An association mapping approach to identify favourable alleles for tomato fruit quality breeding

Abstract

BackgroundGenome Wide Association Studies (GWAS) have been recently used to dissect complex quantitative traits and identify candidate genes affecting phenotype variation of polygenic traits. In order to map loci controlling variation in tomato marketable and nutritional fruit traits, we used a collection of 96 cultivated genotypes, including Italian, Latin American, and other worldwide-spread landraces and varieties. Phenotyping was carried out by measuring ten quality traits and metabolites in red ripe fruits. In parallel, genotyping was carried out by using the Illumina Infinium SolCAP array, which allows data to be collected from 7,720 single nucleotide polymorphism (SNP) markers.ResultsThe Mixed Linear Model used to detect associations between markers and traits allowed population structure and relatedness to be evidenced within our collection, which have been taken into consideration for association analysis. GWAS identified 20 SNPs that were significantly associated with seven out of ten traits considered. In particular, our analysis revealed two markers associated with phenolic compounds, three with ascorbic acid, β-carotene and trans-lycopene, six with titratable acidity, and only one with pH and fresh weight. Co-localization of a group of associated loci with candidate genes/QTLs previously reported in other studies validated the approach. Moreover, 19 putative genes in linkage disequilibrium with markers were found. These genes might be involved in the biosynthetic pathways of the traits analyzed or might be implied in their transcriptional regulation. Finally, favourable allelic combinations between associated loci were identified that could be pyramided to obtain new improved genotypes.ConclusionsOur results led to the identification of promising candidate loci controlling fruit quality that, in the future, might be transferred into tomato genotypes by Marker Assisted Selection or genetic engineering, and highlighted that intraspecific variability might be still exploited for enhancing tomato fruit quality.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0337-9) contains supplementary material, which is available to authorized users.

Genome-wide association-mapping for fruit quality traits in tomato

“…In this study, major associations were detected for AsA, FW, ED, PD and CAR, and minor associations for LYC and SSC. This is in line with earlier association-mapping results (Xu et al 2013;Ruggieri et al 2014).…”

“…In tomato, QTLs detected for the main fruit quality traits have been identified by both linkage-mapping and association-mapping (Paterson et al 1991;Goldman et al 1995;Grandillo and Tanksley 1996;Fulton et al 1997;Chen et al 1999;Frary et al 2000;Causse et al 2002;Causse et al 2004;Mazzucato et al 2008;Berloo et al 2008;Ranc et al 2012;Xu et al 2013;Ruggieri et al 2014). In this study, major associations were detected for AsA, FW, ED, PD and CAR, and minor associations for LYC and SSC.…”

“…Significant associations were detected on both markers, explaining about 16.7 % of the total phenotypic variation. Ruggieri et al (2014) detected one candidate gene Solyc11g071840.1.1 in this region. This gene is significant associated with fruit weight.…”

“…Red boxed are comparisons between previous identified significant associations or genes from the tomato genome and significant associations. Q = QTLs selected from crosses of Cervil 9 Levovil (web: http://www.solgenomics.net); T = QTLs identified in Tanksley et al (1996); C = QTLs identified in Chen et al (1999); F = QTLs identified in Fulton et al (1997); X = Significant associations detected in Xu et al (2013); R = Significant association detected in Ruggieri et al (2014) Euphytica However, markers TES734 and TGS602 were located less than 1 cM to dmw4.1. No significant associations were detected on both markers.…”

“…It can be first used to detect the preliminary locations of QTLs to study the mechanisms underlying phenotypic variation, and then to use linkage analysis for more precise location of those QTLs that are of special interest (Mazzucato et al 2008;Atwell et al 2010;Xu et al 2013). Since the first use to dissect candidate genes on inbred maize lines (Thornsberry et al 2001), GWAS analyses have been widely constructed on a number of major plant species (Ruggieri et al 2014;Wen et al 2014;Hatzig et al 2015;Kumar et al 2015;Matsuda et al 2015;Pers et al 2015). Association mapping-also known as linkage disequilibrium (LD) mapping-is a promising genetic method for the dissection of complex traits in plants (Ersoz et al 2007;Saidou et al 2014).…”