Abstract
Key message
The article presents an optimization of the key parameters for the identification of SNPs in sugarcane using a GBS protocol based on two Illumina NextSeq and NovaSeq platforms.
Abstract
Sugarcane (Saccharum sp.), a world-wide known feedstock for sugar production, bioethanol, and energy, has an extremely complex genome, being highly polyploid and aneuploid. A double-digestion restriction site-associated DNA sequencing protocol (ddRADseq) was tested in four commercial sugarcane hybrids and one high-fibre biotype for the detection of single nucleotide polymorphisms (SNPs). In this work we tested two Illumina sequencing platforms, read size (70 vs. 150 bp), different sequencing coverage per individual (medium and high coverage), and single-reads versus paired-end reads. We also explored different variant calling strategies (with and without reference genome) and filtering schemes [combining two minor allele frequencies (MAFs) with three depth of coverage thresholds]. For the discovery of a large number of novel SNPs in sugarcane, we recommend longer size and paired-end reads, medium sequencing coverage per individual and Illumina platform NovaSeq6000 for a cost-effective approach, and filter parameters of lower MAF and higher depth coverages thresholds. Although the de novo analysis retrieved more SNPs, the reference-based method allows downstream characterization of variants. For the two best performing matrices, the number of SNPs per chromosome correlated positively with chromosome length, demonstrating the presence of variants throughout the genome. Multivariate comparisons, with both matrices, showed closer relationships among commercial hybrids than with the high-fibre biotype. Functional analysis of the SNPs demonstrated that more than half of them landed within regulatory regions, whereas the other half affected coding, intergenic and intronic regions. Allelic distances values were lower than 0.07 when analysing two replicated genotypes, confirming the protocol robustness.
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Data availability
The datasets generated and analysed during the current study are not publicly available because they are part of CM Doctoral Thesis but they are available from the corresponding author on reasonable request.
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Acknowledgements
We specially thank Sergio Gonzalez (Instituto de Agrobiotecnología y Biología Molecular, Instituto Nacional de Tecnología Agropecuaria, INTA-Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET, Argentina), for the assistance and advice on bioinformatics and Giusi Zaina (Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Italy) for improving the quality of the manuscript through her critical reading. We are also grateful to Agencia Nacional de Promoción Científica y Técnica and CONICET for supporting a Doctoral Fellowship to Catalina Molina, and the Unidad de Genómica, INTA, Argentina for laboratory collaboration. Last but not least, we thank Luis Erazzú and José M. García for providing the sugarcane hybrids and the field team of INTA’s sugarcane breeding program for its technical assistance.
Funding
The study was funded by Grants from Agencia Nacional de Promoción Científica y Técnica (PICT 2016 N° 1670), and INTA (PE N° I114 and PE N° 516).
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AA, NBP, SNMP and AFP contributed to the study conception and design. Harvesting and maintenance of plant material were performed by CM. DNA extraction was done by CM. ddRADseq libraries were performed by CM, PAV and AFP. Sequenced data were analysed by CM, CVF and NCA. Funding acquisition by AA, NBP and SNMP. Project administration by AA. Supervision by AA, NBP, SNMP, AFP, CVF and NCA. Writing—original draft preparation by CM and AA. Writing—review and editing by CM, AA, NBP, SNMP, CVF, NCA. All authors read and approved the final manuscript.
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Molina, C., Aguirre, N.C., Vera, P.A. et al. ddRADseq-mediated detection of genetic variants in sugarcane. Plant Mol Biol 111, 205–219 (2023). https://doi.org/10.1007/s11103-022-01322-4
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DOI: https://doi.org/10.1007/s11103-022-01322-4