Advertisement

Euphytica

, 213:107 | Cite as

Genotypic comparisons of chromosomes 01, 04, and 18 from three tetraploid species of Gossypium in topcrosses with five elite cultivars of G. hirsutum L.

  • Johnie N. JenkinsEmail author
  • Jack C. McCartyJr.
  • B. Todd Campbell
  • R. W. Hayes
  • Jixiang Wu
  • Sukumar Saha
  • David Stelly
Article

Abstract

Upland cotton, Gossypium hirsutum L. is the most widely planted cultivated cotton in the United States and the world. The other cultivated tetraploid species G. barbadense L. is planted on considerable less area; however, it produces extra long, strong, and fine fibers which spins into superior yarn. The wild cotton tetraploid species G. tomentosum Nuttall ex Seemann, native to the Hawaiian Archipelago also exhibits traits, such as drought tolerance, that would also be desirable to transfer to Upland cotton. Long-term breeding efforts using whole genome crosses between Upland and these species have not been successful in transferring very many desirable alleles into Upland cotton. Our chromosome substitution lines (CSL) have one chromosome or chromosome arm from an alien species backcrossed into the Upland cotton line,TM-1, via aneuploid technology. Five Upland cultivars were crossed with CS-B01, CS-T01, CS-B04, CS-T04, CS-B18 and CS-T18 and TM-1 the recurrent parent of the CSLs. This provided an opportunity to determine the effects of chromosomes 01, 04, and 18 from the three species in crosses with the five cultivars. Predicted genotypic mean effects of the parents, F2, and F3 generations for eight agronomic and fiber traits of importance were compared. The predicted hybrid mean effects for the three chromosomes from each species were different for several of the traits across cultivars. There was no single chromosome or species that was superior for all traits in crosses. Parental and hybrid lines often differed in the effect of a particular chromosome among the three species. The predicted genotypic mean effects for F2 and F3, with a few exceptions, generally agree with our previous results for additive and dominance genetic effects of these CSL.

Keywords

Cotton Breeding Chromosome substitution Interspecific introgression 

Abbreviations

CSL

Chromosome substitution line

CS-B

Chromosome substitution line from G. barbadense

CS-T

Chromosome substitution line from G. tomentosum

GGE model

Genotype and Genotype-by-environment model

Notes

Acknowledgement

The authors gratefully acknowledge sustained support for this germplasm introgression by our host institutions, Cotton Incorporated, the Texas State Support Committee (state growers and ginners) and the Texas Department of Agriculture Food and Fiber Research Commission. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the U. S. Department of Agriculture and does not imply its approval to the exclusion of other products that may also be suitable.

Compliance with ethical standards

Conflict of interest

None.

