Genomic predictions improve clonal selection in oil palm (Elaeis guineensis Jacq.) hybrids

The prediction of clonal genetic value for yield is challenging in oil palm ( Elaeis guineensis Jacq.). 24 Currently, clonal selection involves two stages of phenotypic selection (PS): ortet preselection on traits 25 with sufficient heritability among a small number of individuals in the best crosses in progeny tests, 26 and final selection on performance in clonal trials. The present study evaluated the efficiency of 27 genomic selection (GS) for clonal selection. The training set comprised almost 300 Deli × La Mé 28 crosses phenotyped for eight palm oil yield components and the validation set 42 Deli × La Mé ortets. 29 Genotyping-by-sequencing (GBS) revealed 15,054 single nucleotide polymorphisms (SNP). The 30 effects of the SNP dataset (density and percentage of missing data) and two GS modeling approaches, 31 ignoring (ASGM) and considering (PSAM) the parental origin of alleles, were assessed. The results 32 showed prediction accuracies ranging from -0.03 to 0.70 for ortet candidates without data records, 33 depending on trait, SNP dataset and modeling. ASGM was better (more robust over traits and SNP 34 datasets, and simpler), although PSAM could slightly improve prediction accuracies for the two traits 35 defining the heterotic groups. The number of SNPs had to reach 7,000, while the percentage of 36 missing data per SNP was of secondary importance. GS prediction accuracies were higher than those 37 of PS for most of the traits. Finally, this makes possible two practical applications of GS, that will 38 increase genetic progress by improving ortet preselection before clonal trials: (1) preselection at the 39 mature stage on all yield components jointly using ortet genotypes and phenotypes, and (2) genomic 40 preselection on more yield components than PS, among a large population of the best possible crosses 41 at nursery stage.


45
The annual yield of palm oil is around four tons per hectare and world production is currently 46 above 75 million tons of crude palm oil [1]. Most cultivated oil palms (Elaeis guineensis Jacq.) are 47 pulp to fruit (PF) and oil to pulp (OP) ratios and number of fruits per bunch (NF). The effect of the 128 SNP dataset (SNP density and percentage of missing data) was studied by filtering SNPs with 129 different maximum percentages of missing data.  Table 1). The pedigrees of these populations are known 140 over several generations (see Cros et al. [12]). ALT is located at 2° 39′ N -99° 42′ E in North 141 Sumatra, on the SOCFINDO estate (Indonesia) and is constituted of 28 trials planted on deep loamy 142 sand soils, with low water deficit and high insolation, and benefiting from standard cultural practices 143 [24]. The experimental design used in these trials was either a balanced lattice of four to five ranks or 144 randomized complete block designs (RCBD), described in detail by Cros et al. [15]. 145 The validation set was composed of 42 Deli × La Mé tenera ortets, evaluated in clonal trials 146 involving on average 69 ramets per clone for production traits and a subset of 34 ramets per clone for 147 quality traits. The ramets were established in three out of the 28 trials of ALT and were planted in 148 1995 and 1998 ( Table 1). The 42 ortets were chosen among individuals from various hybrid crosses 149 planted on seven trials of an earlier set of progeny tests, located at Aek Kwasan 1 (AK1), which was 150 also located on the SOCFINDO estate and benefited from the same agricultural practices. The All the individuals, i.e. the training hybrid crosses, the 42 hybrid ortets and their ramets, were 157 phenotyped for eight traits. Five traits were assessed for bunch quality: average fruit weight (AFW), 158 fruit to bunch (FB), pulp to fruit (PF), and oil to pulp (OP) ratios, and number of fruits per bunch 159 (NF); and three traits for bunch production: bunch number (BN), average bunch weight (ABW), and 160 total bunch production (FFB). For quality traits, data were collected when plants were from five to 161 nine years old at ALT and from six to nine years old at AK1. For production traits, data were collected 162 when the plants were from three to seven years old in both sites.  (Table 1). Ortets genotypes were obtained from two or three samples 171 collected on different ramets (thus allowing controlling the legitimacy of the ramets). DNA extraction 172 and GBS were performed as described in Cros et al. [15], using the PstI and HhaI restriction enzymes. 173 The raw fastq sequence data were processed with Tassel GBS v. 5.2.44 [27], using the Bowtie2 174 software for alignment [28], and VCFtools 0.1.14 [29]. The indels were discarded, the datapoints with 175 depth below five were set to missing, the SNPs that were not biallelic, with more than 75% of missing 176 data or on the unassembled part of the genome were discarded (see Cros  software can consider the family relationships (i.e. parent-offspring) and infers missing genotypes 187 using genotype likelihood computed from the pedigree. The process followed to impute and phase the 188 SNP data is given in Fig. 1

