Mode of Inheritance of Primary Metabolic Traits in Tomato  

Abstract

To evaluate components of fruit metabolic composition, we have previously metabolically phenotyped tomato (Solanum lycopersicum) introgression lines containing segmental substitutions of wild species chromosome in the genetic background of a cultivated variety. Here, we studied the hereditability of the fruit metabolome by analyzing an additional year's harvest and evaluating the metabolite profiles of lines heterozygous for the introgression (ILHs), allowing the evaluation of putative quantitative trait locus (QTL) mode of inheritance. These studies revealed that most of the metabolic QTL (174 of 332) were dominantly inherited, with relatively high proportions of additively (61 of 332) or recessively (80 of 332) inherited QTL and a negligible number displaying the characteristics of overdominant inheritance. Comparison of the mode of inheritance of QTL revealed that several metabolite pairs displayed a similar mode of inheritance of QTL at the same chromosomal loci. Evaluation of the association between morphological and metabolic traits in the ILHs revealed that this correlation was far less prominent, due to a reduced variance in the harvest index within this population. These data are discussed in the context of genomics-assisted breeding for crop improvement, with particular focus on the exploitation of wide biodiversity.

Evolutionarily Distinct BAHD N-acyltransferases are Responsible for Natural Variation of Aromatic Amine Conjugates in Rice

“…The variation of almost all PAs in this study was controlled by a few loci with large effects, which is in accordance with a handful of previous studies on specialized metabolites and distinct from those described for QTLs of primary traits (Fridman et al, 2004;Rowe et al, 2008;Schauer et al, 2008;Chan et al, 2010;Joseph et al, 2013;Alseekh et al, 2015;Wen et al, 2015), for which a large number of minor loci are usually detected. Tissue-specific accumulation of metabolites, representing one of the main contributions to metabolite diversity, is of great interest to plant scientists (Schilmiller et al, 2010;Chan et al, 2011;Watanabe et al, 2013).…”

Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits

“…In a previous investigation of QTLs related to metabolic traits using a lower number of lines (n = 76), Schauer et al (2008) detected 104 metabolite QTLs for 22 distinct amino acids in tomato. Our results obtained using a GWAS approach contrast with these results in terms of the number of QTLs.…”

“…Over the last two decades, numerous QTLs have been identified for traits such as fresh weight using linkage approaches (Frary et al, 2000;Zhang et al, 2012;Chakrabarti et al, 2013) but also for other fruit-related traits such as fruit ascorbic acid levels (Stevens et al, 2007), sensory and instrumental quality traits (Causse et al, 2002), sugar and organic acids (Fulton et al, 2002), and metabolic components (Schauer et al, 2008). Large tomato germplasm collections have been characterized at the molecular level using simple sequence repeat (Ranc et al, 2008) and single-nucleotide polymorphism (SNP) markers (Blanca et al, 2012;Shirasawa et al, 2013), giving insights into population structure, tomato evolutionary history, and the genetic architecture of traits of agronomic interest.…”

Synthetic biology of metabolism: using natural variation to reverse engineer systems

“…This difference in power between the studies is more critical because the epistasis typically found for primary metabolism is not pairwise interactions of loci but instead higher-order interactions involving three or more loci leading to large allele specific effects [50 ,51]. The observation of multi-locus epistasis in metabolism is supported by other observations but more work on larger biparental populations needs to be conducted to test if these gene interactions are additive or higher-order epistasis [39,52,53].The ultimate goal of testing if the 1000+ causal genes is to help guide efforts at computational modeling to improve our power to predictively engineer plant metabolism. Quantitative trait locus mapping with human insight has enabled the characterization and prediction of numerous pathways.…”