Factors controlling accuracy of genomic selection in oil palm (Elaeis guineensis) : W528

Wong and Bernardo (2008) showed in a simulation study that genomic selection could increase the rate of genetic gain in oil palm (Elaeis guineensis). In order to estimate the accuracy of genomic selection in a real breeding program, we applied a cross-validation approach to the largest dataset of progeny tests reported in oil palm breeding. It included two breeding populations of 100 and 130 individuals, genotyped with 225 SSR and evaluated for ten traits. Deregressed estimated breeding values were used as observations in a weighted analysis to derive genomic estimated breeding values (Garrick et al. 2009). Two strategies were used for sampling training populations: within population structure based on K-means clustering (Saatchi et al. 2011) and across population structure. Five statistical methods were compared. The strategy for sampling training populations had the strongest effect on the accuracy of genomic selection in the test population. Its effect was related to the maximum relationship coefficient between test and training individuals. Also, trait, population and trait by population interaction had a significant effect on accuracy. We hypothesized the trait effect was related to the genetic architecture of each trait. The population effect was correlated to the effective size of each population. The trait by population interaction was correlated with the trait variability existing in each population. Finally, our real data confirmed the usefulness of genomic selection for oil palm breeding. Our results should be valuable for all breeding programs where populations are small and have a reduced effective size. (Résumé d'auteur

Inbreeding management and optimization of genetic gain with phenotypic and genomic selection in oil palm (Elaeis guineensis)[W776]

Oil palm breeding relies on reciprocal recurrent selection between two heterotic groups complementary for bunch number and average bunch weight. Given the long generation interval and the limited selection intensity imposed by the progeny tests currently used in the program, genomic selection (GS) is a very promising solution for this species. However, GS also accelerates the annual increase in inbreeding in oil palm parental populations. This can generate inbreeding depression, which can be detrimental for seed production, and cause the loss of favourable alleles, which can reduce the long-term genetic progress. Here, we investigated the effect of three approaches of inbreeding management on parental inbreeding and genetic progress in hybrids. We simulated two widely used parental populations, La Mé and Deli, and four generations of selection. Inbreeding was measured in La Mé and genetic progress on hybrids bunch production. Inbreeding management in La Mé was made by: (i) mate selection, which uses the simulated annealing optimization algorithm, (ii) limiting deterministically the number of full-sibs selected and (iii) prohibiting selfings. The results showed that all methods slowed down the increase in parental inbreeding. Mate selection was also able to simultaneously increase the genetic progress. Stronger slowing-down in inbreeding were achieved with deterministic methods, in particular by selecting at best one individual per full-sib family and prohibiting selfings. However, this was associated with a decreased genetic progress. Finally, mate selection will allow oil palm breeders to control the rate of increase in inbreeding in the parental populations while maximizing the genetic gain

Pedigree reconstruction for cosexual species using simulated annealing: case study of oil palm (Elaeis guineensis) : P-124

The approach of Fernandez and Toro (2006) to reconstruct pedigrees from molecular data of contemporaneous individuals using a simulated annealing algorithm was extended to hermaphroditic and monoecious species. New features also include the possibility of selfings, accounting for a predefined coancestry matrix between founders and specifying different number of individuals per generation. The new method was validated using 16 individuals from the last generation of the Yangambi breeding population of oil palm. Their pedigree was known for 5 generations and they were genotyped with 166 SSR. In the study, the number of used SSR varied from 6 to 166 and the percentage of unknown parentages from 20% to 100%. The Pearson correlation between the pedigree-based coancestries calculated on the true and on the reconstructed genealogies ranged from 0.74 to 0.99. The RMSE ranged from 0.02 to 0.12. When pedigree was assumed completely unknown, reliable reconstruction required at least 38 SSR. Using 100 SSR or more, the Pearson correlation was very high (0.98) and the RMSE very low (0.06). The new method was also applied to 104 individuals from the last generation of a key breeding population (Deli) originated from 4 oil palms. The individuals were genotyped with 160 SSR. Records of their pedigree only existed for the recent past. Results of pedigree reconstruction detected a family coming from old selfings looking as outliers, with pedigree-based coancestries much higher than molecular coancestries, indicating old selfings were erroneous. After correcting the recorded pedigree, pedigree-based coancestries calculated on the reconstructed genealogy and molecular coancestries were highly correlated (> 0.9) when using 80 markers or more. In conclusion, this method gave likely pedigrees with satisfactory reliability for cosexual species, using a realistic number of polymorphic markers. Also, it seems very helpful to correct historical pedigrees. The methodology has been implemented in the software MOL_COANC_v2. (Texte integral

Genomic predictions improve the performance of clonal cultivars in oil palm. [PE0834]

Prediction of clonal genetic value is among the difficulties of the genetic improvement of oil palm (Elaeis guineensis Jacq.) yield. Presently, clonal selection requires two stages of phenotypic selection (PS): preselection on the phenotypic values of one or two yield components having high heritability, and final selection on performances in clonal trials. The current study evaluated the efficiency of genomic selection (GS) for clonal selection on eight traits. The GS models were trained on 295 and 279 Deli × La Mé crosses for bunch production and quality components, respectively, and were validated on 42 Deli × La Mé ortets of known clonal value. Genotyping by sequencing led to a dense genome coverage with 15,054 single nucleotide polymorphisms (SNP). We assessed the effects of SNP dataset (SNP density and quality) and of two GS modelling approaches on prediction accuracy. The results showed prediction accuracies that ranged between 0.70 and -0.03 according to trait, SNP dataset and model. Modeling disregarding the parental origin of alleles was preferable given the simplicity of implementation and the robustness over traits and SNP datasets, although including parental origin effects could slightly increase prediction accuracies for the traits used to define the two oil palm heterotic groups (bunch number and average bunch weight). The greatest GS prediction accuracies were beyond those of PS for most of the traits. Prediction accuracies from 0.70 to 0.45 for all traits can be achieved combining GS and PS. The best GS prediction accuracies are achieved with at least 7,000 SNPs. This will enable preselecting ortet candidates on all traits before clonal trials, thus increasing the selection intensity and the genetic progress

