Genome-wide association studies identifies genetic loci related to fatty acid and branched-chain amino acid metabolism and histone modifications under varying nitrogen treatments in safflower (Carthamus tinctorius).

Effective identification and usage of genetic variation are prerequisites for developing nutrient-efficient cultivars. A collection of 94 safflower (Carthamus tinctorius ) genotypes (G) was investigated for important morphological and photosynthetic traits at four nitrogen (N) treatments. We found significant variation for all the studied traits except chlorophyll b (chl b ) among safflower genotypes, nitrogen treatments and G×N interaction. The examined traits showed a 2.82-50.00% increase in response to N application. Biological yield (BY) reflected a significantly positive correlation with fresh shoot weight (FSW), root length (RL), fresh root weight (FRW) and number of leaves (NOL), while a significantly positive correlation was also observed among carotenoids (C), chlorophyll a (chl a ), chl b and total chlorophyll content (CT) under all treatments. Superior genotypes with respect to plant height (PH), FSW, NOL, RL, FRW and BY were clustered into Group 3, while genotypes with better mean performance regarding chl a , chl b C and CT were clustered into Group 2 as observed in principal component analysis. The identified eight best-performing genotypes could be useful to develop improved nitrogen efficient cultivars. Genome-wide association analysis resulted in 32 marker-trait associations (MTAs) under four treatments. Markers namely DArT-45481731 , DArT-17812864 , DArT-15670279 and DArT-45482737 were found consistent. Protein-protein interaction networks of loci associated with MTAs were related to fatty acid and branched-chain amino acid metabolism and histone modifications.

Whole-genome resequencing of Sorghum bicolor and S. bicolor × S. halepense lines provides new insights for improving plant agroecological characteristics.

Sorghum (Sorghum bicolor L. (Moench)) is the world's fifth economically most important cereal and is a staple particularly in the semi-arid tropics of Africa and Asia. Genetic gains in this crop can benefit from wild relatives such as Sorghum halepense. Genome sequences including those from this wild species can boost the study of genome-wide and intraspecific variation for dissecting the genetic basis and improving important traits in sorghum. The whole-genome resequencing carried out in this work on a panel of 172 populations of S. bicolor and S. bicolor × S. halepense (SbxSh) advanced lines generated a total of 567,046,841 SNPs, 91,825,474 indels, 1,532,171 SVs, and 4,973,961 CNVs. Clearly, SbxSh accumulated more variants and mutations with powerful effects on genetic differentiation. A total of 5,548 genes private to SbxSh mapped to biological process GO enrichment terms; 34 of these genes mapped to root system development (GO: 0022622). Two of the root specific genes i.e., ROOT PRIMORDIUM DEFECTIVE 1 (RPD1; GeneID: 8054879) and RETARDED ROOT GROWTH (RRG, GeneID: 8072111), were found to exert direct effect on root growth and development. This is the first report on whole-genome resequencing of a sorghum panel that includes S. halepense genome. Mining the private variants and genes of this wild species can provide insights capable of boosting sorghum genetic improvement, particularly the perenniality trait that is compliant with agroecological practices, sustainable agriculture, and climate change resilience.

Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.

Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of absorbed photosynthetically active radiation (fAPAR), chlorophyll concentration (CI) and normalized difference vegetation (NDVI) indices were used in crop monitoring, but their comparative performances in sorghum monitoring is lacking. This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance. Bayesian ridge regression showed good cross-validated performance, and high reliability (R2 = 35%) and low bias (mean absolute prediction error, MAPE = 0.4%) during the validation step. Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to Sentinel-2's fAPAR. The best times to sample the vegetation indices were the months of June (second half) and July. The results obtained in this work will serve several purposes including improvements in plant breeding, farming management and sorghum biomass yield forecasting at extension services and policy making levels.

Genomic Selection for Optimum Index with Dry Biomass Yield, Dry Mass Fraction of Fresh Material, and Plant Height in Biomass Sorghum.

