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Euphytica

, 215:34 | Cite as

Identification of heat tolerance loci in broccoli through bulked segregant analysis using whole genome resequencing

  • Sandra E. BranhamEmail author
  • Mark W. Farnham
Article
  • 41 Downloads

Abstract

Most broccoli cultivars are sensitive to high temperatures during the early stages of floral development causing a severe decline of head quality or even complete lack of head formation under superoptimal crop production temperatures. Several heat tolerant lines have been developed in recent years but there have been few studies of the genetic basis of this complex, polygenic trait. A doubled haploid population of broccoli was evaluated for head quality across two summer field trials with the phenotypic extremes validated in two additional summer fields. Whole-genome resequencing of the bulked segregants was used for a quantitative trait loci (QTL)-seq analysis of heat tolerance. Two novel QTL, which differ from previously reported QTL, were identified. Nonsynonymous SNPs were found in a block of flowering time genes within QHT_C09.2 and may explain the significant negative correlation between time to head maturity and heat tolerance. Breeding further genetic gains in this complex, polygenic trait could be expedited through marker assisted selection and gene pyramiding using markers developed from the QTL identified herein.

Keywords

Broccoli Heat tolerance Bulked segregant analysis QTL-seq Brassica oleracea 

Notes

Acknowledgements

This study was funded by the United States Department of Agriculture, Project No. 6080-21000-018-00 and the National Institute of Food and Agriculture, Project No. 2010-51181-21062. The authors would like to thank Zachary J. Stansell and David M. Couillard for their technical assistance.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10681_2018_2334_MOESM1_ESM.png (645 kb)
Online resource 1 Scatterplots of the SNP-index for the heat sensitive bulk at 654,288 SNPs across nine chromosomes. The red lines represent the results of a sliding window analysis with a 1 Mb interval and a window size of 10 kb (PNG 645 kb)
10681_2018_2334_MOESM2_ESM.png (526 kb)
Online resource 2 Scatterplots of the SNP-index for the heat tolerant bulk at 654,288 SNPs across nine chromosomes. The red lines represent the results of a sliding window analysis with a 1 Mb interval and a window size of 10 kb (PNG 525 kb)
10681_2018_2334_MOESM3_ESM.csv (37 kb)
Online resource 3 Candidate genes based upon functional annotation of the significant SNPs that cause missense or nonsense mutations or were found < 1000 bp upstream of the start codon of the listed gene (CSV 36 kb)

