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Semi-automated feature extraction from RGB images for sorghum panicle architecture GWAS
Plant Physiology
  • Yan Zhou, Iowa State University
  • Srikant Srinivasan, Indian Institute of Technology Mandi
  • Seyed Vahid Mirnezami, Iowa State University
  • Aaron Kusmec, Iowa State University
  • Qi Fu, China Agricultural University
  • Lakshmi Attigala, Iowa State University
  • Maria G. Salas-Fernandez, Iowa State University
  • Baskar Ganapathysubramanian, Iowa State University
  • Patrick Schnable, Iowa State University
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Publication Version
Accepted Manuscript
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Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time-consuming for the large populations needed to conduct GWAS (genome-wide association studies) and is difficult for multi-dimensional traits such as volume. A semi-automated phenotyping pipeline (Toolkit for Inflorescence Measurement, TIM) was developed and used to extract uni- and multi-dimensional features from images of 1,064 sorghum (Sorghum bicolor) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel (SAP). GWAS detected 35 unique SNPs associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated SNPs (TASs) are located within chromosomal regions associated with similar traits in previously published QTL and GWAS analysis of sorghum. Additionally, sorghum homologs of maize (Zea mays) and rice (Oryza sativa) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding.


This is a manuscript of an article published as Zhou, Yan, Srikant Srinivasan, Seyed Vahid Mirnezami, Aaron Kusmec, Qi Fu, Lakshmi Attigala, Maria G. Salas Fernandez, Baskar Ganapathysubramanian, and Patrick S. Schnable. "Semi-automated feature extraction from RGB images for sorghum panicle architecture GWAS." Plant Physiology (2018). DOI: 10.1104/pp.18.00974. Posted with permission.

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American Society of Plant Biologists
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Citation Information
Yan Zhou, Srikant Srinivasan, Seyed Vahid Mirnezami, Aaron Kusmec, et al.. "Semi-automated feature extraction from RGB images for sorghum panicle architecture GWAS" Plant Physiology (2018)
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