Hétérosis et variétés hybrides en amélioration des plantes (Synthèses) (French Edition)

Hétérosis et variétés hybrides en amélioration des plantes. more less. Gallais, A. Versailles (FRA): Editions Quae . Synthèses (Quae).), p.
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Heterosis is the increased vigour noted in the progeny from the cross between two subjects. It is a universal phenomenon in the plant and animal world, demonstrating an advantage of heterozygote beings. The work discusses the experimental facts before showing that nowadays heterosis is no longer considered to be shrouded in mystery. Then follows a description of how it is used in plant breeding by creating hybrid varieties: My account Empty cart.

Leading products Bestsellers New Products Future releases Our free ebooks By categories South countries selection Popular science Natural landscapes and environment Agriculture and crop production Animal rearing and production Fishing. Therefore, the fewer the number of QTL, the stronger the effect of selection was in depleting the additive variance.

The percentage of pleiotropic QTL was the most important factor of the study. The number of QTL also had a strong effect. The selection response increased when the percentage of pleiotropic QTL decreased and when the number of QTL increased. The cumulative response was This resulted from the fact that the genetic progress potential depended mostly on the fixation of favorable alleles at QTL controlling either BN or BW, rather than at the pleiotropic QTL, as they had antagonistic effects on both traits.

Similarly, the genetic progress potential was higher when the total number of QTL controlling each trait increased. Their effects are detailed in the following paragraphs. Therefore, obtaining a higher selection response with RRGS compared to RRS could not be achieved without modifying the breeding scheme in order to reduce the generation interval or to increase the selection intensity.

In this case, decreasing the progeny-test frequency led to a lower cumulative selection response, i. With generations without progeny tests, the selection accuracy was reduced and consequently the cumulative selection response decreased. An opposite effect was noted in the annual response, i. With progeny tests performed every four generations, the generation interval decreased by Thirdly, regarding the RRGS gain, we studied the effect of an increase in selection intensity, which was obtained by increasing the number of candidates, as the number of selected individuals was constant.

With candidates, the best We also found that the number of candidates significantly interacted with the percentage of pleiotropic QTL and the number of QTL on the selection response, although these interactions had less impact than the previously mentioned factors. This was not surprising as, with the largest numbers of non-pleiotropic QTL per trait either due to a high number of QTL or to a low percentage of pleiotropic QTL , selection candidates were not enough to capture all of the existing additive variation.

In this case, using candidates led to a higher selection response. By contrast, with a smaller number of non-pleiotropic QTL, candidates were enough to capture all of the additive variation and an increase in the number of candidates did not markedly increase the selection response. The coefficient of variation CV for the annual selection response of that best scheme reached 0.

The three following breeding schemes in the ranking of gain had similar levels of performance and CV for the annual response: Variation in annual selection response associated with each breeding scheme. The breeding scheme includes the breeding strategy RRS: The filled dots represent the means, calculated over 45 values. As expected, inbreeding increased with the generation turnover see Fig. With candidates, they all belonged to different full-sib families due to the method used to mate the selected individuals.

However, the sets of candidates mostly consisted of pairs of full-sibs, which increased the probability of having full-sib individuals among the selected individuals. Inbreeding according to years and the reciprocal recurrent genomic selection RRGS breeding scheme in the Deli population using a and b candidates. Inbreeding is expressed as a percentage of inbreeding in the parental populations in the initial generation generation 0 per year. Bars indicate standard deviations.

The magnitude of the genetic correlation between BW and BN increased markedly in the generations in which progeny tests were conducted Fig. In absolute value, the increase in the generations with progeny tests was greater than the decrease in the generations without progeny tests, so the correlation thus tended to increase over the four generations, except in the case where progeny tests were only performed in the first generation.

Genetic correlation between BN and BW in the Deli population according to years and the reciprocal recurrent genomic selection RRGS breeding scheme with a selection candidates and b candidates. We showed that reciprocal recurrent genomic selection RRGS was a valuable method to achieve a long-term increase in the performance for a trait showing heterosis due to the multiplicative interaction between additive and negatively correlated components. In our oil palm case study, RRGS was superior to traditional RRS as it allowed accurate selection of individuals without progeny tests.

