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Fulltext Linkage-based genome assembly improvement of oil palm (Elaeis guineensis)

Ong AL, Teh CK, Kwong QB, Tangaya P, Appleton DR, Massawe F, et al.

Sci Rep, 2019 04 29;9(1):6619.
PMID: 31036825 DOI: 10.1038/s41598-019-42989-y

Meiotic crossovers in outbred species, such as oil palm (Elaeis guineensis Jacq., 2n = 32) contribute to allelic re-assortment in the genome. Such genetic variation is usually exploited in breeding to combine positive alleles for trait superiority. A good quality reference genome is essential for identifying the genetic factors underlying traits of interest through linkage or association studies. At the moment, an AVROS pisifera genome is publicly available for oil palm. Distribution and frequency of crossovers throughout chromosomes in different origins of oil palm are still unclear. Hence, an ultrahigh-density genomic linkage map of a commercial Deli dura x AVROS pisifera family was constructed using the OP200K SNP array, to evaluate the genetic alignment with the genome assembly. A total of 27,890 linked SNP markers generated a total map length of 1,151.7 cM and an average mapping interval of 0.04 cM. Nineteen linkage groups represented 16 pseudo-chromosomes of oil palm, with 61.7% of the mapped SNPs present in the published genome. Meanwhile, the physical map was also successfully extended from 658 Mb to 969 Mb by assigning unplaced scaffolds to the pseudo-chromosomes. A genic linkage map with major representation of sugar and lipid biosynthesis pathways was subsequently built for future studies on oil related quantitative trait loci (QTL). This study improves the current physical genome of the commercial oil palm, and provides important insights into its recombination landscape, eventually unlocking the full potential genome sequence-enabled biology for oil palm.
Fulltext Automated stomata detection in oil palm with convolutional neural network

Kwong QB, Wong YC, Lee PL, Sahaini MS, Kon YT, Kulaveerasingam H, et al.

Sci Rep, 2021 07 26;11(1):15210.
PMID: 34312480 DOI: 10.1038/s41598-021-94705-4

Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2-3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance.
Fulltext In-depth tanscriptomic analysis on giant freshwater prawns

Mohd-Shamsudin MI, Kang Y, Lili Z, Tan TT, Kwong QB, Liu H, et al.

PLoS One, 2013;8(5):e60839.
PMID: 23734171 DOI: 10.1371/journal.pone.0060839

Gene discovery in the Malaysian giant freshwater prawn (Macrobrachium rosenbergii) has been limited to small scale data collection, despite great interest in various research fields related to the commercial significance of this species. Next generation sequencing technologies that have been developed recently and enabled whole transcriptome sequencing (RNA-seq), have allowed generation of large scale functional genomics data sets in a shorter time than was previously possible. Using this technology, transcriptome sequencing of three tissue types: hepatopancreas, gill and muscle, has been undertaken to generate functional genomics data for M. rosenbergii at a massive scale. De novo assembly of 75-bp paired end Ilumina reads has generated 102,230 unigenes. Sequence homology search and in silico prediction have identified known and novel protein coding candidate genes (∼24%), non-coding RNA, and repetitive elements in the transcriptome. Potential markers consisting of simple sequence repeats associated with known protein coding genes have been successfully identified. Using KEGG pathway enrichment, differentially expressed genes in different tissues were systematically represented. The functions of gill and hepatopancreas in the context of neuroactive regulation, metabolism, reproduction, environmental stress and disease responses are described and support relevant experimental studies conducted previously in M. rosenbergii and other crustaceans. This large scale gene discovery represents the most extensive transcriptome data for freshwater prawn. Comparison with model organisms has paved the path to address the possible conserved biological entities shared between vertebrates and crustaceans. The functional genomics resources generated from this study provide the basis for constructing hypotheses for future molecular research in the freshwater shrimp.
Fulltext Genome-wide association study identifies three key loci for high mesocarp oil content in perennial crop oil palm

Teh CK, Ong AL, Kwong QB, Apparow S, Chew FT, Mayes S, et al.

