Discovery of the REN11 Locus From Vitis aestivalis for Stable Resistance to Grapevine Powdery Mildew in a Family Segregating for Several Unstable and Tissue-Specific Quantitative Resistance Loci.

Avinash Karn,David Ramming,Siraprapa Brooks,Jonathan Fresnedo-Ramírez,Cheng Zou,Rachel Naegele,Craig Ledbetter,Lance Cadle-Davidson,Franka Gabler,Qi Sun

doi:10.3389/fpls.2021.733899

Abstract

Race-specific resistance loci, whether having qualitative or quantitative effects, present plant-breeding challenges for phenotypic selection and deciding which loci to select or stack with other resistance loci for improved durability. Previously, resistance to grapevine powdery mildew (GPM, caused by Erysiphe necator) was predicted to be conferred by at least three race-specific loci in the mapping family B37-28 × C56-11 segregating for GPM resistance from Vitis aestivalis. In this study, 9 years of vineyard GPM disease severity ratings plus a greenhouse and laboratory assays were genetically mapped, using a rhAmpSeq core genome marker platform with 2,000 local haplotype markers. A new qualitative resistance locus, named REN11, on the chromosome (Chr) 15 was found to be effective in nearly all (11 of 12) vineyard environments on leaves, rachis, berries, and most of the time (7 of 12) stems. REN11 was independently validated in a pseudo-testcross with the grandparent source of resistance, “Tamiami.” Five other loci significantly predicted GPM severity on leaves in only one or two environments, which could indicate race-specific resistance or their roles in different timepoints in epidemic progress. Loci on Chr 8 and 9 reproducibly predicted disease severity on stems but not on other tissues and had additive effects with REN11 on the stems. The rhAmpSeq local haplotype sequences published in this study for REN11 and Chr 8 and 9 stem quantitative trait locus (QTL) can be used directly for marker-assisted selection or converted to SNP assays. In screening for REN11 in a diversity panel of 20,651 vines representing the diversity of Vitis, this rhAmpSeq haplotype had a false positive rate of 0.034% or less. The effects of the other foliar resistance loci detected in this study seem too unstable for genetic improvement regardless of quantitative effect size, whether due to race specificity or other environmental variables.

Highlights

Quantitative resistance (QR) has been of interest to plant breeders to provide more durable resistance than qualitative gene-for-gene resistances that are often race-specific (Poland et al, 2009)
Following natural infection in the vineyard, GPM severity rating distributions were skewed right regardless of whether the data were recorded on a 1–4 severity scale or area under disease progress curve (AUDPC) (Figure 1)
To narrow in on the genetic basis of REN11 resistance, we identified seedlings with recombinations within the quantitative trait locus (QTL) in relation to AUDPC phenotype from 2016. rhAmpSeq markers delimited the region to 13.7–15.3 Mbp on Chr 15, with the marker 15_13822901 being the most predictive of disease severity (Figure 6)

Summary

Introduction

Quantitative resistance (QR) has been of interest to plant breeders to provide more durable resistance than qualitative gene-for-gene resistances that are often race-specific (Poland et al, 2009). While some QR loci confer broad-spectrum resistance, others confer race-specific resistance. At least 13 resistance loci have been characterized against GPM (caused by Erysiphe necator), Few confer qualitative resistance that almost completely restricts pathogen colonization [RUN1, REN4, REN6, and possibly REN5 (Blanc et al, 2012)], and the remainder confer QR of minor-to-moderate effect (CadleDavidson, 2019). Regardless of effect size, with the exception of REN4, all GPM resistance loci characterized in-depth for race specificity have presented at least preliminary indications of being race-specific (Cadle-Davidson, 2019), even resistance sources for which the genetic basis has not to be mapped (Ramming et al, 2012; Barba et al, 2015). These resistance loci are being tracked and stacked primarily with microsatellite markers (Di Gaspero et al, 2012; Pap et al, 2016; Zendler et al, 2017), single nucleotide polymorphism (SNP) markers (Myles et al, 2015; Yang et al, 2016), and/or an integrative amplicon sequencing (AmpSeq) marker multiplex (Fresnedo-Ramírez et al, 2017)

Methods

Results

Conclusion