Genomic prediction of tocochromanols in exotic‐derived maize
Abstract Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exot...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
|
| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20286 |
| _version_ | 1797383573340160000 |
|---|---|
| author | Laura E. Tibbs‐Cortes Tingting Guo Xianran Li Ryokei Tanaka Adam E. Vanous David Peters Candice Gardner Maria Magallanes‐Lundback Nicholas T. Deason Dean DellaPenna Michael A. Gore Jianming Yu |
| author_facet | Laura E. Tibbs‐Cortes Tingting Guo Xianran Li Ryokei Tanaka Adam E. Vanous David Peters Candice Gardner Maria Magallanes‐Lundback Nicholas T. Deason Dean DellaPenna Michael A. Gore Jianming Yu |
| author_sort | Laura E. Tibbs‐Cortes |
| collection | DOAJ |
| description | Abstract Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic‐derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic‐derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic‐derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program. |
| first_indexed | 2024-03-08T21:23:00Z |
| format | Article |
| id | doaj.art-22d643481e7649f684ae29ba6cb7e474 |
| institution | Directory Open Access Journal |
| issn | 1940-3372 |
| language | English |
| last_indexed | 2024-03-08T21:23:00Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj.art-22d643481e7649f684ae29ba6cb7e4742023-12-21T07:55:50ZengWileyThe Plant Genome1940-33722023-12-01164n/an/a10.1002/tpg2.20286Genomic prediction of tocochromanols in exotic‐derived maizeLaura E. Tibbs‐Cortes0Tingting Guo1Xianran Li2Ryokei Tanaka3Adam E. Vanous4David Peters5Candice Gardner6Maria Magallanes‐Lundback7Nicholas T. Deason8Dean DellaPenna9Michael A. Gore10Jianming Yu11Dep. of Agronomy Iowa State Univ. Ames IA USAHubei Hongshan Laboratory Wuhan ChinaUSDA ARS, Wheat Health, Genetics, and Quality Research Unit Pullman WA USAPlant Breeding and Genetics Section, School of Integrative Plant Science Cornell Univ. Ithaca NY USAUSDA ARS, North Central Regional Plant Introduction Station Ames IA USAUSDA ARS, North Central Regional Plant Introduction Station Ames IA USAUSDA ARS, North Central Regional Plant Introduction Station Ames IA USADep. of Biochemistry and Molecular Biology Michigan State Univ. East Lansing MI USADep. of Biochemistry and Molecular Biology Michigan State Univ. East Lansing MI USADep. of Biochemistry and Molecular Biology Michigan State Univ. East Lansing MI USAPlant Breeding and Genetics Section, School of Integrative Plant Science Cornell Univ. Ithaca NY USADep. of Agronomy Iowa State Univ. Ames IA USAAbstract Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic‐derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic‐derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic‐derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.https://doi.org/10.1002/tpg2.20286 |
| spellingShingle | Laura E. Tibbs‐Cortes Tingting Guo Xianran Li Ryokei Tanaka Adam E. Vanous David Peters Candice Gardner Maria Magallanes‐Lundback Nicholas T. Deason Dean DellaPenna Michael A. Gore Jianming Yu Genomic prediction of tocochromanols in exotic‐derived maize The Plant Genome |
| title | Genomic prediction of tocochromanols in exotic‐derived maize |
| title_full | Genomic prediction of tocochromanols in exotic‐derived maize |
| title_fullStr | Genomic prediction of tocochromanols in exotic‐derived maize |
| title_full_unstemmed | Genomic prediction of tocochromanols in exotic‐derived maize |
| title_short | Genomic prediction of tocochromanols in exotic‐derived maize |
| title_sort | genomic prediction of tocochromanols in exotic derived maize |
| url | https://doi.org/10.1002/tpg2.20286 |
| work_keys_str_mv | AT lauraetibbscortes genomicpredictionoftocochromanolsinexoticderivedmaize AT tingtingguo genomicpredictionoftocochromanolsinexoticderivedmaize AT xianranli genomicpredictionoftocochromanolsinexoticderivedmaize AT ryokeitanaka genomicpredictionoftocochromanolsinexoticderivedmaize AT adamevanous genomicpredictionoftocochromanolsinexoticderivedmaize AT davidpeters genomicpredictionoftocochromanolsinexoticderivedmaize AT candicegardner genomicpredictionoftocochromanolsinexoticderivedmaize AT mariamagallaneslundback genomicpredictionoftocochromanolsinexoticderivedmaize AT nicholastdeason genomicpredictionoftocochromanolsinexoticderivedmaize AT deandellapenna genomicpredictionoftocochromanolsinexoticderivedmaize AT michaelagore genomicpredictionoftocochromanolsinexoticderivedmaize AT jianmingyu genomicpredictionoftocochromanolsinexoticderivedmaize |