Genome properties of key oil palm (Elaeis guineensis Jacq.) breeding populations.

A good knowledge of the genome properties of the populations makes it possible to optimize breeding methods, in particular genomic selection (GS). In oil palm (Elaeis guineensis Jacq), the world's main source of vegetable oil, this would provide insight into the promising GS results obtained so far. The present study considered two complex breeding populations, Deli and La Mé, with 943 individuals and 7324 single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Linkage disequilibrium (LD), haplotype sharing, effective size (Ne), and fixation index (Fst) were investigated. A genetic linkage map spanning 1778.52 cM and with a recombination rate of 2.85 cM/Mbp was constructed. The LD at r2=0.3, considered the minimum to get reliable GS results, spanned over 1.05 cM/0.22 Mbp in Deli and 0.9 cM/0.21 Mbp in La Mé. The significant degree of differentiation existing between Deli and La Mé was confirmed by the high Fst value (0.53), the pattern of correlation of SNP heterozygosity and allele frequency among populations, and the decrease of persistence of LD and of haplotype sharing among populations with increasing SNP distance. However, the level of resemblance between the two populations over short genomic distances (correlation of r values between populations >0.6 for SNPs separated by <0.5 cM/1 kbp and percentage of common haplotypes >40% for haplotypes <3600 bp/0.20 cM) likely explains the superiority of GS models ignoring the parental origin of marker alleles over models taking this information into account. The two populations had low Ne (<5). Population-specific genetic maps and reference genomes are recommended for future studies.

Micro-RNA-Regulated SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) Gene Expression and Cytokinin Accumulation Distinguish Early-Developing Male and Female Inflorescences in Oil Palm (Elaeis guineensis).

Sexual differentiation of inflorescences and flowers is important for reproduction and affects crop plant productivity. We report here on a molecular study of the process of sexual differentiation in the immature inflorescence of oil palm (Elaeis guineensis). This species is monoecious and exhibits gender diphasy, producing male and female inflorescences separately on the same plant in alternation. Three main approaches were used: small RNA-seq to characterise and study the expression of miRNA genes; RNA-seq to monitor mRNA accumulation patterns; hormone quantification to assess the role of cytokinins and auxins in inflorescence differentiation. Our study allowed the characterisation of 30 previously unreported palm MIRNA genes. In differential gene and miRNA expression studies, we identified a number of key developmental genes and miRNA-mRNA target modules previously described in relation to their developmental regulatory role in the cereal panicle, notably the miR156/529/535-SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE (SPL) gene regulatory module. Gene enrichment analysis highlighted the importance of hormone-related genes, and this observation was corroborated by the detection of much higher levels of cytokinins in the female inflorescence. Our data illustrate the importance of branching regulation within the developmental window studied, during which the female inflorescence, unlike its male counterpart, produces flower clusters on new successive axes by sympodial growth.

Improving the accuracy of genomic predictions in an outcrossing species with hybrid cultivars between heterozygote parents: a case study of oil palm (Elaeis guineensis Jacq.).

Genomic selection (GS) is a method of marker-assisted selection revolutionizing crop improvement, but it can still be optimized. For hybrid breeding between heterozygote parents of different populations or species, specific aspects can be considered to increase GS accuracy: (1) training population genotyping, i.e., only genotyping the hybrid parents or also a sample of hybrid individuals, and (2) marker effects modeling, i.e., using population-specific effects of single nucleotide polymorphism alleles model (PSAM) or across-population SNP genotype model (ASGM). Here, this was investigated empirically for the prediction of the performances of oil palm hybrids for yield traits. The GS model was trained on 352 hybrid crosses and validated on 213 independent hybrid crosses. The training and validation hybrid parents and 399 training hybrid individuals were genotyping by sequencing. Despite the small proportion of hybrid individuals genotyped and low parental heterozygosity, GS prediction accuracy increased on average by 5% (range 1.4-31.3%, depending on trait and model) when training was done using genomic data on hybrids and parents compared with only parental genomic data. With ASGM, GS prediction accuracy increased on average by 3% (- 10.2 to 40%, depending on trait and genotyping strategy) compared with PSAM. We conclude that the best GS strategy for oil palm is to aggregate genomic data of parents and hybrid individuals and to ignore the parental origin of marker alleles (ASGM). To gain a better insight into these results, future studies should examine the respective effect of capturing genetic variability within crosses and taking segregation distortion into account when genotyping hybrid individuals, and investigate the factors controlling the relative performances of ASGM and PSAM in hybrid crops.

In silico QTL mapping in an oil palm breeding program reveals a quantitative and complex genetic resistance to Ganoderma boninense.

