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

Julian García-Abadillo , Paola Barba , Tiago Carvalho , Viviana Sosa-Zuñiga , Roberto Lozano , Humberto Fanelli Carvalho , Miguel Garcia-Rojas , Erika Salazar , Julio Isidro y Sánchez

Horticulture Research ›› 2024, Vol. 11 ›› Issue (2) : 283

PDF (1627KB)
Horticulture Research ›› 2024, Vol. 11 ›› Issue (2) :283 DOI: 10.1093/hr/uhad283
Articles
research-article
Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies
Author information +
History +
PDF (1627KB)

Abstract

Addressing the pressing challenges in agriculture necessitates swift advancements in breeding programs, particularly for perennial crops like grapevines. Moving beyond the traditional biparental quantitative trait loci (QTL) mapping, we conducted a genome-wide association study (GWAS) 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 best linear unbiased estimators (BLUEs) corrections in our study may have 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.

Cite this article

Download citation ▾
Julian García-Abadillo, Paola Barba, Tiago Carvalho, Viviana Sosa-Zuñiga, Roberto Lozano, Humberto Fanelli Carvalho, Miguel Garcia-Rojas, Erika Salazar, Julio Isidro y Sánchez. Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies. Horticulture Research, 2024, 11 (2) : 283 DOI:10.1093/hr/uhad283

登录浏览全文

4963

注册一个新账户 忘记密码

Acknowledgements

This project was funded by ANID FONDECYT under grant agreement No 11161044. P.B. and M.G.-R. received funding from CORFO INNOVA 09PMG-7229 and INIA/MINAGRI (code 500495-70). J.I.yS. was supported by the Beatriz Galindo Program (BEAGAL18/00115) from the Ministerio de Educación y Formación Profesional of Spain and the Severo Ochoa Program for Centres of Excellence in R&D from the “Agencia Estatal de Investigación” of Spain, grant SEV-2016- 0672 (2017-2021) to the CBGP. J.G.-A. is working under a UPM predoctoral grant as part of the program “Programa Propio I +D+i” financed by the Universidad Politécnica de Madrid. H.F.C. received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 818144. E.S. received funding from CORFO-UC Davis Chile Project 13CEI2-21852 and INIA/MINAGRI Conservation of Genetic Resources Program (Code 501453-70).

Author contribution

The study was conceived by J.I.yS. and P.B., who also obtained funding. J.G.-A. conducted statistical and a part of bioinformatics analysis, created the figures, and wrote a significant portion of the article. J.I.yS. also wrote a large part of the article. P.B. contributed experimental and genomic data. T.C. contributed to pre-data analysis. R.L. and H.F.C. performed bioinformatic analysis and genotyping; E.S. provided genetic resources and phenotypic data; and M.G.-R. collected phenotypic data. The manuscript was drafted by J.I.yS., J.G.-A., V.S.-Z., and P.B. All authors participated in the discussion of the results, reviewed the manuscript, and contributed to the article’s development. The submitted version of the article has been approved by all authors.

Data availability

The data underlying the findings presented in this article are available in the GitHub repository maintained by TheRocinante-lab at https://github.com/TheRocinante-lab/Publications/tree/main/2024/GarciaAbadilloEtAl_Dissecting. This repository contains all the data necessary to reproduce the results presented in this paper. Additionally, the repository is freely accessible to anyone who wishes to explore the data or use it for their own research purposes. We encourage interested researchers to take advantage of this resource and to contact us if they have any questions or would like more information about the data.

Conflict of interest statement

The authors declare no conflict of interest.

Supplementary data

Supplementary data is available at Horticulture Research Journal online.

References

[1]

Bettoni JC, Marković Z, Bi W. et al. Grapevine shoot tip cryopreservation and cryotherapy: secure storage of disease-free plants. Plan Theory. 2021; 10:2190

[2]

OIV.Estadã-sticas mundiales.

[3]

Alston JM, Sambucci O. Grapes in the world economy. In: Cantu D, Andrew Walker M,eds. The Grape Genome. Compendium of Plant Genomes. Cham: Springer International Publishing, 2019,1-24

[4]

Doligez A, Bertrand Y, Farnos M. et al. New stable qtls for berry weight do not colocalize with qtls for seed traits in cultivated grapevine (Vitis vinifera L.). BMC Plant Biol. 2013; 13:217-6

[5]

Piva CR, Garcia JLL, Morgan W. The ideal table grapes for the Spanish market. Rev Bras Frutic. 2006; 28:258-61

[6]

Sato A, Yamada M. Berry texture of table, wine, and dual-purpose grape cultivars quantified. HortScience. 2003; 38:578-81

