Evaluation of methods and marker systems in genomic selection of oil palm (Elaeis guineensis Jacq.)

Kwong, Qi Bin and Teh, Chee Keng and Ong, Ai Ling and Chew, Fook Tim and Mayes, Sean and Kulaveerasingam, Harikrishna and Tammi, Martti and Yeoh, Suat Hui and Appleton, David Ross and Harikrishna, Jennifer Ann (2017) Evaluation of methods and marker systems in genomic selection of oil palm (Elaeis guineensis Jacq.). BMC Genetics, 18 (1). ISSN 1471-2156

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Abstract

Background

Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed dura family derived from a Deli dura x Nigerian dura (Deli x Nigerian) with 112 individuals. This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits.

Results

The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods.

Conclusion

Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.

Item Type: Article
Keywords: Genomic prediction, Complex traits, Machine learning, Predictive modeling, Marker-assisted selection, SSR, SNP, Perennial crop
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Biosciences
Identification Number: 10.1186/s12863-017-0576-5
Depositing User: Eprints, Support
Date Deposited: 08 Jan 2018 09:33
Last Modified: 09 Jan 2018 19:35
URI: http://eprints.nottingham.ac.uk/id/eprint/48972

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