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Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array
BMC Plant Biology
  • Jonathan F. Wendel, Iowa State University
  • Lori L Hinze, USDA-ARS, Crop Germplasm Research Unit
  • Amanda M Hulse-kemp, University of California, Davis
  • Iain W Wilson, CSIRO Agriculture and Food
  • Qian-Hao Zhu, CSIRO Agriculture and Food
  • Danny J Llewellyn, CSIRO Agriculture and Food
  • Jen M Taylor, CSIRO Agriculture and Food
  • Andrew Spriggs, CSIRO Agriculture and Food
  • David D Fang, United States Department of Agriculture
  • Mauricio Ulloa, United States Department of Agriculture
  • John J Burke, United States Department of Agriculture
  • Marc Giband, CIRAD
  • Jean-Marc Lacape, CIRAD
  • Allen Van Deynze, University of California, Davis
  • Joshua A Udall, Brigham Young University
  • Jodi A Scheffler, United States Department of Agriculture
  • Steve Hague, Texas A&M University
  • Alan E Pepper, Texas A&M University
  • James Frelichowski, United States Department of Agriculture
  • Cindy T Lawley, Illumina Inc
  • Don C Jones, Cotton Incorporated
  • Richard G Percy, United States Department of Agriculture
  • David M Stelly, Texas A&M University
Document Type
Article
Publication Version
Published Version
Publication Date
1-1-2017
DOI
10.1186/s12870-017-0981-y
Abstract

Background

Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. Results

The SNP markers distinctly separated G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content. Conclusions

Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton.

Comments

This article is published as Hinze, Lori L., Amanda M. Hulse-Kemp, Iain W. Wilson, Qian-Hao Zhu, Danny J. Llewellyn, Jen M. Taylor, Andrew Spriggs et al. "Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array." BMC plant biology 17, no. 1 (2017): 37. 10.1186/s12870-017-0981-y

Copyright Owner
The Author(s)
Language
en
File Format
application/pdf
Citation Information
Jonathan F. Wendel, Lori L Hinze, Amanda M Hulse-kemp, Iain W Wilson, et al.. "Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array" BMC Plant Biology Vol. 17 Iss. 1 (2017) p. 37
Available at: http://works.bepress.com/jonathan_wendel/71/