Use of genomics and phenomics approaches to characterize agronomic traits and grain yield stability in wheat
Citation
Neupane A, Morrison M, Humphreys G, Cuthbert R, Hiebert C, Kumar S, Brauer E, Griffiths S, Hawkesford M, Riche A, and Burt A (2023) use of genomics and phenomics approaches to characterize agronomic traits and grain yield stability in wheat. Abstract and oral presentation at 5th Canadian Wheat Symposium, 13-16 Nov. 2023, Vancouver, BC, Canada. Oral presentation. Abstract S7.
Résumé en langage clair
This research project aimed to understand why wheat yields vary across different environments. The researchers used three populations of wheat to study their grain yield and other agronomic traits in three locations across Canada over three years. They measured various traits, such as how tall the plants grew, how long it took for them to mature, how much grain they produced, and how heavy the grain was. They did this by taking pictures of the plants throughout the growing season and by using traditional direct measurement methods like weighing the grain. The researchers also looked at how these traits affected the plants' ability to produce grain in different environments. They found that some traits, like plant height and photoperiod (how long the plants were exposed to light), had a significant effect on grain yield.
Overall, 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.
Résumé
This research project used genomics and phenomics approaches to understand variation in agronomic traits and wheat yield stability across multiple environments. The populations used were a double haploid (DH) spring wheat population (AAC Brandon/Pasteur), a near-isogenic line (NIL) spring wheat population in the cv. Paragon background, and a winter wheat diversity panel containing Canadian and UK lines. Populations were phenotyped using image-capture and traditional measures between 2019 and 2022 at Canadian sites (Ottawa, Brandon, Lethbridge, and Swift Current), and in the United Kingdom (Rothamsted). The data set for each population contains six to ten site-years of data. The agronomic and phenometric data were used to develop yield prediction models and to assess yield stability across environments. Genotypic data from SNP-based array platforms was used with the phenotypic data to detect associated QTL. QTL analysis of the spring wheat DH population found QTL for agronomic traits across 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. The preliminary analysis of well-characterized introgressions (plant height, photoperiod, and stay green traits) on the Paragon NIL population showed a significant effect on traits including grain yield. The phenomics data from spring and winter wheat trials will be further analyzed to model grain yield, yield stability, and find associated QTL regions. The aim of this research, conducted as part of the International Wheat Yield Partnership, is to identify traits and markers that can be used to improve the stability of wheat yield gains across diverse growing regions.