Genome-wide SNP-discovery and analysis of genetic diversity in oil palm using double digest restriction site associated DNA sequencing

Nugroho, Yogo Adhi; Tanjung, Zulfikar Achmad; Yono, Dwi; Mulyana, A S; Simbolon, H M; Ardi, A S; Yong, Yange; Utomo, Condro; Liwang, Tony

doi:10.1088/1755-1315/293/1/012041

Cited by 11 publications

(8 citation statements)

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“…Palm oil yield prediction is only indirectly addressed in a few ML-based studies conducted in the industry. So far, there have been five studies examining how well scientists can predict phenotype palm oil yields [56,87,141,150,151]. In light of these studies, it is difficult to determine which algorithm is best for predicting palm oil yields.…”

Section: Analysis and Discussionmentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

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Section: Analysis and Discussionmentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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“…The gDNA was submitted to Novogene Laboratory (Beijing, China) for preparation of ddRAD-seq libraries and the Illumina HiSeq PE150 sequencing. Two restriction enzymes, EcoRI and MseI were used for DNA digestion (Nugroho et al 2019). The DNA fragments were then ligated by a barcode adaptor in both DNA ends.…”

Section: Snp Mining and Discoverymentioning

confidence: 99%

“…Several molecular markers like DNAs, RNAs, proteins, metabolites, enzymes, and hormones can be used for MAS. At DNA level, the single nucleotide polymorphism (SNP) variant is abundant in the genome (Nugroho et al 2019). SNP markers become more effective and efficient to assist the palm selection, through mass genotyping like fluorescence-based genotyping (Jatayev et al 2017) and targeted genotyping by sequencing (tGBS) technology (Ott et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Genomewide SNP marker identification associated with drought tolerance in oil palm

Yono¹,

Nugroho²,

Tanjung³

et al. 2021

Biodiversitas

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Abstract. Yono D, Nugroho YA, Tanjung ZA, Utomo C, Liwang T. 2021. Genomewide SNP marker identification associated with drought tolerance in oil palm. Biodiversitas 22: 3138-3144. Drought stress is one of the abiotic stresses that frequently occurred in the oil palm plantation and has a negative impact on fresh fruit bunch (FFB) production. Therefore, drought-tolerant palms are essential to be selected to mitigate this challenge. In Indonesia, several oil palm plantation areas have a dry climate, such as Lampung province. Distinct yield performance palms were identified from well-recorded agronomic trials in these areas, where the palms are frequently exposed to drought stresses every year and lead them to suffer from water deficit response. Group of high and low-yielding palms was selected based on FFB production of each palm for at least ten constitutive years. The double digest restriction amplified DNA (ddRAD) genotyping methods were used to capture the Single Nucleotide Polymorph (SNP) variant from pools of sample association datasets. At least, 538k SNPs were identified from these pooled datasets. A bulked segregant analysis with a Case-Control approach was implemented to screen the contrast SNP profiles between both pools. A total of 56 association signals was selected from sequential filtering. These SNP sites are located in 21 genes. Further SNP validation and phenotypic verification are necessary to obtain SNPs marker for drought-tolerant palm selections.

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“…With rapid development of molecular technology, genetic evaluation of oil palm populations has been conducted using various markers available, for example, isozymes [10], restriction fragment length polymorphism (RFLP) [11,12], amplified fragment length polymorphism (AFLP) [12], simple sequence repeats (SSR) [13][14][15][16][17][18], and single nucleotide polymorphism (SNP) [19]. The advancement of next-generation sequencing techniques and publication of oil palm genome sequence in 2013 [20] have further accelerated markers development, particularly high-density SNP markers, that are useful for genetic diversity studies of oil palm as well as quantitative trait loci (QTL) and genome-wide association studies [21,22]. Sequence data could be mapped to the oil palm reference genome for SNP calling and identification of genome distribution of markers.…”

Section: Introductionmentioning

confidence: 99%

Assessment of genetic diversity and population structure of oil palm (Elaeis guineensis Jacq.) field genebank: A step towards molecular-assisted germplasm conservation

Gan¹,

Teo²,

Manirasa³

et al. 2021

PLoS ONE

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Oil palm (Elaeis guineensis) germplasm is exclusively maintained as ex situ living collections in the field for genetic conservation and evaluation. However, this is not for long term and the maintenance of field genebanks is expensive and challenging. Large area of land is required and the germplasms are exposed to extreme weather conditions and casualty from pests and diseases. By using 107 SSR markers, this study aimed to examine the genetic diversity and relatedness of 186 palms from a Nigerian-based oil palm germplasm and to identify core collection for conservation. On average, 8.67 alleles per SSR locus were scored with average effective number of alleles per population ranging from 1.96 to 3.34 and private alleles were detected in all populations. Mean expected heterozygosity was 0.576 ranging from 0.437 to 0.661 and the Wright’s fixation index calculated was -0.110. Overall moderate genetic differentiation among populations was detected (mean pairwise population FST = 0.120, gene flow Nm = 1.117 and Nei’s genetic distance = 0.466) and this was further confirmed by AMOVA analysis. UPGMA dendogram and Bayesian structure analysis concomitantly clustered the 12 populations into eight genetic groups. The best core collection assembled by Core Hunter ver. 3.2.1 consisted of 58 palms accounting for 31.2% of the original population, which was a smaller core set than using PowerCore 1.0. This core set attained perfect allelic coverage with good representation, high genetic distance between entries, and maintained genetic diversity and structure of the germplasm. This study reported the first molecular characterization and validation of core collections for oil palm field genebank. The established core collection via molecular approach, which captures maximum genetic diversity with minimum redundancy, would allow effective use of genetic resources for introgression and for sustainable oil palm germplasm conservation. The way forward to efficiently conserve the field genebanks into next generation without losing their diversity was further discussed.

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Genome-wide SNP-discovery and analysis of genetic diversity in oil palm using double digest restriction site associated DNA sequencing

Cited by 11 publications

References 0 publications

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Genomewide SNP marker identification associated with drought tolerance in oil palm

Assessment of genetic diversity and population structure of oil palm (Elaeis guineensis Jacq.) field genebank: A step towards molecular-assisted germplasm conservation

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