Genetic diversity and epidemic histories of rice yellow mottle virus in Ghana.

Rice yellow mottle virus (RYMV) has persisted as a major biotic constraint to rice production in Africa. However, no data on RYMV epidemics were available in Ghana, although it is an intensive rice-producing country. Surveys were performed from 2010 to 2020 in eleven rice-growing regions of Ghana. Symptom observations and serological detections confirmed that RYMV is circulating in most of these regions. Coat protein gene and complete genome sequencings revealed that RYMV in Ghana almost exclusively belongs to the strain S2, one of the strains covering the largest area in West Africa. We also detected the presence of the S1ca strain which is being reported for the first time outside its area of origin. These results suggested a complex epidemiological history of RYMV in Ghana and a recent expansion of S1ca to West Africa. Phylogeographic analyses reconstructed at least five independent RYMV introductions in Ghana for the last 40 years, probably due to rice cultivation intensification in West Africa leading to a better circulation of RYMV. In addition to identifying some routes of RYMV dispersion in Ghana, this study contributes to the epidemiological surveillance of RYMV and helps to design disease management strategies, especially through breeding for rice disease resistance.

Induced variations of ethyl methane sulfonate mutagenized cowpea (Vigna unguiculata L. walp) plants.

Unique variants are desired in the development of genetically improved crops to meet farmer and market needs hence ethyl methane sulfonate (EMS) was used to induce genetic variability in cowpea (Vigna unguiculata cv. Asontem). The main objective of this research was to characterize induced variations in EMS chemically mutagenized population of cowpea (Vigna unguiculata L. Walp Var. Asontem) in the M1 and M2 generations. The optimum concentration (LD50) of EMS for generating the mutagenized population was determined by treating seeds with different concentrations of EMS (0.0, 0.2, 0.4, 0.6, and 0.8% v/v) and observing the germination count after 5 days of planting the seeds in Petri dishes. Three thousand cowpea seeds were treated with the 0.4% EMS to generate the M1 and M2 populations that were evaluated for agronomic and morphological traits with untreated seeds serving as control. Data analysis involved distribution of qualitative and quantitative traits. Germination was significantly reduced in the mutagenized population (17.8%) and compared with that of the wild type (61.6%). Percentage survival was significantly higher in wild type (98.38%) as compared with the M1 population (78.46%). Percentage germination in the M2 population (74.03%) was lower than the wild type (80%). A wide spectrum of agro-morphological abnormalities was observed in the M2 population. Wide variations and uniquely different phenotypic classes were observed in leaf color, leaf shape, growth habit, plant pigmentation, twining tendency, pod curvature, seed shape, and seed coat color. M2 individuals were widely distributed for days to flowering, number of pods per plant, number of seeds per pod, number of locules per pods, percentage seed set, pod length and number of seeds per plant. In conclusion, the EMS mutagenesis was effective in inducing the unique variations that will be useful for breeding and development of new farmer preferred varieties.

Combining Ability and Heterotic Patterns of Tropical Early-Maturing Maize Inbred Lines under Individual and Combined Heat and Drought Environments.

Information on combining ability and heterotic patterns of multiple stress-tolerant inbred lines are fundamental prerequisites for devising appropriate breeding strategies for the development of climate-resilient maize hybrids. In the present study, we evaluated 150 single cross hybrids derived from the North Carolina Design II (NCD II) along with six commercial checks under terminal drought stress (TDS), heat stress (HS), and combined drought and heat stress (CHDS)conditions. The objectives of the study were to: (i) determine the combining ability of the inbred lines and identify the best testers across the stresses; (ii) classify the inbred lines into heterotic groups (HGs) based on the general combining ability of multiple traits (HGCAMT) and sequencing-based diversity array technology (DArTseq) and (iii) assess the performance and stability of the lines in hybrid combinations. The inbred lines showed significantly (p < 0.01 and p < 0.05) positive and negative general combining ability (GCA) and specific combining ability (SCA) effects for grain yield (GY) and most other measured traits. The inbred line TZEI 135 displayed relatively larger positive GCA effects for GY when mated either as male or female and was identified as the best tester. TZEI 135 × TZEI 182 was identified as the best single-cross tester across environments. Results of the assessment of the relative importance of GCA and SCA effects revealed the predominance of additive gene action over the non-additive. Six HGs of inbreds were identified using the HGCAMT and three, based on the DArTseq marker genetic distance method, were the most efficient. The best hybrids in this study significantly out-yielded the best checks by 21, 46, and 70% under CHDS, HS, and TDS, respectively. These hybrids should be extensively tested in on-farm trials for possible commercialization in sub-Saharan Africa.

Genome-Wide Association Study Reveals Genetic Architecture and Candidate Genes for Yield and Related Traits under Terminal Drought, Combined Heat and Drought in Tropical Maize Germplasm.

