From mass selection to genomic selection: one century of breeding for quantitative yield components of oil palm (Elaeis guineensis Jacq.)

International audienceMore 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. This review article analyzes the evolution of breeding schemes for oil palm yield and its quantative components and the changes expected to take place with genomic selection (GS). Genetic improvement of oil palm yield started in the 1920s through mass selection. Later, several disruptive improvements dramatically increased the rate of genetic progress: (1) understanding the heredity of fruit form and the adoption of tenera, with thicker mesocarp, in plantations; (2) the discovery of hybrid vigor and the adoption of modified reciprocal recurrent selection; and (3) clonal selection, exploiting intra-hybrid variability. In addition, the use of linear mixed models to estimate genetic values has made selection more efficient. Today, GS appears to be a new disruptive improvement that can speed up breeding schemes by avoiding field trials in some cycles and increase selection intensity by evaluating more candidates. The genetic potential for oil palm yield has increased considerably over one century of breeding. GS is expected to bring the rate of genetic progress to a previously unprecedented level. The future studies on oil palm GS will aim at making it efficient for all yield components. For this purpose, they should focus in particular on the optimization of training populations and on the improvement of prediction models. Minimizing environmental impacts will also require improvement in other aspects (resistance to diseases, cultural practices, etc.)

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