Quantitative trait locus mapping associated with earliness and fruit weight in tomato

Abstract

The flowering time is regarded as an important factor that affects yield in various crops. In order to understand how the molecular basis controlling main components of earliness in tomato (Solanum lycopersicum L.), and to deduce whether the correlation between fruit weight, days to flowering and seed weight, is caused by pleiotropic effects or genetic linkage, a QTLs analysis was carried out using an F 2 interspecific population derived from the cross of S. lycopersicum and S. pimpinellifolium. The analysis revealed that most of the components related to earliness were independent due to the absence of phenotypic correlation and lack of co-localization of their QTLs. QTLs affecting the flowering time showed considerable variation over time in values of explained phenotypic variation and average effects, which suggested dominance becomes more evident over time. The path analysis showed that traits such as days to flowering, seed weight, and length of the first leaf had a significant effect on the expression of fruit weight, confirming that their correlations were due to linkage. This result was also confirmed in two genomic regions located on chromosomes 1 and 4, where despite showing high colocalization of QTLs associated to days to flowering, seed weight and fruit weight, the presence and absence of epistasis in dfft1.1 × dftt4.1 and fw1.1 × fw4.1, suggested that the linkage was the main cause of the co-localization.

Prediction accuracy of genomic selection models for earliness in tomato

“…The prediction values for days to flowering of the first inflorescence ranged from 0.38 to 0.41, whereas the range increased from 0.53 to 0.55 for days to flowering of the third inflorescence. In a previous study, we detected a major QTL at flowering called dfft1.1, which increased its effect over time when measured from the first to third truss (Hernández-Bautista et al, 2016b). Therefore, the increasing range found in the present study could be explained by the increased effect of dfft1.1, showing that this QTL increases the prediction accuracy of the statistical model.…”

“…This same pattern was observed by Hernández-Bautista et al (2016a), who reported that the prediction accuracy of GBLUP and LASSO increased when it was evaluated for traits with heritability greater than 0.6. Previous classical and molecular studies classified days to ripening and emergence as traits with an intermediate or low heritability, and that they are controlled by small QTL effects (Grandillo and Tanksley, 1996;Haggard et al, 2015;Hernández-Bautista et al, 2016b). Therefore, the poor predictive ability found in the models for both traits occurred because both traits are controlled by many minor QTLs and the limited number of markers used in the present study.…”

“…This site has an average annual temperature of 27 °C and approximately 147 mm yearly rainfall. The F2 population was obtained from hybrid plants derived from the cross between lines LOR82 and 11904 (Hernández-Bautista et al, 2016b). Line LOR82 was obtained from a landrace from Puebla State, Mexico.…”

“…The SSR markers were located on 11 chromosomes, except chromosome 12. Some of these markers have been previously associated with earliness quantitative trait loci (QTLs) in tomato (Hernández-Bautista et al, 2016b). The linkage map spanned 815.71 cM of the tomato genome with mean marker spacing of 13.37 cM.…”