Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population

Noah Dewitt,David Marshall,Jerry Johnson,Mohamed Mergoum,James B Holland,J Paul Murphy,Mohammed Guedira,Gina Brown-Guedira,Edwin Lauer

doi:10.1186/s12864-021-07574-6

Noah Dewitt, David Marshall + Show 7 more

Open Access

https://doi.org/10.1186/s12864-021-07574-6

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Abstract

BackgroundGenetic 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.ResultsWe 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. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. 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.ConclusionsIn a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

Highlights

Wheat is a major food crop, contributing nearly 20% of human calories and protein [1]
A similar pattern was observed for plant height: LA95135 was the only parent genotyped for a major dwarfing allele (Rht-D1b), SS-MPV57 was substantially shorter in all locations (Table 1)
For plant height in Raleigh 2018 and Kinston 2018, the mean genotype value of the recombinant inbred lines (RILs) was closer to the SS-MPV57 parent than the mid-parent value

Summary

Introduction

Wheat is a major food crop, contributing nearly 20% of human calories and protein [1]. Wheat yield is highly polygenic, with variation in yield emerging from variation in other phenotypes each with different genetic bases Plant growth traits such as heading date (when the spike emerges from the flag leaf ) and adult plant height affect yield by both altering resource partitioning between tissues and changing how plants experience environmental factors. Wheat breeders typically select for optimal values of plant height and heading date for a given environment and production system in early generations based on unreplicated head rows. Beyond this selection, improvements in yield resulting from modern plant breeding programs have largely been generated without considering its underlying genetic architecture, including the dependence of final plant yield on variation in plant growth trajectories.

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