Theoretical and Applied Genetics

, Volume 123, Issue 6, pp 881–895 | Cite as

Targeted association analysis identified japonica rice varieties achieving Na+/K+ homeostasis without the allelic make-up of the salt tolerant indica variety Nona Bokra

  • N. Ahmadi
  • S. Negrão
  • D. Katsantonis
  • J. Frouin
  • J. Ploux
  • P. Letourmy
  • G. Droc
  • P. Babo
  • H. Trindade
  • G. Bruschi
  • R. Greco
  • M. M. Oliveira
  • P. Piffanelli
  • B. Courtois
Original Paper

Abstract

During the last decade, a large number of QTLs and candidate genes for rice tolerance to salinity have been reported. Using 124 SNP and 52 SSR markers, we targeted 14 QTLs and 65 candidate genes for association mapping within the European Rice Core collection (ERCC) comprising 180 japonica accessions. Significant differences in phenotypic response to salinity were observed. Nineteen distinct loci significantly associated with one or more phenotypic response traits were detected. Linkage disequilibrium between these loci was extremely low, indicating a random distribution of favourable alleles in the ERCC. Analysis of the function of these loci indicated that all major tolerance mechanisms were present in the ERCC although the useful level of expression of the different mechanisms was scattered among different accessions. Under moderate salinity stress some accessions achieved the same level of control of Na+ concentration and Na+/K+ equilibrium as the indica reference variety for salinity tolerance Nona Bokra, although without sharing the same alleles at several loci associated with Na+ concentration. This suggests (a) differences between indica and japonica subspecies in the effect of QTLs and genes involved in salinity tolerance and (b) further potential for the improvement of tolerance to salinity above the tolerance level of Nona Bokra, provided the underlying mechanisms are complementary at the whole plant level. No accession carried all favourable alleles, or showed the best phenotypic responses for all traits measured. At least nine accessions were needed to assemble the favourable alleles and all the best phenotypic responses. An effective strategy for the accumulation of the favourable alleles would be marker-assisted population improvement.

Supplementary material

122_2011_1634_MOESM1_ESM.xlsx (44 kb)
Table S1: List of accessions of the European Rice Core Collection (ERCC) and their country of origin. (XLSX 44 kb)
122_2011_1634_MOESM2_ESM.pdf (84 kb)
Table S2: List and characteristics of the 124 SNP markers targeting 47 candidate genes for salt tolerance, and list of SNPs significantly associated with tolerance to salinity. (PDF 83 kb)
122_2011_1634_MOESM3_ESM.pdf (70 kb)
Table S3: List and characteristics of the 52 polymorphic SSRs targeting 18 candidate genes and 14 QTLs for salinity tolerance, and list of SSRs significantly associated with tolerance to salinity. (PDF 70 kb)
122_2011_1634_MOESM4_ESM.pdf (84 kb)
Table S4: Response to salinity stress of the 200 accessions of the European Rice Core Collection. Experiment 1: SIS: salinity injury score; LCC_r: leaf chlorophyll content response; SDW16_r: shoot dry weight response at 16 days after sowing. Experiment 2: PH_r: plant height response; TN_r: tiller number response; LN_r: leaf number response; MRL_r: maximum root length response; RDW_r: root dry weight response; SDW_r: shoot dry weight response; K + and N + : leaf concentration of K + and N + ; PCA_1, PCA_2 and PCA_3: coordinates on the first, second and third axes of the Principal Component Analysis; HAC: Class of hierarchical ascendant classification.(PDF 83 kb)
122_2011_1634_MOESM5_ESM.pdf (69 kb)
Table S5: Results of targeted association analysis using three different models: General Linear Model (GLM_Q), Mixed Linear Model (MLM_ K) and Mixed Linear Model (MLM_K + Q), with K kinship matrix and Q population membership matrix. PH_r: plant height response; TN_r: tiller number response; LN_r: leaf number response; MRL_r: maximum root length response; RDW_r: root dry weight response; SDW_r: shoot dry weight response; K+ and N+: leaf concentration of K+ and N+; PCA_1, PCA_2 and PCA_3: coordinates on the first, second and third axes of the Principal Component Analysis; HAC: Class of hierarchical ascendant classification, SIS: salt injury score. (PDF 68 kb)
122_2011_1634_MOESM6_ESM.pdf (376 kb)
Figure S1: Location on the rice chromosomes of the candidate genes, molecular markers and QTLs for salinity tolerance. Positions are in Mb. Genes with validated function in rice are in yellow boxes. Other genes are in black. Molecular markers linked with QTLs are in black. Molecular markers genotyped in this study are in blue. Those for which significant associations were detected are in red. QTLs for K related traits are in red; QTL for Na related traits are in blue; QTLs for Na/K ratio are in violet; QTLs for relative traits (saline versus control conditions) are in grey. QTLs related to salt injury and salt tolerance are in green. For each QTL, the triangle area is proportional to the percentage of variance explained by the QTL. The QTL numbers correspond to their ID in the QTL module of TropgeneDB (http://tropgenedb.cirad.fr/html/rice_QTL.html) where additional details on the QTLs can be found. DSD: Days from Seedling to Death; KCR: K+ Concentration in Roots; KCS: K+ Concentration in Shoots; KQR: K+ Quantity in Roots; KUP: K+ Uptake; NA +/K + : Na+/K+ Ratio; NCR: Na+ Concentration in Roots; NCS: Na+ Concentration in Shoots; NQR: Na+ Quantity in Roots; NQS: Na+ Quantity in Shoots; NUP: Na+ Uptake; RBM: Relative Biomass; RDW: Relative Dry Weight; RFW: Relative Fresh Weight; RGRM: Relative Seed Germination; RLA: Relative Leaf Area; RSH: Relative Seedling Height; RSL: Relative Shoot Length; RSRL: Relative Seminal Root Length; RSV: Relative Seedling Vigor; RSVG: Relative Seedling Vigor; RTN: Relative Tiller Number; SI: Salt Injury; STOL: Salt Tolerance. Most of the information is extracted from Negrao et al (2011). (PDF 375 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • N. Ahmadi
    • 1
  • S. Negrão
    • 2
    • 7
  • D. Katsantonis
    • 3
  • J. Frouin
    • 1
  • J. Ploux
    • 1
  • P. Letourmy
    • 1
  • G. Droc
    • 6
  • P. Babo
    • 2
    • 7
  • H. Trindade
    • 4
  • G. Bruschi
    • 5
  • R. Greco
    • 5
  • M. M. Oliveira
    • 2
    • 7
  • P. Piffanelli
    • 5
  • B. Courtois
    • 6
  1. 1.CIRAD, UPR AIVAMontpellierFrance
  2. 2.ITQB, Instituto de Tecnologia Química e BiológicaUniversidade Nova de Lisboa LisbonPortugal
  3. 3.NAGREF, National Agricultural Research FoundationThermi-ThessalonikiGreece
  4. 4.Dep. Biologia VegetalUniversidade de Lisboa, Faculdade de Ciências de Lisboa, Instituto de Biotecnologia e Bioengenharia, Centro de Biotecnologia VegetalLisbonPortugal
  5. 5.FPTP, Parco Tecnologico Padano Foundation, Via EinsteinLodi (LO)Italy
  6. 6.CIRAD, UMR DAPMontpellierFrance
  7. 7.IBET, Instituto de Biologia Experimental e TecnológicaOeirasPortugal

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