References

  1. Al-Quadhy RS, Morris WR, Mumm RF (1988) Chromosomal locations of genes for traits associated with lodging in winter wheat. Crop Sci 28:631–635CrossRefGoogle Scholar
  2. Berke TG, Baenziger PS, Morris WR (1992a) Chromosomal location of wheat quantitative trait loci affecting agronomic performance using reciprocal chromosome substitutions. Crop Sci 32:621–627CrossRefGoogle Scholar
  3. Berke TG, Baenziger PS, Morris WR (1992b) Chromsomal location of wheat quantitative trait loci affecting stability of six traits, using reciprocal chromosome substitutions. Crop Sci 32:628–633CrossRefGoogle Scholar
  4. Bowman DT, Gutierez OA, Percy RG, Calhoun DS, May OL (2006) Pedigrees of upland and pima cotton cultivars released between 1970 and 2005. Miss Agric For Exp Station Tech Bull 1155:57Google Scholar
  5. Calhoun DS, Bowman DT, May OL (1994) Pedigrees of upland and pima cotton cultivars released between 1970 and 1990. Miss Agric For Exp Station Tech Bull 1017:42Google Scholar
  6. Calhoun DS, Bowman DT, May OL (1997) Pedigrees of upland and pima cotton cultivars released between 1970 and 1995. Miss Agric For Exp Station Tech Bull 1069:53Google Scholar
  7. Campbell BT, Baenziger PS, Gill KS, Eskridge KM, Budak H, Erayman M, Yen Y (2003) Identification of QTL’s and environmental interactions associated with agronomic traits on chromosome 3A of wheat. Crop Sci 43:1493–1505CrossRefGoogle Scholar
  8. Campbell BT, Baenziger PS, Eskridge KM, Budak H, Streck NA, Weiss A, Gill KS, Erayman M (2004) Using environmental covariates to explain Gx E and QTLx E interactions for agronomic traits on choromosome 3A of wheat. Crop Sci 44:620–627CrossRefGoogle Scholar
  9. Endrizzi JE (1963) Genetic analysis of six primary monosomes and one tertiary monosome in Gossypium hirsutum. Genetics 48:1625–1633PubMedPubMedCentralGoogle Scholar
  10. Jenkins JN, McCarty JC, Saha S, Gutierrez O, Hayes R, Stelly DM (2006) Genetic effects of thirteen Gossypium barbadense L. chromosome substitution lines in topcrosses with upland cotton cultivars: I. Yield and yield components. Crop Sci 46:1169–1178CrossRefGoogle Scholar
  11. Jenkins JN, McCarty JC, Saha S, Gutierrez O, Hayes R, Stelly DM (2007) Genetic effects of thirteen Gossypium barbadense L. chromosome substitution lines in topcrosses with Upland cotton cultivars: II. Fiber quality traits. Crop Sci 47:561–571CrossRefGoogle Scholar
  12. Jenkins JN, McCarty JC, Wu J, Hayes R, Stelly DM (2012) Genetic effects of nine Gossypium barbadense L. chromosome substitution lines in top crosses with five elite upland cotton G. hirsutum L. cultivars. Euphytica 187:161–173CrossRefGoogle Scholar
  13. Jenkins JN, McCarty JC Jr, Gutierrez OA, Hayes RW, Jones DC (2013) Registration of RMBUP-C4, a random-mated population with Gossypium barbadense L. alleles introgressed into Upland cotton germplasm. J Plant Reg 7:224–228CrossRefGoogle Scholar
  14. Jenkins JN, McCarty JC Jr, Campbell BT, Hayes RW, Wu J, Saha S, Stelly DM (2017) Genetic effects of chromosomes 01, 04, and 18 from three tetraploid species of Gossypium in topcrosses with five elite cultivars. Crop Sci. doi: 10.2135/cropsc:2016.06.0528 Google Scholar
  15. Kaeppler SM (1997) Quantitative trait locus mapping using set of near-isogenic lines: relative power comparisons and technical considerations. Theor Appl Gent 95:384–392CrossRefGoogle Scholar
  16. Law CN (1966) The location of genetic factors affecting a quantitative character in wheat. Genetics 53:487–498PubMedPubMedCentralGoogle Scholar
  17. Mansur LM, Qualset CO, Kasarda DD, Morris R (1990) Effects of ‘Cheyenne’ chromosome on milling quality and baking quality of ‘Chinese Spring’ wheat in relation to glutenin and gliadin storage protein. Crop Sci 30:593–602CrossRefGoogle Scholar
