Open AccessA novel approach to identify genes that determine grain protein deviation in cereals
Author(s) -
Mosleth Ellen F.,
Wan Yongfang,
Lysenko Artem,
Chope Gemma A.,
Penson Simon P.,
Shewry Peter R.,
Hawkesford Malcolm J.
Publication year - 2015
Publication title -
plant biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.525
H-Index - 115
eISSN - 1467-7652
pISSN - 1467-7644
DOI - 10.1111/pbi.12285
Subject(s) - biology , cultivar , principal component analysis , trait , genetic variation , multivariate statistics , explained variation , gene , yield (engineering) , genetic variability , agronomy , genetics , statistics , mathematics , genotype , materials science , computer science , metallurgy , programming language
Summary Grain yield and protein content were determined for six wheat cultivars grown over 3 years at multiple sites and at multiple nitrogen (N) fertilizer inputs. Although grain protein content 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 identify gene transcripts significantly related to GPD across environments. The yield and protein content were initially adjusted for nitrogen fertilizer inputs and then adjusted for yield (to remove the negative correlation with protein content), resulting in a parameter termed corrected GPD. Significant genetic variation in corrected GPD was observed for six cultivars grown over a range of environmental conditions (a total of 584 samples). Gene transcript profiles were determined in a subset of 161 samples of developing grain to identify transcripts contributing to GPD. Principal component analysis (PCA), analysis of variance (ANOVA) and means of scores regression (MSR) were used to identify individual principal components (PCs) correlating with GPD alone. Scores of the selected PCs, which were significantly related to GPD and protein content but not to the yield and significantly affected by cultivar, were identified as reflecting a multivariate pattern of gene expression related to genetic variation in GPD. Transcripts with consistent variation along the selected PCs were identified by an approach hereby called one‐block means of scores regression (one‐block MSR).