References

  1. Bita CE, Gerats T (2013) Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops. Front Plant Sci 4:273.  https://doi.org/10.3389/fpls.2013.00273 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bjorkman T, Pearson KJ (1998) High temperature arrest of inflorescence development in broccoli (Brassica oleracea var. italica L.). J Exp Bot 49:101–106.  https://doi.org/10.1093/jxb/49.318.101 CrossRefGoogle Scholar
  3. Branham SE, Stansell ZJ, Couillard DM, Farnham MW (2017) Quantitative trait loci mapping of heat tolerance in broccoli (Brassica oleracea var. italica) using genotyping-by-sequencing. Theor Appl Genet 130:529–538.  https://doi.org/10.1007/s00122-016-2832-x CrossRefPubMedGoogle Scholar
  4. Chiang MS, Chong C, Landry BS, Crete R (1993) Cabbage. In: Kalloo G, Berg BO (eds) Genetic improvement of vegetable crops. Pergamon Press, New York, pp 100–152Google Scholar
  5. Ellis RJ (1990) Molecular chaperones: the plant connection. Science (New York, NY) 250:954–959.  https://doi.org/10.1126/science.250.4983.954 CrossRefGoogle Scholar
  6. Fadina OA, Pankin AA, Khavkin EE (2013) Molecular characterization of the flowering time gene FRIGIDA in Brassica genomes A and C. Russ J Plant Physiol 60(2):279–289CrossRefGoogle Scholar
  7. Farnham MW, Björkman T (2011a) Breeding vegetables adapted to high temperatures: a case study with broccoli. HortScience 46:1093–1097CrossRefGoogle Scholar
  8. Farnham M, Björkman T (2011b) Evaluation of experimental broccoli hybrids developed for summer production in the eastern United States. HortScience 46:858–863CrossRefGoogle Scholar
  9. Fontes MR, Ozbun JL, Sadik S (1967) Influence of temperature on initiation of floral primordia in green sprouting broccoli. Proc Am Soc Horticult Sci 91:315Google Scholar
  10. Gauss JF, Taylor GA (1969) Environmental factors influencing reproductive differentiation and the subsequent formation of the inflorescence of Brassica oleracea L. var. italica, Plenck, cv. ‘Coastal’. J Amer Soc Horticult Sci 94:275–280Google Scholar
  11. Heather D, Sieczka J, Dickson M, Wolfe D (1992) Heat tolerance and holding ability in broccoli. J Am Soc Horticult Sci 117:887–892CrossRefGoogle Scholar
  12. Hisano H, Sakamoto K, Takagi H et al (2017) Exome QTL-seq maps monogenic locus and QTLs in barley. BMC Genom 18:1–9.  https://doi.org/10.1186/s12864-017-3511-2 CrossRefGoogle Scholar
  13. Irwin JA, Lister C, Soumpourou E et al (2012) Functional alleles of the flowering time regulator FRIGIDA in the Brassica oleracea genome. BMC Plant Biol.  https://doi.org/10.1186/1471-2229-12-21 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Irwin JA, Soumpourou E, Lister C et al (2016) Nucleotide polymorphism affecting FLC expression underpins heading date variation in horticultural brassicas. Plant J Cell Mol Biol 87:597–605.  https://doi.org/10.1111/tpj.13221 CrossRefGoogle Scholar
  15. Jia Q, Tan C, Wang J et al (2016) Marker development using SLAF-seq and whole-genome shotgun strategy to fine-map the semi-dwarf gene ari-e in barley. BMC Genom 17:911.  https://doi.org/10.1186/s12864-016-3247-4 CrossRefGoogle Scholar
  16. Johanson U, West J, Lister C et al (2000) Molecular analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis flowering time. Science (New York, NY) 290:344–347.  https://doi.org/10.1126/science.290.5490.344 CrossRefGoogle Scholar
  17. Kong F, Deng Y, Wang G et al (2014) LeCDJ1, a chloroplast DnaJ protein, facilitates heat tolerance in transgenic tomatoes. J Integr Plant Biol 56:63–74.  https://doi.org/10.1111/jipb.12119 CrossRefPubMedGoogle Scholar
  18. Kosugi S, Natsume S, Yoshida K et al (2013) Coval: improving alignment quality and variant calling accuracy for next-generation sequencing data. PLoS ONE.  https://doi.org/10.1371/journal.pone.0075402 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Le Strange M, Cahn M, Koike S et al (2010) Broccoli production in California. Veg Prod Ser 7216:1–6Google Scholar
  20. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25:1754–1760.  https://doi.org/10.1093/bioinformatics/btp324 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079.  https://doi.org/10.1093/bioinformatics/btp352 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Lin KH, Chang LC, Lai CD, Lo HF (2013) AFLP mapping of quantitative trait loci influencing seven head-related traits in broccoli (Brassica oleracea var. italica). J Horticult Sci Biotechnol 88:257–268.  https://doi.org/10.1080/14620316.2013.11512964 CrossRefGoogle Scholar
