ArticleThe plant genome2024
Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis.
Article in The plant genome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrating genomic and phenomic data improves prediction of key traits in potato breeding.Horticulture research · 2026Article
- Integrating genomic selection into potato breeding: A comparison of genotyping platforms and cross-environmental predictions.The plant genome · 2026Article
- An Effective YOLOv11 Grain Detection Model Trained on Intact Barley Spikes Reveals a QTL Containing a Pivotal Regulator of Lateral Spikelet Formation.Plants (Basel, Switzerland) · 2026Article
- Article
- PotatoMASH is a cost-effective marker system for genomic prediction in potato based on short-read haplotypes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Leveraging unmanned aerial vehicle derived multispectral data for improved genomic prediction in potato (Solanum tuberosum).The plant genome · 2025Article
- Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis.The plant genome · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Potato (Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as shape and skin color, making these traits important targets for breeders. Achieving and evaluating genetic gain is facilitated by precise and accurate trait measures. Historically, quality traits have been measured using visual rating scales, which are subject to human error and necessarily lump individuals with distinct characteristics into categories. Image analysis offers a method of generating quantitative measures of quality traits. In this study, we use TubAR, an image-analysis R package, to generate quantitative measures of shape and skin color traits for use in genomic prediction. We developed and compared different genomic models based on additive and additive plus non-additive relationship matrices for two aspects of skin color, redness, and lightness, and two aspects of shape, roundness, and length-to-width ratio for fresh market red and yellow potatoes grown in Minnesota between 2020 and 2022. Similarly, we used the much larger chipping potato population grown during the same time to develop a multi-trait selection index including roundness, specific gravity, and yield. Traits ranged in heritability with shape traits falling between 0.23 and 0.85, and color traits falling between 0.34 and 0.91. Genetic effects were primarily additive with color traits showing the strongest effect (0.47), while shape traits varied based on market class. Modeling non-additive effects did not significantly improve prediction models for quality traits. The combination of image analysis and genomic prediction presents a promising avenue for improving potato quality traits.
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