[7]

Varoquaux F, Blanvillain R, Delseny M. et al. Less is better: new approaches for seedless fruit production. Trends Biotechnol. 2000; 18:233-42

[8]

Carvajal-Millán E, Carvallo T, Orozco JA. et al. Polyphenol oxidase activity, color changes, and dehydration in table grape rachis during development and storage as affected by n-(2-chloro-4-pyridyl)-n-phenylurea. J Agric Food Chem. 2001; 49:946-51

[9]

Gardea AA, Martinez-Tellez MA, Sanchez A. et al. Post-Harvestweight Loss of Flame Seedless Clusters. In Rantz, J.M., Ed. Proceedings of the International Symposium of Table Grape Production, Pages 203-206. Davis, CA, USA: American Society of Enology and Viticulture; 1994:

[10]

Lichter A, Kaplunov T, Zutahy Y. et al. Physical and visual properties of grape rachis as affected by water vapor pressure deficit. Postharvest Biol Technol. 2011; 59:25-33

[11]

Ramteke SD, Vikas Urkude SP,Bhagwat SR. Berry cracking; its causes and remedies in grapes-a review. Trends in Biosciences. 2017; 10:549-56

[12]

Reisch BI, Owens CL, Cousins PS. Grape. In: Badenes ML, Byrne DH,eds. Fruit Breeding. Handbook of Plant Breeding, vol. 8. Boston, MA: Springer, 2012,225-62

[13]

Rolle L, Giacosa S, Gerbi V. et al. Varietal comparison of the chemical, physical, and mechanical properties of five colored table grapes. Int J Food Prop. 2013; 16:598-612

[14]

Ejsmentewicz T, Balic I, Sanhueza D. et al. Comparative study of two table grape varieties with contrasting texture during cold storage. Molecules. 2015; 20:3667-80

[15]

Lobato-Gómez M, Hewitt S, Capell T. et al. Transgenic and genome-edited fruits: background, constraints, benefits, and commercial opportunities. Horticulture Research. 2021; 8:166

[16]

Mirdehghan SH, Rahimi S. Pre-harvest application of polyamines enhances antioxidants and table grape (Vitis vinifera L.) quality during postharvest period. Food Chem. 2016; 196:1040-7

[17]

Savadi S, Mangalassery S, Sandesh MS. Advances in genomics and genome editing for breeding next generation of fruit and nut crops. Genomics. 2021; 113:3718-34

[18]

Migicovsky Z, Sawler J, Gardner KM. et al. Patterns of genomic and phenomic diversity in wine and table grapes. Horticulture research. 2017; 4:17035

[19]

Edge-Garza DA, Luby JJ, Peace C. Decision support for cost-efficient and logistically feasible marker-assisted seedling selection in fruit breeding. Mol Breed. 2015; 35:1-15

[20]

Muñoz-Espinoza C, Di Genova A, Correa J. et al. Transcriptome profiling of grapevine seedless segregants during berry development reveals candidate genes associated with berry weight. BMC Plant Biol. 2016; 16:1-17

[21]

Töpfer R, Hausmann L, Harst M. et al. New horizons for grapevine breeding. Fruit, Vegetable and Cereal Science and Biotechnology. 2011; 5:79-100

[22]

Bouquet A, Danglot Y. Inheritance of seedlessness in grapevine (Vitis vinifera L.). Vitis. 1996; 35:35-42

[23]

Lahogue F, This P, Bouquet A. Identification of a codominant scar marker linked to the seedlessness character in grapevine. Theor Appl Genet. 1998; 97:950-9

[24]

Mejía N, Gebauer M, Muñoz L. et al. Identification of qtls for seedlessness, berry size, and ripening date in a seedless x seedless table grape progeny. Am J Enol Vitic. 2007; 58:499-507

[25]

Mejia N, Soto B, Guerrero M. et al. Molecular, genetic and transcriptional evidence for a role of vvagl 11 in stenospermocarpic seedlessness in grapevine. BMC Plant Biol. 2011; 11:57-19

[26]

Ocarez N, Jiménez N, Núñez R. et al. Unraveling the deep genetic architecture for seedlessness in grapevine and the development and validation of a new set of markers for vviagl11-based gene-assisted selection. Genes. 2020; 11:151

[27]

Royo C, Torres-Pérez R, Mauri N. et al. The major origin of seedless grapes is associated with a missense mutation in the mads-box gene vviagl11. Plant Physiol. 2018; 177:1234-53

[28]

Cabezas JA, Cervera MT, Leonor Ruiz-García J. et al. A genetic analysis of seed and berry weight in grapevine. Genome. 2006; 49:1572-85

