese-making ability of milk from this breed. SI

Wampee [Clausena lansium (Lour.) Skeels] was originally native to the southern part of China, and had a long history of cultivation. Wampee has been an important species in the development of the fruit breeding program in the genus Clausena, family Rutaceae. In addition to fruit characteristics and bark color, it is difficult to distinguish different varieties of wampee, more difficult of identification of seedling stage. In this study, we determined and analyzed the complete chloroplast genome of Jixin, seedless, and sweet wampee varieties. By comparison, it was found that the sweet wampee is 150 bp less than the other two varieties. We designed a pair of specific primers to distinguish between sweet wampee and other varieties, and it also has better discriminating ability through the analysis of seedlings. The complete chloroplast genome will provide valuable information for the development of highly variable DNA markers for future variety identification and population analysis studies of wampee.

Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

An ambitious vanilla (Vanilla planifolia Jacks. ex Andrews) breeding program was started in Madagascar in the early 1950s to control Fusariosis, a serious crop disease caused by the soil fungus Fusarium oxysporum. The breeding program has produced tens of thousands of new vanilla genotypes of which a small number have been selected and conserved. This article examines the history of the Malagasy breeding work during the second half of the twentieth century, and outlines a possible strategy to exploit the data and exceptional genetic resources it has produced. The National Center for Research Applied to Rural Development has preserved 19 of the very diverse and original hybrids at the vanilla station in Ambohitsara (Antalaha, Madagascar). Two of them are particularly remarkable, namely Manitra ampotony for its exceptionally high vanillin content and Tsy taitra for its strong resistance to Fusariosis and its aromatic profile, which is similar to that of Bourbon vanilla. However, the global vanilla industry remains fragile because varietal diversity now available cannot overcome current and foreseeable constraints. The hybrids formed from multiple crosses between V. planifolia and three aromatic species, (V. pompona Schiede, V. tahitensis J.W. Moore and V. phaeantha Rchb.f.), constitute a preferred resource for further vanilla breeding. Facilitating its wide use by current research and extension teams is, in our opinion, essential to meet the growing need of producers for better-adapted varieties to supply the market of one of the most highly prized natural flavors in the world.

Curcuma kwangsiensis S. K. Lee & C. F. Liang, a traditional herb in southern China, accumulates substantial amounts of active components, curcuminoids and volatile oil. However, the limited hereditary information and SSR markers have hindered its breeding program and genetic analysis. Here, we examined the transcriptome of C. kwangsiensis using next-generation sequencing (NGS) technology. A dataset with 8.17 Gb of raw reads was generated and assembled into 77,976 unigenes. Moreover, 11,678 EST-SSR markers were screened from transcriptome data. Of the 800 synthesized primer pairs, 486 (60.8%) exhibited successful amplification and 115 (23.7%) were polymorphic. A set of 24 selected markers showed high cross-species transferabilities among 13 Curcuma species. In total, 277 alleles (6–19 alleles per locus) were discovered, and the polymorphic information content (PIC) ranged from 0.496 to 0.905. Based on the results of cluster and structure analyses, the 75 accessions were classified into four major groups with some admixtures. Finally, a core collection (22 accessions) was identified, exhibiting Na, Ne, I, and PIC values with retention rates of 87.7%, 107.8%, 102.5% and 102%, respectively. All these unigenes and EST-SSRs will be useful for germplasm resource evaluation and for diversifying the potential of product derivatives from Curcuma.

General combining ability (GCA) has been successfully used to evaluate the parental performance in hybrid breeding program, while the molecular basis of GCA still remains unknown. In this study, RNA-Seq was employed to compare the transcriptome profiles that determined the GCA for yield-related traits in two different barley genotypes. A total of 1051 differentially expressed unigenes (DEGs) including 599 up-regulated DEGs and 452 down-regulated DEGs were found between Baodamai 12 and Ximala 22. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis revealed that many DEGs participated in metabolic process, stimulus response, catalytic activity and biosynthesis. Furthermore, 11 DEGs involved in the starch and sucrose metabolism pathway were discovered, among which eight up-regulated genes were related to beta-fructofuranosidase, pectinesterase, beta-glucosidase and hexokinase, while the other three down-regulated genes were related to sucrose-phosphate synthase, beta-glucosidase and hexokinase. These DEGs might contribute to the differences of GCA for grain weight between the two genotypes. The results may provide a new insight in the studying of the molecular basis of GCA and establish a foundation for further genomic research on the improvement of combining ability in barley breeding program.

