Association study of phenology, yield and quality related traits in table grapes using SSR and SNP markers

Zarouri, Belkacem

doi:10.20868/upm.thesis.43315

Cited by 4 publications

(9 citation statements)

References 11 publications

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“…Zarouri 2016, but did not recover other GWAS hits from Zarouri 2016 new QTLs on chromosomes 1 and 3 but did not recover the GWAS hits from the other studies, on chromosomes 5 (Zarouri, 2016) and 13 (Laucou et al, 2018).…”

Section: Qtl Detection By Gwas and Identification Of Candidate Genesmentioning

confidence: 83%

“…We also compared our reliable QTLs with significant GWAS hits from other publications in grapevine. Only two traits (cluster and berry weights) were phenotyped in at least one other study and for which at least one significant GWAS hit was found (Zarouri, 2016;Laucou et al, 2018;Guo et al, 2019). For berry weight, out of the 10 QTLs we found, 8 were deemed new on chromosomes 1, 2, 8, 11, 15 and 17.…”

Section: Qtl Detection By Gwas and Identification Of Candidate Genesmentioning

confidence: 94%

“…Other research groups are invited to contact us for more details. In parallel, the panel will be studied for traits related to drought in more controlled environments, extending what was done on a bi-parental cross (Coupel-Ledru et al, 2014, 2016.…”

Section: Genomic Prediction and The Wider Goal Of Understanding Genementioning

confidence: 99%

“…Yang et al (2017) used only 187 SSRs and 96 genotypes. Moreover, most of these studies, as well as Zarouri (2016) which analyzed 36k SNPs in 242 cultivars, and Guo et al (2019) which analyzed 32k SNPs in 179 cultivars, used SNP-by-SNP models to test for association.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limits, a few genome-wide association studies (GWASs) were performed in cultivated grapevine diversity panels but did not identify many new QTLs, due to various reasons. Several articles (Myles et al 2011;Zarouri 2016;Migicovsky et al 2017;Laucou et al 2018) harnessed phenotypic data from genetic resources repositories collected without a proper experimental design. Moreover, the first three cited articles used at most 10k SNPs despite the low extent of linkage disequilibrium (Myles et al 2011;Nicolas et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

Flutre

Cunff

Fodor

et al. 2020

Preprint

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SummaryTo cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennials, but requires to understand the genetic architecture of important traits. To go beyond QTL mapping in bi-parental crosses, we exploited a diverse panel of 279 Vitis vinifera L. cultivars. This panel planted in five blocks in the vineyard was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. Such an experimental design allowed us to reliably assess the genotypic values for most traits. The panel was genotyped for 60k SNPs by combining an 18K microarray and sequencing (GBS). Marker densification via GBS markedly increased the proportion of genetic variance explained by SNPs, and two multi-SNP models identified QTLs not found by a SNP-by-SNP model. This led to 489 reliable QTLs using the combined microarray-GBS SNPs for 41% more response variables than a SNP-by-SNP model applied to microarray-only SNPs, and many QTLs were new compared to the results from bi-parental crosses. Prediction accuracy ranging from 0.14 to 0.84 for 80% of the response variables was promising for genomic selection, and provided insights into the genetic architecture of each trait when put in perspective with the number of QTLs and heritability.

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Section: Qtl Detection By Gwas and Identification Of Candidate Genesmentioning

confidence: 83%

Section: Qtl Detection By Gwas and Identification Of Candidate Genesmentioning

confidence: 94%

Section: Genomic Prediction and The Wider Goal Of Understanding Genementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

Flutre

Cunff

Fodor

et al. 2020

Preprint

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A genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

Flutre

Cunff

Fodor³

et al. 2022

G3 Genes|Genomes|Genetics

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To cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennial species such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait loci (QTLs) in bi-parental crosses, we exploited a diversity panel of 279 Vitis vinifera L. cultivars planted in five blocks in the vineyard. This panel was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms (SNPs) by combining an 18K microarray and genotyping-by-sequencing (GBS). The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via GBS markedly increased the proportion of genetic variance explained by SNPs, and two multi-SNP models identified QTLs not found by a SNP-by-SNP model. Overall, 489 reliable QTLs were detected for 41% more response variables than by a SNP-by-SNP model with microarray-only SNPs, many new ones compared to the results from bi-parental crosses. A prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as QTL and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application into breeding are discussed.

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Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies

García-Abadillo,

Barba,

Carvalho

et al. 2024

Horticulture Research

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Addressing the pressing challenges in agriculture necessitates swift advancements in breeding programs, particularly for perennial crops like grapevines. Moving beyond the traditional bi-parental quantitative trait loci (QTL) mapping, we conducted a genome-wide association study encompassing 588 Vitis vinifera L. cultivars from a Chilean breeding program, spanning three seasons and testing 13 key yield-related traits. A strong candidate gene, Vitvi11g000454, located on chromosome 11 and related to plant response to biotic and abiotic stresses through jasmonic acid signaling, was associated with berry width and holds potential for enhancing berry size in grape breeding. We also mapped novel QTL associated with post-harvest traits across chromosomes 2, 4, 9, 11, 15, 18, and 19, broadening our grasp on the genetic intricacies dictating fruit post-harvest behavior, including decay, shriveling, and weight loss. Leveraging gene ontology annotations, we drew parallels between traits and scrutinized candidate genes, laying a robust groundwork for future trait-feature identification endeavors in plant breeding. We also highlighted the importance of carefully considering the choice of the response variable in GWAS analyses, as the use of BLUEs corrections in our study may led to the suppression of some common QTL in grapevine traits. Our results underscore the imperative of pioneering non-destructive evaluation techniques for long-term conservation traits, offering grape breeders and cultivators insights to improve post-harvest table grape quality and minimize waste.

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Association study of phenology, yield and quality related traits in table grapes using SSR and SNP markers

Cited by 4 publications

References 11 publications

Genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

Genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

A genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies

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