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. Lynch

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

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. Abstract: 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.

Original languageEnglish (US)
Pages (from-to)419-431
Number of pages13
JournalTheoretical and Applied Genetics
Volume130
Issue number2
DOIs
StatePublished - Feb 1 2017

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Agronomy and Crop Science
  • Genetics

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