Training population optimization algorithms are useful for efficiently training genomic prediction models for single-cross performance, especially if the population is extended beyond only realized crosses to all possible single crosses. Genomic prediction of single-cross performance could allow effective evaluation of all possible single crosses between all inbreds developed in a hybrid breeding program. The objectives of the present study were to investigate the effect of different levels of relatedness on genomic predictive ability of single crosses, evaluate the usefulness of deterministic formula to forecast prediction accuracy in advance, and determine the potential for TRS optimization based on prediction error variance (PEVmean) and coefficient of determination (CDmean) criteria. We used 481 single crosses made by crossing 89 random recombinant inbred lines (RILs) belonging to the Iowa stiff stalk synthetic group with 103 random RILs belonging to the non-stiff stalk synthetic heterotic group. As expected, predictive ability was enhanced by ensuring close relationships between TRSs and target sets, even when TRS sizes were smaller. We found that designing a TRS based on PEVmean or CDmean criteria is useful for increasing the efficiency of genomic prediction of maize single crosses. We went further and extended the sampling space from that of all observed single crosses to all possible single crosses, providing a much larger genetic space within which to design a training population. Using all possible single crosses increased the advantage of the PEVmean and CDmean methods based on expected prediction accuracy. This finding suggests that it may be worthwhile using an optimization algorithm to select a training population from all possible single crosses to maximize efficiency in training accurate models for hybrid genomic prediction.

Background Improving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments. Results A set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents. Conclusion Although G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (Ndams = 1,000) that were selected for the same breeding goal trait (h2 = 0.3) under G×E correlation (rg) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (Gd), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 Gd_rg combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21–1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain.

Climate change is rapidly changing how we live, what we eat and produce, the crops we breed and the target traits. Previously underutilized orphan crops that are climate resilient are receiving much attention from the crops research community, as they are often the only crops left in the field after periods of extreme weather conditions. There are several orphan crops with incredible resilience to biotic and abiotic stresses. Some are nutritious, while others provide good sources of biofuel, medicine and other industrial raw materials. Despite these benefits, orphan crops are still lacking in important genetic and genomic resources that could be used to fast track their improvement and make their production profitable. Progress has been made in generating draft genomes of at least 28 orphan crops over the last decade, thanks to the reducing cost of sequencing. The implementation of a structured breeding program that takes advantage of additional modern crop improvement tools such as genomic selection, speed breeding, genome editing, high throughput phenotyping and breeding digitization would make rapid improvement of these orphan crops possible, but would require coordinated research investment. Other production challenges such as lack of adequate germplasm conservation, poor/non-existent seed systems and agricultural extension services, as well as poor marketing channels will also need to be improved if orphan crops were to be profitable. We review the importance of breeding orphan crops under the increasing effects of climate change, highlight existing gaps that need to be addressed and share some lessons to be learned from major crops. Supplementary Information The online version of this article (10.1007/s00122-020-03755-1) contains supplementary material, which is available to authorized users.