Sci Rep, 2016;6:19075.
PMID: 26743827 DOI: 10.1038/srep19075

GWAS in out-crossing perennial crops is typically limited by insufficient marker density to account for population diversity and effects of population structure resulting in high false positive rates. The perennial crop oil palm is the most productive oil crop. We performed GWAS for oil-to-dry-mesocarp content (O/DM) on 2,045 genotyped tenera palms using 200K SNPs that were selected based on the short-range linkage disequilibrium distance, which is inherent with long breeding cycles and heterogeneous breeding populations. Eighty loci were significantly associated with O/DM (p ≤ 10(-4)) and three key signals were found. We then evaluated the progeny of a Deli x AVROS breeding trial and a 4% higher O/DM was observed amongst those having the beneficial genotypes at two of the three key loci (p < 0.05). We have initiated MAS and large-scale planting of elite dura and pisifera parents to generate the new commercial tenera palms with higher O/DM potential.
Fulltext Genomic Selection in Commercial Perennial Crops: Applicability and Improvement in Oil Palm (Elaeis guineensis Jacq.)

Kwong QB, Ong AL, Teh CK, Chew FT, Tammi M, Mayes S, et al.

Sci Rep, 2017 06 06;7(1):2872.
PMID: 28588233 DOI: 10.1038/s41598-017-02602-6

Genomic selection (GS) uses genome-wide markers to select individuals with the desired overall combination of breeding traits. A total of 1,218 individuals from a commercial population of Ulu Remis x AVROS (UR x AVROS) were genotyped using the OP200K array. The traits of interest included: shell-to-fruit ratio (S/F, %), mesocarp-to-fruit ratio (M/F, %), kernel-to-fruit ratio (K/F, %), fruit per bunch (F/B, %), oil per bunch (O/B, %) and oil per palm (O/P, kg/palm/year). Genomic heritabilities of these traits were estimated to be in the range of 0.40 to 0.80. GS methods assessed were RR-BLUP, Bayes A (BA), Cπ (BC), Lasso (BL) and Ridge Regression (BRR). All methods resulted in almost equal prediction accuracy. The accuracy achieved ranged from 0.40 to 0.70, correlating with the heritability of traits. By selecting the most important markers, RR-BLUP B has the potential to outperform other methods. The marker density for certain traits can be further reduced based on the linkage disequilibrium (LD). Together with in silico breeding, GS is now being used in oil palm breeding programs to hasten parental palm selection.
Fulltext Evaluation of methods and marker Systems in Genomic Selection of oil palm (Elaeis guineensis Jacq.)

Kwong QB, Teh CK, Ong AL, Chew FT, Mayes S, Kulaveerasingam H, et al.

BMC Genet, 2017 Dec 11;18(1):107.
PMID: 29228905 DOI: 10.1186/s12863-017-0576-5

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.
Hormones, polyamines, and cell wall metabolism during oil palm fruit mesocarp development and ripening

Teh HF, Neoh BK, Wong YC, Kwong QB, Ooi TE, Ng TL, et al.

J Agric Food Chem, 2014 Aug 13;62(32):8143-52.
PMID: 25032485 DOI: 10.1021/jf500975h

Oil palm is one of the most productive oil-producing crops and can store up to 90% oil in its fruit mesocarp. Oil palm fruit is a sessile drupe consisting of a fleshy mesocarp from which palm oil is extracted. Biochemical changes in the mesocarp cell walls, polyamines, and hormones at different ripening stages of oil palm fruits were studied, and the relationship between the structural and the biochemical metabolism of oil palm fruits during ripening is discussed. Time-course analysis of the changes in expression of polyamines, hormones, and cell-wall-related genes and metabolites provided insights into the complex processes and interactions involved in fruit development. Overall, a strong reduction in auxin-responsive gene expression was observed from 18 to 22 weeks after pollination. High polyamine concentrations coincided with fruit enlargement during lipid accumulation and latter stages of maturation. The trend of abscisic acid (ABA) concentration was concordant with GA₄ but opposite to the GA₃ profile such that as ABA levels increase the resulting elevated ABA/GA₃ ratio clearly coincides with maturation. Polygalacturonase, expansin, and actin gene expressions were also observed to increase during fruit maturation. The identification of the master regulators of these coordinated processes may allow screening for oil palm variants with altered ripening profiles.
Fulltext Development and Validation of a High-Density SNP Genotyping Array for African Oil Palm

Kwong QB, Teh CK, Ong AL, Heng HY, Lee HL, Mohamed M, et al.