Basal stem rot caused by Ganoderma boninense is the major threat to oil palm cultivation in Southeast Asia, which accounts for 80% of palm oil production worldwide, and this disease is increasing in Africa. The use of resistant planting material as part of an integrated pest management of this disease is one sustainable solution. However, breeding for Ganoderma resistance requires long-term and costly research, which could greatly benefit from marker-assisted selection (MAS). In this study, we evaluated the effectiveness of an in silico genetic mapping approach that took advantage of extensive data recorded in an ongoing breeding program. A pedigree-based QTL mapping approach applied to more than 10 years' worth of data collected during pre-nursery tests revealed the quantitative nature of Ganoderma resistance and identified underlying loci segregating in genetic diversity that is directly relevant for the breeding program supporting the study. To assess the consistency of QTL effects between pre-nursery and field environments, information was collected on the disease status of the genitors planted in genealogical gardens and modeled with pre-nursery-based QTL genotypes. In the field, individuals were less likely to be infected with Ganoderma when they carried more favorable alleles at the pre-nursery QTL. Our results pave the way for a MAS of Ganoderma resistant and high yielding planting material, and the provided proof-of-concept of this efficient and cost-effective approach could motivate similar studies based on diverse breeding programs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01246-9.

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.). Currently, clonal selection involves two stages of phenotypic selection (PS): ortet preselection on traits with sufficient heritability among a small number of individuals in the best crosses in progeny tests, and final selection on performance in clonal trials. The present study evaluated the efficiency of genomic selection (GS) for clonal selection. The training set comprised almost 300 Deli × La Mé crosses phenotyped for eight palm oil yield components and the validation set 42 Deli × La Mé ortets. Genotyping-by-sequencing (GBS) revealed 15,054 single nucleotide polymorphisms (SNP). The effects of the SNP dataset (density and percentage of missing data) and two GS modeling approaches, ignoring (ASGM) and considering (PSAM) the parental origin of alleles, were assessed. The results showed prediction accuracies ranging from 0.08 to 0.70 for ortet candidates without data records, depending on trait, SNP dataset and modeling. ASGM was better (on average slightly more accurate, less sensitive to SNP dataset and simpler), although PSAM appeared interesting for a few traits. With ASGM, the number of SNPs had to reach 7,000, while the percentage of missing data per SNP was of secondary importance, and GS prediction accuracies were higher than those of PS for most of the traits. Finally, this makes possible two practical applications of GS, that will increase genetic progress by improving ortet preselection before clonal trials: (1) preselection at the mature stage on all yield components jointly using ortet genotypes and phenotypes, and (2) genomic preselection on more yield components than PS, among a large population of the best possible crosses at nursery stage.

Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm.

In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software.

Genomic preselection with genotyping-by-sequencing increases performance of commercial oil palm hybrid crosses.

BACKGROUND: There is great potential for the genetic improvement of oil palm yield. Traditional progeny tests allow accurate selection but limit the number of individuals evaluated. Genomic selection (GS) could overcome this constraint. We estimated the accuracy of GS prediction of seven oil yield components using A × B hybrid progeny tests with almost 500 crosses for training and 200 crosses for independent validation. Genotyping-by-sequencing (GBS) yielded +5000 single nucleotide polymorphisms (SNPs) on the parents of the crosses. The genomic best linear unbiased prediction method gave genomic predictions using the SNPs of the training and validation sets and the phenotypes of the training crosses. The practical impact was illustrated by quantifying the additional bunch production of the crosses selected in the validation experiment if genomic preselection had been applied in the parental populations before progeny tests. RESULTS: We found that prediction accuracies for cross values plateaued at 500 to 2000 SNPs, with high (0.73) or low (0.28) values depending on traits. Similar results were obtained when parental breeding values were predicted. GS was able to capture genetic differences within parental families, requiring at least 2000 SNPs with less than 5% missing data, imputed using pedigrees. Genomic preselection could have increased the selected hybrids bunch production by more than 10%. CONCLUSIONS: Finally, preselection for yield components using GBS is the first possible application of GS in oil palm. This will increase selection intensity, thus improving the performance of commercial hybrids. Further research is required to increase the benefits from GS, which should revolutionize oil palm breeding.

Identification of Ganoderma Disease Resistance Loci Using Natural Field Infection of an Oil Palm Multiparental Population.

Multi-parental populations are promising tools for identifying quantitative disease resistance loci. Stem rot caused by Ganoderma boninense is a major threat to palm oil production, with yield losses of up to 80% prompting premature replantation of palms. There is evidence of genetic resistance sources, but the genetic architecture of Ganoderma resistance has not yet been investigated. This study aimed to identify Ganoderma resistance loci using an oil palm multi-parental population derived from nine major founders of ongoing breeding programs. A total of 1200 palm trees of the multi-parental population was planted in plots naturally infected by Ganoderma, and their health status was assessed biannually over 25 yr. The data were treated as survival data, and modeled using the Cox regression model, including a spatial effect to take the spatial component in the spread of Ganoderma into account. Based on the genotypes of 757 palm trees out of the 1200 planted, and on pedigree information, resistance loci were identified using a random effect with identity-by-descent kinship matrices as covariance matrices in the Cox model. Four Ganoderma resistance loci were identified, two controlling the occurrence of the first Ganoderma symptoms, and two the death of palm trees, while favorable haplotypes were identified among a major gene pool for ongoing breeding programs. This study implemented an efficient and flexible QTL mapping approach, and generated unique valuable information for the selection of oil palm varieties resistant to Ganoderma disease.

