Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis
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 accu...
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| Published in | The plant genome Vol. 17; no. 4; pp. e20507 - n/a |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
John Wiley & Sons, Inc
01.12.2024
John Wiley and Sons Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1940-3372 1940-3372 |
| DOI | 10.1002/tpg2.20507 |
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| 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.
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. |
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| AbstractList | 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.
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. 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. 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. 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. 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.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. 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. 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. 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. |
| Author | Endelman, Jeffrey B. Thompson, Asunta L. Miller, Michael D. Shannon, Laura M. Yusuf, Muyideen Stefaniak, Thomas R. Haagenson, Darrin |
| AuthorAffiliation | 3 USDA‐ARS, Edward T. Schafer Agricultural Research Center Fargo North Dakota USA 2 Seneca Foods Corporation Le Sueur Minnesota USA 4 Department of Plant & Agroecosystem Sciences University of Wisconsin Madison Wisconsin USA 5 Department of Plant Sciences North Dakota State University Fargo North Dakota USA 1 Department of Horticultural Science University of Minnesota Saint Paul Minnesota USA |
| AuthorAffiliation_xml | – name: 2 Seneca Foods Corporation Le Sueur Minnesota USA – name: 3 USDA‐ARS, Edward T. Schafer Agricultural Research Center Fargo North Dakota USA – name: 4 Department of Plant & Agroecosystem Sciences University of Wisconsin Madison Wisconsin USA – name: 1 Department of Horticultural Science University of Minnesota Saint Paul Minnesota USA – name: 5 Department of Plant Sciences North Dakota State University Fargo North Dakota USA |
| Author_xml | – sequence: 1 givenname: Muyideen orcidid: 0000-0002-4932-7827 surname: Yusuf fullname: Yusuf, Muyideen organization: University of Minnesota – sequence: 2 givenname: Michael D. orcidid: 0009-0007-2202-7158 surname: Miller fullname: Miller, Michael D. organization: Seneca Foods Corporation – sequence: 3 givenname: Thomas R. orcidid: 0009-0001-0455-4233 surname: Stefaniak fullname: Stefaniak, Thomas R. organization: University of Minnesota – sequence: 4 givenname: Darrin orcidid: 0000-0002-2425-123X surname: Haagenson fullname: Haagenson, Darrin organization: USDA‐ARS, Edward T. Schafer Agricultural Research Center – sequence: 5 givenname: Jeffrey B. orcidid: 0000-0003-0957-4337 surname: Endelman fullname: Endelman, Jeffrey B. organization: University of Wisconsin – sequence: 6 givenname: Asunta L. orcidid: 0000-0002-1879-5265 surname: Thompson fullname: Thompson, Asunta L. organization: North Dakota State University – sequence: 7 givenname: Laura M. orcidid: 0000-0003-3935-4909 surname: Shannon fullname: Shannon, Laura M. email: lmshannon@umn.edu organization: University of Minnesota |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39256988$$D View this record in MEDLINE/PubMed |
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| Snippet | 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... Potato ( Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as... Abstract Potato (Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such... |
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| SubjectTerms | Accuracy Agricultural production class Cloning Color Density Digital imaging Estimates fresh market genetic improvement genome Genome, Plant Genomic analysis genomics Genomics - methods Heritability image analysis Image processing Image Processing, Computer-Assisted - methods markets Minnesota multiple trait selection Original Phenotype Polymorphism Potatoes prediction Prediction models selection index Skin Skin pigmentation Solanum tuberosum Solanum tuberosum - genetics specific gravity |
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| Title | Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis |
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