Location: Plant Science ResearchTitle: Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population
|De Witt, Noah|
|GUEDIRA, MOHAMMED - North Carolina State University|
|LAUER, EDWIN - North Carolina State University|
|MURPHY, J - North Carolina State University|
|MERGOUM, MOHAMED - University Of Georgia|
|JOHNSON, JERRY - University Of Georgia|
|Holland, Jim - Jim|
Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/25/2021
Publication Date: 3/2/2021
Citation: De Witt, N., Guedira, M., Lauer, E., Murphy, J.P., Marshall, D.S., Mergoum, M., Johnson, J., Holland, J.B., Brown Guedira, G.L. 2021. Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population. BMC Genomics. 22:402. https://doi.org/10.1186/s12864-021-07574-6.
Interpretive Summary: Heading date (when the wheat spike emerges just prior to flowering) and plant height are two traits collected by breeders and geneticists to understand differences in plants’ growth and development over the course of a season. Plants that show extreme values for these traits – that are too short or tall, or flower too early or late – maybe be sub-optimal for the environmental conditions in a given location. Breeders typically select against plants with extreme values for these traits over multiple seasons of field evaluations, but under some breeding schemes it may be advantageous to predict these values for a given wheat line without having to plant it in the field. The best model to predict values for plant height and heading date will vary based on the number and effect size of the genes that create variation among individuals for these traits. Here we find that for both heading date and plant height in a wheat population, only a small number of genes are responsible for creating genetic differences between individuals. Most models commonly used for phenotype prediction rely on assumptions that the trait is controlled by a large number of small-effect genes, as is the case for yield. We also demonstrate that simpler models considering just the identified genes out-perform standard prediction models. Both the specific novel genes and the optimal model identified in this study can be used to breed wheat cultivars that are better adapted to the local environment.
Technical Abstract: Background: Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. Results: We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. Conclusions: In a population constructed from parents sharing only one known plant height or heading date QTL, almost all additive genetic variation in plant growth traits is associated with those major variants or novel moderate-effect QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, this suggests that genetic variation in plant height heading date likely emerges from similar combinations of major and moderate effect QTL in most of these breeding populations. We may make accurate and much more cost-effective prediction models by targeted genotyping of key SNPs.