Insights into callus induction by GWAS and development of SNP marker for donor plants selection in oil palm tissue culture

Insights into callus induction by GWAS and development of SNP marker for donor plants selection in oil palm tissue culture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Insights into callus induction by GWAS and development of SNP marker for donor plants selection in oil palm tissue culture Yin Min Htwe, Peng Shi, Dapeng Zhang, Zhiying Li, Qun Yu, Yong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3829704/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Efficient callus induction is vital for successful tissue culture of oil palm, yet identifying genomic loci and markers for early identification of specific individuals with high potential of callus induction is still unclear. In this study, callus induction rate at 1-, 2-, and 3-months after inoculation (C1, C2 and C3) of 198 oil palm individuals were investigated as phenotypes, and totally 11,475,258 high quality single nucleotide polymorphisms (SNPs) were generated by resequencing as genotypes. Genome-wide association study (GWAS) was performed accordingly using these phenotypes and genotypes. Correlation analysis revealed a positive association of C1 with both C2 (R = 0.81) and C3 (R = 0.50). Therefore, only SNPs in C1 were identified to develop markers for screening individuals capable of callus induction at early stage. A total of 21 significant SNPs were observed in C1, in which six of them on chromosome 12 (Chr12) potentially linked to callus induction were further revealed by the linkage disequilibrium (LD) block analysis. Totally 13 SNP markers from these six loci were tested accordingly and only the marker C-12 at locus Chr12_12704856 effectively distinguishing the GG allele, which showed the highest probability (69%) of callus induction. Moreover, the method for rapid SNP variant detection without electrophoresis was established via qPCR analysis. Notably, individuals S30 and S46, carrying the GG allele, consistently showed high callus induction rates (> 50%) from C1 to C3. Our findings facilitated marker-assisted selection for specific individuals with high potential of callus induction, thereby providing valuable assistance for donor plants selection in oil palm tissue culture. GWAS Allele SNPs Callus induction Marker-assisted selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Message Six loci potentially linked to callus induction was identified by GWAS and a SNP marker that can select the individuals with high potential of callus induction was developed. Introduction Oil palm ( Elaeis guineensis Jacq.) is one of the most important tropical oil crops in the world. With the growing global population, ensuring the sustainable development of the palm oil industry is crucial to meet the increasing demand for palm oil consumption (Wang et al. 2019 ). Somatic embryogenesis (SE) is an effective technique for the mass plant production, particularly in perennial crops like oil palm (Zhang et al. 2021 ). The successful development of somatic embryos relies on efficient callus induction, making it a critical step for subsequent somatic embryo development. Furthermore, efficient callus generation is vital for tissue culture propagation of plant species that are difficult to regenerate, as well as in the process of genetic engineering to achieve desired phenotypic traits (Tuskan et al. 2018 ). Recent research on maize has highlighted the genotype-dependent nature of embryonic callus induction (Liang et al. 2023 ). Several genes responsible for callus induction have been identified in numerous plant species. For instance, the WAK gene family in Chinese cabbage has shown potential involvement in callus cell growth and reproduction (Zhang et al. 2020a ). In Arabidopsis , LATERAL ORGAN BOUNDARIES DOMAIN ( LBD ) genes play a key role in callus induction (Fan et al. 2012 ). The AtbZIP59–LBD complex is crucial in regulating auxin-induced callus formation (Xu et al. 2018 ), while CYCLIN D3 ( CYCD3 ) is involved in wounding-induced callus formation (Ikeuchi et al. 2017 ). Moreover, NAC DOMAIN CONTAINING PROTEIN71 ( ANAC071 ) and AP2/ERF transcription factor RAP2.6L have been identified as essential regulators during wound-induced callus formation process in Arabidopsis (Ikeuchi et al. 2013 ). Studies in cotton have indicated that the WOX genes play a pivotal role in callus induction (Muhammad Tajo et al. 2022 ). Overexpression of ZmBBM2 promotes callus formation (Du et al. 2019 ), and ZmMYB138 transcription factor being another promoter of callus formation via GA signal transduction in maize (Ge et al. 2016 ). Additionally, ZmARF23 mediates callus induction by binding to the ZmSAUR15 promoter and enhancing its transcriptional expression (Liang et al. 2023 ). In Panax ginseng , silencing the PgWRKY6 gene reduced the rate of embryogenic callus induction (Yang et al. 2020 ). As genetic factors play a crucial role in callus induction and proliferation, identifying genes and regulatory elements responsible for controlling callus formation can provide valuable insights into developing in vitro systems for recalcitrant plant species especially in oil palm (Tuskan et al. 2018 ; Weckx et al. 2019 ). Genome-wide association study (GWAS) has emerged as an effective tool to investigate the associations between genotypes and phenotypes, enabling the identification of causal loci/genes (Alqudah et al. 2020 ). This approach has been widely adopted in studying various plant species (Yano et al. 2016 ; Alqudah et al. 2020 ; Guo et al. 2020 ; Cui et al. 2021 ; Zhao et al. 2021 ; Hu et al. 2022 ; Ma et al. 2023 ; Gudi et al. 2023 ), providing valuable insights into the genetic basis of agronomic traits. A GWAS identifies clusters of linked single nucleotide polymorphisms (SNPs) associated with the target trait, known as genomic risk loci, shedding light on the genetic foundations of the trait (Uffelmann et al. 2021 ). Previous studies have utilized GWAS to identify genetic factors associated with callus formation in various plant species, such as rose (Nguyen et al. 2020 ), rice(Zhang et al. 2019b ) and Populus trichocarpa (Tuskan et al. 2018 ). Furthermore, it has proven to be a valuable tool in identifying stress-tolerant maize inbred lines (Shikha et al. 2021 ). Through the application of GWAS, researchers have made significant progress in identifying associated SNPs and candidate genes related to target traits in various crops (Qu et al. 2017 ; Alqudah et al. 2020 ; Ahn et al. 2023 ). Moreover, the development of molecular markers linked to a trait of interest facilitates marker-assisted selection (MAS) during the early stages of plant development (Rahman et al. 2007 ). Extensive studies have successfully detected SNP markers for selecting desired traits (Hayashi et al. 2004 ; Sattarzadeh et al. 2006 ; Gaudet et al. 2007 ; Kim et al. 2022 ), supporting the effectiveness of MAS. However, genomic loci and effective markers related to callus induction in oil palm remains unclear and related studies is required. In this study, callus induction rates at 1-, 2-, and 3-months after inoculation (C1, C2 and C3) of 198 oil palm individuals were observed for phenotyping, along with genotyping by resequencing. GWAS was then conducted accordingly to identify SNP loci linked to callus induction. Subsequently, the SNP marker capable of distinguishing alleles with a high potential for callus induction was developed to facilitate donor plants selection at early stage. Materials and methods Plant materials Oil palm trees were planted in the National Germplasm Nursery for Tropical Palms in Wenchang, Hainan, China. A total of 198 oil palm trees around 12 years old were selected in the present study, and immature male inflorescence located at the 14th leaf of each individual was collected for callus induction. Sampling period was in the local drought season from March to September of the same year. Phenotyping The immature spikelets of the male inflorescences were used as explants. Callus induction was performed according to our previous method (Zhang et al. 2021 ). Briefly, 100 immature spikelets from each individual were cultured in Y3 medium supplemented with 4 g/L Phytagel (Solarbio, P8170), 30 g/L Sucrose (Solarbio, S8270), 2.5 g/L activated charcoal (Solarbio, C7261) and 120 mg/L of 2,4-dichlorophenoxyaceticacid (2,4-D) (Solarbio, D8100). After 1-, 2-, and 3-months inoculation (C1, C2 and C3), the callus induction rate of each individual was recorded using the formula: $$\text{C}\text{a}\text{l}\text{l}\text{u}\text{s} \text{i}\text{n}\text{d}\text{u}\text{c}\text{t}\text{i}\text{o}\text{n} \text{r}\text{a}\text{t}\text{e} \left(\text{%}\right) =\frac{Total number of explants formed callus}{Total number of explants cultured}\times 100$$ Subsequently, samples from C1, C2 and C3 of all 198 individuals were collected, immediately frozen in liquid nitrogen and stored at -80˚C for subsequent analyses. Genotyping The DNA extraction and purification was performed using the CTAB method according to the BGI (Beijing Genomics Institute) manufacturer’s protocol. The DNA quantity and quality were measured with a DS-11 Spectrophotometer (DeNovix) and agarose gel electrophoresis. Whole genome resequencing for association mapping was performed using whole genome sequencing library preparation method (DNBSEQ, BGI, China). Resequencing data was filtered using SOAPnuke software (v1.5.6) (Chen et al. 2018 ) with the following parameters: -n 0.01 -l 20 -q 0.3 --adaMR 0.25 --ada_trim --polyX 50. All clean reads were aligned against the oil palm reference genome (Genome assembly EG5) using BWA (v0.7.17-r1188) with ‘mem’ algorithm (Li and Durbin 2009 ), and the mapped results were sorted and filtered with SAMtools (v1.9) (Danecek et al. 2021 ). The SNP calling was performed with the software Genome Analysis Tool kit (v4.1.2.0) (McKenna et al. 2010 ) under default parameter values. After filtering