It led to a significant increase in the annual selection response, through generations of selection based on markers alone and, to a lesser extent, through an increase in the selection intensity. In our oil palm example, the annual selection response of this RRGS best strategy reached 0. Therefore, it seemed that genotyping more individuals would have been useless here. However, the number of hybrids to genotype should be dependent on the heterozygosity in the parental populations.

With a higher level of heterozygosity in the parental population, the phenotypic variation within crosses would likely increase, making more relevant hybrid genotyping of hybrids in order to capture this variation. Aspects other than just the expected annual selection response need to be taken into account when choosing an optimal breeding scheme.

Furthermore, the efficiency cost and operational complexity must be considered. Other breeding schemes could thus offer interesting alternatives, with good compromises between costs, operational complexity, expected annual selection response, risk regarding this expectation and evolution of inbreeding.

Indeed, it was among the best four breeding schemes in terms of annual selection response 0. The relative importance of the decrease in generation interval and the increase in selection intensity depends on the characteristics of the species. This makes the species an excellent candidate for the implementation of early genomic evaluation, with RRGS having a high potential compared to RRS. By contrast, oil palm breeding populations have a quite narrow genetic base, with effective sizes under 10 [ 27 ], and this creates relatively small additive variances, therefore reducing the interest of increasing the number of candidates.

Our results confirmed the usefulness of GS for oil palm, in line with the simulation results of Wong and Bernardo [ 14 ]. However, we extended their results to a more general situation, closer to actual oil palm breeding program conditions, by applying GS to complex breeding populations and by considering two antagonistic traits, i.

BN and ABW, which are crucial in oil palm breeding. The accuracies we obtained in this previous study when applying GS to full-sibs of the training individuals could be compared with the accuracies we obtained here on the full-sibs of the progeny-tested individuals. We previously obtained mean accuracies of 0. This was likely due to the fact that our previous training population was smaller than that used here The consistency between the empirical results and our present simulations suggests that the actual genetic architecture for BN and BW could be close to the average scenario of our simulations, i.

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This would result in a high computation time that would be hard to manage in a simulation study due to the many replicates, and memory problems. However, this should not be a problem here as the mixed models were used to predict GEBV, not to estimate genetic parameters. According to these authors, this occurred because the Mendelian sampling terms i. We assumed the different trend existing in our study was related to the drop in accuracy observed for non-progeny-tested individuals, which occurred because the calibration of the GS model was based on the progeny tests of only individuals per population.

Consequently, our estimates of Mendelian sampling terms were likely not as accurate as those obtained in animal species. Good genetic variability management is necessary to avoid inbreeding depression in parental populations, which has been reported in oil palm [ 10 , 29 , 30 ], and to maintain the genetic progress potential over the long term. Therefore, the RRGS breeding schemes we presented here should be combined with methods to explicitly manage genetic diversity and inbreeding. The simplest inbreeding management method is to increase the number of selected individuals, which would slow down the increase in inbreeding, possibly with only a small reduction in selection response [ 28 ].

Another option that does not necessarily lead to gain losses is optimal contribution selection [ 32 ] and its extension in the GS context [ 33 ]. This involves the use of the genetic value of individuals and their relationships with other individuals to determine their contribution to the following generation, in order to maximize genetic gain at a desired inbreeding rate under the assumption of random mating among selections. A step further is mate selection [ 34 , 35 ], where the optimum contribution is applied to mates among all candidates, so that selection and mating are simultaneously handled for improved management of inbreeding beyond what is expected by random mating.

Mate selection optimizes the number of parents to be selected, the actual matings between them and the distribution of the contribution in the offspring of these mates, in order to maximize the expected selection response in the following generation while respecting a restriction on the expected increase in inbreeding.

Here we studied a GS approach to select individuals within two parental populations for their crossbred performance, as in several animal studies [ 6 , 36 — 38 ] and in maize [ 39 ]. In this study we considered that heterosis in bunch production was a consequence of the multiplicative interaction between the negatively correlated bunch number and bunch weight, both assumed here to have complete additive genetic determinism. This multiplicative interaction between complementary component differences in the parents is a heterosis model without dominance, but heterosis in a multiplicative trait can also be due to the multiplicative interaction of component dominance [ 7 ].

