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This article in TPG

  1. Vol. 10 No. 2
    unlockOPEN ACCESS
     
    Received: Dec 21, 2016
    Accepted: Mar 01, 2017
    Published: June 8, 2017


    * Corresponding author(s): sarah.battenfield@syngenta.com
    j.crossa@cgiar.org
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doi:10.3835/plantgenome2016.12.0130

Increasing Genomic-Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat

  1. Diego Jarquína,
  2. Cristiano Lemes da Silvab,
  3. R. Chris Gaynorc,
  4. Jesse Polandd,
  5. Allan Fritzb,
  6. Reka Howarde,
  7. Sarah Battenfield *f and
  8. Jose Crossa *g
  1. a Dep. of Agronomy and Horticulture, Univ. of Nebraska, Lincoln, NE 68583
    b Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506
    c The Roslin Institute and Royal School of Veterinary Studies, Univ. of Edinburgh, Easter Bush, Midlothian, UK
    d Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., Manhattan, KS 66506
    e Dep. of Statistics. Univ. of Nebraska, Lincoln, NE
    f AgriPro Wheat, Syngenta, Junction City, KS 66441
    g Biometrics and Statistics Unit, CIMMYT, El Batan, Mexico
Core Ideas:
  • Incorporating environmental covariates increases genomic selection accuracy.
  • G × E models can impute known lines into known environments with good accuracy.
  • Breeding programs may exploit genomic selection cross-validation schemes in trial designs.

Abstract

Wheat (Triticum aestivum L.) breeding programs test experimental lines in multiple locations over multiple years to get an accurate assessment of grain yield and yield stability. Selections in early generations of the breeding pipeline are based on information from only one or few locations and thus materials are advanced with little knowledge of the genotype × environment interaction (G × E) effects. Later, large trials are conducted in several locations to assess the performance of more advanced lines across environments. Genomic selection (GS) models that include G × E covariates allow us to borrow information not only from related materials, but also from historical and correlated environments to better predict performance within and across specific environments. We used reaction norm models with several cross-validation schemes to demonstrate the increased breeding efficiency of Kansas State University’s hard red winter wheat breeding program. The GS reaction norm models line effect (L) + environment effect (E), L + E + genotype environment (G), and L + E + G + (G × E) effects) showed high accuracy values (>0.4) when predicting the yield performance in untested environments, sites or both. The GS model L + E + G + (G × E) presented the highest prediction ability (r = 0.54) when predicting yield in incomplete field trials for locations with a moderate number of lines. The difficulty of predicting future years (forward prediction) is indicated by the relatively low accuracy (r = 0.171) seen even when environments with 300+ lines were included.

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