Highly divergent isolates of chrysanthemum virus B and chrysanthemum virus R infecting chrysanthemum in Russia

Background Chrysanthemum is a popular ornamental and medicinal plant that suffers from many viruses and viroids. Among them, chrysanthemum virus B (CVB, genus Carlavirus, family Betaflexiviridae) is widespread in all chrysanthemum-growing regions. Another carlavirus, chrysanthemum virus R (CVR), has been recently discovered in China. Information about chrysanthemum viruses in Russia is very scarce. The objective of this work was to study the prevalence and genetic diversity of CVB and CVR in Russia. Methods We surveyed the chrysanthemum (Chrysanthemum morifolium Ramat.) germplasm collection in the Nikita Botanical Gardens, Yalta, Russia. To detect CVB and CVR, we used RT-PCR with virus-specific primers. To reveal the complete genome sequences of CVB and CVR isolates, metatransciptomic analysis of the cultivars Ribonette, Fiji Yellow, and Golden Standard plants, naturally co-infected with CVB and CVR, was performed using Illumina high-throughput sequencing. The recombination detection tool (RDP4) was employed to search for recombination in assembled genomes. Results A total of 90 plants of 23 local and introduced chrysanthemum cultivars were surveyed. From these, 58 and 43% plants tested positive for CVB and CVR, respectively. RNA-Seq analysis confirmed the presence of CVB and CVR, and revealed tomato aspermy virus in each of the three transcriptomes. Six near complete genomes of CVB and CVR were assembled from the RNA-Seq reads. The CVR isolate X21 from the cultivar Golden Standard was 92% identical to the Chinese isolate BJ. In contrast, genomes of the CVR isolates X6 and X13 (from the cultivars Ribonette and Fiji Yellow, respectively), were only 76% to 77% identical to the X21 and BJ, and shared 95% identity to one another and appear to represent a divergent group of the CVR. Two distantly related CVB isolates, GS1 and GS2, were found in a plant of the cultivar Golden Standard. Their genomes shared from 82% to 87% identity to each other and the CVB genome from the cultivar Fiji Yellow (isolate FY), as well as to CVB isolates from Japan and China. A recombination event of 3,720 nucleotides long was predicted in the replicase gene of the FY genome. It was supported by seven algorithms implemented in RDP4 with statistically significant P-values. The inferred major parent was the Indian isolate Uttar Pradesh (AM765837), and minor parent was unknown. Conclusion We found a wide distribution of CVB and CVR in the chrysanthemum germplasm collection of the Nikita Botanical Gardens, which is the largest in Russia. Six near complete genomes of CVR and CVB isolates from Russia were assembled and characterized for the first time. This is the first report of CVR in Russia and outside of China thus expanding the information on the geographical distribution of the virus. Highly divergent CVB and CVR isolates have been identified that contributes the better understanding the genetic diversity of these viruses.

Genetic Diversity and Population Structure of Chionanthus virginicus

The genus Chionanthus, known as fringetrees, is a member of the olive family (Oleaceae). Chionanthus virginicus is an understory tree or shrub with a wide range in forests of the eastern United States and is used as an ornamental tree that is known to be free of insects and disease in the wild. The species is tolerant of a wide range of environmental conditions, and there is interest in developing new cultivars with improved horticultural traits, such as tree form or upright growth habit and superior flowering display that are widely adapted. To identify genepools in the native range of C. virginicus for use in breeding programs, the genetic diversity and population structure were assessed for 274 individuals from 12 locations in four states (Florida, Maryland, North Carolina, and Texas) using 26 simple sequence repeats (SSRs). An average of 12.54 alleles/locus were detected, allelic richness averaged 2.80. Genetic differentiation was 0.11, indicating moderate differentiation among subpopulations. Despite the high genetic diversity and low population differentiation, Bayesian clustering analysis identified six genetic groups that match the geographic distribution of collection sites. Analysis of molecular variance indicated that most (82%) of the variation is explained within individuals, and 11% and 7% of the variation is due to differences among individuals within populations and among populations. Analysis of isolation by distance across all samples showed a weak positive relationship between geographic distance and genetic distance. The C. virginicus samples analyzed in this study indicate there is sufficient diversity for germplasm collection for use in breeding programs. Given the relatively moderate genetic differentiation, there are not likely to be unique islands of genetic diversity that may be missed when gathering parental materials for a breeding program

Genetic diversity of yield related traits in a large subset of exotic and indigenous rice germplasm collection maintained at Tamil Nadu Agricultural University gene bank

The present investigation was carried out to explore the extent of genetic divergence in 95 rice germplasm accessions for twelve characters during kharif, 2018. In D2 analysis, the 95 genotypes were grouped into fifteen clusters. The clustering pattern indicated that there was no parallelism between genetic diversity and geographical origin as the genotypes from same origin were included in different clusters and vice versa. The highest intra cluster distance was registered in cluster V (215.183) followed by cluster IX (209.831), cluster VIII (204.057) and cluster XIV (202.623).The maximum inter cluster distance was observed between cluster II and cluster III (991.049) followed by cluster II and cluster X (974.960), cluster III and cluster XI (963.826), cluster II and XII (962.013), cluster X and cluster XI (932.469) and cluster XI and cluster XII (919.151). Genetically distant parents from those clusters could be able to produce higher heterosis in progenies on hybridization. Grain yield per plant, 100 grain weight and days to 50% flowering were the major contributors towards the total genetic divergence among the genotypes studied. Thus selection could be made based on grain yield per plant, hundred grain weight and days to 50%flowering for the progenies identified.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction’s potential to a set of 482 pea (Pisum sativum L.) accessions—genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components—for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

