Turmeric is a beneficial crop with notable health advantages, yet limited genetic data poses challenges for developing new varieties. This study assessed the genetic diversity of 40 turmeric genotypes using single nucleotide polymorphism (SNP) markers. We used dartseq technology to genotype the SNP data then we perform several filtering process maintain the SNP quality of data. Filtering procedures involved parameters such as removal of secondary loci, call rate <90%, elimination of monomorphic loci, reproducibility <99%, read depth <5, and minor allele frequency (MAF) <0.0 resulted 10092 SNPs. We then used this SNP to perform association study for rhizome weight and curcuminoid content.

Data Collection

A diversity panel of 40 turmeric (Curcuma longa) genotypes from various regions of Indonesia, including six widely marketed commercial accessions, was established. These genotypes were cultivated at the Ciparanje Experimental Field, Faculty of Agriculture, Padjadjaran University, from January to October 2023. The field is located at 753 meters above sea level, with coordinates of 6°55’0.72804’’ S latitude and 107°46’18.46056’’ E longitude.

Genomic DNA was extracted from fresh leaves of three-month-old plants, using a single plant per genotype. DNA extraction followed the manufacturer's protocols, utilizing the Wizard® Genomic DNA Purification Kit (Promega, USA) in combination with the Geneaid Plant Genomic DNA Mini Kit (GP100) columns (Geneaid, Taiwan). DNA concentration and purity were verified with a Thermo Scientific NanoDrop Lite Spectrophotometer and confirmed by 2% agarose gel electrophoresis. Samples with a minimum concentration of 50 ng/µl and a 260/280 absorbance ratio of 1.8-2.0 were selected for SNP genotyping. SNP genotyping was conducted in Australia using the Diversity Arrays Technology (DArT) sequencing approach. Genomic representations were generated by digesting 100 ng of DNA with PstI and either BstNI or TaqI frequent cutter enzymes in a solution of 10 mM Tris-OAc, 50 mM KOAc, 10 mM Mg(OAc)2, and 5 mM DTT (Jaccoud et al., 2001; Kilian et al., 2012). The resulting amplicons were organized in a 97-well microtiter plate and sequenced on the Illumina HiSeq2500 platform for 77 cycles. DNA sequences were processed with the DArT pipeline, which filters out low-quality sequences by setting a minimum Phred score of 30 for barcoded regions and a Phred score of 10 for the remaining sequences. Approximately 2.5 million sequences per sample were retained for SNP marker calling. Data analysis was conducted using DArTsoft14 and DArTdb (Adu et al., 2021). Filtering criteria included the exclusion of secondary loci, call rates below 90%, monomorphic loci, reproducibility less than 99%, read depth below 5, and a minor allele frequency (MAF) threshold of 0.01 (Gruber et al., 2018).

Analyses Performed using the Data Set

In our study, we analyzed the population structure and kinship of turmeric accessions using a combination of Bayesian clustering and principal component analysis (PCA). Population structure was inferred using STRUCTURE software version 2.3.4 (Pritchard et al., 2000), which employs a Bayesian model-based clustering algorithm to estimate individual membership coefficients across potentially mixed populations. To determine the optimal number of genetic clusters, we evaluated K values ranging from 1 to 10 with 10,000 burn-in generations followed by 10,000 Markov Chain Monte Carlo (MCMC) iterations (Zenetta et al., 2024). The best-fit delta K was determined according to Evanno et al. (2005), using StructureSelector (https://lmme.ac.cn/StructureSelector/(opens in new window) (Li & Liu, 2018), enhancing our understanding of the subpopulation structure within the accessions. Kinship coefficients between pairs of accessions were calculated using TASSEL v5.2.93, providing insight into genetic relatedness across the population. The PCA and kinship results were visualized using the GAPIT v3 package in RStudio 4.3.2, allowing us to identify major patterns of genetic variation and relatedness (Qu et al., 2015). This combined approach enabled a detailed examination of the genetic grouping and the extent of admixture among the turmeric accessions.

We measured rhizome weight and curcuminoid content (curcumin, demethoxycurcumin, and bisdemethoxycurcumin) in 40 turmeric accessions to assess phenotypic variation across accessions. Furthermore, we used these phenotypic data to assess phenotype-genotype associations using GWAS. Genome-wide association studies (GWAS) were conducted using the Genomic Association and Prediction Integrated Tool (GAPIT) in RStudio 4.3.2 (Wang & Zhang, 2021). The FarmCPU model was applied with PCA (Q-matrix) and kinship (K-matrix) as covariates to identify significant associations between SNP markers and rhizome weight and curcuminoid content (Bararyenya et al., 2020). Candidate genes were identified by annotating regions flanking the significant SNPs (500 bp upstream and downstream) using BLASTn against the NCBI nucleotide database, focusing on regions with the highest similarity and lowest e-value. This comprehensive genetic analysis provided insights into the genetic basis of key agronomic traits, informing potential breeding strategies for turmeric improvement (Jiang et al., 2021).

References

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