Theoretical and Applied Genetics

, Volume 124, Issue 4, pp 665–683

cDNA-AFLP-based genetical genomics in cotton fibers

  • Michel Claverie
  • Marlène Souquet
  • Janine Jean
  • Nelly Forestier-Chiron
  • Vincent Lepitre
  • Martial Pré
  • John Jacobs
  • Danny Llewellyn
  • Jean-Marc Lacape
Original Paper
  • 491 Downloads

Abstract

Genetical genomics, or genetic analysis applied to gene expression data, has not been widely used in plants. We used quantitative cDNA-AFLP to monitor the variation in the expression level of cotton fiber transcripts among a population of inter-specific Gossypium hirsutum × G. barbadense recombinant inbred lines (RILs). Two key fiber developmental stages, elongation (10 days post anthesis, dpa), and secondary cell wall thickening (22 dpa), were studied. Normalized intensity ratios of 3,263 and 1,201 transcript-derived fragments (TDFs) segregating over 88 RILs were analyzed for quantitative trait loci (QTL) mapping for the 10 and 22 dpa fibers, respectively. Two-thirds of all TDFs mapped between 1 and 6 eQTLs (LOD > 3.5). Chromosome 21 had a higher density of eQTLs than other chromosomes in both data sets and, within chromosomes, hotspots of presumably trans-acting eQTLs were identified. The eQTL hotspots were compared to the location of phenotypic QTLs for fiber characteristics among the RILs, and several cases of co-localization were detected. Quantitative RT-PCR for 15 sequenced TDFs showed that 3 TDFs had at least one eQTL at a similar location to those identified by cDNA-AFLP, while 3 other TDFs mapped an eQTL at a similar location but with opposite additive effect. In conclusion, cDNA-AFLP proved to be a cost-effective and highly transferable platform for genome-wide and population-wide gene expression profiling. Because TDFs are anonymous, further validation and interpretation (in silico analysis, qPCR gene profiling) of the eQTL and eQTL hotspots will be facilitated by the increasing availability of cDNA and genomic sequence resources in cotton.

Supplementary material

122_2011_1738_MOESM1_ESM.doc (44 kb)
ESM1 (esm1.doc) – cDNA-AFLP protocol (DOC 44 kb)
122_2011_1738_MOESM2_ESM.doc (48 kb)
ESM2 (esm2.doc) - List of TDFs selected from the 10 dpa experiment for validation by quantitative RT-PCR. The TDF size, Genbank accession hit and its corresponding (putative) function, as well as the primers used for qPCR are indicated. In the cases where Blast results of the TDF sequence suggested several possible candidates, each accession was tested in qPCR (* indicates the 3 accessions/TDF combinations that showed congruence in the qPCR-derived and AFLP-derived eQTL mapping experiments) (DOC 47 kb)
122_2011_1738_MOESM3_ESM.pdf (83 kb)
ESM3 (esm3.pdf) - Same title and legend as Fig. 3 - Comparative localization of eQTLs with meta-clusters for fiber phenotypic QTLs Localization of eQTLs from fibers at 10 (blue bars) and 22 dpa (red bars) are compared with regions containing meta-clusters of fiber phenotypic QTLs (phQTLs) for major fiber quality trait categories, including fineness (FIN), length (LEN), strength (STR), elongation (ELO) and color (COL) as reported in Lacape et al. (2010). The observed parental effect (either Gh or Gb) of each meta-cluster of phQTLs, as indicated in the legends, corresponds to an improvement of the trait value, higher length, strength, and elongation or lower fineness and yellowness. The horizontal lines represent the 1000 permutation-based (P < 0.05) thresholds in the 10 and 22 dpa experiments. (PDF 82 kb)
122_2011_1738_MOESM4_ESM.pdf (85 kb)
ESM4 (esm4.pdf) - List of the 227 AFLP-derived transcript fragments mapping an eQTL in the 10 dpa and/or in the 22 dpa eQTL experiment and which matched with a TDF predicted from the AFLP-in silico digestion of the annotated EST unigene displaying differential (digital) expression between the 2 genotypes (fold change larger than 2) for the same stage of fiber development (either 10 and/or 22 dpa). (PDF 100 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Michel Claverie
    • 1
  • Marlène Souquet
    • 1
  • Janine Jean
    • 2
  • Nelly Forestier-Chiron
    • 2
  • Vincent Lepitre
    • 1
  • Martial Pré
    • 1
  • John Jacobs
    • 3
  • Danny Llewellyn
    • 4
  • Jean-Marc Lacape
    • 1
  1. 1.UMR AGAP, CIRADMontpellierFrance
  2. 2.UR SCA, CIRADMontpellierFrance
  3. 3.Bayer BioScience N.V.GhentBelgium
  4. 4.CSIRO Plant IndustryCanberraAustralia

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