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APPLICATIONS OF HIGH-THROUGHPUT PHENOTYPING IN SOYBEAN (Glycine max L. Merr) BREEDING
thesisposted on 01.05.2020 by Fabiana Freitas Moreira
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
The rapid expansion of high-throughput phenotyping (HTP) platforms in agronomic research has led to a major shift in plant science towards time-series phenotyping that can track plant development through its life stages, providing an opportunity to dissect the genetic basis of longitudinal traits. Plant breeders can now phenotype large populations during the growing season and promote the desirable genetic gain for the traits of interest in specific time points within their breeding program. The biggest challenge is to use the various tools in a practical way to understand the many complexities of plant growth and development and breeding implications. This dissertation explores interdisciplinary frameworks to assess different applications of HTP for longitudinal traits in soybean breeding. We provide a review outlining the current analytical approaches in quantitative genetics and genomics to adequately use high-dimensional phenomic data. Examples, advantages, and pitfalls of each approach, and future research directions and opportunities are explored. Among longitudinal traits in soybean, average canopy coverage (ACC) and above-ground biomass (AGB) are promising traits to strategic improve yield gain. Soybean ACC is highly heritable, with a high genetic correlation to yield and can be effectively measured by unmanned aerial systems (UAS). This study reveals that progeny rows selection using yield given ACC (Yield|ACC) selected the most top-ranking lines in advanced yield trials, which emphasizes the value of HTP of ACC for selection in the early stages of soybean breeding. In addition, we developed a HTP methodology to predict soybean AGB over several days after planting (DAP) and assessed the quantitative genomic properties of temporal AGB using random regression models (RRM). Our results show that AGB narrow-sense heritability estimates fluctuated over time and the genetic correlation of AGB between DAP decreased as the days went further apart. Considering the trait heritability, high prediction accuracies suggest that AGB is a good indicator trait for genomic selection in soybean breeding. Different genomic regions were found to be associated with AGB over time with potential time-specific SNPs playing a role in the trait expression. Similarly, candidate genes were identified with potential different patterns of expression over time. This study presents novel genetic knowledge for longitudinal traits in soybean and may contribute to the development of new cultivars with high yield and optimized AGB. This is the first application of RRM for genomic evaluation of a longitudinal trait in soybean and provides a foundation that RRM can be an effective approach to understand the temporal genetic architecture of a longitudinal trait in other crops.