, 214:41 | Cite as

Genotyping by sequencing of rice interspecific backcross inbred lines identifies QTLs for grain weight and grain length

  • Dharminder Bhatia
  • Rod A. Wing
  • Yeisoo Yu
  • Kapeel Chougule
  • Dave Kudrna
  • Seunghee Lee
  • Allah Rang
  • Kuldeep Singh


Grain weight and grain length are the most stable components of rice yield and important indicators of consumer preference. Considering the potentials of wild rice and to enhance the rice yields to meet the increasing demands, 185 Backcross Inbred Lines (BILs) in the background of O. sativa ssp. indica cv. PR114, including 63 rufi-BILs derived from O. rufipogon IRGC104433 and 122 glumae-BILs from O. glumaepatula IRGC104387 were evaluated for mapping QTLs for yield and yield component traits using Genotyping by Sequencing (GBS). Phenotypic evaluation of BILs in three seasons spanning two locations revealed significant differences compared with recurrent parent. BILs which did not show significant differences for any trait under investigation, or similar based on pedigree, were excluded from GBS. Some glumae-BILs had to be excluded from mapping QTLs due to less sequence information. A custom designed approach for GBS data analysis identified 3322 informative SNPs in 55 rufi-BILs and 3437 informative SNPs in 79 glumae-BILs. QTL mapping identified one QTL for thousand grain weight (qtgw5.1), two for grain width (qgw5.1, qgw5.2) and one for grain length (qgl7.1) in rufi-BILs. In the glumae-BILs, three QTL for thousand grain weight (qtgw2.1, qtgw3.1, qtgw6.1) and two for grain length (qgl3.1, qgl7.1) were identified. Most of the grain weight and width QTL showed positive additive effect contributed by wild species allele, whereas the grain length QTL showed positive additive effect contributed by recurrent parent allele. Based on their physical position, none of the QTLs were found similar to previously cloned QTLs. QTLs for grain traits identified from low yielding wild relatives of rice reveals their significance in improving further the rice yields and widen the genetic base of cultivated rice.


Backcross inbred lines (BILs) O. rufipogon O. glumaepatula Grain weight Grain length Genotyping by sequencing (GBS) Quantitative trait loci (QTL) 



This work was supported by the Monsanto Beachell Borlaug International Scholarship Programme (MBBISP) to DB, which is gratefully acknowledged.

Author contributions

DB, KS, RAW, AR designed and conducted the study, DB did field evaluation; DB, YY, DK prepared GBS library; SL, YY sequenced the GBS library; DB, KC analysed GBS data; DB did QTL mapping; DB, KS, RAW wrote and edited the manuscript.

Supplementary material

10681_2018_2119_MOESM1_ESM.tif (619 kb)
Supplementary material 1 (TIFF 619 kb). Fig. S1. Custom designed GBS data analysis workflow used for SNP identification and mapping of target traits in both rufi-BILs and glumae-BILs
10681_2018_2119_MOESM2_ESM.tif (363 kb)
Supplementary material 2 (TIFF 362 kb). Fig. S2. Graphical plots of phenotypic variation in DF, PH, PS and PY in BILs. Plotted values are the adjusted means of square lattice design. SI, SII, SIII represent three different seasons. Arrow indicates where the recurrent parent PR114 falls
10681_2018_2119_MOESM3_ESM.tif (1 mb)
Supplementary material 3 (TIFF 1045 kb). Fig. S3. Graphical representation of read alignment statistics of (a) rufi-BILs; (b) glumae-BILs aligned to PR114 fake pseudomolecule
10681_2018_2119_MOESM4_ESM.tif (2.8 mb)
Supplementary material 4 (TIFF 2839 kb). Fig. S4. Distribution of (a) 3322 informative SNPs of rufi-BILs and (b) 3437 informative SNPs of glumae-BILs on twelve chromosomes of rice based on their physical distance. Red lines in the bars indicate the SNP
10681_2018_2119_MOESM5_ESM.tif (6.1 mb)
Supplementary material 5 (TIFF 6245 kb). Fig. S5. Graphical genotypes of (a) rufi-BILs based on 3322 informative SNPs (b) glumae-BILs based on 3437 informative markers. Red colour indicates the recurrent parent, purple colour indicates the introgressions from donor parent and white colour indicates missing data points
10681_2018_2119_MOESM6_ESM.xls (47 kb)
Supplementary material 6 (XLS 47 kb)
10681_2018_2119_MOESM7_ESM.xlsx (24 kb)
Supplementary material 7 (XLSX 23 kb)
10681_2018_2119_MOESM8_ESM.docx (23 kb)
Supplementary material 8 (DOCX 22 kb)


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Plant Breeding and GeneticsPunjab Agricultural UniversityLudhianaIndia
  2. 2.School of Agricultural BiotechnologyPunjab Agricultural UniversityLudhianaIndia
  3. 3.Arizona Genomics Institute, School of Plant SciencesUniversity of ArizonaTucsonUSA
  4. 4.National Bureau of Plant Genetic ResourcesNew DelhiIndia

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