Prediction models and computation of genetic values of unobserved clones 208
Two approaches were implemented to predict the genetic value of the validation clones: the 209 across-population SNP genotype model (ASGM) and the population-specific effects of SNP alleles 210 model (PSAM). In addition, for both approaches, two models were tested: a purely additive model 211 (ASGM_A and PSAM_A) and a model combining additive and dominance effects (ASGM_AD and 212 PSAM_AD). The ASGM_A approach used a model with a single random genetic effect, 213 corresponding to the additive genetic value of the parents of the training hybrid crosses and of the 214 validation clones. The ASGM_AD and PSAM_AD model also included a random dominance effect of 215 crosses and ortets. The PSAM_A approach used two random effects partitioning the additive genetic 216 values of each individual into two parts originating from Deli and La Mé alleles. All these four models 217 were implemented separately on each trait (univariate models). For GS, the GBLUP statistical 218 approach was used [31,32], and the corresponding models were termed G_ASGM_A, G_ASGM_AD, 219 G_PSAM_A, and G_PSAM_AD. In addition, to evaluate the usefulness of the SNP data, these four 220 models were implemented with pedigree data instead of SNPs (control PBLUP models, termed 221 P_ASGM_A, P_ASGM_AD, P_PSAM_A, and P_PSAM_AD). 222 In all cases, the models were trained with the phenotypic data of ALT hybrids and the genomic 223 data of their parents, and the genetic values of the 42 validation clones were predicted. For all the 224 models mentioned above, no phenotypic data of the validation clones were provided to the prediction 225 models. This corresponds to a breeding situation where predictions are made for immature individuals 226 (e.g. nursery plantlets belonging to crosses that were not evaluated in progeny-tests but were produced 227 by mating the best parents selected at the end of the progeny-tests). However, ortet selection can also 228 be made within the crosses evaluated in progeny tests. In this case, the ortet candidates have 229 phenotypic data records, which should be taken into consideration along with their SNP data when 230 predicting their clonal value. This was evaluated with the G_ASGM_A model, simply including the 231 adjusted phenotypic value of the validation ortets (see below) to the phenotypic dataset used to train 232 the model, and is referred to as the G_ASGM_A+pheno approach. 233 All GS analyses were run on a server of the CIRAD-UMR AGAP HPC data center of the 234 South Green bioinformatics platform (http://www.southgreen.fr/), using a homemade R script. 235 236 2.6.1. Across-population SNP genotype models (ASGM) 237 The model used for the G_ASGM_AD approach was as follows: 238 with: the observed phenotypes of the training hybrid individuals, the vector of fixed effects 240 (phenotypic mean, trial effects, block effects and, for bunch production traits, age), 241 ~ N(0,  To 247 implement this model in practice, two specificities of our dataset had to be taken into account. First, a 248 few parents of the training crosses were not genotyped (Table 1), and the . matrices had therefore to 249 be made with the genealogical data of hybrid crosses with ungenotyped parents and with the SNP data 250 of hybrid crosses with genotyped parents (computed with the SNP data of their parents, see below) 251 and of the ortets. All . matrices subsequently in this paper will refer to matrices combining 252 genealogical and genomic information. 0 is the inverse of , computed according to Misztal et al. 253 [33] as: 0 1 0 + 2 0 0 0 3 0 − 1 0 5, where 3 0 and 1 0 are the inverse of the realized and the 254 genealogical additive relationship matrices, respectively, of the 42 ortets and the hybrid crosses with 255 genotyped parents, and 1 0 is the inverse of the genealogical relationship matrix of all hybrid crosses 256 (i.e. the few with ungenotyped parents and the ones with genotyped parents) and the 42 ortets. Second, 257 the phenotyped individuals constituting the hybrid crosses were not genotyped while they had to be connected to the validation ortets through their genomic relationships (only the parents of the hybrids 259 were genotyped, except a few parents that were not genotyped and for which the genealogical 260 relationships were used, as explained above). To get genotypes for the hybrid crosses with genotyped 261 parents, we computed for each cross the mean genotypes expected from the parental genotypes (i.e. for 262 SNP j in cross i, the mean number of copies of the minor allele of SNP j expected to be found in the 263 hybrid individuals of i), assuming this was relevant considering the relatively large number of 264 individuals per cross ( Table 1). The genomic additive relationship matrix G was obtained as: 3 265