Mol Plant, 2016 Aug 01;9(8):1132-1141.
PMID: 27112659 DOI: 10.1016/j.molp.2016.04.010

High-density single nucleotide polymorphism (SNP) genotyping arrays are powerful tools that can measure the level of genetic polymorphism within a population. To develop a whole-genome SNP array for oil palms, SNP discovery was performed using deep resequencing of eight libraries derived from 132 Elaeis guineensis and Elaeis oleifera palms belonging to 59 origins, resulting in the discovery of >3 million putative SNPs. After SNP filtering, the Illumina OP200K custom array was built with 170 860 successful probes. Phenetic clustering analysis revealed that the array could distinguish between palms of different origins in a way consistent with pedigree records. Genome-wide linkage disequilibrium declined more slowly for the commercial populations (ranging from 120 kb at r(2) = 0.43 to 146 kb at r(2) = 0.50) when compared with the semi-wild populations (19.5 kb at r(2) = 0.22). Genetic fixation mapping comparing the semi-wild and commercial population identified 321 selective sweeps. A genome-wide association study (GWAS) detected a significant peak on chromosome 2 associated with the polygenic component of the shell thickness trait (based on the trait shell-to-fruit; S/F %) in tenera palms. Testing of a genomic selection model on the same trait resulted in good prediction accuracy (r = 0.65) with 42% of the S/F % variation explained. The first high-density SNP genotyping array for oil palm has been developed and shown to be robust for use in genetic studies and with potential for developing early trait prediction to shorten the oil palm breeding cycle.
Fulltext Differential gene expression at different stages of mesocarp development in high- and low-yielding oil palm

Wong YC, Teh HF, Mebus K, Ooi TEK, Kwong QB, Koo KL, et al.

BMC Genomics, 2017 06 21;18(1):470.
PMID: 28637447 DOI: 10.1186/s12864-017-3855-7

BACKGROUND: The oil yield trait of oil palm is expected to involve multiple genes, environmental influences and interactions. Many of the underlying mechanisms that contribute to oil yield are still poorly understood. In this study, we used a microarray approach to study the gene expression profiles of mesocarp tissue at different developmental stages, comparing genetically related high- and low- oil yielding palms to identify genes that contributed to the higher oil-yielding palm and might contribute to the wider genetic improvement of oil palm breeding populations.
RESULTS: A total of 3412 (2001 annotated) gene candidates were found to be significantly differentially expressed between high- and low-yielding palms at at least one of the different stages of mesocarp development evaluated. Gene Ontologies (GO) enrichment analysis identified 28 significantly enriched GO terms, including regulation of transcription, fatty acid biosynthesis and metabolic processes. These differentially expressed genes comprise several transcription factors, such as, bHLH, Dof zinc finger proteins and MADS box proteins. Several genes involved in glycolysis, TCA, and fatty acid biosynthesis pathways were also found up-regulated in high-yielding oil palm, among them; pyruvate dehydrogenase E1 component Subunit Beta (PDH), ATP-citrate lyase, β- ketoacyl-ACP synthases I (KAS I), β- ketoacyl-ACP synthases III (KAS III) and ketoacyl-ACP reductase (KAR). Sucrose metabolism-related genes such as Invertase, Sucrose Synthase 2 and Sucrose Phosphatase 2 were found to be down-regulated in high-yielding oil palms, compared to the lower yield palms.
CONCLUSIONS: Our findings indicate that a higher carbon flux (channeled through down-regulation of the Sucrose Synthase 2 pathway) was being utilized by up-regulated genes involved in glycolysis, TCA and fatty acid biosynthesis leading to enhanced oil production in the high-yielding oil palm. These findings are an important stepping stone to understand the processes that lead to production of high-yielding oil palms and have implications for breeding to maximize oil production.