Mixed model approach for IBD-based QTL mapping in a complex oil palm pedigree.

BACKGROUND: Elaeis guineensis is the world's leading source of vegetable oil, and the demand is still increasing. Oil palm breeding would benefit from marker-assisted selection but genetic studies are scarce and inconclusive. This study aims to identify genetic bases of oil palm production using a pedigree-based approach that is innovative in plant genetics. RESULTS: A quantitative trait locus (QTL) mapping approach involving two-step variance component analysis was employed using phenotypic data on 30852 palms from crosses between more than 300 genotyped parents of two heterotic groups. Genome scans were performed at parental level by modeling QTL effects as random terms in linear mixed models with identity-by-descent (IBD) kinship matrices. Eighteen QTL regions controlling production traits were identified among a large genetically diversified sample from breeding program. QTL patterns depended on the genetic origin, with only one region shared between heterotic groups. Contrasting effects of QTLs on bunch number and weights reflected the close negative correlation between the two traits. CONCLUSIONS: The pedigree-based approach using data from ongoing breeding programs is a powerful, relevant and economic approach to map QTLs. Genetic determinisms contributing to heterotic effects have been identified and provide valuable information for orienting oil palm breeding strategies.

Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.).

KEY MESSAGE: Genomic selection empirically appeared valuable for reciprocal recurrent selection in oil palm as it could account for family effects and Mendelian sampling terms, despite small populations and low marker density. Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from -0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size.

Genetic architecture of palm oil fatty acid composition in cultivated oil palm (Elaeis guineensis Jacq.) compared to its wild relative E. oleifera (H.B.K) Cortés.

We searched for quantitative trait loci (QTL) associated with the palm oil fatty acid composition of mature fruits of the oil palm E. guineensis Jacq. in comparison with its wild relative E. oleifera (H.B.K) Cortés. The oil palm cross LM2T x DA10D between two heterozygous parents was considered in our experiment as an intraspecific representative of E. guineensis. Its QTLs were compared to QTLs published for the same traits in an interspecific Elaeis pseudo-backcross used as an indirect representative of E. oleifera. Few correlations were found in E. guineensis between pulp fatty acid proportions and yield traits, allowing for the rather independent selection of both types of traits. Sixteen QTLs affecting palm oil fatty acid proportions and iodine value were identified in oil palm. The phenotypic variation explained by the detected QTLs was low to medium in E. guineensis, ranging between 10% and 36%. The explained cumulative variation was 29% for palmitic acid C16:0 (one QTL), 68% for stearic acid C18:0 (two QTLs), 50% for oleic acid C18:1 (three QTLs), 25% for linoleic acid C18:2 (one QTL), and 40% (two QTLs) for the iodine value. Good marker co-linearity was observed between the intraspecific and interspecific Simple Sequence Repeat (SSR) linkage maps. Specific QTL regions for several traits were found in each mapping population. Our comparative QTL results in both E. guineensis and interspecific materials strongly suggest that, apart from two common QTL zones, there are two specific QTL regions with major effects, which might be one in E. guineensis, the other in E. oleifera, which are independent of each other and harbor QTLs for several traits, indicating either pleiotropic effects or linkage. Using QTL maps connected by highly transferable SSR markers, our study established a good basis to decipher in the future such hypothesis at the Elaeis genus level.

Estimation of genealogical coancestry in plant species using a pedigree reconstruction algorithm and application to an oil palm breeding population.

KEY MESSAGE: Explicit pedigree reconstruction by simulated annealing gave reliable estimates of genealogical coancestry in plant species, especially when selfing rate was lower than 0.6, using a realistic number of markers. Genealogical coancestry information is crucial in plant breeding to estimate genetic parameters and breeding values. The approach of Fernández and Toro (Mol Ecol 15:1657-1667, 2006) to estimate genealogical coancestries from molecular data through pedigree reconstruction was limited to species with separate sexes. In this study it was extended to plants, allowing hermaphroditism and monoecy, with possible selfing. Moreover, some improvements were made to take previous knowledge on the population demographic history into account. The new method was validated using simulated and real datasets. Simulations showed that accuracy of estimates was high with 30 microsatellites, with the best results obtained for selfing rates below 0.6. In these conditions, the root mean square error (RMSE) between the true and estimated genealogical coancestry was small (<0.07), although the number of ancestors was overestimated and the selfing rate could be biased. Simulations also showed that linkage disequilibrium between markers and departure from the Hardy-Weinberg equilibrium in the founder population did not affect the efficiency of the method. Real oil palm data confirmed the simulation results, with a high correlation between the true and estimated genealogical coancestry (>0.9) and a low RMSE (<0.08) using 38 markers. The method was applied to the Deli oil palm population for which pedigree data were scarce. The estimated genealogical coancestries were highly correlated (>0.9) with the molecular coancestries using 100 markers. Reconstructed pedigrees were used to estimate effective population sizes. In conclusion, this method gave reliable genealogical coancestry estimates. The strategy was implemented in the software MOLCOANC 3.0.