Population-specific effects of SNP alleles models (PSAM) 284
The model used for G_PSAM_AD was as follows: 285 The ability of each model to predict the reference clonal value of the 42 validation clones (see 314 below) was evaluated through their prediction accuracy, computed as the correlation between the 315 reference value and the predicted clonal values. 316 Pairwise comparisons of prediction accuracies among models were made for each trait using 317 the Hotelling-Williams t-test [36]. This test compares two non-independent correlations, i.e. having 318 one variable in common, which in our case is the reference value of the 42 clones. This test was 319 applied using the R package psych [37]. 320 321

Determination of the reference clonal values predicted by the models 322
In order to validate the different prediction models, clonal genetic values were obtained for 323 each clone from the phenotypic data collected on their ramets. Subsequently in this paper, they will be 324 referred to as reference genetic values. They were computed using a simple linear mixed model to 325 adjust the phenotypic values of the ramets for the effects of experimental design, i.e. clonal trials, 326 blocks, incomplete blocks, elementary plots and, for bunch production traits, age. In this model, clones 327 were included as a fixed effect. 328 329

Accuracy of phenotypic selection before clonal trials 330
To evaluate the possibility of using GS instead of the current phenotypic selection (PS) to 331 select the hybrid individuals to test in the clonal trials, the PS accuracy was computed for each trait. It 332 was defined as the correlation between the ortet adjusted phenotypes and the reference clonal genetic 333 values. The adjusted phenotype was obtained for each ortet from its phenotypic data collected in AK1, 334 using a simple linear mixed model with individuals as random effect and hybrid crosses and all the 335 effects related to the experimental design, i.e. trials, blocks, incomplete blocks, elementary plots and, 336 for bunch production traits, age, as fixed effects. Finally, each ortet had for each trait an adjusted 337 phenotype that was equal to the sum of the individual effect of the ortet, the effect of its cross and the 338 mean residual effect over its phenotypic data records. On average over traits and SNP datasets, G_ASGM_A was more accurate (0.45) than 377 G_PSAM_A (0.37), with the mean prediction accuracy per trait over SNP datasets ranging from 0.14 378 (PF) to 0.65 (FB) for G_ASGM_A and from 0.09 (PF) to 0.58 (FB) for G_PSAM_A. G_ASGM_A 379 obtained a mean prediction accuracy greater than G_PSAM_A for six traits out of eight, with 380 G_PSAM_A being on average slightly more accurate than G_ASGM_A for ABW and equal for BN 381 (Table 3). Considering the maximum accuracy over all SNP datasets, the prediction accuracy ranged 382 from 0.18 (PF) to 0.70 (FB) for G_ASGM_A and from 0.16 (PF) to 0.65 (FB) for G_PSAM_A (Table  383 3), and, here, G_PSAM_A was more accurate for both BN and ABW, although slightly. Considering 384 the different SNP datasets and traits, large differences in prediction accuracy between G_ASGM_A 385 and G_PSAM_A were observed, up to +0.36 in favour of G_ASGM_A with OP at pmax = 45%-nSNP = 386 11,707 ( Fig. 3 and Table 4). The differences in prediction accuracies between G_ASGM_A and nSNP=11,707 and pmax=75%-nSNP=15,054), FB and FFB in one dataset each, pmax=10%-nSNP=6,898 and 390 pmax=5%-nSNP=5,620 respectively. In rare cases, low and non-significant differences (up to +0.16) were 391 observed in favor of G_PSAM_A. G_ASGM_A, therefore, appeared to be a better approach (i.e. more 392 accurate and easier to implement) for predicting clonal values for oil palm yield components. 