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.)

International audienceGenomic 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

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 x La Me crosses phenotyped for eight palm oil yield components and the validation set 42 Deli x La Me 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

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

International audienceA 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 Me, with 943 individuals and 7324 single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Linkage disequilibrium (LD), haplotype sharing, effective size (N-e), and fixation index (F-st) 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 r(2)=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 Me. The significant degree of differentiation existing between Deli and La Me was confirmed by the high F-st 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 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 N-e (<5). Population-specific genetic maps and reference genomes are recommended for future studies

Identification of Fusarium wilt resistance loci in two major genetic backgrounds for oil palm breeding

International audienceFusarium wilt (FW) caused by Fusarium oxysporum f. sp. elaeidis is the main disease of oil palm in Africa and is a latent threat to other producing regions. A long-term work based on a genetic survey of FW resistance using field observations and pre-nursery screening tests with inoculation resulted in FW resistant planting material whose resistance has not been overcome in 40 years. The genetic determinism of FW resistance has never been studied. Such information would eventually enable marker assisted selection (MAS) to ensure continuous improvement of planting material, through multi-trait selection (yield and disease or multiple diseases) and diversification of the genetic base. In this study, we used a pedigree-based QTL mapping approach that took advantage of the extensive pre-nursery FW resistance data recorded in the framework of a reciprocal recurrent selection program in Benin Republic. Eight QTL regions were mapped in two major genetic backgrounds and favorable QTL alleles were identified in the current breeding populations. The QTL pattern was specific to the population studied in terms of number, location and genetic variance explained, highlighting the different genetic architecture of FW resistance depending on the genetic background. To investigate a putative trade-off between FW resistance and yield, FW resistance QTL genotypes were predicted in a population that was formerly evaluated for yield in the field. The effects on yield of a FW QTL were offset in the commercial planting material thanks to different effects on different yield components in both heterotic groups. These results shed light on FW resistance in oil palm and provide valuable information for the implementation of MAS in the breeding program. Considering that prenursery screening tests are widely used, the approach presented here could be implemented in other breeding programs to provide further insights into FW resistance, especially in the context of wider genetic diversity

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

International audienceBasal 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