Sorghum is one of the world's major crops, expresses traits for resilience to climate change, and can be used for several purposes including food and clean fuels. Multiple-trait genomic prediction and selection models were implemented using genotyping-by-sequencing single nucleotide polymorphism markers and phenotypic data information. We demonstrated for the first time the efficiency genomic selection modelling of index selection including biofuel traits such as aboveground biomass yield, plant height, and dry mass fraction of the fresh material. This work also sheds light, for the first time, on the promising potential of using the information from the populations grown from seed to predict the performance of the populations regrown from the rhizomes-even two winter seasons after the original trial was sown. Genomic selection modelling of the optimum index selection including the three traits of interest (plant height, aboveground dry biomass yield, and dry mass fraction of fresh mass material) was the most promising. Since the plant characteristics evaluated herein are routinely measured in cereal and other plant species of agricultural interest, it can be inferred that the findings can be transferred in other major crops.

Genome-wide association mapping of total antioxidant capacity, phenols, tannins, and flavonoids in a panel of Sorghum bicolor and S. bicolor × S. halepense populations using multi-locus models.

Sorghum is widely used for producing food, feed, and biofuel, and it is increasingly grown to produce grains rich in health-promoting antioxidants. The conventional use of grain color as a proxy to indirectly select against or for antioxidants polyphenols in sorghum grain was hampered by the lack of consistency between grain color and the expected antioxidants concentration. Marker-assisted selection built upon significant loci identified through linkage disequilibrium studies showed interesting potential in several plant breeding and animal husbandry programs, and can be used in sorghum breeding for consumer-tailored antioxidant production. The purpose of this work was therefore to conduct genome-wide association study of sorghum grain antioxidants using single nucleotide polymorphisms in a novel diversity panel of Sorghum bicolor landraces and S. bicolor × S. halepense recombinant inbred lines. The recombinant inbred lines outperformed landraces for antioxidant production and contributed novel polymorphism. Antioxidant traits were highly correlated and showed very high broad-sense heritability. The genome-wide association analysis uncovered 96 associations 55 of which were major quantitative trait loci (QTLs) explaining 15 to 31% of the observed antioxidants variability. Eight major QTLs localized in novel chromosomal regions. Twenty-four pleiotropic major effect markers and two novel functional markers (Chr9_1550093, Chr10_50169631) were discovered. A novel pleiotropic major effect marker (Chr1_61095994) explained the highest proportion (R2 = 27-31%) of the variance observed in most traits evaluated in this work, and was in linkage disequilibrium with a hotspot of 19 putative glutathione S-transferase genes conjugating anthocyanins into vacuoles. On chromosome four, a hotspot region was observed involving major effect markers linked with putative MYB-bHLH-WD40 complex genes involved in the biosynthesis of the polyphenol class of flavonoids. The findings in this work are expected to help the scientific community particularly involved in marker assisted breeding for the development of sorghum cultivars with consumer-tailored antioxidants concentration.

Diversity of macro- and micronutrients in the seeds of lentil landraces.

Increasing the amount of bioavailable mineral elements in plant foods would help to improve the nutritional status of populations in developing countries. Legume seeds have the potential to provide many essential nutrients. It is important to have information on genetic variations among different lentil populations so that plant breeding programs can use new varieties in cross-breeding programs. The main objective of this study was to characterize the micro- and macronutrient concentrations of lentil landraces seeds collected from South-Eastern Turkey. We found impressive variation in the micro- and macroelement concentrations in 39 lentil landraces and 7 cultivars. We investigated the relationships of traits by correlation analysis and principal component analysis (PCA). The concentrations of several minerals, particularly Zn, were positively correlated with other minerals, suggesting that similar pathways or transporters control the uptake and transport of these minerals. Some genotypes had high mineral and protein content and potential to improve the nutritional value of cultivated lentil. Cross-breeding of numerous lentil landraces from Turkey with currently cultivated varieties could improve the levels of micro- and macronutrients of lentil and may contribute to the worldwide lentil quality breeding program.