Open AccessAn Assessment of the Relative Influences of Genetic Background, Functional Diversity at Major Regulatory Genes, and Transgenic Constructs on the Tomato Fruit Metabolome
Author(s) -
DiLeo Matthew V.,
Bakker Meghan den,
Chu Elly Yiyi,
Hoekenga Owen A.
Publication year - 2014
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.3835/plantgenome2013.06.0021
Subject(s) - biology , linear discriminant analysis , principal component analysis , metabolomics , metabolome , transgene , genetics , computational biology , genotype , false discovery rate , genetic diversity , phenotype , microbiology and biotechnology , gene , evolutionary biology , statistics , bioinformatics , population , mathematics , demography , sociology
While the greatest strength of systems biology may be to measure tens of thousands of variables across different genotypes, this simultaneously presents an enormous challenge to statistical analysis that cannot be completely solved with conventional approaches that identify and rank differences. Here we examine a diverse panel of conventional and transgenic, field‐grown tomato fruits ( Solanum lycopersicum L.) by liquid chromatography–mass spectrometry (LC‐MS) metabolic fingerprinting. We used a progression of statistics to examine phenotypic variation observed. While clear trends were found by principal component analysis (PCA) related to genetic background and ripeness, it could not detect differences between transgenic genotypes and their nontransgenic parent variety. Partial least squares discriminant analysis (PLS‐DA), a supervised method, identified 15 metabolic features of potential interest, but only five were significantly different between the transgenic lines and their nontransgenic parent. Weighted correlation network analysis (WGCNA) recognized relationships among these features and others, suggesting that a small suite of highly correlated compounds accumulated to significantly lower levels in the transgenic genotypes. We assert that metabolic fingerprinting with a series of statistical methods is an efficient and powerful approach to examine both large and small genetic effects on phenotypes of high value or interest.