SNPs with allele number > 2, missing data > 20%, minor allele frequency (MAF) < 5%, minor allele count (MAC) 80%, the remaining high-quality SNPs were retained for GWAS analysis. SNP density was plotted using R package CMplot (Yin et al. 2021 ). Population genetic analysis The ADMIXTURE (v1.3)(Alexander et al. 2009 ) program was used for genetic assignment using an unlinked set of SNPs. This unlinked SNP set was selected from filtered SNPs by removing SNPs with linkage disequilibrium (LD, r 2 ) above 0.2 using plink (v1.9) (Chang et al. 2015 ). The ADMIXTURE was run with the cross validation (CV) flag specifying from K = 1 to K = 9 clusters, and the one with lowest cross-validation error was chosen as the best K. The R package pophelper (v2.2.7)(Francis 2017 ) was used to generate the ancestry barplots. Principal component analysis (PCA)(Price et al. 2006 ) was performed based on filtered SNPs using GCTA (v1.92.2)(Yang et al. 2011b ) software. Pair-wise relationship matrix (kinship matrix) was calculated with all filtered SNPs using GCTA (v1.92.2) (Yang et al. 2011b ). The LD decay of whole population and each sub-population were analyzed using PopldDecay (v3.41)(Zhang et al. 2019a ) software suite base on filtered SNPs. Phylogenetic study was carried out using neighbor-joining (NJ) method in MEGA-X (Kumar et al. 2018 ), with 500 bootstraps. GWAS for callus induction The GWAS for callus induction trait was performed using Gemma software (v0.98.1) (Zhou and Stephens 2012 ) to implement the calculation of four models: General Linear Model (GLM), GLM with population structure (GLM-Q), Mixed linear model (MLM) with kinship matrix (MLM-K), and MLM with both population structure and kinship matrix (MLM-QK). The population structure matrix corresponding to the optimal K value of Admixture is used as the Q matrix of the corresponding model, and the inter-sample affinity matrix calculated by the GCTA software is used as the K matrix of the corresponding model. The R package ggplot2 (Wickham 2016 ) was used to visualize the Manhattan and quantile quantile (QQ) plots. Bonferroni correction threshold (p-value = 0.01/marker number or 0.05/marker number) was used to identify significant associations. Candidate genes located within the 50kb region upstream or downstream of significant associated makers were identified. All significant SNPs (P ≤ 0.5) obtained from MLM(Q + K) model GWAS were used to identify potential candidate genes associated with callus induction trait. The functional annotation information of candidate genes was obtained from National Center for Biotechnology Information Database (NCBI, https://www.ncbi.nlm.nih.gov/ ). The LD was visualized and haplotype blocks were constructed using the LDBlockShow software with the following parameter: -SeleVar 2 (Dong et al. 2021 ), and the correlation coefficient (R 2 ) was calculated to determine pairwise LD decay. Peak SNPs in LD regions were used to predict candidate genes. Subsequently, we performed comparative analyses of callus induction rate in individuals with different SNPs. Then, the probability of callus induction (PCI) for each haplotype was observed using the formula: $$\text{P}\text{r}\text{o}\text{b}\text{a}\text{b}\text{i}\text{l}\text{i}\text{t}\text{y} \text{o}\text{f} \text{c}\text{a}\text{l}\text{l}\text{u}\text{s} \text{i}\text{n}\text{d}\text{u}\text{c}\text{t}\text{i}\text{o}\text{n} \left(\text{%}\right) =\frac{Total number of individuals formed callus}{Total number ofindividuals}\times 100$$ Development of SNP primers Isolated DNA samples were further diluted to a working concentration of 100ng/µl with Tris-EDTA (TE) buffer and used for SNP marker analysis. Allele specific primers were designed using WASP: a Web-based Allele-Specific PCR assay designing tool ( https://bioinfo.biotec.or.th/WASP ) (Wangkumhang et al. 2007 ). To enhance reaction specificity, a mismatched artificial base pair consisting of C/T and G/A was added at the penultimate position from the 3´end (SNP site) (Table 1 ). This particular mismatch has a strong destabilizing effect, which helps to improve the efficiency of the amplification reaction (Wangkumhang et al. 2007 ). Amplification of SNP primers was performed on Biometra Thermal Cycler (Analytik Jena, Germany). The PCR reaction contained 1µL DNA, 10µL of 2X M5 HiPer plus Taq HiFi PCR mix (with blue dye) (Mei5 Biotechnology, Co., Ltd, Beijing, China) and 0.5µL each of 10µM forward and reverse primer in a final volume of 20µL. PCR parameters were as follows: initial denaturation of 95°C for 3 min, 32 cycles of amplification (94°C for 25s, 53°C for 25s and 72°C for 5s) and a final extension at 72°C for 5 min. Agarose gel electrophoresis was employed to detect the polymorphism assay of SNP primers. Table 1 Sequences of SNP primers used in this study Primer name Forward (5' to 3') Reverse (5' to 3') Product size (bp) C-1 GCCTTTGGCTTCATTCAGCC ACTGAAAGCAAGATGGAAGA 242 C-2 CCTTTGGCTTCATTCAGCC TCTGATGCAAATCACTGAAA 254 C-3 CTTTGGCTTCATTCAGTTGAG ACTGAAAGCAAGATGGAAGA 240 C-4 TGGCTTCATTCAGTTGAG ACTGAAAGCAAGATGGAAGA 237 C-5 GGCTTCATTCAGTTGGATTTCC TACCAAATAGGTAGAAGCCG 273 C-6 CTTCATTCAGTTGGATTTCC ACTGAAAGCAAGATGGAAGA 234 C-7 AGCTTGCTGTTGGAGTTCTA ATAATCAGATAATTCTGCACGGGAT 272 C-8 GTCGGTATGTGGAGAGTCAT TAATCAGATAATTCTGCACGGGAT 114 C-9 TCAAAGTTGTTCGGTTCACT ATAATCAGATAATTCTGCACTA 223 C-10 AGCTTGCTGTTGGAGTTCTA TAATCAGATAATTCTGCACTA 271 C-11 GCCCGTGCAGAATTATCTAG TGAAAGCAAGATGGAAGACT 211 C-12 CCCGTGCAGAATTATCTAA ACTGAAAGCAAGATGGAAGA 212 C-13 CCCGTGCAGAATTATCTAG ACTGAAAGCAAGATGGAAGA 212 Quantitative PCR (qPCR) analysis The qPCR analysis was performed on qTOWER3 G (Analytik Jena AG, Germany) under the following conditions: initial denaturation at 95˚C for 5min, followed by 40 cycles of denaturation process at 95˚C for 30s, annealing at 53˚C for 30s, and extension at 72 ˚C for 30s. Subsequently, the melting curve analysis was performed immediately at melting rate value of 5˚C/s, from 60–95 ˚C. Each reaction mixture contained 1µL of 100ng/µL DNA, 5µL MonAmp™ ChemoHS qPCR Mix, 0.2µL of each 10µM forward and reverse primer in a final volume of 10µL. The housekeeping gene Actin was used as an endogenous control. Results Phenotypic variation In this study, a total of 198 oil palm individuals were used, and callus induction rates were recorded after 1-,2-, and 3-months inoculation (C1, C2 and C3) (Fig. 1 a). The callus induction rate ranged from 0–86% in C1, 0–92% in C2 and 0–100% in C3. Notably, individuals S30 and S46 demonstrated consistently high induction rates, maintaining above 50% throughout all time points. This pronounced induction proficiency suggests a potential intrinsic cellular predisposition towards callus formation within these genotypes (Fig. 1 b). Pearson correlation coefficients delineating a strong positive relationship between early and later stages of callus induction (C1 with C2: R = 0.81; C1 with C3: R = 0.50) (Fig. 1 c). These correlations highlight the inherent stability of this trait over time. Genetic variation Totally 11,624,016 SNPs were generated by resequencing. After filtering low quality SNPs (minor allele frequency 20%, and heterozygosity > 80%), a total of 11,475,258 high quality SNPs were conserved for subsequent analysis. Additionally, the distribution of high-quality SNPs was investigated, and the results showed that high-quality SNPs were roughly evenly distributed in 16 chromosomes of oil palm (Fig. 2 ). This indicates that the SNPs used in the subsequent analysis represent a comprehensive coverage of the oil palm genome. Population structure and LD decay analysis A total of 11,475,258 high quality SNPs obtained after screening were utilized for population structure, PCA, and phylogenetic analysis. Dynamic changes in population structure were explored using different K values (K = 2–9) (Fig. S1 a), revealing the smallest cross-validation error (CV error) at K = 3, indicating the presence of three subpopulations among the 198 oil palm individuals (Fig. S1 b). Neighbour-joining, kinship and PCA analyses further demonstrated distant relationships among the majority of individuals (Fig. S1 c, d and e), indicating the collected individuals' diversity. These findings collectively supported the suitability of the selected population for GWAS analysis. Furthermore, LD decay analysis revealed that r 2 decreased to half of its maximum value at approximately 50kb (Fig. S1 f), suggesting that genes located within the 50kb region around SNPs could be potential candidate genes associated with callus induction. GWAS analysis To explore the genetic factors associated with callus induction, GWAS analysis was performed using four linear regression models: GLM, GLM-Q, MLM-K, and MLM-QK (Fig. S2 ). After generating QQ plots for each of the four GWAS models, the model that exhibited the best fit between the expected and observed values was selected as the optimal model for each trait. This model was then used in subsequent analyses. The Q + K mixed linear model (MLM) is widely recognized as the most popular method for GWAS (Wang and Xu 2019 ). Our findings confirm the suitability of the MLM-QK model for GWAS analysis (Fig. S2 ), consequently leading us to employ this model for further analyses. Analysis of SNP loci linked to callus induction As C1 showed positive association with both C2 (R = 0.81) and C3 (R = 0.50) (Fig. 1 c), only SNPs in C1 were identified to develop markers for screening individuals capable of callus induction at early stage. Results showed that a total of 21 high quality SNPs were significantly associated with C1. These SNPs are distributed across Chr2, Chr5, Chr6, Chr7, Chr8, Chr9, Chr12, Chr14 and Chr16 (Table S1 ). Furthermore, 35 promising candidate genes associated with C1 were identified (Table S2 ). The analysis showed that these SNPs contributed to a phenotypic variation (R 2 ) ranging from 11–22% (Table S1 ), with Chr12 contained largest number of high-quality SNPs (7 SNPs), including