393 Prediction accuracies were broadly improved for three traits (FB, BN and ABW) when 394 relationship matrices were computed using SNPs (G_ASGM_A and G_PSAM_A) instead of 395 genealogical data (control pedigree-based models P_ASGM_A and P_PSAM_A). The maximum 396 prediction accuracies of GS over all SNP datasets outperformed pedigree-based models for seven traits 397 out of eight (except for AFW) ( Table 5). The largest difference was observed in BN for pmax=75%-398 nsnp=15,054, with G_ASGM_A accuracy being 0.67 higher than P_ASGM_A. Accuracies of pedigree-399 based models exceeded GS in almost every SNP dataset for AFW ( Fig. 3 and Table 5). The 400 differences between GS models and their pedigree-based control models were significant for five 401 traits, with four traits (FB, OP, BN and ABW) where GS was the best and one trait (AFW) where 402 pedigree-based models were more accurate (Table 5). 403 The SNP dataset affected the prediction accuracy differently according to the trait and the 404 model. However, the use of a different SNP dataset for each combination of trait and model seems 405 unrealistic for the practical application of GS. Therefore, in order to identify the optimal SNP 406 dataset(s) that would maximize GS accuracy, we computed for each SNP dataset the mean 407 G_ASGM_A prediction accuracy over the traits. This value increased with the SNP density (0.41 with 408 SNP dataset pmax=0%-nsnp=2,447 and 0.43 with pmax=5%-nsnp=5,620), before plateauing at 0.46 with 409 the subsequent SNP datasets. Mean prediction accuracy over the SNP datasets forming the plateau 410 ranged from 0.17 (PF) to 0.66 (FB), and were close to the highest accuracies achieved over all the 411 SNP datasets (Table 3). There was therefore a minimum of 6,898 SNPs required to reach maximum 412 prediction accuracy on average over all traits. 413 Accuracies were more variable among SNP datasets and traits with G_PSAM_A than with 414 G_ASGM_A. With G_ASGM_A, prediction accuracies tended to increase with SNP density before plateauing (except for AFW) and slightly decreasing in some cases. This suggested that more useful 416 information was captured for prediction purposes when using more SNPs (to a certain limit) and, 417 again, that the percentage of missing data was of lesser importance. On the other hand, a reduction of 418 accuracies was observed with SNP density for AFW. For G_PSAM_A, prediction accuracies 419 increased, and usually plateaued, for only four traits (FB, PF, NF and ABW). For the other traits, 420 prediction accuracies remained stable or tended to decrease with increasing marker density and 421 maximum percentage of missing SNP data. Thus, the accuracy of OP, for instance, decreased around 422 59.6% from pmax=0%-nsnp=1,497 to pmax=45%-nsnp=11,425 (Fig. 3). 423 Figure 4 presents the prediction accuracies of PS and the mean prediction accuracy of 425 G_ASGM_A over the best datasets (i.e. with pmax from 10% to 75% and nsnp from 6,898 to 15,054), 426 with (G_ASGM_A+pheno) and without phenotypic data of the ortets. Variation of PS accuracy was 427 large between traits, going from -0.03 for ABW to 0.63 for OP. Very low PS accuracies (<0.1) were 428 obtained for ABW and FFB, meaning that PS would have been inefficient for these two traits. The 429

Comparison of prediction accuracies of PS and GS 424
highest PS accuracies were achieved in OP (0.63) and PF (0.59) ( Table 6 and Fig. 4). These two traits 430 are known to have moderate to high heritability in the oil palm [2] and are consequently routinely used 431 for preselection before clonal trials. This was the case here, as indicated by the intensity of PS for 432 these two traits, which was the highest among the eight traits studied (Table 6). 433 The GS prediction accuracy obtained with the best SNP datasets was generally higher with 434 G_ASGM_A+pheno than with G_ASGM_A (except for AFW, where a slight decrease was found) 435 ( Fig. 4). On average over all the traits, G_ASGM_A+pheno thus reached 0.53, against 0.46 for 436 G_ASGM_A (i.e. +15.2%). The prediction accuracy of G_ASGM_A and G_ASGM_A+pheno 437 obtained with the best SNP datasets was above PS prediction accuracies for six and seven traits,

