A novel approach to identify genes that determine grain protein deviation in cereals

Grain yield and protein content were determined for six wheat cultivars grown over three years at multiple sites and at multiple N-fertilizer inputs. Although grain protein was negatively correlated with yield, some grain samples had higher protein contents than expected based on their yields, a trait referred to as grain protein deviation (GPD). We used novel statistical approaches to calculate GPD across environment and to correlate gene expression in the developing caryopsis with this trait. The yield and protein content were initially adjusted for nitrogen fertilizer inputs, and then adjusted for yield (to remove the negative correlation) resulting in environmental corrected GPD. The transcriptome data for all samples were subjected to Principal Component Analysis (PCA) and ANOVA to identify individual Principal Components (PCs) correlating with GPD alone. Scores of the selected PCs significantly related to cultivar differences and GPD but not to the yield or protein content were identified as reflecting a multivariate pattern of gene expression related to genetic variation in GPD. Sets of genes significant for these PCs and hence GPD were identified as candidate genes determining cultivar differences in GPD.

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