Chr12_12696848, Chr12_12704827, Chr12_12704830, Chr12_12704835, Chr12_12704836, Chr12_12704839, and Chr12_12704856 (Fig. 3 a, Table S1 ). The LD block analysis shows a high level of linkage relationship among six of these SNP loci, with the exception of Chr12_12696848 (Fig. 3 b). The allelic variants of those six SNPs are “T/C”, “A/G”, “T/C”, “C/A”, “C/T” and “A/G” (Table S1 ), and there are three haplotypes (Hap1, Hap2 and Hap3) in each SNP locus. Genotype analyses revealed that accessions carrying Hap1 were associated with low callus induction efficiency, while those with Hap3 demonstrated a higher potential for callus induction. Callus induction efficiency of the accessions with Hap2 was between those of Hap1 and Hap3 (Fig. 3 c). Further analysis revealed that all six loci were closely located at approximately 25kb downstream region of LOC105054851, which was annotated as wall-associated receptor kinase 2-like ( WAK2 ) (Fig. 3 d). The WAK genes were reported to play an important role in cell wall and callus formation (Zhang et al. 2020a ). Analysis of SNP primers for allele discrimination The allele discrimination efficiency of 13 developed SNP primers was assessed using 2% agarose gel. Among the developed primers, the one derived from SNP Chr12_12704856 (C-12) effectively differentiated the GG from both AG and AA alleles. In individuals carrying AG and AA alleles, a visible band of 212bp was amplified, while no band was observed in individuals with the GG allele (Fig. 4 a). Further analysis revealed variations in the probability of callus induction (PCI) across the different genotypes at this locus. The GG genotype exhibited a markedly higher PCI, with a rate of 69%, compared to AG and AA genotypes (Fig. 4 b). Notably, individuals S30 and S46, which consistently exhibited high callus induction rates (> 50%) from C1 to C3 (Fig. 1 b), possessed the GG allele. This indicates that the presence of a discriminative marker for the GG allele holds promising potential for enhanced callus induction in oil palm tissue culture. Melting curve analysis by qPCR is a powerful and reliable technique for variant scanning and genotyping (Wittwer 2009 ; Hung et al. 2011 ). Our previous study on date palm (Wang et al. 2020 ) utilized qPCR melting curve analysis to determine sex alleles (X and Y). This technique doesn’t need the electrophoresis steps and reduce the time. The entire process, including amplification and melting curve analysis, can be completed in a single run within a short period of time. Thus, in this study, qPCR analysis was performed to detect allelic variants of SNP locus Chr12_12704856. The results demonstrated successful amplification of the target samples using the C-12 marker (Fig. 5 a). Thereafter, the melting peaks corresponding to different alleles were determined. Results showed that the Tm value for AA and AG were 78.1˚C and 78˚C, respectively. For GG, the melt peaks appeared at temperature of 80.7˚C and 86.6˚C (Fig. 5 b). The efficiency of the qPCR assay is evident as it allows for rapid and precise genotyping without the need for traditional gel electrophoresis, streamlining the workflow and reducing analysis time significantly. Discussion Effective callus induction is pivotal for plant regeneration, especially for species like oil palm, where tissue culture recalcitrance presents a major bottleneck (Weckx et al. 2019 ; Zhang et al. 2020b ). Identifying molecular markers associated with callus induction can significantly advance oil palm propagation techniques. Despite the importance, relevant studies in this area remain sparse. Genome-wide association study (GWAS) has been widely used to identify genetic variants linked to agronomic traits across various plant species (Yano et al. 2016 ; Qu et al. 2017 ; Zhao et al. 2021 ; Kim et al. 2022 ; Li et al. 2022 ; Ahn et al. 2023 ). Similar marker-trait associations have been explored in plants like rose (Nguyen et al. 2020 ), Populus (Tuskan et al. 2018 ; Zhang et al. 2020b ), soybean (Yang et al. 2011a ), rice (Zhang et al. 2019b ) and maize (Ma et al. 2018 ) for callus induction, yet oil palm research is limited in this aspect. In our current study, callus induction rates at 1-, 2-, and 3-months after inoculation (C1, C2 and C3) in 198 oil palm individuals were investigated as phenotypes. A total of 11,475,258 high quality single nucleotide polymorphisms (SNPs) obtained through resequencing were employed as genotypes. Phylogenetic and population structure analyses revealed noticeable variations among the 198 oil palm individuals (Fig. S1 c, d, and e), indicating the diversity within the sampled population. These findings support the suitability of the selected population for conducting GWAS analysis. Subsequently, GWAS was conducted to explore the loci associated with callus induction in oil palm. The correlation coefficient analysis revealed a positive correlation of C1 with both C2 (R = 0.81) and C3 (R = 0.5) (Fig. 1 c). Since C1 is the early callus induction rate at one month, we focused on identifying significant SNPs (P < 0.05) only in C1 to develop markers for screening individuals capable of callus induction at early stage. By conducting GWAS, a total of 21 significant SNPs associated with C1 were identified (Table S1 ). Based on LD decay distance, genes located within the 50kb region around SNPs associated with callus induction were identified as potential candidate genes. Consequently, 35 promising candidate genes were identified in C1 (Table S2 ). Notably, six SNPs on chromosome 12 formed a high-linkage LD block (Fig. 3 b), near the gene encoding wall-associated receptor kinase 2-like (WAK2) (Fig. 3 d). The wall-associated kinase ( WAK ) gene, a subfamily of the receptor-like kinase (RLK) gene family, is known to be associated with the plant cell wall and plays a crucial role in cell expansion (Zhang et al. 2020a ). Previous study conducted on Nicotiana benthamiana have identified various cis-acting elements within the promoter regions of wall-associated kinases (WAKs) and WAK-like kinases (WAKLs). These cis-acting elements were found to be associated with phytohormone and/or stress responses (Zhong et al. 2023 ). This suggests that these genes play a role in coordinating plant responses to phytohormones and stress factors. Another study conducted on Chinese cabbage has suggested the potential importance of the WAK-like genes in callus formation (Zhang et al. 2020a ). Our finding supports the hypothesis that WAK2 likely play an important role in callus induction. Further research may provide insights into the specific mechanisms by which WAK2 regulate cell proliferation and differentiation during callus induction. Genotype analysis in this study revealed that the callus induction potential of Hap3 was higher than Hap1 and Hap2 (Figs. 3 c), suggesting that genotype plays a critical role in determining callus induction efficiency. Further analysis of allele discrimination using SNP primers showed that the marker (C-12) effectively distinguished GG allele (Fig. 4 a), which showed the highest probability (69%) of callus induction (Fig. 4 b). Furthermore, qPCR analysis provides evidence for the effectiveness of qPCR in rapidly detecting sequence variants without electrophoresis (Fig. 5 ). Among the tested oil palm individuals, S30 and S46 consistently showed high callus induction rates (> 50%) from C1 to C3. Further analysis revealed that these individuals possessed the GG allele. This evidence strongly suggests that the implementation of a highly efficient SNP marker that can identify the GG allele would greatly benefit oil palm tissue culture. Conclusions GWAS was applied to investigate the associations between SNPs and callus induction phenotypes in 198 oil palm individuals. A total of 21 significant SNPs were identified in C1, in which 6 SNPs potentially linked to callus induction were further revealed by LD block analysis. A total of 13 markers were then assessed accordingly, in which the marker C-12 from the locus Chr12_12704856 can identify the individuals with GG allele, which showed the highest probability (69%) of callus induction. The marker-assisted selection for specific individuals with high potential of callus induction will enhance the selection efficiency for donor plants in oil palm tissue culture. Abbreviations Chr, Chromosome; GLM: General lineral model; GWAS: Genome-wide association analysis; Hap: Haplotype; H: heterozygous allele; LD: Linkage disequilibrium; MAF: Minor allele frequency; MAS: Marker-assisted selection; MLM: Mixed linear model; PCA: Principal component analysis; PCI: Probability of callus induction; qPCR: Quantitative PCR; QQ: Quantile-quantile; SNP: Single nucleotide polymorphism; WAK2 : Wall-associated receptor kinase 2-like. Declarations Acknowledgements We are very grateful to the National Germplasm Nursery for Tropical Palms for providing plant materials. Authors contributions YW conceived and supervised the project. YMH and PS conducted the experiments and wrote the article draft. YMH, PS, DZ and YW contributed to the methodology, data collection and analysis. ZL and QY contributed to material preparation. DZ and YW revised the manuscript. All authors read and approved the final manuscript. Funding The research was supported by the National Natural Science Foundation of China (No. 32071740), the post-doc project of Hainan Yazhou Bay Seed Laboratory (No. B21Y10301/B22C10303), the National Key R&D Program of China (No. 2023YFD2200700) and the earmarked fund for CARS-14 (China Agriculture Research System-Specific Oilseed Crops). Data availability The data sets supporting the results of this article are included within the article and its additional files. Competing Interests The authors have no competing interests to disclose. 