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.

植物的生长，发育和营养品质取决于氨基酸稳态，尤其是种子中的稳态。然而，我们对影响氨基酸含量和组成的潜在遗传学的理解仍然有限，迄今为止仅鉴定了少数候选基因和数量性状基因座。对决定氨基酸水平的遗传学和生物学过程的了解不断增强，将使研究人员能够利用此信息进行植物育种和生物学发现。为了实现这一目标，我们使用基因组预测来识别与拟南芥模型植物种子中的游离氨基酸（FAA）组成相关并因此可能对其产生影响的生物学过程。。根据代谢途径注释将标记分为几类，并使用基因组划分模型进行拟合，以评估每种途径对解释的遗传力，模型拟合和预测能力的影响。选定的途径包括已知的影响FAA组成的过程，尽管程度不明，但涉及四类：氨基酸，核心，特化和蛋白质代谢。使用这种方法，除了找到新的性状-途径关联外，我们还鉴定了包含FAA性状已知变体的途径的关联。直接参与FAA调节的与氨基酸代谢有关的标记物改善了对支链氨基酸和组氨酸的预测能力。基因组划分的使用还揭示了跨生化家族的模式，其中丝氨酸衍生的FAA与蛋白质相关注释相关，芳香族FAA与专门的代谢途径相关。综上所述，这些发现提供了证据，证明基因组分配是揭示种子中FAA性状的生物学过程的相对贡献的可行策略，为指导假设检验和缩小候选基因的搜索空间提供了有希望的框架。