In this case, dominance in the component traits generates heterosis in the complex trait, to a greater extent than the dominance in the components, due to the multiplicative nature of the complex trait. Here we did not study the effect of this type of genetic determinism or a combination of the two types. This would require further investigation, which could be done by modifying the script used for our simulations and including dominance effects in the GS models see for instance Su et al. Presumably their result applied in the case where selection was highly accurate, such as when based on progeny tests, which was not the case here when selection was made on markers alone using a GS model calibrated with a small training set.

Reciprocal recurrent genomic selection RRGS appeared as a valuable method to achieve a long-term increase in the performance for a trait showing heterosis due to the multiplicative interaction between additive and negatively correlated components. In our oil palm case study, RRGS was superior to traditional RRS in terms of annual selection response as it could decrease the generation interval and increase the selection intensity. With genotyped hybrids, calibration every four generations and candidates per generation and population, selection response of RRGS was RRGS without genotyping hybrid individuals, with calibration every two generations and candidates was a relevant alternative, as a good compromise between the annual response, risk around the expected response, increased inbreeding and cost.

DC carried out the simulations, performed the statistical analysis of the results and drafted the manuscript. LS and JMB contributed to the writing of the paper. MD participated in the statistical analysis. All authors read and approved the final manuscript. National Center for Biotechnology Information , U. Published online Aug Received Dec 14; Accepted Aug This article has been cited by other articles in PMC. Abstract Background To study the potential of genomic selection for heterosis resulting from multiplicative interactions between additive and antagonistic components, we focused on oil palm, where bunch production is the product of bunch weight and bunch number.

Results We concluded that RRGS could increase the annual selection response compared to RRS by decreasing the generation interval and by increasing the selection intensity. Conclusions RRGS appeared as a valuable method to achieve a long-term increase in the performance for a trait showing heterosis due to the multiplicative interaction between additive and negatively correlated components, such as oil palm bunch production.

Background Genomic selection GS [ 1 ] is the state-of-the-art method of marker-assisted selection for complex traits. Methods Simulation overview The overall simulation process is summarized in Fig. Open in a separate window.


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Simulation of the equilibrium base population We simulated a population over discrete generations with a constant size of individuals having an equal contribution to the following generation and reproducing by random mating with the exclusion of selfing. The model was of the following form: Genotyping-by-sequencing GBS is an appropriate and cost effective way to achieve this goal [ 23 ].

GBS relies on the sequencing of the genome regions delimited by the restriction site of enzymes used to reduce genome complexity [ 24 ]. GBS enables multiplexed sequencing and can easily be applied to large populations, scoring thousands of SNPs. In oil palm, the only study using GBS indicated it was an efficient genotyping approach for quantitative trait loci QTL detection [ 25 ]. Finally, as in many GS papers, these empirical studies only focused on GS accuracy and did not investigate the additional gain that could result from the actual use of GS in a breeding scheme. The present study had two goals: In detail, to reach our first goal, we trained the GS model with the data of all the individuals progeny tested in the most recent breeding cycle of the PalmElit commercial breeding program, and validated it with an independent dataset of progeny tests.

The almost hybrid crosses planted in Site 1 were used to train the GS model that predicted the genetic value of around crosses planted in Site 2, as well as the GCAs of their 67 A and 42 B parents validation sets. The study considered seven traits that are key components of palm oil yield. To reach our second goal, we considered FFB fresh fruit bunches, or annual cumulative bunch production and used the empirical values estimated in Site 2 for this trait, i.

From these values, we ran a simulation in which A and B individuals were subjected to two breeding approaches: The empirical estimation of the GS prediction accuracies took place in two steps: In the first step, reference cross values were obtained from the observed cross values with adjustment to remove the effects of the experimental design trial, block, etc. For this purpose, we used the phenotypic data from Site 2 and a linear mixed model TBLUP, see mixed model analyses section below.

The reference cross values were defined as the sum of the GCA of their A and B parents and the specific combining ability SCA, corresponding here to the dominance effect of the crosses. Here, the goal was to obtain reference cross values as close as possible to the observed values avoiding any possible bias due to the experimental design. For this reason, relationships between parents were not taken into account i.

The reference parental GCAs were obtained with the same model but using genealogical coancestries i. The goal of PBLUP was to assess the usefulness of marker data, in particular their ability to capture genetic differences within full-sib families of parents i. Mendelian segregation terms , which is necessary because the aim of oil palm breeding is to select the best individuals in the best families [ 27 ].