Meiotic disturbances in F1 hybrids and their progenies are still major problems in wide hybridization. To investigate the genome affinity reflected in chromosome pairing and segregation, we studied chromosome behaviors during meiosis in two interspecific F1 hybrids [O. minuta × O. australiensis (Om × Oa, BCE genome) and Oa × O. ridleyi (Or, EHJ genome)] by using both traditional staining methods and genomic in situ hybridization (GISH). GISH analysis has been successfully performed on mitotic chromosomes to distinguish different Oryza genomes, but relatively fewer systematic analyses of meiotic chromosomes of interspecific hybrids have been reported. In the hybrids, highly irregular chromosome behaviors through meiosis resulted in producing microspores with unbalanced genome. At diakinesis of these two hybrids, most chromosomes present as univalent, with low frequency as bivalents and occasionally as trivalents. In a pollen mother cell, 2 to 8 bivalents and 0 to 4 trivalents were observed in the hybrid Oa × Or, and 1 to 8 bivalents and 0 to 5 trivalents were observed in the hybrid Om × Oa. GISH results indicated that 51.52% bivalents in Oa × Or and 79.65% bivalents in Om × Oa involved allosyndetic association, which indicates that recombination and introgression should be possible if viable backcrosses can be recovered even from triploid hybrids. In this study, we revealed that the meiotic disturbance due to low affinities between parental genomes is the major reason for the sterility of these two triploid interspecific hybrids. The two hybrids showing vigor in reproductive growth are potential genetic resources in future breeding programs. A better understanding of genomic affinities between these distant Oryza species can facilitate planning an effective breeding program by using wide hybridization, and efficient and routine GISH analysis is helpful to monitor alien introgression in the process.

Cucumis melo L. is characterized with a great polymorphism according to many agronomical features. Study the variability of melon flowering and fruit traits is an initial step in the breeding program. The current study aimed to identify the genetic variability in the melon collection according to flowering and fruit traits in order to be used in breeding programs. It was evaluated fifty melon genotypes belong to different groups - cantalupensis, reticulatus, makuwa, inodorus, and agrestis. The studied collection consists of Bulgarian old cultivars, hybrids, landraces, PI, and introduced lines. Investigated genotypes were characterized by nine quantitative fruit traits - days to flowering, sex expression, days to ripening, fruit length, fruit diameter, flesh thickness, fruit weight, seed cavity diameter, and total soluble solids. It was established great variability in studied characteristics measured by a coefficient of variation from 7.73 to 39.85%. Significant correlations between fruit length, fruit diameter, flesh thickness, cavity diameter, and fruit weight were established. Principal component analysis grouped variables into three components which explain 69.89% of the total variation. Cluster analysis divided genotypes into six groups of similarities. Among the genotypes examined, they have a relatively high level of variability and their polymorphism could be used in a breeding program aimed at combining valuable characters.

Drought stress is one of the main problems for the rice crop, as it reduces the production and productivity of the grain yield significantly. In Egypt, many restrictions were made on the cultivation of rice due to its high-water demand. Producing promising drought-tolerant rice cultivars combined with high yielding is one of the main targets for rice breeders. A set of 22 highly diverse rice genotypes were evaluated under normal and drought conditions. Morphological, physiological, and yield traits were recorded on each genotype. Drought susceptibility index (DSI) was estimated for six yield traits to identify the most drought-tolerant rice genotypes. A high genetic variation was found among genotypes tested in the experiment. Under normal conditions, the highest phenotypic correlation was found between grain yield (GY) and sterility percentage (SP) (− 0.73**), while it was among GY and chlorophyll content (CC) (0.82**) under drought conditions. To identify quantitative trait loci (QTL) controlling yielding traits under drought and normal, a single marker analysis was performed between all yield traits under both conditions and a set of 106 simple sequence repeat (SSR) marker alleles. The genetic association analysis revealed 14 and 17 QTL under drought and normal conditions, respectively. The most drought-tolerant genotypes were selected based on phenotypic traits, the number of QTL in each selected genotype, and the level of genetic diversity existed among the genotypes. As a result, five genotypes (Giza 178, IET1444, GZ1368-S-5-4, Nahda, Giza 14) were identified as the most promising drought-tolerant rice genotypes. Eight QTL controlling drought tolerance were identified in Giza 178, Nahda, and GZ1368-S-5-4, while four QTL were found in IET1444. The number of different QTLs were estimated among the five selected genotypes. Giza 178 and GZ1368-S-5-4 shared the same QTLs. Seven different QTLs were found among Nahda, IET1444, GZ1368-S-5-4, and Giza 14. Combining information from phenotypic traits, genetic diversity analysis, and QTL analysis was very useful in identifying the true drought-tolerant rice genotypes that can be used for crossing in the future breeding program.