445
In this paper, we evaluated the possibility of predicting the genetic value of oil palm ortet 446 selection candidates, using GS models and high throughput SNP genotyping (GBS). We considered 447 two breeding situations consisting of candidate ortets with or without phenotypic values. We assessed 448 the effect on prediction accuracy of marker datasets and of two approaches for modeling the parental 449 origin of marker alleles (across-population SNP genotype models, ASGM, and population-specific 450 effects of SNP alleles models, PSAM). 451 452

Improving the genetic progress of clonal breeding with GS 453
In the current clonal breeding methodology, ortets that will be evaluated in clonal trials are 454 selected on the few traits with high H² value among a limited number of phenotyped candidates at the 455 mature stage and belonging to the best crosses evaluated in progeny tests. Based on the results 456 presented here, annual genetic progress can be improved by selecting ortets (1) among a large 457 population of the best possible crosses (produced based on the results of the progeny tests) at the 458 juvenile (e.g. nursery) stage with GS models on most of the yield components or, (2) at the mature 459 stage on all the yield components, using jointly the genomic and phenotypic data of the ortet selection 460

candidates. 461
In detail, in the first GS approach that is now possible, the best crosses identified based on the 462 results of the progeny test (i.e. with the best performance expected from the parental GCAs and the 463 crosses' specific combining abilities [SCAs]) would be produced to generate a large number of 464 seedlings, that would be submitted to GS on the traits with satisfactory GS accuracy. This would 465 improve the genetic progress at three levels. First, most of the breeding programs consider that there 466 are six traits of interest for palm oil yield breeding (FB, PF, OP, ABW, BN and FFB), and PS before 467 clonal trials is usually applied to PF and OP, as they have the highest H² [39]. In our dataset, these 468 traits indeed had high H², with PS prediction accuracy >0.5 (Fig. 4)