Advances in oil palm genomic selection

More efficient methods are required to breed oil palm (Elaeis guineensis Jacq.) for yield maximization, in order to meet the increased demand for palm oil while limiting environmental impacts. Today, genomic selection (GS) appears to be a disruptive improvement that can speed up breeding schemes by avoiding field trials in some cycles and increase selection intensity by the application of selection to a larger number of candidates than with the current methods. Oil palm is becoming a model species for GS, as it is one of the perennial crops with the largest number of published articles. GS was evaluated in oil palm for the prediction of parental general combining abilities and performances of hybrid crosses and clones. In all cases, GS accuracies high enough to allow selection were obtained for some traits. Best accuracies were obtained when training and validation populations were highly related, such as full-sibs or progenies. Array-based SNPs and GBS-derived SNPs allowed cost effective GS predictions, with densities of a few thousand markers being sufficient. Widely used statistical methods of GS predictions GBLUP and rrBLUP appeared efficient, and could be optimized by SNP filtering methods. Approaches to limit the increase in the rate of inbreeding associated with GS were identified. Evaluations of the annual genetic progress showed that GS should bring it to an unprecedented level. Further studies remain required for the optimal application of GS in oil palm. They should focus in particular on the optimization of training populations, the improvement of prediction models, the variation of GS accuracy between families, the use of multi-omics data (transcriptomics, proteomics, etc.), the modeling of G × E interactions and inter-specific selection

Discovery of Bragg confined hybrid modes with high Q-factor in a hollow dielectric resonator

The authors report on observation of Bragg confined mode in a hollow cylindrical dielectric cavity. A resonance was observed at 13.4 $GHz$ with an unloaded Q-factor of order $2\times10^5$, which is more than a factor of 6 above the dielectric loss limit. Previously such modes have only been realized from pure Transverse Electric modes with no azimuthal variations and only the $E_{\phi}$ component. From rigorous numeric simulations it is shown that the mode is a hybrid mode with non-zero azimuthal variations and with dominant $E_r$ and $E_{\phi}$ electric field components and $H_z$ magnetic field component.Comment: Accepted to be published in Applied Physics Letter

Potential of genomic selection in perennial crops: preliminary results in the context of Eucalyptus and oil palm breeding : P-180

The methodology of selection in plant breeding has markedly evolved with the advent of high throughput molecular technology, the increasingly reasonable cost of genotyping, and the implementation of genomic selection (GS). For perennial crops, the potential of GS is high and gives the opportunity to shorten the breeding cycle by selecting at the juvenile stage using marker information. Here we present preliminary results of GS experiments for two perennials crop, Eucalyptus and oil palm, that play an important economical role in tropical regions. In the case of Eucalyptus, a simulation study was developed to test the efficiency of GS in the frame of a recurrent selection scheme for clone production over four breeding cycles. Scenarios crossing broad sense heritabilities (H²=0.6 and 0.1), dominance to additive variance ratios (R=0.1; 0.5 and 1) and training population structure were compared using Bayesian LASSO method. Models including dominance effects are all the more relevant when the R ratio and the training population size are high. The genetic gain per unit time with GS was 1.5 to 3 times higher than with phenotypic selection at mature stage for breeding and clone populations. For oil palm, we implemented a cross-validation approach with 111 individuals of the last generation of a key breeding population, evaluated through progeny tests including 40,000 individuals and genotyped with 140 microsatellites. The accuracy of GS increased when increasing the training population size and reached 0.6-0.7, according to the trait, with a 3:1 ratio for training and validation populations respectively. The small effective population size detected in this breeding population explains the good GS performance even with a limited panel of markers. Our studies based on two perennials crops presenting different biological patterns and different breeding contexts suggest very promising results of GS for long rotation plant species. (Texte integral

Detrapping and retrapping of free carriers in nominally pure single crystal GaP, GaAs and 4H-SiC semiconductors under light illumination at cryogenic temperatures

We report on extremely sensitive measurements of changes in the microwave properties of high purity non-intentionally-doped single-crystal semiconductor samples of gallium phosphide, gallium arsenide and 4H-silicon carbide when illuminated with light of different wavelengths at cryogenic temperatures. Whispering gallery modes were excited in the semiconductors whilst they were cooled on the coldfinger of a single-stage cryocooler and their frequencies and Q-factors measured under light and dark conditions. With these materials, the whispering gallery mode technique is able to resolve changes of a few parts per million in the permittivity and the microwave losses as compared with those measured in darkness. A phenomenological model is proposed to explain the observed changes, which result not from direct valence to conduction band transitions but from detrapping and retrapping of carriers from impurity/defect sites with ionization energies that lay in the semiconductor band gap. Detrapping and retrapping relaxation times have been evaluated from comparison with measured data.Comment: 7 pages, 6 figure