Theoretical and Applied Genetics

, Volume 130, Issue 2, pp 419–431 | Cite as

Genome-wide association mapping and agronomic impact of cowpea root architecture

  • James D. Burridge
  • Hannah M. Schneider
  • Bao-Lam Huynh
  • Philip A. Roberts
  • Alexander Bucksch
  • Jonathan P. LynchEmail author
Original Article


Key message

Genetic analysis of data produced by novel root phenotyping tools was used to establish relationships between cowpea root traits and performance indicators as well between root traits and Striga tolerance.


Selection and breeding for better root phenotypes can improve acquisition of soil resources and hence crop production in marginal environments. We hypothesized that biologically relevant variation is measurable in cowpea root architecture. This study implemented manual phenotyping (shovelomics) and automated image phenotyping (DIRT) on a 189-entry diversity panel of cowpea to reveal biologically important variation and genome regions affecting root architecture phenes. Significant variation in root phenes was found and relatively high heritabilities were detected for root traits assessed manually (0.4 for nodulation and 0.8 for number of larger laterals) as well as repeatability traits phenotyped via DIRT (0.5 for a measure of root width and 0.3 for a measure of root tips). Genome-wide association study identified 11 significant quantitative trait loci (QTL) from manually scored root architecture traits and 21 QTL from root architecture traits phenotyped by DIRT image analysis. Subsequent comparisons of results from this root study with other field studies revealed QTL co-localizations between root traits and performance indicators including seed weight per plant, pod number, and Striga (Striga gesnerioides) tolerance. The data suggest selection for root phenotypes could be employed by breeding programs to improve production in multiple constraint environments.


Quantitative Trait Locus Root Trait Root Architecture Phene Root Crown 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Howard G. Buffet Foundation, the USAID Feed the Future Innovation Laboratory for Climate Resilient Beans, and the Feed the Future Innovation Lab for Collaborative Research on Grain Legumes. Genotyping was supported by the CGIAR Generation Challenge Program. This work was also supported by the USDA National Institute of Food and Agriculture, Hatch Project 4372, the NSF Plant Genome Research Program, NSF 0820624 and the Center for Data Analytics, Georgia, Institute of Technology, Spatial Networks in Biology: Organizing and Analyzing the Structure of Distributed Biological Systems. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the National Institute of Food and Agriculture (NIFA) or the United States Department of Agriculture (USDA).

Compliance with ethical standards

The authors declare that they have no conflict of interest.

Supplementary material

122_2016_2823_MOESM1_ESM.docx (100 kb)
Supplementary material 1 (DOCX 100 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • James D. Burridge
    • 1
  • Hannah M. Schneider
    • 1
  • Bao-Lam Huynh
    • 2
  • Philip A. Roberts
    • 2
  • Alexander Bucksch
    • 3
  • Jonathan P. Lynch
    • 1
    Email author
  1. 1.Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of NematologyUniversity of CaliforniaRiversideUSA
  3. 3.Schools of Biology and Interactive ComputingGeorge Institute of TechnologyAtlantaUSA

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