(although it was not clear why FB 469
had a similar H², while it is usually among the traits with low H²). Therefore, considering that breeders 470 use 0.5 as the minimum prediction accuracy for applying PS before clonal trials, they would now 471 apply GS to four traits (FB, OP, FFB and ABW) (Fig. 4), with a similar mean prediction accuracy over 472 these traits with GS (0.56) compared to PS (0.60 over FB, PF and OP). Interestingly, the two traits that 473 had a prediction accuracy lower with G_ASGM_A than with PS, i.e. PF and OP, were the ones for 474 which the 42 ortets were submitted to the strongest phenotypic selection before clonal trials. In 475 particular, PF had the highest intensity of phenotypic selection (0.68) and also had much lower 476 prediction accuracy with G_ASGM_A than with PS. We hypothesized this occurred as the phenotypic 477 preselection led to the fixation of many genes controlling these traits, and in particular PF, in the 42 478 ortets, thus making that the relationships computed over the genome-wide SNPs no longer matched 479 with the relationships at the genes. This hypothesis could be investigated using a validation set that 480 was not submitted to phenotypic preselection. Such a study would be of great interest as, in case our 481 hypothesis could be confirmed, the breeders would likely get in practice a higher GS accuracy for PF 482 and OP, as the seedlings comprising the population of application would not be preselected. In this 483 case, GS before the clonal trials would be even more useful. Second, a GS-based approach would also 484 increase the genetic progress by higher selection intensity compared to PS: GS would be applied to 485 nursery individuals, i.e. possibly in the thousands, while PS is currently applied to the small number of 486 individuals planted in the progeny tests trials (i.e. normally 10 to 50 per cross) [9]. Third, making the 487 selection in the best possible crosses instead of the best crosses evaluated would be an improvement in 488 terms of genetic progress, as the best possible crosses were likely not present in the progeny tests, due 489 to the high degree of incompleteness of the mating designs. It is also possible to make these crosses in 490 the context of phenotypic clonal selection, but in this case, the selection process would require around 491 10 more years of phenotypic evaluations in these elite crosses to identify the candidate ortets for the 492 clonal trials [16]. 493 In the second GS approach, i.e. the selection of ortets among mature hybrid individuals, it is 494 now possible to apply this selection to all the yield components. Indeed, for individuals at the mature 495 stage, which thus may have phenotypic records, for each of the six commonly selected oil yield 496 components it is possible to reach a prediction accuracy of 0.5 (or almost, in the case of BN), using 497 conventional PS for PF and G_ASGM_A+pheno for the other traits. In practice, increasing the number 498 of traits on which ortets are selected before clonal trials will increase selection intensity and thus the 499 genetic progress. 500 Another possible approach to improve the genetic progress would be to use genomic 501 predictions to identify, before the progeny tests, the best possible crosses, and to use them to 502 implement the first approach of clonal GS suggested here. For that purpose, progeny tests from the 503 previous cycle could be used as a training population, and genomic ortet selection would be applied at 504 the nursery stage in the best possible crosses. This approach would, therefore, have the additional 505 advantage of shortening the breeding cycle (as it makes it possible to run the clonal trials 506 simultaneously with the progeny tests), but it should be investigated in greater details as its efficiency 507 also depends on the accuracy of the genomic estimated breeding values of the parents. considers population-specific SNP variances and SNP effects. For that reason, we expected 512 G_PSAM_A to perform better than G_ASGM_A for many traits, considering the marked genetic 513 difference between Deli and La Mé, with Fst around 0.55 [38]. However, G_PSAM_A only performed 514 better than G_ASGM_A for BN and, to a lesser extent, ABW. We hypothesized that this was the 515 bunch production traits, i.e. six in group A and ten in group B, against only one common QTL. This is 521 also in agreement with the fact that a large part of the SNPs in the two populations have opposite 522 minor alleles, with differences as extreme as having one allele fixed in one population and the other allele fixed in the other population (Fig. 2b, c). However, not all SNPs showed these types of 524 differences and similar segregation patterns among populations were also observed, which is likely 525 related to the similar performance of G_ASGM_A and G_PSAM_A for the other traits. In order to 526 help to understand the results obtained here, it would be useful to investigate whether the QTLs Although G_PSAM_A has the potential to model genetic differences between parental 532 populations, it also has a drawback, which is that it has to estimate more parameters than G_ASGM_A 533 G_ASGM_A and G_PSAM_A with high SNP density (31,930 SNPs). We did not find SNP density to 555 have such an effect on the prediction accuracy of G_PSAM_A or on the relative performance of 556 G_PSAM_A and G_ASGM_A. This likely results from the fact that, in our study, SNP density varied 557 with SNP quality, with higher SNP numbers meaning a higher percentage of missing data. These two 558 parameters, therefore, seem to interact on the prediction accuracy of the two models investigated. 559 However, the fact that the mean GS accuracy over the traits increased with the number of SNPs and 560 plateaued from 6,898 SNPs indicated that SNP density was of greater importance for the prediction 561 accuracy than the percentage of missing data per SNP. 562 We found that, in order to maximize the efficiency of GS, the prediction of the genetic values 563 must be done using G_ASGM_A with an SNP density ranging from around 7,000 to 15,000 for all 564 traits. Another possibility would be to use a different SNP dataset for each trait, maximizing the 565 accuracy for the considered trait. However, as previously mentioned, this does not seem convenient for 566 the practical application of GS. The variation in prediction accuracy among SNP datasets might also 567 have been exacerbated by the small size of our validation population (due to the difficulty of obtaining 568 a large number of clones in trials, mainly because of the mantled anomaly [8]), and therefore so far it 569 seems wiser to identify the best SNP datasets on average over several traits. 570 GS prediction models (G_ASGM_A and G_PSAM_A) were usually more accurate than their 571 respective control pedigree-based models (P_ASGM_A and P_PSAM_A). The superiority of GS 572 models shows that, even for unobserved individuals, GS models can account for both Mendelian 573 sampling terms of siblings in a family and for family effects, while pedigree-based models can only 574 account, at best, for family effects, as already found in previous oil palm GS studies [16]. 575 However, G_ASGM_A outperformed its control pedigree-based model more often than 576 G_PSAM_A. Thus, G_PSAM_A remained less accurate than P_PSAM_A for all the SNP datasets in 577 three traits, while that never happened with G_ASGM_A. Also, the overall inferiority of G_PSAM_A to G_ASGM_A occurred while P_PSAM_A was actually better than P_ASGM_A for five traits out of 579 eight. This looks contradictory and suggests that the performance of G_PSAM_A could have been 580 reduced by phasing errors. Also, many studies comparing G_ASGM_A and G_PSAM_A were carried 581 out by simulation with known phases [22,42,43], and therefore possible phasing errors in our study 582 could also be the cause of the discrepancies observed between our results and the results obtained in 583 simulation studies. Investigating other phasing approaches seems therefore of interest in the oil palm 584 context. 585 586

