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. 2018 Jun 22:9:849.
doi: 10.3389/fpls.2018.00849. eCollection 2018.

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

Jauhar Ali  1 , Umair M Aslam  1   2 , Rida Tariq  1   3 , Varunseelan Murugaiyan  1 , Patrick S Schnable  4   5   6 , Delin Li  4   6 , Corinne M Marfori-Nazarea  1   2 , Jose E Hernandez  2 , Muhammad Arif  3 , Jianlong Xu  6   7 , Zhikang Li  7   8
Affiliations

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

Jauhar Ali et al. Front Plant Sci. .

Abstract

This study demonstrates genotyping-by-sequencing-based single-nucleotide polymorphism (SNP)-typing in 11 early-backcross introgression populations of rice (at BC1F5), 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.

Keywords: SNP-typing; conventional genotyping by sequencing (cGBS); introgression breeding; marker-assisted breeding; non-synonymous SNPs; tunable genotyping by sequencing (tGBS).

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Figures

FIGURE 1
FIGURE 1
Illustrated breeding scheme.
FIGURE 2
FIGURE 2
Annotation of LMD50 SNPs. Intergenic and genic proportions of identified SNPs.
FIGURE 3
FIGURE 3
Genomic distribution of LMD50 SNPs. The size of each chromosome is based on actual chromosome lengths from the Rice Genome Annotation Project (MSU7; http://rice.plantbiology.msu.edu/index.shtml) database.
FIGURE 4
FIGURE 4
LMD50 SNPs’ genotype summary by sample for sub-population 1. (A) Including missing SNP calls. (B) Considering only non-missing SNP calls. Red color represents homozygous donor parent alleles, blue represents homozygous recurrent parent alleles, and green for heterozygous.
FIGURE 5
FIGURE 5
Diversity and phylogenetic patterns among 11 sub-populations. Neighbor-joining tree based on LMD50 SNPs.
FIGURE 6
FIGURE 6
Distribution of non-synonymous SNPs that have a deleterious effect on proteins. Circos diagrams representing annotated gene locations in (A) sub-population 1 (DP: Haoannong), (B) sub-population 9 (DP: Basmati-385), (C) sub-population 10 (DP: M-401), and (D) sub-population 11 (DP: X-21). The outer numbering shows the 12 rice chromosomes. Red color shows the abiotic stress-related loci, green color shows biotic stress-related loci, and blue color shows the loci other than stress-related families.
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References

    1. Akpinar B. A., Lucas S., Budak H. (2017). A large-scale chromosome-specific SNP discovery guideline. Funct. Integr. Genomics 17 97–105. 10.1007/s10142-016-0536-6 - DOI - PubMed
    1. Ali A. J., Xu J. L., Ismail A. M., Fu B. Y., Vijaykumar C. H. M., Gao Y. M., et al. (2006). Hidden diversity for abiotic and biotic stress tolerances in the primary gene pool of rice revealed by a large backcross breeding program. Field Crops Res. 97 66–76. 10.1016/j.fcr.2005.08.016 - DOI
    1. Ali J., Xu J. L., Gao Y. M., Fontanilla M., Li Z. K. (2013). “Breeding for yield potential and enhanced productivity across different rice ecologies through green super rice (GSR) breeding strategy,” in International Dialogue on Perception and Prospects of Designer Rice eds Muralidharan K., Siddiq E. A. (Hyderabad: Society for Advancement of Rice Research, Directorate of Rice Research; ).
    1. Ali J., Xu J. L., Gao Y. M., Fontanilla M. A., Li Z. K. (2012a). “Green super rice (GSR) technology: an innovative breeding strategy-achievements & advances,” in Proceedings of the 12th SABRAO Congress-Plant Breeding Towards Chiang Mai: 16–17.
    1. Ali J., Xu J. L., Gao Y. M., Fontanillo M. A., Marfori C. M., Li Z., et al. (2012b). Innovative Green Super Rice (GSR) molecular breeding strategy: achievements and advances. Philipp. J. Crop Sci. 37:49.

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