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Nat Genet 48:927–934. https://doi.org/10.1038/ng.3596 Yin L, Zhang H, Tang Z et al (2021) rMVP: A Memory-efficient, visualization-enhanced, and parallel-accelerated tool for Genome-wide association study. Genomics Proteom Bioinf 19:619–628. https://doi.org/10.1016/j.gpb.2020.10.007 Zhang B, Li P, Su T et al (2020a) Comprehensive analysis of wall-associated kinase genes and their expression under abiotic and biotic stress in chinese cabbage ( Brassica rapa ssp. pekinensis ). J Plant Growth Regul 39:72–86. https://doi.org/10.1007/s00344-019-09964-3 Zhang C, Dong SS, Xu JY et al (2019a) PopLDdecay: A fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35:1786–1788. https://doi.org/10.1093/bioinformatics/bty875 Zhang D, Shi P, Htwe YM et al (2021) Caffeate may play an important role in the somatic embryogenesis of oil palm ( Elaeis guineensis Jacq). 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BMC Plant Biol 23:146. https://doi.org/10.1186/s12870-023-04112-2 Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824. https://doi.org/10.1038/ng.2310 Supplementary Files Supplementaryfigures.docx TableS1.SNPsassociatedwithC1.xlsx Table S1 SNPs associated with C1. TableS2.ListofcandidategenesassociatedwithC1.xlsx Table S2 List of candidate genes associated with C1. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Jan, 2024 Reviewers invited by journal 03 Jan, 2024 Editor assigned by journal 03 Jan, 2024 First submitted to journal 02 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3829704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265206417,"identity":"22eee4b0-8915-4eaa-b09b-3d16713ea786","order_by":0,"name":"Yin Min Htwe","email":"","orcid":"","institution":"Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"Min","lastName":"Htwe","suffix":""},{"id":265206418,"identity":"4e98b5c4-bb0c-43a8-b5e0-9e689989e1a3","order_by":1,"name":"Peng Shi","email":"","orcid":"","institution":"Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Shi","suffix":""},{"id":265206419,"identity":"bf4bb09a-3719-4f95-aee7-3a54f48a99da","order_by":2,"name":"Dapeng Zhang","email":"","orcid":"","institution":"Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Dapeng","middleName":"","lastName":"Zhang","suffix":""},{"id":265206420,"identity":"ddcaa108-c6f0-4ad4-9582-5ee87f3211b1","order_by":3,"name":"Zhiying Li","email":"","orcid":"","institution":"Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Zhiying","middleName":"","lastName":"Li","suffix":""},{"id":265206421,"identity":"c233cdeb-124e-4e95-85d7-7ba418fb6ca1","order_by":4,"name":"Qun Yu","email":"","orcid":"","institution":"Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Qun","middleName":"","lastName":"Yu","suffix":""},{"id":265206422,"identity":"5bae187e-198c-4071-ac24-0a88e3b8c7ad","order_by":5,"name":"Yong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBAC+3YgkcBgQ4IWA2awljQGBjaStDAwHCZJC/PDBw/+nJfnn9/8+ANDzR3CWuyZ2YwNEttuG844xmYmwXDsGVEOM5NIbLidwHCMwYyBseEwMVrYv/9I+HMuQf4Y++cPRGrhMWNIYDuQYHCMx0CCWC3FEoltyYYbj+WUSSQcI0KLfXv7xo8//tjJyx0+vvnDhxoitKCCBFI1jIJRMApGwSjADgB9uzZcfSQauwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8473-5386","institution":"Chinese Academy of Tropical Agricultural Sciences Coconut Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-01-02 14:21:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3829704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3829704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49324656,"identity":"48b4938d-47a8-4b04-9d70-5f67916ea804","added_by":"auto","created_at":"2024-01-08 17:19:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2500508,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of callus induction efficiency in oil palm. \u003cstrong\u003e(a)\u003c/strong\u003e Callus induction observed in male inflorescences of oil palm individual S30 cultivated on Y3 medium, across three time points. \u0026nbsp;\u003cstrong\u003e(b) \u003c/strong\u003eBox plot showing variation and distribution of callus induction rates at 1-, 2-, and 3-months after inoculation (C1, C2 and C3). The threshold for high callus induction (\u0026gt;50%) is annotated to the left of the corresponding data points. 'S' denotes individual samples.\u003cstrong\u003e (c)\u003c/strong\u003e Scatter plots depicting the correlation analyses between the callus induction rates at successive time intervals, with Pearson correlation coefficients (R) indicating the strength and significance of the linear relationships (C1 with C2: R=0.81; C1 with C3: R=0.50).\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/daa3b49fe88071f342357458.jpg"},{"id":49325625,"identity":"abf828ec-3574-484e-833f-eaf9e5bf2264","added_by":"auto","created_at":"2024-01-08 17:27:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":732129,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SNPs on 16 chromosomes of oil palm plant.\u003cstrong\u003e \u003c/strong\u003eThe plot shows the number of SNPs within 0.5Mb window size.\u003cstrong\u003e \u003c/strong\u003eThe horizontal axis shows the chromosome length (Mb). The different colors depict SNP density (the number of SNPs per window), with green indicating lower densities and red indicating higher densities.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/9bef6bddcb230eeec01833c4.jpg"},{"id":49325627,"identity":"64f7f881-62dd-4f31-bf82-484b3a6cbfbf","added_by":"auto","created_at":"2024-01-08 17:27:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1531469,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and analyses of major loci associated with C1. \u003cstrong\u003e(a) \u003c/strong\u003eManhattan plots of the significant SNPs associated with C1. Each point represents a SNP. Dotted blue line indicates the significant threshold of the P-value (0.05), while dotted red line indicates the significant threshold of the P-value (0.01). \u003cstrong\u003e(b)\u003c/strong\u003e Detailed plots selected from representative GWAS results in region 12.697–12.707 Mb on chromosome 12 (x-axis). Significant SNPs are marked with red dots. The linkage disequilibrium (LD) pattern around the peak SNP (red dot) is depicted below the regional plot, with red shading indicating high LD (R² value) between SNPs. \u003cstrong\u003e(c)\u003c/strong\u003e Haplotypes (Hap1, Hap2, Hap3) observed in 198 individuals using the six SNPs.\u003cstrong\u003e (d)\u003c/strong\u003e Genomic location of six SNP loci and candidate gene surrounding SNPs on Chr12. Exons (coding sequences, CDS) are represented by orange boxes, untranslated regions (UTRs) by blue boxes, and introns by lines. The SNP position is marked by dashed lines, and the direction of gene transcription is indicated by arrows.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/cc8c078229d9cc7046d72b13.jpg"},{"id":49324654,"identity":"a2fc7fd8-47ee-40ae-9055-06e9e1dd521f","added_by":"auto","created_at":"2024-01-08 17:19:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":532135,"visible":true,"origin":"","legend":"\u003cp\u003eAllele specific discrimination and correlated callus induction probabilities. \u003cstrong\u003e(a)\u003c/strong\u003e Gel electrophoresis results for SNP primer-based allele discrimination. The primer derived from SNP Chr12_12704856 (C-12) successfully differentiated GG from AG and AA in a 2% agarose gel. GG show no band, while both AG and AA present a clear 212 bp band, where M represents 2kb DNA ladder. \u003cstrong\u003e(b)\u003c/strong\u003e Correlation between genotypes and probability of callus induction (PCI). The GG genotype is associated with the highest PCI at 69%, followed by AG at 25% and AA at 20%.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/4814752889d3e173a32ac916.jpg"},{"id":49324660,"identity":"a45462a9-5ee6-45a5-b48f-05de756b30d2","added_by":"auto","created_at":"2024-01-08 17:19:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":635648,"visible":true,"origin":"","legend":"\u003cp\u003eDiscrimination of SNP alleles by\u003cstrong\u003e \u003c/strong\u003eqPCR analysis.\u003cstrong\u003e(a) \u003c/strong\u003eAmplification curve showing the amplification profiles of different genotypes at SNP locus Chr12_12704856. Each genotype (GG, AG, and AA) exhibits a characteristic curve with distinct plateau phases, indicating the specificity of the amplification. \u003cstrong\u003e(b) \u003c/strong\u003eMelting curve analysis showing the clear genotype differentiation by distinct melt peaks. The GG genotype show higher melting temperature peaks at 80.7˚C and 86.6˚C, while the AG genotype has a peak at 78˚C, and the AA genotype has a peak at 78.1˚C. These peaks correspond to the dissociation characteristics of each DNA duplex, confirming the allele-specific melting properties.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/0aa6956c3420fb13d65a726e.jpg"},{"id":49327091,"identity":"bbd6b2d3-2183-4771-bec3-e9c0a23ba30d","added_by":"auto","created_at":"2024-01-08 17:35:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":919018,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/0ecdbd6e-d957-452c-ae1e-785f1950a26d.pdf"},{"id":49325626,"identity":"b2ce4bfa-b345-44f3-b2b8-cc368d1b58c5","added_by":"auto","created_at":"2024-01-08 17:27:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":498507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/6b8d8770f00e4003fcf2dc0e.docx"},{"id":49324653,"identity":"f2d05780-974b-4ebc-8bd9-de58c636a132","added_by":"auto","created_at":"2024-01-08 17:19:49","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e SNPs associated with C1.\u003c/p\u003e","description":"","filename":"TableS1.SNPsassociatedwithC1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/f542fdcb536ff480d2a65bcc.xlsx"},{"id":49324658,"identity":"fca4a17c-f0ac-4e02-95d5-6f9e08117cdd","added_by":"auto","created_at":"2024-01-08 17:19:49","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2 \u003c/strong\u003eList of candidate genes associated with C1.