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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
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Edité par CCSD ; Genetics Society of America -
The data that support the findings of this study are openly available, for sequences at the NCBI as BioProject PRJNA489354 and, for all the rest, at the Data INRAE repository at https://doi.org/10.15454/8DHKGL. The code that supports the findings of this study is openly available at the Data INRAE repository at https://doi.org/10.15454/8DHKGL. Many of the custom functions we used are available in a R package for reproducibility purposes, rutilstimflutre (Flutre 2019).. International audience. 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 in bi-parental crosses, we exploited a diversity panel of 279 Vitis vinifera L. cultivars planted in 5 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 by combining an 18K microarray and genotyping-by-sequencing. The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via genotyping-by-sequencing markedly increased the proportion of genetic variance explained by single nucleotide polymorphisms, and 2 multi-single nucleotide polymorphism models identified quantitative trait loci not found by a single nucleotide polymorphism-by-single nucleotide polymorphism model. Overall, 489 reliable quantitative trait loci were detected for 41% more response variables than by a single nucleotide polymorphism-by-single nucleotide polymorphism model with microarray-only single nucleotide polymorphisms, many new ones compared with 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 quantitative trait locus 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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