[29]

Houel C, Chatbanyong R, Doligez A. et al. Identification of stable qtls for vegetative and reproductive traits in the microvine (Vitis vinifera L.) using the 18 k infinium chip. BMC Plant Biol. 2015; 15:1-19

[30]

Viana AP, Riaz S, Walker MA. et al. Genetic dissection of agronomic traits within a segregating population of breeding table grapes. Genet Mol Res. 2013; 12:951-64

[31]

Costantini L, Battilana J, Lamaj F. et al. Berry and phenology-related traits in grapevine (Vitis vinifera L.): from quantitative trait loci to underlying genes. BMC Plant Biol. 2008; 8:1-17

[32]

Correa J, Mamani M, Muñoz-Espinoza C. et al. New stable qtls for berry firmness in table grapes. Am J Enol Vitic. 2016; 67:212-7

[33]

Correa J, Ravest G, Laborie D. et al. Quantitative trait loci for the response to gibberellic acid of berry size and seed mass in tablegrape (Vitis vinifera L.). Aust J Grape Wine Res. 2015; 21:496-507

[34]

Zarouri B. Association Study of Phenology, Yield and Quality Related Traits in Table Grapes Using SSR and SNP Markers, PhD thesis,. Agronomos, Universidad Politécnica de Madrid (Spain); 2016:

[35]

Ban Y, Mitani N, Sato A. et al. Genetic dissection of quantitative trait loci for berry traits in interspecific hybrid grape (Vitis labruscana × Vitis vinifera). Euphytica. 2016; 211:295-310

[36]

Fanizza G, Lamaj F, Costantini L. et al. Qtl analysis for fruit yield components in table grapes (Vitis vinifera). Theor Appl Genet. 2005; 111:658-64

[37]

Fischer BM, Salakhutdinov I, Akkurt M. et al. Quantitative trait locus analysis of fungal disease resistance factors on a molecular map of grapevine. Theor Appl Genet. 2004; 108:501-15

[38]

Smoliga JM, Baur JA, Hausenblas HA. Resveratrol and health-a comprehensive review of human clinical trials. Mol Nutr Food Res. 2011; 55:1129-41

[39]

Zhao YH, Guo YS, Lin H. et al. Quantitative trait locus analysis of grape weight and soluble solid content. Genet Mol Res. 2015; 14:9872-81

[40]

Richter R, Gabriel D, Rist F. et al. Identification of co-located qtls and genomic regions affecting grapevine cluster architecture. Theor Appl Genet. 2019; 132:1159-77

[41]

Liang Z, Duan S, Sheng J. et al. Whole-genome resequencing of 472 vitis accessions for grapevine diversity and demographic history analyses. Nat Commun. 2019; 10:1190

[42]

Zhang C, Cui L, Fang J. Genome-wide association study of the candidate genes for grape berry shape-related traits. BMC Plant Biol. 2022; 22:42:5-20

[43]

Buckler E, Gore M, Zhu C. et al. Status and prospects of association mapping in plants. The plant genome. 2008; 1:

[44]

Flutre T, Le Cunff L, Fodor A. et al. A genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs. G3. 2022; 12:

[45]

Guo D-L, Zhao H-L, Li Q. et al. Genome-wide association study of berry-related traits in grape [Vitis vinifera L.] based on genotyping-by-sequencing markers. Horticulture research. 2019; 6:11

[46]

Laucou V, Launay A, Bacilieri R. et al. Extended diversity analysis of cultivated grapevine Vitis vinifera with 10k genome-wide snps. PLoS One. 2018; 13:e0192540

[47]

Liu H-J, Yan J. Crop genome-wide association study: a harvest of biological relevance. Plant J. 2019; 97:8-18

[48]

Tello J, Ibáñez J. Status and prospects of association mapping in grapevine. Plant Sci. 2023; 327:111539

[49]

Chuan Z, Jiu-yun WU, Li-wen CUI. et al. Mining of candidate genes for grape berry cracking using a genome-wide association study. Journal of integrative Agriculture. 2022; 21:2291-304

[50]

Peterson RE, Kuchenbaecker K, Walters RK. et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019; 179:589-603

[51]

Breiman L. Bagging predictors. Mach Learn. 1996; 24:123-40

[52]

Zhang H, Fan X, Zhang Y. et al. Identification of favorable snp alleles and candidate genes for seedlessness in Vitis vinifera L. using genome-wide association mapping. Euphytica. 2017; 213:1-13

[53]

Larsson SJ, Lipka AE, Buckler ES. Lessons from dwarf8 on the strengths and weaknesses of structured association mapping. PLoS Genet. 2013; 9:e1003246