  23. Michaels SD, Bezerra IC, Amasino RM (2004) FRIGIDA-related genes are required for the winter-annual habit in Arabidopsis. Proc Natl Acad Sci USA 101:3281–3285.  https://doi.org/10.1073/pnas.0306778101 CrossRefPubMedGoogle Scholar
  24. Nover L, Miernyk JA (2001) A genomics approach to the chaperone network of Arabidopsis thaliana. Cell Stress Chaperones 6:175–176CrossRefGoogle Scholar
  25. Parkin IP, Koh C, Tang H et al (2014) Transcriptome and methylome profiling reveals relics of genome dominance in the mesopolyploid Brassica oleracea. Genome Biol 15:R77.  https://doi.org/10.1186/gb-2014-15-6-r77 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Pink D, Bailey L, McClement S et al (2008) Double haploids, markers and QTL analysis in vegetable brassicas. Euphytica 164:509–514.  https://doi.org/10.1007/s10681-008-9742-1 CrossRefGoogle Scholar
  27. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  28. Schlappi MR (2006) FRIGIDA LIKE 2 is a functional allele in Landsberg erecta and compensates for a nonsense allele of FRIGIDA LIKE 1. Plant Physiol 142:1728–1738.  https://doi.org/10.1104/pp.106.085571 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Shimizu M, Fujimoto R, Ying H et al (2014) Identification of candidate genes for fusarium yellows resistance in Chinese cabbage by differential expression analysis. Plant Mol Biol 85:247–257.  https://doi.org/10.1007/s11103-014-0182-0 CrossRefPubMedGoogle Scholar
  30. Shindo C, Aranzana MJ, Lister C et al (2005) Role of FRIGIDA and FLOWERING LOCUS C in determining variation in flowering time of Arabidopsis. Plant Physiol 138:1163–1173.  https://doi.org/10.1104/pp.105.061309.1 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Stansell Z, Björkman T, Branham S et al (2017) Use of a quality trait index to increase the reliability of phenotypic evaluations in broccoli. HortScience 52:1490–1495.  https://doi.org/10.21273/HORTSCI12202-17 CrossRefGoogle Scholar
  32. Stinchcombe JR, Weinig C, Ungerer M et al (2004) A latitudinal cline in flowering time in Arabidopsis thaliana modulated by the flowering time gene FRIGIDA. Proc Natl Acad Sci 101:4712–4717.  https://doi.org/10.1073/pnas.0306401101 CrossRefPubMedGoogle Scholar
  33. Takagi H, Abe A, Yoshida K et al (2013) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant J 74:174–183.  https://doi.org/10.1111/tpj.12105 CrossRefPubMedGoogle Scholar
  34. United States Department of Agriculture, National Agricultural Statistics Service (2014) Vegetable and pulses yearbook. USDA/NAAS, WashingtonGoogle Scholar
  35. Walley PG, Carder J, Skipper E et al (2012) A new broccoli × broccoli immortal mapping population and framework genetic map: tools for breeders and complex trait analysis. Theor Appl Genet 124:467–484.  https://doi.org/10.1007/s00122-011-1721-6 CrossRefPubMedGoogle Scholar
  36. Wang M, Farnham M, Nannes J (1999) Ploidy of broccoli regenerated from microspore culture versus anther culture. Plant Breed 118:249–252CrossRefGoogle Scholar
  37. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:1–7.  https://doi.org/10.1093/nar/gkq603 CrossRefGoogle Scholar
  38. Wang N, Qian W, Suppanz I et al (2011) Flowering time variation in oilseed rape (Brassica napus L.) is associated with allelic variation in the FRIGIDA homologue BnaA.FRI.a. J Exp Bot 62:5641–5658.  https://doi.org/10.1093/jxb/err249 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Wurr DCE, Fellows JR, Phelps K, Reader RJ (1995) Vernalization in calabrese (Brassica oleracea var. italica): a model for apex development. J Exp Bot 46:1487–1496.  https://doi.org/10.1093/jxb/46.10.1487 CrossRefGoogle Scholar
  40. Yi L, Chen C, Yin S et al (2018) Sequence variation and functional analysis of a FRIGIDA orthologue (BnaA3. FRI) in Brassica napus. BMC Plant Biol 18(1):32CrossRefGoogle Scholar
  41. Zheng W, Wang Y, Wang L et al (2016) Genetic mapping and molecular marker development for Pi65(t), a novel broad-spectrum resistance gene to rice blast using next-generation sequencing. Theor Appl Genet 129:1035–1044.  https://doi.org/10.1007/s00122-016-2681-7 CrossRefPubMedGoogle Scholar
  42. Zhou W, Zhou T, Li MX et al (2012) The Arabidopsis J-protein AtDjB1 facilitates thermotolerance by protecting cells against heat-induced oxidative damage. New Phytol 194:364–378.  https://doi.org/10.1111/j.1469-8137.2012.04070.x CrossRefPubMedGoogle Scholar
  43. Zou C, Wang P, Xu Y (2016) Bulked sample analysis in genetics, genomics and crop improvement. Plant Biotechnol J 14:1941–1955.  https://doi.org/10.1111/pbi.12559 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.USDA-ARS, U.S. Vegetable LaboratoryCharlestonUSA

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