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Title: Genomic prediction informed by biological processes expands our understanding of the genetic architecture underlying free amino acid traits in dry Arabidopsis seeds

Author
item TURNER-HISSONG, SARAH - University Of Missouri
item BIRD, KEVIN - University Of Missouri
item LIPKA, ALEXANDER - University Of Illinois
item KING, ELIZABETH - University Of Missouri
item Beissinger, Timothy
item ANGELOVICI, RUTHIE - University Of Missouri

Submitted to: G3, Genes/Genomes/Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/20/2020
Publication Date: 11/1/2020
Citation: Turner-Hissong, S.D., Bird, K., Lipka, A.E., King, E.G., Beissinger, T.M., Angelovici, R. 2020. Genomic prediction informed by biological processes expands our understanding of the genetic architecture underlying free amino acid traits in dry Arabidopsis seeds. G3, Genes/Genomes/Genetics. 10(11):4227-4239. https://doi.org/10.1534/g3.120.401240.
DOI: https://doi.org/10.1534/g3.120.401240

Interpretive Summary: Amino acids play a central role in plant growth, development, and human nutrition. A better understanding of the genetic control of amino acid traits will enable researchers to integrate this information into plant breeding and biotechnology. However, despite advances in genetics and genomics, it is difficult to estimate the number of genes controlling complex traits such as amino acid levels in seeds or the magnitude of their effects. In this study we apply a technique known as "genomic prediction" to specific subsets of genes to evaluate how many genes contribute to variability in amino acid composition, how these genes are distributed in the genomes of plants, and the magnitude of the effects that these genes have on changes amino acid composition in seeds. This approach will enhance the ability of scientists to study complex plant traits, such as the amino acid composition of seeds, and to generate the new crop germplasm that is crucial to the future for U.S. farmers and consumers.

Technical Abstract: Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana. Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.