Genetic architecture and genomic predictive ability of apple quantitative traits across environments

Jung, Michaela; Keller, Beat; Roth, Morgane; Aranzana, María José; Auwerkerken, Annemarie; Guerra, Walter; Al-Rifaï, Mehdi; Lewandowski, M.; Sanin, Nadia; Rymenants, Marijn; Didelot, Frédérique; Dujak, Christian; Forcada, Carolina Fonti; Knauf, Andrea; Laurens, François; Studer, Bruno; Muranty, Hélène; Patocch, Andrea

doi:10.1093/hr/uhac028

Cited by 35 publications

(70 citation statements)

References 80 publications

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“…Therefore, genome-wide prediction might be a more appropriate breeding approach when targeting SSC. Previous studies have reported moderate predictive abilities for the genome-wide prediction of SSC in the apple [38,39]. The SSC QTLs detected in this study could be included in genome-wide prediction models as fixed effects.…”

Section: Breeding Implicationsmentioning

confidence: 72%

Large-Effect QTLs for Titratable Acidity and Soluble Solids Content Validated in ‘Honeycrisp’-Derived Apple Germplasm

2022

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Fruit acidity and sweetness are important fruit quality traits in the apple and are therefore targets in apple breeding programs. Multiple quantitative trait loci (QTLs) associated with titratable acidity (TA) and soluble solids content (SSC) have been previously detected. In this study a pedigree-based QTL analysis approach was used to validate QTLs associated with TA and SSC in a ‘Honeycrisp’-derived germplasm set. TA and SSC data collected from 2014 to 2018 and curated genome-wide single nucleotide polymorphism (SNP) data were leveraged to validate three TA QTLs on linkage groups (LGs) 1, 8, and 16 and three SSC QTLs on LGs 1, 13, and 16. TA and SSC QTL haplotypes were characterized in six University of Minnesota apple breeding families representing eight breeding parents including ‘Honeycrisp’ and ‘Minneiska’. Six high-TA haplotypes, four low-TA haplotypes, 14 high-SSC haplotypes, and eight low-SSC haplotypes were characterized. The results of this study will enable more informed selection in apple breeding programs.

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Section: Breeding Implicationsmentioning

confidence: 72%

Large-Effect QTLs for Titratable Acidity and Soluble Solids Content Validated in ‘Honeycrisp’-Derived Apple Germplasm

2022

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“…The results for genetic subpopulation obtained suggested that the non-Spanish reference cultivars were slightly more similar among them than with the Spanish accessions. In fact, several studies have shown that the European germplasm core collections shared a lot of the non-Spanish reference plant material conserved [ 1 , 6 , 10 , 11 , 16 , 66 , 75 , 76 ]. Moreover, according to the autochthone accessions from the different germplasm collections around the world, domestication and breeding could have caused diversity loss.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the bibliography, scarce studies of phenotypic associations in apples using SSRs can be found [ 80 ]. The most recent studies analyzed several parameters, such as flowering time, harvest date, flesh firmness, ripening index, or polyphenols, but based only on SNP markers [ 12 , 32 , 33 , 34 , 35 , 75 , 76 ]. However, Tsykun et al [ 81 ] reported that the multi-allelic SSRs markers seemed to be best suited for detecting genetic structure than SNPs markers because the SSRs markers had a higher discrimination power than bi-allelic SNP markers [ 82 ].…”

Section: Discussionmentioning

confidence: 99%

Population Structure and Association Mapping for Agronomical and Biochemical Traits of a Large Spanish Apple Germplasm

Mignard

Forcada

Giménez

et al. 2023

Plants

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A basic knowledge of linkage disequilibrium and population structure is necessary in order to determine the genetic control and identify significant associations with agronomical and phytochemical compounds in apple (Malus × domestica Borkh). In this study, 186 apple accessions (Pop1), representing both Spanish native accessions (94) and non-Spanish cultivars (92) from the EEAD-CSIC apple core collection, were assessed using 23 SSRs markers. Four populations were considered: Pop1, Pop2, Pop3, and Pop4. The initial Pop1 was divided into 150 diploid (Pop2) and 36 triploid accessions (Pop3), while for the inter-chromosomal linkage disequilibrium and the association mapping analysis, 118 phenotype diploid accessions were considered Pop4. Thus, the average number of alleles per locus and observed heterozygosity for the overall sample set (Pop1) were 15.65 and 0.75, respectively. The population structure analysis identified two subpopulations in the diploid accessions (Pop2 and Pop4) and four in the triploids (Pop3). Regarding the Pop4, the population structure with K = 2 subpopulations segregation was in agreement with the UPGMA cluster analysis according to the genetic pairwise distances. Moreover, the accessions seemed to be segregated by their origin (Spanish/non-Spanish) in the clustering analysis. One of the two subpopulations encountered was quite-exclusively formed by non-Spanish accessions (30 out of 33). Furthermore, agronomical and basic fruit quality parameters, antioxidant traits, individual sugars, and organic acids were assessed for the association mapping analysis. A high level of biodiversity was exhibited in the phenotypic characterization of Pop4, and a total of 126 significant associations were found between the 23 SSR markers and the 21 phenotypic traits evaluated. This study also identified many new marker-locus trait associations for the first time, such as in the antioxidant traits or in sugars and organic acids, which may be useful for predictions and for a better understanding of the apple genome.

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“…To train the predictor model, two SNP datasets were extracted from the matrix: one of 150 size and shape-related SNPs (tSNPs) from literature [13] or identified by GWAS in our laboratory (data not published), and the one of 150 randomly selected SNPs (rSNPs) using NumPy library [8]. The random selection of SNPs was renewed every time a new model was trained.…”

Section: Genotypic Datamentioning

confidence: 99%

GenoDrawing: An autoencoder framework for image prediction from SNP markers

Jurado-Ruiz

Rousseau

Botía

et al. 2023

Preprint

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Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation. Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically significant heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and improved dataset balance in terms of shape for more precise outcomes.

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Genetic architecture and genomic predictive ability of apple quantitative traits across environments

Cited by 35 publications

References 80 publications

Large-Effect QTLs for Titratable Acidity and Soluble Solids Content Validated in ‘Honeycrisp’-Derived Apple Germplasm

Large-Effect QTLs for Titratable Acidity and Soluble Solids Content Validated in ‘Honeycrisp’-Derived Apple Germplasm

Population Structure and Association Mapping for Agronomical and Biochemical Traits of a Large Spanish Apple Germplasm

GenoDrawing: An autoencoder framework for image prediction from SNP markers

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