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

Kwong, Qi Bin; Teh, Chee Keng; Ong, Ai Ling; Chew, Fook Tim; Mayes, Sean; Kulaveerasingam, Harikrishna; Tammi, Martti; Yeoh, Suat Hui; Appleton, David Ross; Harikrishna, Jennifer Ann

Authors

Qi Bin Kwong

Chee Keng Teh

Ai Ling Ong

Fook Tim Chew

Sean Mayes

Harikrishna Kulaveerasingam

Martti Tammi

Suat Hui Yeoh

David Ross Appleton

Jennifer Ann Harikrishna



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.

Journal Article Type Article
Publication Date Dec 11, 2017
Journal BMC Genetics
Electronic ISSN 1471-2156
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 18
Issue 1
APA6 Citation Kwong, Q. B., Teh, C. K., Ong, A. L., Chew, F. T., Mayes, S., Kulaveerasingam, H., …Harikrishna, J. A. (2017). Evaluation of methods and marker systems in genomic selection of oil palm (Elaeis guineensis Jacq.). BMC Genetics, 18(1), https://doi.org/10.1186/s12863-017-0576-5
DOI https://doi.org/10.1186/s12863-017-0576-5
Keywords Genomic prediction, Complex traits, Machine learning, Predictive modeling, Marker-assisted selection, SSR,
SNP, Perennial crop
Publisher URL https://bmcgenet.biomedcentral.com/articles/10.1186/s12863-017-0576-5
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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