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.

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.