Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis - Yusuf, Muyideen; Miller, Michael D.; Stefaniak, Thomas R.; Haagenson, Darrin; Endelman, Jeffrey B.; Thompson, Asunta L.; Shannon, Laura M. (The plant genome, 2024)
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. Core Ideas Quality traits are essential to the marketability of potato. Phenotyping through image analysis results in more precise genomic selection models for potato quality traits. Additive models were sufficient for skin color traits, but including dominance improved estimates of roundness. Increasing population size improves prediction accuracy, suggesting a role for multi‐program models. Plain Language Summary Consumers choose potatoes based on visual appeal, including traits like skin color and tuber shape. When breeding new potato varieties, selecting for these appearance traits is crucial, but it is hard to select for something you cannot measure well. Historically, people have measured these quality traits by visually rating them on a 1–5 scale. We used a digital image analysis to generate precise and accurate measures of skin color and tuber shape. These measures were then used to build mathematical models, which relate whole genome markers to phenotypes and allow us to determine which clones will make the best parents, in a process called genomic selection (GS). We tried several different models and determined the best one for identifying heritable effects. Including data from other breeding programs improved the prediction ability of our models. Combining image analysis, GS, and multi‐program data is a promising avenue for improving quality traits in potato.