Details

Autor(en) / Beteiligte

• Nyouma, Achille
• Bell, Joseph Martin
• Jacob, Florence
• Riou, Virginie
• Manez, Aurore
• Pomiès, Virginie
• Nodichao, Leifi
• Syahputra, Indra
• Affandi, Dadang
• Cochard, Benoit
• Durand-Gasselin, Tristan
• Cros, David

Titel

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

Ist Teil von

• Plant science (Limerick), 2020-10, Vol.299, p.110547-110547, Article 110547

Ort / Verlag

Ireland: Elsevier B.V

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• •Genomic selection can be used to select hybrid individuals to test in clonal trials.•A joint use of genomic and phenotypic selection enables selection of hybrid individuals in all the yield components with accuracies ranging from 0.33 to 0.66.•Genomic predictions largely improve clonal selection and increase genetic progress in oil palm.•The minimum number of SNPs was 7,000, the percentage of missing data per SNP was of secondary importance, and it was usually better not to model parental origin of alleles. 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.