QTL mapping and underlying genes for heat tolerance in grapevine (Rhine Riesling × Cabernet Sauvignon) under field conditions

Abstract

QTL analysis for key physiological traits assessed during hot days highlighted 26 genomic regions and promising candidate genes for thermotolerance and response to light stress under field conditions in grapevine. Grapevine is one of the most widely cultivated perennial fruit crops in the world, with its economic relevance mainly related to wine production. Climate change, with global warming and increased frequency of intense phenomena, is greatly affecting viticulture and the wine sector. Thus, studying the genetic factors involved in grapevine response to high temperatures can help to improve vineyard management strategies and support plant breeding innovations. In this experiment, a mapping population (Rhine Riesling × Cabernet Sauvignon) was used to perform a genetic dissection of the physiological response to increased temperatures under vineyard conditions. Photosynthetic activity and stomatal dynamics were evaluated for three seasons during hot days at different plant developmental stages. Results of quantitative trait loci (QTL) analysis highlighted 26 genomic regions that consistently contribute to the eight tested traits. Candidate genes with supporting evidence, underlying QTL clusters with explained variance above 10%, are those associated with signal perception and transduction, protein homeostasis, osmoprotection, photosynthesis and response to radiation which are relevant mechanisms for plant heat acclimation. Within the stable chromosomal intervals identified by this exploratory analysis, other gene predictions emerged that may be tested for their involvement in grapevine resilience to increasing temperatures. The genetic architecture of quantitative traits linked to grapevine heat tolerance investigated under real field conditions, helps to define key targets for adapting an important traditional crop to environmental changes.

Fig. 1

Climograph reporting precipitations (mm) and air temperatures (mean in green, maximum in red and minimum in light-blue) in Giaroni experimental field (Trentino Alto Adige, Italy, 46°18′N, 11°13′E), for seasons 2021–2023 in the period between bud burst-harvest (April–September) (colour figure online)

Fig. 2

Reduction in F_v/F_m (% relative to control) in different phenotyping sessions for all the individuals of the mapping population (‘Rhine Riesling’ × ’Cabernet Sauvignon’) reported with their numerical codes. Phenotyping sessions are indicated as follows: ▲ flowering, ■ berry pea-size, ● pre-véraison, × véraison, * véraison full; while dots are colored based on the year: 2021 (blue), 2022 (red), 2023 (green) (colour figure online)

Fig. 3

Results of MQM analysis (2021–2023) on chlorophyll fluorescence parameters. Parameters are represented with different colors (F_v/F_m in green, Area in red, Phi(Eo) in blue and Phi(Ro) in yellow) while years are reported with different shapes (● 2021, ▲ 2022, ■ 2023). Chromosomes are colored following the increasing frequency with which a genomic region harbors significant QTL according to MQM analysis: from 0 (gray) to 5 repetitions (red) across different datasets and/or parameters. The size of genomic regions highlighted in the figure is reported in basepairs (colour figure online)

Fig. 4

Results of MQM analysis (2021–2023) on leaf transpirational parameters. Parameters are represented with different colors (g_s in green, E in red, VPleaf in blue and Tleaf, in yellow) while years are highlighted with different shapes (● 2021, ▲ 2022, ■ 2023). Chromosomes are colored following the increasing frequency with which a genomic region harbors significant QTL according to MQM analysis: from 0 (gray) to 5 repetitions (red) across different datasets and/or parameters. The size of genomic regions highlighted in the figure is reported in basepairs (colour figure online)