Genome-wide association analysis of resistance to Pseudoperonospora cubensis in citron watermelon

Cultivated sweet watermelon (Citrullus lanatus) is an important vegetable crop for millions of people around the world. There are limited sources of resistance to economically important diseases within C. lanatus, whereas Citrullus amarus has a reservoir of traits that can be exploited to improve C. lanatus for resistance to biotic and abiotic stresses. Cucurbit downy mildew (CDM), caused by Pseudoperonospora cubensis, is an emerging threat to watermelon production. We screened 122 C. amarus accessions for resistance to CDM over two tests (environments). The accessions were genotyped by whole-genome resequencing to generate 2,126,759 single nucleotide polymorphic (SNP) markers. A genome-wide association study was deployed to uncover marker-trait associations and identify candidate genes underlying resistance to CDM. Our results indicate the presence of wide phenotypic variability (1.1 - 57.8%) for leaf area infection, representing a 50.7-fold variation for CDM resistance across the C. amarus germplasm collection. Broad-sense heritability estimate was 0.55, implying the presence of moderate genetic effects for resistance to CDM. The peak SNP markers associated with resistance to P. cubensis were located on chromosomes Ca03, Ca05, Ca07, and Ca11. The significant SNP markers accounted for up to 30% of the phenotypic variation and were associated with promising candidate genes encoding disease resistance proteins, leucine-rich repeat receptor-like protein kinase, and WRKY transcription factor. This information will be useful in understanding the genetic architecture of the P. cubensis-Citrullus spp. patho-system as well as development of resources for genomics-assisted breeding for resistance to CDM in watermelon.

Evaluation of Borage (Borago officinalis L.) Genotypes for Nutraceutical Value Based on Leaves Fatty Acids Composition

Borage (Borago officinalis L.) is a traditional vegetable grown and consumed in some Spanish regions. The objective of this study was to determine the variability and evolution of fatty acid composition in a borage germplasm collection formed by wild types, breeding lines, commercial varieties, and landraces. Fatty acids were analysed in petioles, the commonly edible part of the leaves, and the leaf blades, the by-product of the borage industry, in two growth stages: at the optimal harvest period (120 days after sowing) and at the end of the harvest period (150 days after sowing). The results showed that for each of the eight fatty acids identified, there were significant differences among the twelve borage genotypes depending on the developmental plant stage at sampling date and the part of the leaf analysed, the interaction effect also being statistically significant. The main polyunsaturated fatty acids identified were: linoleic acid (18:2 n6, LA), α-linolenic acid (18:3 n3, ALA), γ-linolenic acid (18:3 n6, GLA), and stearidonic acid (SDA, 18:4, n-3), account for approximately 70% of polyunsaturated fatty acids. Blue-flowered genotypes differ from white-flowered genotypes by their high content of ALA and SDA, which can be exploited in borage breeding programs. Petioles from young plants present higher n6 fatty acids, while older plants produce a great amount of n3 fatty acids. Besides, the higher content of ALA in the leaf blades gives them a good dietary potential. All these fatty acids, with multiple health benefits, support the nutraceutical interest of borage leaves (both petioles and leaf blades) for human consumption, animal feeding, medicine, and pharmacy.

Studying germplasm collection accessions of naked oats

Background. The breeding value of accessions from the VIR collection can be estimated only under specific climate and soil conditions. Studying such accessions helps to include them in the breeding process.Materials and methods. Forty-two accessions of naked oats of various origin were studied at the FARC of the North-East. Biochemical analysis of grain and statistical data processing using descriptive statistics and correlation analysis techniques were carried out.Results. The accessions were divided into groups according to their ripening schedule: early (up to 80 days), medium (81–84 days), and mid-late (85–89 days). High yielding genotypes were identified in each ripeness group. The plant height depended on the hydrothermal conditions of the growing season (r = 0.25). The studied accessions were all classified as undersized (64.0–99.1 cm), including the reference cv. ‘Vyatsky’ (88.1 cm). A significant contribution of some panicle productivity components to an increase in yield was observed. The protein and fat content in grain determines its nutritional and energy value. The results showed a positive relationship between yield and protein content (r = 0.44). A significant negative correlation between fat content and protein content (r = –0.61) was registered. The study of naked oat accessions showed different fat content in grain across the ripeness groups.Conclusion. According to the results of the study, some accessions were identified as sources of traits useful for breeding. The following accessions were selected as the best according to a set of characters (yield, plant height, number of grains per panicle, panicle weight, protein and fat content in grain): early ‘Litovsij nagij’ (k-15234, Lithuania), ‘Gkzalon’ (k-15299, Mongolia) and ‘MF9224-164’ (k-15090, USA); medium k-15248 (local, Poland) and MF9521-281 (k-15095, USA); and mid-late ‘Bai Yan 2’ (k-15525, China), ‘Progress’ (k-15339, Russia), ‘Visit’ (k-15501, Ukraine) and ‘Mina’ (k-15192, Bulgaria).