Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain
Abstract With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large‐effect genes with cis‐acting variants affecting messenger RNA (mRNA) expre...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20276 |
| _version_ | 1797383629251280896 |
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| author | Ryokei Tanaka Di Wu Xiaowei Li Laura E. Tibbs‐Cortes Joshua C. Wood Maria Magallanes‐Lundback Nolan Bornowski John P. Hamilton Brieanne Vaillancourt Xianran Li Nicholas T. Deason Gregory R. Schoenbaum C. Robin Buell Dean DellaPenna Jianming Yu Michael A. Gore |
| author_facet | Ryokei Tanaka Di Wu Xiaowei Li Laura E. Tibbs‐Cortes Joshua C. Wood Maria Magallanes‐Lundback Nolan Bornowski John P. Hamilton Brieanne Vaillancourt Xianran Li Nicholas T. Deason Gregory R. Schoenbaum C. Robin Buell Dean DellaPenna Jianming Yu Michael A. Gore |
| author_sort | Ryokei Tanaka |
| collection | DOAJ |
| description | Abstract With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large‐effect genes with cis‐acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK‐GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12–21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK‐GBLUP models improved predictive abilities by 7.0–13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi‐trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large‐effect candidate causal genes (1–3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes. |
| first_indexed | 2024-03-08T21:23:49Z |
| format | Article |
| id | doaj.art-fe98d3e9faef41a18481bedbc749d9bd |
| institution | Directory Open Access Journal |
| issn | 1940-3372 |
| language | English |
| last_indexed | 2024-03-08T21:23:49Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj.art-fe98d3e9faef41a18481bedbc749d9bd2023-12-21T07:55:50ZengWileyThe Plant Genome1940-33722023-12-01164n/an/a10.1002/tpg2.20276Leveraging prior biological knowledge improves prediction of tocochromanols in maize grainRyokei Tanaka0Di Wu1Xiaowei Li2Laura E. Tibbs‐Cortes3Joshua C. Wood4Maria Magallanes‐Lundback5Nolan Bornowski6John P. Hamilton7Brieanne Vaillancourt8Xianran Li9Nicholas T. Deason10Gregory R. Schoenbaum11C. Robin Buell12Dean DellaPenna13Jianming Yu14Michael A. Gore15Plant Breeding and Genetics Section, School of Integrative Plant Science Cornell Univ. Ithaca NY 14853 USAPlant Breeding and Genetics Section, School of Integrative Plant Science Cornell Univ. Ithaca NY 14853 USAPlant Breeding and Genetics Section, School of Integrative Plant Science Cornell Univ. Ithaca NY 14853 USADep. of Agronomy Iowa State Univ. Ames IA 50011 USAInstitute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences Univ. of Georgia Athens GA 30602 USADep. of Biochemistry and Molecular Biology Michigan State Univ. East Lansing MI 48824 USADep. of Plant Biology Michigan State Univ. East Lansing MI 48824 USAInstitute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences Univ. of Georgia Athens GA 30602 USAInstitute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences Univ. of Georgia Athens GA 30602 USAUSDA ARS, Wheat Health, Genetics, and Quality Research Unit Pullman WA 99164 USADep. of Biochemistry and Molecular Biology Michigan State Univ. East Lansing MI 48824 USADep. of Agronomy Iowa State Univ. Ames IA 50011 USAInstitute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences Univ. of Georgia Athens GA 30602 USADep. of Biochemistry and Molecular Biology Michigan State Univ. East Lansing MI 48824 USADep. of Agronomy Iowa State Univ. Ames IA 50011 USAPlant Breeding and Genetics Section, School of Integrative Plant Science Cornell Univ. Ithaca NY 14853 USAAbstract With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large‐effect genes with cis‐acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK‐GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12–21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK‐GBLUP models improved predictive abilities by 7.0–13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi‐trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large‐effect candidate causal genes (1–3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.https://doi.org/10.1002/tpg2.20276 |
| spellingShingle | Ryokei Tanaka Di Wu Xiaowei Li Laura E. Tibbs‐Cortes Joshua C. Wood Maria Magallanes‐Lundback Nolan Bornowski John P. Hamilton Brieanne Vaillancourt Xianran Li Nicholas T. Deason Gregory R. Schoenbaum C. Robin Buell Dean DellaPenna Jianming Yu Michael A. Gore Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain The Plant Genome |
| title | Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain |
| title_full | Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain |
| title_fullStr | Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain |
| title_full_unstemmed | Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain |
| title_short | Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain |
| title_sort | leveraging prior biological knowledge improves prediction of tocochromanols in maize grain |
| url | https://doi.org/10.1002/tpg2.20276 |
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