\u003c/p\u003e","description":"","filename":"TableS2.ListofcandidategenesassociatedwithC1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3829704/v1/c2d3380754a9c8e95509d406.xlsx"}],"financialInterests":"","formattedTitle":"Insights into callus induction by GWAS and development of SNP marker for donor plants selection in oil palm tissue culture","fulltext":[{"header":"Key Message","content":"\u003cp\u003e\u003cstrong\u003eSix loci potentially linked to callus induction was identified by GWAS and a SNP marker that can select the individuals with high potential of callus induction was developed.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eOil palm (\u003cem\u003eElaeis guineensis\u003c/em\u003e Jacq.) is one of the most important tropical oil crops in the world. With the growing global population, ensuring the sustainable development of the palm oil industry is crucial to meet the increasing demand for palm oil consumption (Wang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSomatic embryogenesis (SE) is an effective technique for the mass plant production, particularly in perennial crops like oil palm (Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The successful development of somatic embryos relies on efficient callus induction, making it a critical step for subsequent somatic embryo development. Furthermore, efficient callus generation is vital for tissue culture propagation of plant species that are difficult to regenerate, as well as in the process of genetic engineering to achieve desired phenotypic traits (Tuskan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent research on maize has highlighted the genotype-dependent nature of embryonic callus induction (Liang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several genes responsible for callus induction have been identified in numerous plant species. For instance, the \u003cem\u003eWAK\u003c/em\u003e gene family in Chinese cabbage has shown potential involvement in callus cell growth and reproduction (Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). In \u003cem\u003eArabidopsis\u003c/em\u003e, LATERAL ORGAN BOUNDARIES DOMAIN (\u003cem\u003eLBD\u003c/em\u003e) genes play a key role in callus induction (Fan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The \u003cem\u003eAtbZIP59\u0026ndash;LBD\u003c/em\u003e complex is crucial in regulating auxin-induced callus formation (Xu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while CYCLIN D3 (\u003cem\u003eCYCD3\u003c/em\u003e) is involved in wounding-induced callus formation (Ikeuchi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, NAC DOMAIN CONTAINING PROTEIN71 (\u003cem\u003eANAC071\u003c/em\u003e) and AP2/ERF transcription factor \u003cem\u003eRAP2.6L\u003c/em\u003e have been identified as essential regulators during wound-induced callus formation process in \u003cem\u003eArabidopsis\u003c/em\u003e (Ikeuchi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies in cotton have indicated that the \u003cem\u003eWOX\u003c/em\u003e genes play a pivotal role in callus induction (Muhammad Tajo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overexpression of \u003cem\u003eZmBBM2\u003c/em\u003e promotes callus formation (Du et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and \u003cem\u003eZmMYB138\u003c/em\u003e transcription factor being another promoter of callus formation via GA signal transduction in maize (Ge et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, \u003cem\u003eZmARF23\u003c/em\u003e mediates callus induction by binding to the \u003cem\u003eZmSAUR15\u003c/em\u003e promoter and enhancing its transcriptional expression (Liang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In \u003cem\u003ePanax ginseng\u003c/em\u003e, silencing the \u003cem\u003ePgWRKY6\u003c/em\u003e gene reduced the rate of embryogenic callus induction (Yang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As genetic factors play a crucial role in callus induction and proliferation, identifying genes and regulatory elements responsible for controlling callus formation can provide valuable insights into developing \u003cem\u003ein vitro\u003c/em\u003e systems for recalcitrant plant species especially in oil palm (Tuskan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Weckx et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenome-wide association study (GWAS) has emerged as an effective tool to investigate the associations between genotypes and phenotypes, enabling the identification of causal loci/genes (Alqudah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This approach has been widely adopted in studying various plant species (Yano et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alqudah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gudi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), providing valuable insights into the genetic basis of agronomic traits. A GWAS identifies clusters of linked single nucleotide polymorphisms (SNPs) associated with the target trait, known as genomic risk loci, shedding light on the genetic foundations of the trait (Uffelmann et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies have utilized GWAS to identify genetic factors associated with callus formation in various plant species, such as rose (Nguyen et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), rice(Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) and \u003cem\u003ePopulus trichocarpa\u003c/em\u003e (Tuskan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, it has proven to be a valuable tool in identifying stress-tolerant maize inbred lines (Shikha et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Through the application of GWAS, researchers have made significant progress in identifying associated SNPs and candidate genes related to target traits in various crops (Qu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alqudah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ahn et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the development of molecular markers linked to a trait of interest facilitates marker-assisted selection (MAS) during the early stages of plant development (Rahman et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Extensive studies have successfully detected SNP markers for selecting desired traits (Hayashi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sattarzadeh et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gaudet et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), supporting the effectiveness of MAS. However, genomic loci and effective markers related to callus induction in oil palm remains unclear and related studies is required.\u003c/p\u003e \u003cp\u003eIn this study, callus induction rates at 1-, 2-, and 3-months after inoculation (C1, C2 and C3) of 198 oil palm individuals were observed for phenotyping, along with genotyping by resequencing. GWAS was then conducted accordingly to identify SNP loci linked to callus induction. Subsequently, the SNP marker capable of distinguishing alleles with a high potential for callus induction was developed to facilitate donor plants selection at early stage.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eOil palm trees were planted in the National Germplasm Nursery for Tropical Palms in Wenchang, Hainan, China. A total of 198 oil palm trees around 12 years old were selected in the present study, and immature male inflorescence located at the 14th leaf of each individual was collected for callus induction. Sampling period was in the local drought season from March to September of the same year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotyping\u003c/h2\u003e \u003cp\u003eThe immature spikelets of the male inflorescences were used as explants. Callus induction was performed according to our previous method (Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Briefly, 100 immature spikelets from each individual were cultured in Y3 medium supplemented with 4 g/L Phytagel (Solarbio, P8170), 30 g/L Sucrose (Solarbio, S8270), 2.5 g/L activated charcoal (Solarbio, C7261) and 120 mg/L of 2,4-dichlorophenoxyaceticacid (2,4-D) (Solarbio, D8100). After 1-, 2-, and 3-months inoculation (C1, C2 and C3), the callus induction rate of each individual was recorded using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{C}\\text{a}\\text{l}\\text{l}\\text{u}\\text{s} \\text{i}\\text{n}\\text{d}\\text{u}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n} \\text{r}\\text{a}\\text{t}\\text{e} \\left(\\text{%}\\right) =\\frac{Total number of explants formed callus}{Total number of explants cultured}\\times 100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSubsequently, samples from C1, C2 and C3 of all 198 individuals were collected, immediately frozen in liquid nitrogen and stored at -80˚C for subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eThe DNA extraction and purification was performed using the CTAB method according to the BGI (Beijing Genomics Institute) manufacturer\u0026rsquo;s protocol. The DNA quantity and quality were measured with a DS-11 Spectrophotometer (DeNovix) and agarose gel electrophoresis. Whole genome resequencing for association mapping was performed using whole genome sequencing library preparation method (DNBSEQ, BGI, China). Resequencing data was filtered using SOAPnuke software (v1.5.6) (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) with the following parameters: -n 0.01 -l 20 -q 0.3 --adaMR 0.25 --ada_trim --polyX 50. All clean reads were aligned against the oil palm reference genome (Genome assembly EG5) using BWA (v0.7.17-r1188) with \u0026lsquo;mem\u0026rsquo; algorithm (Li and Durbin \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the mapped results were sorted and filtered with SAMtools (v1.9) (Danecek et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SNP calling was performed with the software Genome Analysis Tool kit (v4.1.2.0) (McKenna et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) under default parameter values. After filtering SNPs with allele number\u0026thinsp;\u0026gt;\u0026thinsp;2, missing data\u0026thinsp;\u0026gt;\u0026thinsp;20%, minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;5%, minor