Genotyped individuals for training 587
In this study, to make GS predictions more cost-effective, the genotypes of the phenotyped 588 hybrid individuals constituting the training set were reconstructed using the molecular data of their 589 parents, with G_ASGM, or not used in the model, with G_PSAM. Both modeling approaches 590 therefore assume that the mean genotype in a hybrid family (i.e. the mean number of copies of the 591 minor allele over the individuals making the family) expected from the parental genotypes is the same 592 as the actual mean genotype. Nevertheless, in the case of allele segregation distortion at a locus, the 593 mean genotype in a hybrid family would significantly deviate from the mean genotype expected from 594 the parental genotypes, and this could reduce the GS accuracy. Indeed, high numbers of distorted 595 markers can be found in plants: Zuo  consequence of reciprocal recurrent selection, which generated the contrasted allele frequencies we 607 observed across Deli and La Mé populations (Fig. 2)

611
This work showed that GS can largely improve clonal selection in oil palm (Elaeis 612 guineensis). GS prediction accuracies for ortets without phenotypic data records extended from -0.03 613 to 0.7 according to the trait, GS model and SNP dataset. The G_ASGM_A approach was better for 614 predicting clonal values than G_PSAM_A (more robust over traits and SNP datasets, easier to 615 implement), although G_PSAM_A could, in some cases, slightly improve prediction accuracies for the 616 two traits defining the heterotic groups. G_ASGM_A gave higher prediction accuracies than current 617 phenotypic selection for six traits out of eight. GS models required at least 7,000 SNPs to perform 618 best, with the percentage of missing data per SNP being of secondary importance. 619 The annual genetic progress of clonal oil palm breeding for yield can be increased by 620 replacing the current phenotypic ortet preselection before clonal trials by (1) genomic ortet 621 preselection on most of the yield components among a large population of the best possible crosses 622 (produced based on the results of the progeny tests) at the juvenile stage or, (2) ortet preselection at the 623 mature stage on all the yield components using jointly the genomic and phenotypic data of the ortet 624 selection candidates. GS can, therefore, enhance oil palm production. Further studies should be 625 conducted, for example considering other traits (vegetative growth, resistance to diseases) and using a 626 different phasing approach. The molecular data were generated by AM, VR and VP. 651   Bunch production: bunch number (BN), average bunch weight (ABW) and total bunch production 789 (FFB); bunch quality: average fruit weight (AFW), fruit to bunch (FB), pulp to fruit (PF), and oil to 790 pulp (OP) ratios, and number of fruits per bunch (NF); genomic prediction models: across-population 791 SNP genotype models (ASGM_A), population-specific effects of SNP alleles models (PSAM_A

Table 4 797
Pairwise comparison of prediction accuracies among genomic selection and pedigree-based models, 798 according to SNP dataset and trait. For any pair of models, the values indicate the difference in 799 prediction accuracy between the two models (model1 -model2). SNP datasets are defined based on 800 the maximum percentage of missing data allowed per SNP pmax and the resulting number of SNPs nSNP 801 and are labeled pmax%-nSNP. Significance of pairwise comparisons by Hotelling-Williams t-test: *0.05 802 > P ≥ 0.01; **0.01 > P ≥ 0.001; ***P < 0.001.  Pairwise comparison of prediction accuracies among genomic selection and pedigree-based models, 806 according to SNP dataset and trait. For any pair of models, the values indicate the difference in 807 prediction accuracy between the two models (model1 -model2). SNP datasets are defined based on 808 the maximum percentage of missing data allowed per SNP pmax and the resulting number of SNPs nSNP 809 and are labeled pmax%-nSNP. Significance of pairwise comparisons by Hotelling-Williams t-test: *0.05 810 > P ≥ 0.01; **0.01 > P ≥ 0.001; ***P < 0.001.