Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles.

Abstract

Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56kb to 6.37Mb, with a median of 921 kb, of which 27% had a CI lower than 500kb and 53% had a CI lower than 1Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.