allele count (MAC)\u0026thinsp;\u0026lt;\u0026thinsp;3 and heterozygosity\u0026thinsp;\u0026gt;\u0026thinsp;80%, the remaining high-quality SNPs were retained for GWAS analysis. SNP density was plotted using R package CMplot (Yin et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genetic analysis\u003c/h2\u003e \u003cp\u003eThe ADMIXTURE (v1.3)(Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) program was used for genetic assignment using an unlinked set of SNPs. This unlinked SNP set was selected from filtered SNPs by removing SNPs with linkage disequilibrium (LD, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) above 0.2 using plink (v1.9) (Chang et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The ADMIXTURE was run with the cross validation (CV) flag specifying from K\u0026thinsp;=\u0026thinsp;1 to K\u0026thinsp;=\u0026thinsp;9 clusters, and the one with lowest cross-validation error was chosen as the best K. The R package pophelper (v2.2.7)(Francis \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was used to generate the ancestry barplots. Principal component analysis (PCA)(Price et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was performed based on filtered SNPs using GCTA (v1.92.2)(Yang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e) software. Pair-wise relationship matrix (kinship matrix) was calculated with all filtered SNPs using GCTA (v1.92.2) (Yang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). The LD decay of whole population and each sub-population were analyzed using PopldDecay (v3.41)(Zhang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) software suite base on filtered SNPs. Phylogenetic study was carried out using neighbor-joining (NJ) method in MEGA-X (Kumar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with 500 bootstraps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGWAS for callus induction\u003c/h2\u003e \u003cp\u003eThe GWAS for callus induction trait was performed using Gemma software (v0.98.1) (Zhou and Stephens \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to implement the calculation of four models: General Linear Model (GLM), GLM with population structure (GLM-Q), Mixed linear model (MLM) with kinship matrix (MLM-K), and MLM with both population structure and kinship matrix (MLM-QK). The population structure matrix corresponding to the optimal K value of Admixture is used as the Q matrix of the corresponding model, and the inter-sample affinity matrix calculated by the GCTA software is used as the K matrix of the corresponding model. The R package ggplot2 (Wickham \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was used to visualize the Manhattan and quantile quantile (QQ) plots.\u003c/p\u003e \u003cp\u003eBonferroni correction threshold (p-value\u0026thinsp;=\u0026thinsp;0.01/marker number or 0.05/marker number) was used to identify significant associations. Candidate genes located within the 50kb region upstream or downstream of significant associated makers were identified. All significant SNPs (P\u0026thinsp;\u0026le;\u0026thinsp;0.5) obtained from MLM(Q\u0026thinsp;+\u0026thinsp;K) model GWAS were used to identify potential candidate genes associated with callus induction trait. The functional annotation information of candidate genes was obtained from National Center for Biotechnology Information Database (NCBI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The LD was visualized and haplotype blocks were constructed using the LDBlockShow software with the following parameter: -SeleVar 2 (Dong et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) was calculated to determine pairwise LD decay. Peak SNPs in LD regions were used to predict candidate genes. Subsequently, we performed comparative analyses of callus induction rate in individuals with different SNPs. Then, the probability of callus induction (PCI) for each haplotype was observed using the formula:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{P}\\text{r}\\text{o}\\text{b}\\text{a}\\text{b}\\text{i}\\text{l}\\text{i}\\text{t}\\text{y} \\text{o}\\text{f} \\text{c}\\text{a}\\text{l}\\text{l}\\text{u}\\text{s} \\text{i}\\text{n}\\text{d}\\text{u}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n} \\left(\\text{%}\\right) =\\frac{Total number of individuals formed callus}{Total number ofindividuals}\\times 100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of SNP primers\u003c/h2\u003e \u003cp\u003eIsolated DNA samples were further diluted to a working concentration of 100ng/\u0026micro;l with Tris-EDTA (TE) buffer and used for SNP marker analysis. Allele specific primers were designed using WASP: a Web-based Allele-Specific PCR assay designing tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo.biotec.or.th/WASP\u003c/span\u003e\u003cspan address=\"https://bioinfo.biotec.or.th/WASP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Wangkumhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To enhance reaction specificity, a mismatched artificial base pair consisting of C/T and G/A was added at the penultimate position from the 3\u0026acute;end (SNP site) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This particular mismatch has a strong destabilizing effect, which helps to improve the efficiency of the amplification reaction (Wangkumhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Amplification of SNP primers was performed on Biometra Thermal Cycler (Analytik Jena, Germany). The PCR reaction contained 1\u0026micro;L DNA, 10\u0026micro;L of 2X M5 HiPer plus Taq HiFi PCR mix (with blue dye) (Mei5 Biotechnology, Co., Ltd, Beijing, China) and 0.5\u0026micro;L each of 10\u0026micro;M forward and reverse primer in a final volume of 20\u0026micro;L. PCR parameters were as follows: initial denaturation of 95\u0026deg;C for 3 min, 32 cycles of amplification (94\u0026deg;C for 25s, 53\u0026deg;C for 25s and 72\u0026deg;C for 5s) and a final extension at 72\u0026deg;C for 5 min. Agarose gel electrophoresis was employed to detect the polymorphism assay of SNP primers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of SNP primers used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer\u003c/p\u003e \u003cp\u003ename\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward (5' to 3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse (5' to 3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProduct size (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCTTTGGCTTCATTCAGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTGAAAGCAAGATGGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTTTGGCTTCATTCAGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCTGATGCAAATCACTGAAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTTTGGCTTCATTCAGTTGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTGAAAGCAAGATGGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGCTTCATTCAGTTGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTGAAAGCAAGATGGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGCTTCATTCAGTTGGATTTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACCAAATAGGTAGAAGCCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTTCATTCAGTTGGATTTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTGAAAGCAAGATGGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCTTGCTGTTGGAGTTCTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATAATCAGATAATTCTGCACGGGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTCGGTATGTGGAGAGTCAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAATCAGATAATTCTGCACGGGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCAAAGTTGTTCGGTTCACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATAATCAGATAATTCTGCACTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCTTGCTGTTGGAGTTCTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAATCAGATAATTCTGCACTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCCGTGCAGAATTATCTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGAAAGCAAGATGGAAGACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCGTGCAGAATTATCTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTGAAAGCAAGATGGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCGTGCAGAATTATCTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTGAAAGCAAGATGGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative PCR (qPCR) analysis\u003c/h2\u003e \u003cp\u003eThe qPCR analysis was performed on qTOWER3 G (Analytik Jena AG, Germany) under the following conditions: initial denaturation at 95˚C for 5min, followed by 40 cycles of denaturation process at 95˚C for 30s, annealing at 53˚C for 30s, and extension at 72 ˚C for 30s. Subsequently, the melting curve analysis was performed immediately at melting rate value of 5˚C/s, from 60\u0026ndash;95 ˚C. Each reaction mixture contained 1\u0026micro;L of 100ng/\u0026micro;L DNA, 5\u0026micro;L MonAmp\u0026trade; ChemoHS qPCR Mix, 0.2\u0026micro;L of each 10\u0026micro;M forward and reverse primer in a final volume of 10\u0026micro;L. The housekeeping gene Actin was used as an endogenous control.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation\u003c/h2\u003e \u003cp\u003eIn this study, a total of 198 oil palm individuals were used, and callus induction rates were recorded after 1-,2-, and 3-months inoculation (C1, C2 and C3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The callus induction rate ranged from 0\u0026ndash;86% in C1, 0\u0026ndash;92% in C2 and 0\u0026ndash;100% in C3. Notably, individuals S30 and S46 demonstrated consistently high induction rates, maintaining above 50% throughout all time points. This pronounced induction proficiency suggests a potential intrinsic cellular predisposition towards callus formation within these genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Pearson correlation coefficients delineating a strong positive relationship between early and later stages of callus induction (C1 with C2: R\u0026thinsp;=\u0026thinsp;0.81; C1 with C3: R\u0026thinsp;=\u0026thinsp;0.50) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). These correlations highlight the inherent stability of this trait over time.