Methods: First, studies of codon usage in monocots were reviewed. The current information was then extended regarding codon usage, as well as codon-pair context bias, using four completely sequenced non-grass monocot genomes (Musa acuminata, Musa balbisiana, Phoenix dactylifera and Spirodela polyrhiza) for which comparable transcriptome datasets are available. Measurements were taken regarding relative synonymous codon usage, effective number of codons, derived optimal codon and GC content and then the relationships investigated to infer the underlying evolutionary forces.
Key Results: The research identified optimal codons, rare codons and preferred codon-pair context in the non-grass monocot species studied. In contrast to the bimodal distribution of GC3 (GC content in third codon position) in grasses, non-grass monocots showed a unimodal distribution. Disproportionate use of G and C (and of A and T) in two- and four-codon amino acids detected in the analysis rules out the mutational bias hypothesis as an explanation of genomic variation in GC content. There was found to be a positive relationship between CAI (codon adaptation index; predicts the level of expression of a gene) and GC3. In addition, a strong correlation was observed between coding and genomic GC content and negative correlation of GC3 with gene length, indicating a strong impact of GC-biased gene conversion (gBGC) in shaping codon usage and nucleotide composition in non-grass monocots.
Conclusion: Optimal codons in these non-grass monocots show a preference for G/C in the third codon position. These results support the concept that codon usage and nucleotide composition in non-grass monocots are mainly driven by gBGC.
RESULTS: Here, we analyzed genetic data of 230 B. flabellifer accessions across Thailand using 17 EST-SSR and 12 gSSR polymorphic markers. Clustering analysis revealed that the population consisted of two genetic clusters (STRUCTURE K = 2). Cluster I is found mainly in southern Thailand, while Cluster II is found mainly in the northeastern. Those found in the central are of an extensive mix between the two. These two clusters are in moderate differentiation (F ST = 0.066 and N M = 3.532) and have low genetic diversity (HO = 0.371 and 0.416; AR = 2.99 and 3.19, for the cluster I and II respectively). The minimum numbers of founders for each genetic group varies from 3 to 4 individuals, based on simulation using different allele frequency assumptions. These numbers coincide with that B. flabellifer is dioecious, and a number of seeds had to be simultaneously introduced for obtaining both male and female founders.
CONCLUSIONS: From these data and geographical and historical evidence, we hypothesize that there were at least two different invasive events of B. flabellifer in Thailand. B. flabellifer was likely brought through the Straits of Malacca to be propagated in the southern Thailand as one of the invasive events before spreading to the central Thailand. The second event likely occurred in Khmer Empire, currently Cambodia, before spreading to the northeastern Thailand.
RESULTS: Our results indicate that the SHELL markers can theoretically reduce the major losses due to dura contamination of tenera planting material. However, these markers cannot distinguish illegitimate tenera, which reduces the value of having bred elite tenera for commercial planting and in the breeding programme, where fruit form is of limited utility, and incorrect identity could lead to significant problems. We propose an optimised approach using SNPs for routine quality control.
CONCLUSIONS: Both dura and tenera contamination can be identified and removed at or before the nursery stage. An optimised legitimacy assay using SNP markers coupled with a suitable sampling scheme is now ready to be deployed as a standard control for seed production and breeding in oil palm. The same approach will also be an effective solution for other perennial crops, such as coconut and date palm.
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.