As PBLUP only used pedigrees to model genetic covariances between training and validation individuals, it cannot account for the Mendelian sampling term and predicted identical GCAs to parental full-sibs in the test set. The A and B parents of the training and validation crosses formed two complex populations with high relatedness but with some variation in this parameter, which reflects the way GS could be implemented in oil palm to predict the cross value of hybrids obtained by mating individuals that could be related to the training individuals to different degrees full-sibs, half-sibs, progeny, cousins, etc.

Two types of GS prediction accuracies were computed: From these results, we simulated large A and B populations of selection candidates with genetic variances obtained from Site 2 data and two breeding approaches aiming at improving FFB in hybrid crosses. First, we simulated the conventional RRS methodology that was used to set up Site 2 progeny tests, in which progeny tested A and B individuals can be considered as random samples of the populations of candidates in terms of FFB i.

Second, we simulated RRS with genomic preselection prior to progeny tests, using the GS prediction accuracies obtained in the first part of the study. Group A was mostly made up of Deli individuals. The Deli breeding population originated from four ancestral oil palms planted in in Indonesia. The population was selected for yield at least in the early twentieth century and inbreeding was commonly used, by selfing or by mating related selected individuals [ 12 ]. Group A also included individuals from the Angola population resulting from material collected before the s. Group B was made up of several breeding populations of African origin.

The African populations also derived from a few founders collected during the first half of the twentieth century. African populations were also subject to inbreeding and selection for yield. Table S1 lists the number of A and B individual progeny tested per parental group in the two sites, as well as their status genotyped or not, present in only one site or in both. Figures S1 and S2, for groups A and B, respectively with training individuals in blue and validation individuals in red. In Group A, the mean maximum genealogical coancestry [ 29 ] between each validation individual and the training individual f max V-T , ranged from 0 to 0.

In Group B, f max V-T ranged from 0 to 0. The validation sets were therefore closely related to the training set, which corresponds to the way GS would be applied in oil palm breeding to predict the breeding value of individuals of the same generation or of the following generation compared to the progeny tested individuals or the genetic values of crosses between them. They both have deep well drained soils developed over reworked Toba Tuffs haplic arenosols and dystric cambisol types in Site 1 and haplic acrisols type in Site 2.

The same standard cultural practices were used in both sites, and the same protocol was used for recording data. Table S1 summarizes the characteristics of the experimental designs in the two sites. The data from Site 1 are also described in detail in Cros et al.


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    • The total number of crosses in Site 2 was with data for bunch production for bunch quality but only were used as the validation set, the others being excluded because they were also present in Site 1 9 crosses or because their parents were not genotyped. All the data used were collected on tenera thin-shelled commercial type individuals. The plant material belongs to the PalmElit www. PalmElit is a leading oil palm breeding and seed production company.

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    Phenotypic data on hybrid individuals were available for three bunch production traits: Data from palms aged three to seven were used for bunch production traits. Data from palms aged five and six were used for bunch quality traits. Between-site phenotypic correlations were estimated on the nine crosses common to both sites.

    Background

    The correlations were on average 0. Coefficient of variation CV and skewness in the two sites and between-site correlations for the reference adjusted for experimental design cross values of the seven traits studied. Correlations were computed over the nine crosses common to both sites. The reverse adapter contained the flowcell attachment region and the Hha I-compatible overhang sequence.

    Next, PCR equimolar amounts of amplification products from each sample in the well microtiter plate were bulked and applied to c-Bot Illumina bridge PCR followed by sequencing on Illumina HiSeq From the total number of good barcoded reads ,, out of ,, , the pipeline found , tags, aligned with Bowtie2 software.

    The tag mapping and the polymorphism calling identified , polymorphic sites. The data were further processed with VCFtools [ 34 ]. Indels and SNPs that were not biallelic were discarded. Data points with a sequencing depth of less than five were set to missing. Using a custom R script [ 35 ], the SNPs appearing as outliers in terms of mean depth i. This resulted in 19, SNPs. The molecular dataset was split into two, one for Group A and the other for Group B. The SNPs that mapped on the unassembled part of the genome were discarded, as the imputation of sporadic missing data required known positions.