[54]

Costantini L, Battilana J, Lamaj F. et al. Berry and phenology-related traits in grapevine (Vitis vinifera L.): from quantitative trait loci to underlying genes. BMC Plant Biol. 2008; 8:1-17

[55]

Du Plessis L, ˇSkunca N, Dessimoz C. The what, where, how and why of gene ontology-a primer for bioinformaticians. Brief Bioinform. 2011; 12:723-35

[56]

Gaudet P, Livstone MS, Lewis SE. et al. Phylogenetic-based propagation of functional annotations within the gene ontology consortium. Brief Bioinform. 2011; 12:449-62

[57]

Holmans P, Green EK, Pahwa JS. et al. Gene ontology analysis of gwa study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet. 2009; 85:13-24

[58]

Lebrec JJ, Huizinga TW, Toes RE. et al. Integration o f gene ontology pathways with North American Rheumatoid Arthritis Consortium genome-wide association data via linear modeling. BMC Proc. 2009; 3:S94

[59]

Huang M, Liu X, Zhou Y. et al. Blink: a package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience. 2019; 8(2):giy154.

[60]

Wang J, Zhang Z. Gapit version 3: boosting power and accuracy for genomic association and prediction. Genomics, Proteomics & Bioinformatics. 2021; 19:629-40

[61]

Wang M, Vannozzi A, Wang G. et al. Genome and transcriptome analysis of the grapevine (Vitis vinifera L.) wrky gene family. Horticulture Research. 2014; 1:14016

[62]

Li S, Geng X, Chen S. et al. The co-expression of genes involved in seed coat and endosperm development promotes seed abortion in grapevine. Planta. 2021; 254:1-16

[63]

Yandi W, Wang Y, Fan X. et al. Qtl mapping for berry shape based on a high-density genetic map constructed by whole-genome resequencing in grape. Horticultural Plant J. 2022; 9:729-42

[64]

Berardini TZ, Reiser L, Li D. et al. The arabidopsis information resource: making and mining the “gold standard”-annotated reference plant genome. Genesis. 2015; 53:474-85

[65]

Hector RD, Burlacu E, Aitken S. et al. Snapshots of pre-rrna structural flexibility reveal eukaryotic 40s assembly dynamics at nucleotide resolution. Nucleic Acids Res. 2014; 42:12138-54

[66]

Elshire RJ, Glaubitz JC, Sun Q. et al. A robust, simple genotyping-by-sequencing (gbs) approach for high diversity species. PLoS One. 2011; 6:e19379

[67]

Glaubitz JC, Casstevens TM, Fei L. et al. Tassel-gbs: a high capacity genotyping by sequencing analysis pipeline. PLoS One. 2014; 9:e90346

[68]

Hyma KE, Barba P, Wang M. et al. Heterozygous mapping strategy (hetmapps) for high resolution genotyping-by-sequencing markers: a case study in grapevine. PLoS One. 2015; 10:e0134880

[69]

Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012; 9:357-9

[70]

Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:1207. 3907, 2012.

[71]

Danecek P, Auton A, Abecasis G. et al. The variant call format and vcftools. Bioinformatics. 2011; 27:2156-8

[72]

Browning BL, Tian X, Zhou Y. et al. Fast two-stage phasing of large-scale sequence data. Am J Hum Genet. 2021; 108:1880-90

[73]

Amadeu RR, Cellon C, Olmestead JW. et al. Aghmatrix: R package to construct relationship matrices for autotetraploid and diploid species: a blueberry example. The Plant Genome. 2016; 9:1-10

[74]

VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008; 91:4414-23

[75]

Zheng X, Levine D, Shen J. et al. A high-performance computing toolset for relatedness and principal component analysis of snp data. Bioinformatics. 2012; 28:3326-8

[76]

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2022:

[77]

Bush WS, Moore JH. Chapter 11: genome-wide association studies. PLoS Comput Biol. 2012; 8:e1002822

[78]

Quinlan AR, Hall IM. Bedtools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841-2

[79]

UniProt Consortium. Uniprot: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019; 47:D506-15

[80]

Wickham H. Httr: Tools for Working with URLs and HTTP, 2023. R package version 1.4.5.

[81]

Wickham H, Averick M, Bryan J. et al. Welcome to the tidyverse. Journal of Open Source Software. 2019; 4:1686

[82]

Wickham H, François R, Henry L. et al. R package version. Biometrics. 2011; 67:678-79

[83]

Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2016:

PDF (1627KB)

91

Accesses

0

Citation

Detail

Sections
Recommended

/