Genetic analysis of yield and yield stability traits in spring wheat across diverse Canadian environments


Neupane A, Morrison M, Humphreys G, Cuthbert R, Knox R, Hiebert C, Kumar S, Brauer E, Griffiths S, Hawkesford M, Riche A, and Burt A (2023) Genetic analysis of yield and yield stability traits in spring wheat across diverse Canadian environments. The Canadian Tri-Society meeting of the Canadian Phytopathological Society (CPS), the Canadian Society of Agronomy (CSA) and Canadian Society for Horticultural Science (CSHS), June 17-21, 2023, Ottawa, ON, Canada. Oral presentation, abstract 47.

Plain language summary

This research aims to understand how different traits in wheat affect its yield and stability in different environments. They studied a group of 88 wheat plants in Canada from 2019 to 2022. They looked at how genes and traits like plant height, days to maturity, and grain yield affect wheat's ability to grow and produce grain. This research is part of a larger effort to improve wheat yields in different parts of the world. By understanding how genes affect wheat traits, researchers hope to develop new varieties of wheat that can produce more grain in a wider range of environments.


The goal of this research project is to improve the understanding of the agronomic traits that support yield and yield stability across diverse growing environments. A subset of 88 lines from a double haploid spring wheat population (AAC Brandon/Pasteur) was grown in yield trials at three Canadian sites, Ottawa, Brandon, and Swift Current from 2019 to 2022. Agronomic data were analyzed to model grain yield and to capture yield stability across multiple environments. Genotypic data from SNP-based array platforms was used with the phenotypic data to detect associated QTL. The linkage map of the population was constructed containing 2350 SNP and a total map length of 4096 cM. Major agronomic traits QTL were detected across multiple environments and located on multiple chromosomes, including plant height on chromosomes 4A, 4B, and 5B, days to maturity on 5B, and 7D, grain yield on 6D, test weight on 3B, 4B and 5A, thousand kernel weight on 3B, 4B and 5A, and protein content on 4B and 7A. Analysis for grain yield stability QTL is in progress. Additional image data was captured and used to model biomass accumulation throughout the growing season at the Ottawa site. This phenomics data will be used with multiple yield components to create yield prediction models and to find associated QTL. This project, part of the International Wheat Yield Partnership, aims to identify yield-related traits and linked markers that can reliably improve yield across diverse environments.