  18. McCarty JC, Jenkins JN, Wu J (2004a) Primitive accession derived germplasm by cultivar crosses as sources for cotton improvement. I. Phenotypic values and variance components. Crop Sci 44:1226–1230. doi: 10.2135/cropsci:2004.1226 CrossRefGoogle Scholar
  19. McCarty JC, Jenkins JN, Wu J (2004b) Primitive accession derived germplasm by cultivar crosses as sources for cotton improvement. II. Genetic effects and genotypic values. Crop Sci 44:1231–1236. doi: 10.2135/cropsci:2004.1231 CrossRefGoogle Scholar
  20. Mengistu N, Baenziger PS, Eskridge KM, Dweikat I, Wegulo SN, Gill KS, Mujeeb-Kazi A (2012) Validation of QTL for grain yield-related traits on wheat chromosome 3A using recombinant inbred chromosome lines. Crop Sci 52:1622–1632CrossRefGoogle Scholar
  21. R Core Team (2014) R: A language and environment for statistical computing. R foundation for statistical computing. Vienna. https://www.r-project.org/
  22. Rao CR (1971) Estimation of variance and covariance components MINQUE theory. J Multivar Anal 1:257–275CrossRefGoogle Scholar
  23. Shah MM, Gill KS, Baenziger PS, Yen Y, Kaeppler SM, Ariyarathne HM (1999) Molecular mapping of loci for agronomic traits on chromosome 3A of bread wheat. Crop Sci 39:1728–1732CrossRefGoogle Scholar
  24. Stelly DM, Saha S, Raska DA, Jenkins JN, McCarty JC, Gutierrez OA (2005) Registration of 17 Upland (Gossypium hirsutum) cotton germplasm lines disomic for different G. barbadense chromosome or arm substitution. Crop Sci 45:2663–2665CrossRefGoogle Scholar
  25. Wu J (2014) Minique: An R package for linear mixed model analyses. http://cran.r-project.org
  26. Wu J, Jenkins JN, McCarty JC (2010) A generalized approach and computer tool for quantitative genetics study. In: W. Song, sditor, Proceedings of the 22nd Annual Confrence on Applied Statistics in Agriculture, 25–27 April 2010. Kansas State University, Manhattan, KS p. 85–106Google Scholar
  27. Wu J, Jenkins JN, McCarty JC, Glover K (2012) Detecting epistatic effects associated with cotton traits by a modified MDR approach. Euphytica 197:289–301CrossRefGoogle Scholar
  28. Wu J, Bondalapati K, Glover K, Berzonsky W, Jenkins JN, McCarty JC (2013) Genetic analysis without replications: model evaluation and application in spring wheat. Euphytica 190:447–458CrossRefGoogle Scholar
  29. Wu J, Jenkins JN, McCarty JC (2014) Qgtools: Tools for quantitative genetics data analyses. https://cran.r-project.org/web/packages/qgtools/indes.html
  30. Yen Y, Baenziger PS (1992) A better way to construct recombinant chromosome lines and their controls. Genome 35:827–830CrossRefGoogle Scholar
  31. Yen Y, Baenziger PS, Bruns Reeder J, Moreno-Sevilla B, Budak N (1997) Agronomic performance of hybrids between clutivars and chromosome substitution lines. Crop Sci 37:396–399CrossRefGoogle Scholar
  32. Zemetra RS, Morris WR (1988) Effects of an intercultivaral chromosome substitution on winterhardiness and vernalization in wheat. Genetics 119:453–456PubMedPubMedCentralGoogle Scholar
  33. Zemetra RS, Morris WR, Schmidt JW (1986) Gene locations for heading date using reciprocal chromosome substitutions in winter wheat. Crop Sci 26:531–533CrossRefGoogle Scholar
  34. Zhang J, Percy RG, McCarty JC Jr (2014) Introgression genetics and breeding between upland and pima cotton: a review. Euphytica 198:1–12CrossRefGoogle Scholar
  35. Zhu J (1989) Estimation of genetic variance components in the general mixed model. Ph. D. Dissertation, North Carolina State University, Raleigh, NCGoogle Scholar
  36. Zhu J (1993) Methods of predicting genotype value and heterosis for offspring of hybrids. J Biomath 8:32–40Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2017

Authors and Affiliations

  1. 1.USDA-ARSStarkvilleUSA
  2. 2.USDA-ARSFlorenceUSA
  3. 3.Agronomy, Horticulture and Plant Science DepartmentSouth Dakota State UniversityBrookingsUSA
  4. 4.Department of Soil and Crop SciencesTexas A&M UniversityCollege StationUSA

Personalised recommendations