Dataset Information

Genomic Prediction of Sunflower Hybrids Oil Content.

ABSTRACT: Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

SUBMITTER: Mangin B

PROVIDER: S-EPMC5613134 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Genomic Prediction of Sunflower Hybrids Oil Content.

Mangin Brigitte B Bonnafous Fanny F Blanchet Nicolas N Boniface Marie-Claude MC Bret-Mestries Emmanuelle E Carrère Sébastien S Cottret Ludovic L Legrand Ludovic L Marage Gwenola G Pegot-Espagnet Prune P Munos Stéphane S Pouilly Nicolas N Vear Felicity F Vincourt Patrick P Langlade Nicolas B NB

Frontiers in plant science 20170921

Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily i ...[more]

PMID: 28983306

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Project description:Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors' investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl millet. Despite the fact that 6× more sequencing data were generated per sample for RAD-seq than for tGBS, tGBS yielded more than 2× as many informative SNPs (defined as those having MAF > 0.05) than RAD-seq. A genomic prediction scheme utilizing only data from hybrids generated prediction accuracies (median) ranging from 0.73-0.74 (1000-grain weight), 0.87-0.89 (days to flowering time), 0.48-0.51 (grain yield) and 0.72-0.73 (plant height). For traits with little to no heterosis, hybrid only and hybrid/inbred prediction schemes performed almost equivalently. For traits with significant mid-parent heterosis, the direct inclusion of phenotypic data from inbred lines significantly (P < 0.05) reduced prediction accuracy when all lines were analyzed together. However, when inbreds and hybrid trait values were both scored relative to the mean trait values for the respective populations, the inclusion of inbred phenotypic datasets moderately improved genomic predictions of the hybrid genomic estimated breeding values. Here we show that modern approaches to genotyping by sequencing can enable genomic selection in pearl millet. While historical pearl millet breeding records include a wealth of phenotypic data from inbred lines, we demonstrate that the naive incorporation of this data into a hybrid breeding program can reduce prediction accuracy, while controlling for the effects of heterosis per se allowed inbred genotype and trait data to improve the accuracy of genomic estimated breeding values for pearl millet hybrids.

Project description:BACKGROUND:There are a lot of different types of sunscreen products (oils, sticks, gels, creams, lotions) which can be found on the world's market. Sunscreen product that contains active chemical ingredients sometimes has harmful effects on the skin. Sunflower oil contains vitamin E and acts as a natural sunscreen which can absorb UVB light. The average droplet size of nanoemulsion is between 100 and 500 nm and do not show the problems of stability (creaming, flocculation, coalescence, and sedimentation), which are commonly associated with macroemulsions. AIM:The aim of this study was to prepare and evaluate the sunflower oil nanoemulsion as a sunscreen. METHODS:Sunflower oil nanoemulsions were prepared by spontaneous emulsification method with 3 formulas F1 (Tween 80 38%, sorbitol 22%), F2 (Tween 80 36%, sorbitol 24%), F3 (Tween 80 34%, sorbitol 26%) and 5% sunflower oil as a sunscreen substance. The nanoemulsions were evaluated for particle size, physical stability in room temperature (25 ± 2°C), low temperature (4 ± 2°C) and high temperature (40 ± 2°C) during experiment for 12 weeks of storage, centrifugation at 3750 rpm for 5 hours, viscosity, pH, freeze-thaw test and sun protection value (SPF) value by in vitro. RESULTS:The results of nanoemulsion evaluation showed that nanoemulsion formula F1 had the smallest average particle size of 124.47 nm with yellowish colour, clear, transparent, pH value (6.5 ± 0.1), viscosity value (225 ± 25 cP), did not show any separation or creaming in the centrifugation, and stable during experiment for 12 weeks of storage at room temperature, low temperature and high temperature. The SPF value of all nanoemulsion preparations was higher than that of the emulsion. CONCLUSION:The preparation of the sunflower oil nanoemulsion with a ratio of Tween 80 and sorbitol (38: 22) produces a stable nanoemulsion during the experiment for 12 weeks storage at the room, low and high temperature. The nanoemulsion preparation has higher SPF values compared to the emulsion. This nanoemulsion formulation could be considered more effective in sunscreen cosmetic use compare to the emulsion.

Dataset Information

Genomic Prediction of Sunflower Hybrids Oil Content.

Publications

Genomic Prediction of Sunflower Hybrids Oil Content.

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