Maize (Zea mays L.) production is constrained by drought and heat stresses. The combination of these two stresses is likely to be more detrimental. To breed for maize cultivars tolerant of these stresses, 162 tropical maize inbred lines were evaluated under combined heat and drought (CHD) and terminal drought (TD) conditions. The mixed linear model was employed for the genome-wide association study using 7834 SNP markers and several phenotypic data including, days to 50% anthesis (AD) and silking (SD), husk cover (HUSKC), and grain yield (GY). In total, 66, 27, and 24 SNPs were associated with the traits evaluated under CHD, TD, and their combined effects, respectively. Of these, four single nucleotide polymorphism (SNP) markers (SNP_161703060 on Chr01, SNP_196800695 on Chr02, SNP_195454836 on Chr05, and SNP_51772182 on Chr07) had pleiotropic effects on both AD and SD under CHD conditions. Four SNPs (SNP_138825271 (Chr03), SNP_244895453 (Chr04), SNP_168561609 (Chr05), and SNP_62970998 (Chr06)) were associated with AD, SD, and HUSKC under TD. Twelve candidate genes containing phytohormone cis-acting regulating elements were implicated in the regulation of plant responses to multiple stress conditions including heat and drought. The SNPs and candidate genes identified in the study will provide invaluable information for breeding climate smart maize varieties under tropical conditions following validation of the SNP markers.

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage.

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.

Genomic and Phenotypic Diversity of Cultivated and Wild Tomatoes with Varying Levels of Heat Tolerance.

Assessment of genetic variability in heat-tolerant tomato germplasm is a pre-requisite to improve yield and fruit quality under heat stress. We assessed the population structure and diversity in a panel of three Solanum pimpinellifolium (wild tomatoes) and 42 S. lycopersicum (cultivated tomatoes) lines and accessions with varying heat tolerance levels. The DArTseq marker was used for the sequencing and 5270 informative single nucleotide polymorphism (SNP) markers were retained for the genomic analysis. The germplasm was evaluated under two heat stress environments for five yield and flower related traits. The phenotypic evaluation revealed moderate broad-sense heritabilities for fruit weight per plant and high broad-sense heritabilities for fruit weight, number of inflorescences per plant, and number of flowers per inflorescence. The hierarchical clustering based on identity by state dissimilarity matrix and UPGMA grouped the germplasm into three clusters. The cluster analysis based on heat-tolerance traits separated the germplasm collection into five clusters. The correlation between the phenotypic and genomic-based distance matrices was low (r = 0.2, p < 0.05). The joint phenotypic and genomic-based clustering grouped the germplasm collection into five clusters well defined for their response to heat stress ranging from highly sensitive to highly tolerant groups. The heat-sensitive and heat-tolerant clusters of S. lycopersicum lines were differentiated by a specific pattern of minor allele frequency distribution on chromosome 11. The joint phenotypic and genomic analysis revealed important diversity within the germplasm collection. This study provides the basis for efficient selection of parental lines to breed heat-tolerant varieties.

Mapping chromosomal regions associated with anther indehiscence with exerted stigmas in CRI-48 and Jasmine 85 cross of rice (Oryza sativa L).

Anther indehiscence in certain wide crosses combines male sterility with stigma exertion, a phenomenon that is desirable for hybrid rice seed production. This study sought to identify chromosomal region(s) that combine anther indehiscence with exerted stigmas. A mapping population consisting of 189 BC1F1 plants was derived from a cross between CRI-48 and Jasmine 85 and backcrossing the resulting F1 to Jasmine 85. Contrary to the three complementary genes mode of inheritance reported earlier, a single locus (AI6-1) was mapped on chromosome 6 at 27.4 cM for anther indehiscence with exerted stigmas through a mixed model-based composite interval mapping (MCIM). This locus was flanked by two single nucleotide polymorphism (SNP) markers, K_ID6002884 and K_ID6003341 within a range of 23.1-28.9 cM. The allele at the locus was contributed by the CRI-48 parent which has Oryza glaberrima ancestry. This locus is suggested to control anther indehiscence and stigma exertion through pleiotropic gene action or cluster of genes.

Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program.

KEY MESSAGE: Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or "test-half-predict-half" can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

Optimizing pollencounter for high throughput phenotyping of pollen quality in tomatoes.

The macro "PollenCounter" in ImageJ was initially developed to assess pollen viability in grapevine. We set out to see if PollenCounter could be used to assess pollen number and viability in tomatoes.•We tested different optimization scenarios by adjusting the pollen size (100-900, 200-900 pixel2) and circularity of pollen grains (0.4-1, 0.5-1, and 0.6-1) on 31 microscopic images of stained tomato pollen. Both total pollen number and proportion of viable pollen were positively and significantly correlated with the outputs from manual counting. The scenario with 100-900 pixel2 pollen size and 0.4-1 circularity had the highest association for pollen number (r = 0.99) and pollen viability (r = 0.86). PollenCounter is 32-fold faster than manual counting.•We added a command to the macro to automatically save the outputs containing the number of total and viable pollen, avoiding transcription errors inherent to manual counting.•We successfully applied the optimized PollenCounter to discriminate tomato genotypes based on pollen number and pollen viability under heat stress. Our results show that PollenCounter, as an open-access macro, can be customized and improved to meet users' needs. The use of PollenCounter can save time and money in pollen quality assessment. We outline the steps to optimize the macro for other samples or crop species. The optimized macro could allow efficient screening of a large germplasm collection for pollen thermo-tolerance and selection of best thermo-tolerant individuals in breeding programs.