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Exploiting the Genomic Diversity of Rice ( L.): SNP-Typing in 11 Early-Backcross Introgression-Breeding Populations. | LitMetric

AI Article Synopsis

  • This study used genotyping-by-sequencing to analyze 11 rice populations, identifying 794,297 polymorphic SNPs after thorough sequencing and quality control.
  • The findings highlighted unique donor introgression patterns and lower heterozygosity in introgression lines, linked to selection under abiotic stress.
  • The research revealed 426 deleterious non-synonymous SNPs and 120 novel SNPs, which will aid in developing markers for molecular breeding programs.

Article Abstract

This study demonstrates genotyping-by-sequencing-based single-nucleotide polymorphism (SNP)-typing in 11 early-backcross introgression populations of rice (at BCF), comprising a set of 564 diverse introgression lines and 12 parents. Sequencing using 10 Ion Proton runs generated a total of ∼943.4 million raw reads, out of which ∼881.6 million reads remained after trimming for low-quality bases. After alignment, 794,297 polymorphic SNPs were identified, and filtering resulted in LMD50 SNPs (low missing data, with each SNP, genotyped in at least 50% of the samples) for each sub-population. Every data point was supported by actual sequencing data without any imputation, eliminating imputation-induced errors in SNP calling. Genotyping substantiated the impacts of novel breeding strategy revealing: (a) the donor introgression patterns in ILs were characteristic with variable introgression frequency in different genomic regions, attributed mainly to stringent selection under abiotic stress and (b) considerably lower heterozygosity was observed in ILs. Functional annotation revealed 426 non-synonymous deleterious SNPs present in 102 loci with a range of 1-4 SNPs per locus and 120 novel SNPs. SNP-typing this diversity panel will further assist in the development of markers supporting genomic applications in molecular breeding programs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6024854PMC
http://dx.doi.org/10.3389/fpls.2018.00849DOI Listing

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