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Using drone retrieved multispectral data for phenomic selection in potato breeding
Saturday, 2024/03/09 | 07:33:19

Alessio Maggiorelli, Nadia Baig, Vanessa Prigge, Julien Bruckmüller, Benjamin Stich

Theoretical and Applied Genetics; March 2024; vol. 137; article 70

 

Abstract

 

Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral refectance data in potato breeding by testing diferent prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral refectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of − 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.

 

See file:///C:/Users/Admin/Downloads/s00122-024-04567-3.pdf

 

Fig. 2:  Predictive abilities (PAs) for every potato trait in every prediction case. The median PA is depicted in each tile as a decimal number and is a result of a fvefold cross-validated prediction with 25 repetitions either with multispectral best linear unbiased prediction (MBLUP) for scenarios one to fve (S1–S5) or with genomic BLUP (GBLUP) for scenario six and seven (S6 & S7). Traits are ordered on the x-axis according to the mean PA of phenomic prediction scenarios (S1–S5) in descending order. The Y axis shows the prediction case. Each prediction case shows the general prediction scenario (S1–S7). The code before the x specifes which phenotypic data is predicted and the one after the x states from which data the relationship matrix was derived. G and W are the code for the locations Gransebieth and Windeby, respectively, while 19, 20, and 21 represent the year. AEM before the x means that the phenotypic adjusted entry means (AEMs) derived from all five available environments were predicted while MAEM after the x specifes that the AEMs derived from both multispectral data sets, namely W20 and W21, were used for predictions. cjM stands for “column-joined” and means that the two aforementioned matrices of spectra were joined into one single matrix before computing the relationship matrix M for prediction. SNPs stands for the GBLUP model where data from a SNP array were used to build the relationship matrix. Missing tiles could either not be calculated due to missing trait data for a specifc environment or because the predictions did not pass the quality control of an absolute coefcient of variation ≤ 150 or ≤ 100 missing PAs. Every prediction was carried out using respective relationship matrices only considering additive efects. The mean PA across all traits per prediction case is shown in the last column while the mean PA across each MBLUP (S1–S5) or GBLUP (S6–S7) scenario is shown per trait in the last two rows.

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