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic variation\u003c/h2\u003e \u003cp\u003eTotally 11,624,016 SNPs were generated by resequencing. After filtering low quality SNPs (minor allele frequency\u0026thinsp;\u0026lt;\u0026thinsp;5%, missing data\u0026thinsp;\u0026gt;\u0026thinsp;20%, and heterozygosity\u0026thinsp;\u0026gt;\u0026thinsp;80%), a total of 11,475,258 high quality SNPs were conserved for subsequent analysis. Additionally, the distribution of high-quality SNPs was investigated, and the results showed that high-quality SNPs were roughly evenly distributed in 16 chromosomes of oil palm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This indicates that the SNPs used in the subsequent analysis represent a comprehensive coverage of the oil palm genome.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure and LD decay analysis\u003c/h2\u003e \u003cp\u003eA total of 11,475,258 high quality SNPs obtained after screening were utilized for population structure, PCA, and phylogenetic analysis. Dynamic changes in population structure were explored using different K values (K\u0026thinsp;=\u0026thinsp;2\u0026ndash;9) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea), revealing the smallest cross-validation error (CV error) at K\u0026thinsp;=\u0026thinsp;3, indicating the presence of three subpopulations among the 198 oil palm individuals (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Neighbour-joining, kinship and PCA analyses further demonstrated distant relationships among the majority of individuals (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec, d and e), indicating the collected individuals' diversity. These findings collectively supported the suitability of the selected population for GWAS analysis. Furthermore, LD decay analysis revealed that r\u003csup\u003e2\u003c/sup\u003e decreased to half of its maximum value at approximately 50kb (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ef), suggesting that genes located within the 50kb region around SNPs could be potential candidate genes associated with callus induction.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGWAS analysis\u003c/h2\u003e \u003cp\u003eTo explore the genetic factors associated with callus induction, GWAS analysis was performed using four linear regression models: GLM, GLM-Q, MLM-K, and MLM-QK (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). After generating QQ plots for each of the four GWAS models, the model that exhibited the best fit between the expected and observed values was selected as the optimal model for each trait. This model was then used in subsequent analyses. The Q\u0026thinsp;+\u0026thinsp;K mixed linear model (MLM) is widely recognized as the most popular method for GWAS (Wang and Xu \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our findings confirm the suitability of the MLM-QK model for GWAS analysis (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), consequently leading us to employ this model for further analyses.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of SNP loci linked to callus induction\u003c/h2\u003e \u003cp\u003eAs C1 showed positive association with both C2 (R\u0026thinsp;=\u0026thinsp;0.81) and C3 (R\u0026thinsp;=\u0026thinsp;0.50) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), only SNPs in C1 were identified to develop markers for screening individuals capable of callus induction at early stage. Results showed that a total of 21 high quality SNPs were significantly associated with C1. These SNPs are distributed across Chr2, Chr5, Chr6, Chr7, Chr8, Chr9, Chr12, Chr14 and Chr16 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Furthermore, 35 promising candidate genes associated with C1 were identified (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The analysis showed that these SNPs contributed to a phenotypic variation (R\u003csup\u003e2\u003c/sup\u003e) ranging from 11\u0026ndash;22% (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), with Chr12 contained largest number of high-quality SNPs (7 SNPs), including Chr12_12696848, Chr12_12704827, Chr12_12704830, Chr12_12704835, Chr12_12704836, Chr12_12704839, and Chr12_12704856 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The LD block analysis shows a high level of linkage relationship among six of these SNP loci, with the exception of Chr12_12696848 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The allelic variants of those six SNPs are \u0026ldquo;T/C\u0026rdquo;, \u0026ldquo;A/G\u0026rdquo;, \u0026ldquo;T/C\u0026rdquo;, \u0026ldquo;C/A\u0026rdquo;, \u0026ldquo;C/T\u0026rdquo; and \u0026ldquo;A/G\u0026rdquo; (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and there are three haplotypes (Hap1, Hap2 and Hap3) in each SNP locus. Genotype analyses revealed that accessions carrying Hap1 were associated with low callus induction efficiency, while those with Hap3 demonstrated a higher potential for callus induction. Callus induction efficiency of the accessions with Hap2 was between those of Hap1 and Hap3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Further analysis revealed that all six loci were closely located at approximately 25kb downstream region of LOC105054851, which was annotated as wall-associated receptor kinase 2-like (\u003cem\u003eWAK2\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The \u003cem\u003eWAK\u003c/em\u003e genes were reported to play an important role in cell wall and callus formation (Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of SNP primers for allele discrimination\u003c/h2\u003e \u003cp\u003eThe allele discrimination efficiency of 13 developed SNP primers was assessed using 2% agarose gel. Among the developed primers, the one derived from SNP Chr12_12704856 (C-12) effectively differentiated the GG from both AG and AA alleles. In individuals carrying AG and AA alleles, a visible band of 212bp was amplified, while no band was observed in individuals with the GG allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Further analysis revealed variations in the probability of callus induction (PCI) across the different genotypes at this locus. The GG genotype exhibited a markedly higher PCI, with a rate of 69%, compared to AG and AA genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Notably, individuals S30 and S46, which consistently exhibited high callus induction rates (\u0026gt;\u0026thinsp;50%) from C1 to C3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), possessed the GG allele. This indicates that the presence of a discriminative marker for the GG allele holds promising potential for enhanced callus induction in oil palm tissue culture.\u003c/p\u003e\u003cp\u003eMelting curve analysis by qPCR is a powerful and reliable technique for variant scanning and genotyping (Wittwer \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hung et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our previous study on date palm (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) utilized qPCR melting curve analysis to determine sex alleles (X and Y). This technique doesn\u0026rsquo;t need the electrophoresis steps and reduce the time. The entire process, including amplification and melting curve analysis, can be completed in a single run within a short period of time. Thus, in this study, qPCR analysis was performed to detect allelic variants of SNP locus Chr12_12704856. The results demonstrated successful amplification of the target samples using the C-12 marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Thereafter, the melting peaks corresponding to different alleles were determined. Results showed that the Tm value for AA and AG were 78.1˚C and 78˚C, respectively. For GG, the melt peaks appeared at temperature of 80.7˚C and 86.6˚C (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The efficiency of the qPCR assay is evident as it allows for rapid and precise genotyping without the need for traditional gel electrophoresis, streamlining the workflow and reducing analysis time significantly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEffective callus induction is pivotal for plant regeneration, especially for species like oil palm, where tissue culture recalcitrance presents a major bottleneck (Weckx et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Identifying molecular markers associated with callus induction can significantly advance oil palm propagation techniques. Despite the importance, relevant studies in this area remain sparse.