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Canadian Field Crop Genetics Improvement Cluster, Activity 9: Canadian research consortium for next generation selection in soybean

Principal Investigator

François Belzile

Research Institution

Université Laval

External Funding Partners

This project is part of the $10.3 million Canadian Field Crop Genetics Improvement Cluster funded by the Canadian Field Crop Research Alliance (CFCRA) and Agriculture and Agri-Food Canada (AAFC) through the Industry-Led Research and Development Stream of the Growing Forward 2 AgriInnovation Program. Grain Farmers of Ontario is a founding member of the CFCRA.

Project Start

April 2013

Project End

March 2018


  • Develop a genomic selection model in soybean germplasm that can accurately infer field performance strictly from genetic information.
  • Optimize high-throughput methods to genotype 1,000 individual plants and to retain the individuals in each cross that have the highest predicted yield.
  • Compare how the lines selected strictly on the basis of their genotype compare with those selected by the breeder based on his/her individual plant selections in field trials for 2 years to determine their yield.


  • The development of a genomic selection model that potentially allows breeders to accurately predict the field performance of a soybean line on the basis of its genetic makeup.
  • The improvement of a rapid and cost-effective genetic characterization process for large numbers of individual soybean plants may lead to the commercialization of superior soybean cultivars.

Scientific Summary

In plant breeding, one of the key challenges is identifying the best progeny obtained in a cross. At advanced stages, the information derived from extensive field testing is of excellent quality and provides a good basis for selection. However, in earlier generations, breeders need to make decisions based on the appearance of a single plant. This provides for a very limited amount of information on which decisions to keep or discard a line must be based. It is at this step that we hypothesize that technology can provide a means to improve selection in the participating public soybean breeding programs. Recent technological advances in the areas of DNA sequencing and genotyping now make it possible to rapidly and cost-effectively examine tens of thousands of genetic markers in hundreds of individual plants. These technological advances create the opportunity to innovate radically in how marker information can be used in support of breeding efforts, such as genomic selection (GS). The premise behind GS is that, given sufficient marker information and a good model linking genetic information with actual agronomic performance, it is possible to predict the performance of a line simply based on its genetic makeup.

We propose to test this hypothesis by carrying out GS in the three public breeding programs in three phases. In phase 1, we will build our model to predict yield based solely on the genetic makeup of an individual soybean line or cultivar using existing/new field trial and marker data from a total of 300-400 lines. In phase 2, we will use powerful genetic analysis tools to characterize the genetic makeup of 1,000 individual progeny plants from crosses. In phase 3, the progeny of selected plants will be tested in field trials to determine their actual performance and examine how the predicted performance based on our GS model compares with the observed performance under field conditions.