    Mendelian segregation between parents and offspring was checked and the inconsistent data points were set to missing. The depth per data point was on average The mean depth per SNP was The mean minor allele frequency MAF was 0. The percentage of missing data was on average Molecular coancestries between genotyped individuals were calculated according to Lynch [ 38 ] and Li et al. For each validation individual, the maximum coancestry with the training individuals was computed, and the mean value over all the validation individuals was calculated. The proportion of dominance variance between crosses over the total genetic variance between crosses was calculated for Site 2 from the TBLUP model including genealogical information.

    The additive genomic relationship matrices G. In order to investigate the usefulness of estimating the dominance effects SCAs , the predicted cross values where obtained with or without SCA, i. The validation crosses were initially divided into six random sets of equal size, and for each validation set, the prediction accuracy for cross values was obtained as the Pearson correlation between the reference and the predicted cross values. GCA prediction accuracy was obtained as the Pearson correlation between the reference and the predicted GCAs on the 67 Group A individuals and 42 B available for validation, with no replicates due to small population sizes.

    In order to investigate the effect of taking pedigree information into account when imputing the missing molecular data with BEAGLE 4. To investigate the effect of marker density on prediction accuracy, we varied the number of SNPs used to construct the genomic matrices of GBLUP from SNPs to using the same number simultaneously in both parental groups. For a given level of SNP density, we made 26 replicates of random samples of SNPs, using the same replicates for all the traits. In order to study whether filtering SNPs based on their percentage of missing data affected prediction accuracies, the variation in the number of SNPs was also implemented using the SNPs with the lowest percentage of missing data, with three replicates of random SNPs with same percentage of missing data for each level of numbers of SNP.

    An ANOVA similar to the one explained above was performed to assess whether the effect of SNP filtering minimized the percentage of missing data on the prediction accuracies of the cross values. The variance-covariance matrices were:.

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    This was obtained as the mean selection accuracy of the 67 A and 42 B individuals, computed from the prediction error variances as described in Marchal et al. For RRS, the A individuals and B included in the progeny tests were randomly chosen from the populations of candidates. For RRS with genomic preselection, the A individuals and B individuals included in the progeny tests were those with the highest genomic estimated GCA among each population of candidates.

    To investigate how the number of A and B candidates subjected to genomic preselection affected the performance of the selected hybrids, we first considered candidates per parental population and then increased the number from to , with a step of For each level of number of candidates, 20, replicates were made by generating random populations of candidates for each replicate. All analyses were conducted using R software version 3. Molecular coancestries between training and validation individuals were similar in Group A and Group B, the mean value of the maximum molecular coancestries between validation individuals and training individuals being 0.

    Using the pedigrees when imputing the missing SNP data increased the prediction accuracy of cross values for two traits, OP oil-to-pulp ratio and FFB fresh fruit bunches , but did not affect the other traits see Fig. For OP, the prediction accuracy increased by 4. For the remainder of the study, we consequently only used the molecular dataset imputed with the pedigree. Prediction accuracies of cross values of the genomic model GBLUP according to the imputation method and trait concerned.

    All SNPs were used for predictions. Values are means over six sets of training crosses. The prediction accuracies of the cross values were the same whether or not the predicted cross values included the SCA specific combining ability term. The proportion of SCA variance between crosses in total genetic variance between crosses reached a maximum for FB For the remainder of the study, we only present the results obtained for cross values prediction accuracies when the SCA term was not used in the prediction.

    When SNPs were used, the prediction accuracy of the cross values ranged from very low to intermediate 0. The variation in GS prediction accuracy for intermediate marker densities indicated that some SNP subsets enabled higher prediction accuracy than when all the markers were used not shown. With to SNPs, the increase reached In the other traits, this method of SNP sampling led to prediction accuracies with to SNPs similar to random sampling.

    Percentage of missing molecular data in group a left panel and group b right panel according to the method used to sample SNPs random sampling, solid black line, or selecting SNPs with the lowest percentage of missing data, red line. Vertical bars are standard deviations. Using all SNPs, they ranged from 0. The prediction accuracies of the cross values were usually closer to the GCA prediction accuracies obtained in Group B than in Group A. As expected, the magnitude of the increase was affected by the number of A and B individuals subjected to preselection, although the number of B candidates had a greater effect than the number of A candidates.

    Thus, with a fixed number of A candidates subjected to genomic preselection, increasing the number of B candidates from to increased the FFB from 2.