\u003c/p\u003e \u003cp\u003eGenome-wide association study (GWAS) has been widely used to identify genetic variants linked to agronomic traits across various plant species (Yano et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Qu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ahn et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar marker-trait associations have been explored in plants like rose (Nguyen et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003ePopulus\u003c/em\u003e (Tuskan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), soybean (Yang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e), rice (Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) and maize (Ma et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for callus induction, yet oil palm research is limited in this aspect. In our current study, callus induction rates at 1-, 2-, and 3-months after inoculation (C1, C2 and C3) in 198 oil palm individuals were investigated as phenotypes. A total of 11,475,258 high quality single nucleotide polymorphisms (SNPs) obtained through resequencing were employed as genotypes. Phylogenetic and population structure analyses revealed noticeable variations among the 198 oil palm individuals (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec, d, and e), indicating the diversity within the sampled population. These findings support the suitability of the selected population for conducting GWAS analysis. Subsequently, GWAS was conducted to explore the loci associated with callus induction in oil palm. The correlation coefficient analysis revealed a positive correlation of C1 with both C2 (R\u0026thinsp;=\u0026thinsp;0.81) and C3 (R\u0026thinsp;=\u0026thinsp;0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Since C1 is the early callus induction rate at one month, we focused on identifying significant SNPs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) only in C1 to develop markers for screening individuals capable of callus induction at early stage. By conducting GWAS, a total of 21 significant SNPs associated with C1 were identified (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Based on LD decay distance, genes located within the 50kb region around SNPs associated with callus induction were identified as potential candidate genes. Consequently, 35 promising candidate genes were identified in C1 (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Notably, six SNPs on chromosome 12 formed a high-linkage LD block (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), near the gene encoding wall-associated receptor kinase 2-like (WAK2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The wall-associated kinase (\u003cem\u003eWAK\u003c/em\u003e) gene, a subfamily of the receptor-like kinase (RLK) gene family, is known to be associated with the plant cell wall and plays a crucial role in cell expansion (Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Previous study conducted on \u003cem\u003eNicotiana benthamiana\u003c/em\u003e have identified various cis-acting elements within the promoter regions of wall-associated kinases (WAKs) and WAK-like kinases (WAKLs). These cis-acting elements were found to be associated with phytohormone and/or stress responses (Zhong et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This suggests that these genes play a role in coordinating plant responses to phytohormones and stress factors. Another study conducted on Chinese cabbage has suggested the potential importance of the WAK-like genes in callus formation (Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Our finding supports the hypothesis that \u003cem\u003eWAK2\u003c/em\u003e likely play an important role in callus induction. Further research may provide insights into the specific mechanisms by which \u003cem\u003eWAK2\u003c/em\u003e regulate cell proliferation and differentiation during callus induction.\u003c/p\u003e \u003cp\u003eGenotype analysis in this study revealed that the callus induction potential of Hap3 was higher than Hap1 and Hap2 (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), suggesting that genotype plays a critical role in determining callus induction efficiency. Further analysis of allele discrimination using SNP primers showed that the marker (C-12) effectively distinguished GG allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), which showed the highest probability (69%) of callus induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Furthermore, qPCR analysis provides evidence for the effectiveness of qPCR in rapidly detecting sequence variants without electrophoresis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the tested oil palm individuals, S30 and S46 consistently showed high callus induction rates (\u0026gt;\u0026thinsp;50%) from C1 to C3. Further analysis revealed that these individuals possessed the GG allele. This evidence strongly suggests that the implementation of a highly efficient SNP marker that can identify the GG allele would greatly benefit oil palm tissue culture.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eGWAS was applied to investigate the associations between SNPs and callus induction phenotypes in 198 oil palm individuals. A total of 21 significant SNPs were identified in C1, in which 6 SNPs potentially linked to callus induction were further revealed by LD block analysis. A total of 13 markers were then assessed accordingly, in which the marker C-12 from the locus Chr12_12704856 can identify the individuals with GG allele, which showed the highest probability (69%) of callus induction. The marker-assisted selection for specific individuals with high potential of callus induction will enhance the selection efficiency for donor plants in oil palm tissue culture.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eChr, Chromosome;\u0026nbsp;GLM: General lineral model; GWAS: Genome-wide association analysis;\u0026nbsp;Hap: Haplotype; H: heterozygous allele; LD: Linkage disequilibrium; MAF: Minor allele frequency; MAS: Marker-assisted selection; MLM: Mixed linear model; PCA: Principal component analysis; PCI: Probability of callus induction; qPCR: Quantitative PCR;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eQQ: Quantile-quantile; SNP: Single nucleotide polymorphism; \u003cem\u003eWAK2\u003c/em\u003e: Wall-associated receptor kinase 2-like.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to the National Germplasm Nursery for Tropical Palms for providing plant materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYW conceived and supervised the project. YMH and PS conducted the experiments and wrote the article draft. YMH, PS, DZ and YW contributed to the methodology, data collection and analysis. ZL and QY contributed to material preparation. DZ and YW revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by the National Natural Science Foundation of China (No. 32071740), the post-doc project of Hainan Yazhou Bay Seed Laboratory (No. B21Y10301/B22C10303), the National Key R\u0026amp;D Program of China (No. 2023YFD2200700) and the earmarked fund for CARS-14 (China Agriculture Research System-Specific Oilseed Crops).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets supporting the results of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhn E, Botkin J, Ellur V et al (2023) Genome-wide association study of seed morphology traits in \u003cem\u003eSenegalese sorghum\u003c/em\u003e cultivars. 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Nat Genet 44:821\u0026ndash;824. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.2310\u003c/span\u003e\u003cspan address=\"10.1038/ng.2310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-cell-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcre","sideBox":"Learn more about [Plant Cell Reports](https://www.springer.com/journal/299)","snPcode":"299","submissionUrl":"https://submission.nature.com/new-submission/299/3","title":"Plant Cell Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"GWAS, Allele, SNPs, Callus induction, Marker-assisted selection","lastPublishedDoi":"10.21203/rs.3.rs-3829704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3829704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEfficient callus induction is vital for successful tissue culture of oil palm, yet identifying genomic loci and markers for early identification of specific individuals with high potential of callus induction is still unclear. In this study, callus induction rate at 1-, 2-, and 3-months after inoculation (C1, C2 and C3) of 198 oil palm individuals were investigated as phenotypes, and totally 11,475,258 high quality single nucleotide polymorphisms (SNPs) were generated by resequencing as genotypes. Genome-wide association study (GWAS) was performed accordingly using these phenotypes and genotypes. Correlation analysis revealed a positive association of C1 with both C2 (R\u0026thinsp;=\u0026thinsp;0.81) and C3 (R\u0026thinsp;=\u0026thinsp;0.50). Therefore, only SNPs in C1 were identified to develop markers for screening individuals capable of callus induction at early stage. A total of 21 significant SNPs were observed in C1, in which six of them on chromosome 12 (Chr12) potentially linked to callus induction were further revealed by the linkage disequilibrium (LD) block analysis. Totally 13 SNP markers from these six loci were tested accordingly and only the marker C-12 at locus Chr12_12704856 effectively distinguishing the GG allele, which showed the highest probability (69%) of callus induction. Moreover, the method for rapid SNP variant detection without electrophoresis was established via qPCR analysis. Notably, individuals S30 and S46, carrying the GG allele, consistently showed high callus induction rates (\u0026gt;\u0026thinsp;50%) from C1 to C3. Our findings facilitated marker-assisted selection for specific individuals with high potential of callus induction, thereby providing valuable assistance for donor plants selection in oil palm tissue culture.\u003c/p\u003e","manuscriptTitle":"Insights into callus induction by GWAS and development of SNP marker for donor plants selection in oil palm tissue culture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 17:19:44","doi":"10.21203/rs.3.rs-3829704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-04T00:19:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-03T18:25:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-03T14:39:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Cell Reports","date":"2024-01-02T05:30:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-cell-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcre","sideBox":"Learn more about [Plant Cell Reports](https://www.springer.com/journal/299)","snPcode":"299","submissionUrl":"https://submission.nature.com/new-submission/299/3","title":"Plant Cell Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c6e0dee7-47ae-4e50-b995-b06c7946ffc6","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-01-08T17:19:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 17:19:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3829704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3829704","identity":"rs-3829704","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}