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Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array

  • Author(s): Wang, S
  • Wong, D
  • Forrest, K
  • Allen, A
  • Chao, S
  • Huang, BE
  • Maccaferri, M
  • Salvi, S
  • Milner, SG
  • Cattivelli, L
  • Mastrangelo, AM
  • Whan, A
  • Stephen, S
  • Barker, G
  • Wieseke, R
  • Plieske, J
  • Lillemo, M
  • Mather, D
  • Appels, R
  • Dolferus, R
  • Brown-Guedira, G
  • Korol, A
  • Akhunova, AR
  • Feuillet, C
  • Salse, J
  • Morgante, M
  • Pozniak, C
  • Luo, MC
  • Dvorak, J
  • Morell, M
  • Dubcovsky, J
  • Ganal, M
  • Tuberosa, R
  • Lawley, C
  • Mikoulitch, I
  • Cavanagh, C
  • Edwards, KJ
  • Hayden, M
  • Akhunov, E
  • et al.

Published Web Location

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4265271/
No data is associated with this publication.
Abstract

High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker-trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence-absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat. © 2014 The Authors Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

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