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Renk JS, Gilbert AM, Hattery TJ, O'Connor CH, Monnahan PJ, Anderson N, Waters AJ, Eickholt DP, Flint-Garcia SA, Yandeau-Nelson MD, Hirsch CN. Genetic control of kernel compositional variation in a maize diversity panel. Plant Genome 2021; 14:e20115. [PMID: 34197039 DOI: 10.1002/tpg2.20115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/07/2021] [Indexed: 06/13/2023]
Abstract
Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9-71.0% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole-genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality.
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Affiliation(s)
- Jonathan S Renk
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Travis J Hattery
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA
| | | | | | | | - Sherry A Flint-Garcia
- United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA
| | - Marna D Yandeau-Nelson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
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Renk JS, Gilbert AM, Hattery TJ, O'Connor CH, Monnahan PJ, Anderson N, Waters AJ, Eickholt DP, Flint-Garcia SA, Yandeau-Nelson MD, Hirsch CN. Genetic control of kernel compositional variation in a maize diversity panel. Plant Genome 2021. [PMID: 34197039 DOI: 10.1101/2021.03.29.436703v1.full] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9-71.0% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole-genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality.
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Affiliation(s)
- Jonathan S Renk
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Travis J Hattery
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA
| | | | | | | | - Sherry A Flint-Garcia
- United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA
| | - Marna D Yandeau-Nelson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
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Burns MJ, Renk JS, Eickholt DP, Gilbert AM, Hattery TJ, Holmes M, Anderson N, Waters AJ, Kalambur S, Flint-Garcia SA, Yandeau-Nelson MD, Annor GA, Hirsch CN. Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy. Theor Appl Genet 2021; 134:3743-3757. [PMID: 34345971 DOI: 10.1007/s00122-021-03926-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/23/2021] [Indexed: 05/10/2023]
Abstract
Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high-throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman's rank correlation coefficient of 0.852 between wet laboratory and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR spectra data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.
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Affiliation(s)
- Michael J Burns
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Jonathan S Renk
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | | | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Travis J Hattery
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Mark Holmes
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | | | | | | | - Sherry A Flint-Garcia
- United States Department of Agriculture, Agriculture Research Service, Columbia, MO, 65211, USA
- Division of Plant Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Marna D Yandeau-Nelson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - George A Annor
- Department of Food Science and Nutrition, University of Minnesota, St Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA.
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Qiu Y, O’Connor CH, Della Coletta R, Renk JS, Monnahan PJ, Noshay JM, Liang Z, Gilbert A, Anderson SN, McGaugh SE, Springer NM, Hirsch CN. Whole-genome variation of transposable element insertions in a maize diversity panel. G3 (Bethesda) 2021; 11:jkab238. [PMID: 34568911 PMCID: PMC8473971 DOI: 10.1093/g3journal/jkab238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/29/2021] [Indexed: 01/09/2023]
Abstract
Intact transposable elements (TEs) account for 65% of the maize genome and can impact gene function and regulation. Although TEs comprise the majority of the maize genome and affect important phenotypes, genome-wide patterns of TE polymorphisms in maize have only been studied in a handful of maize genotypes, due to the challenging nature of assessing highly repetitive sequences. We implemented a method to use short-read sequencing data from 509 diverse inbred lines to classify the presence/absence of 445,418 nonredundant TEs that were previously annotated in four genome assemblies including B73, Mo17, PH207, and W22. Different orders of TEs (i.e., LTRs, Helitrons, and TIRs) had different frequency distributions within the population. LTRs with lower LTR similarity were generally more frequent in the population than LTRs with higher LTR similarity, though high-frequency insertions with very high LTR similarity were observed. LTR similarity and frequency estimates of nested elements and the outer elements in which they insert revealed that most nesting events occurred very near the timing of the outer element insertion. TEs within genes were at higher frequency than those that were outside of genes and this is particularly true for those not inserted into introns. Many TE insertional polymorphisms observed in this population were tagged by SNP markers. However, there were also 19.9% of the TE polymorphisms that were not well tagged by SNPs (R2 < 0.5) that potentially represent information that has not been well captured in previous SNP-based marker-trait association studies. This study provides a population scale genome-wide assessment of TE variation in maize and provides valuable insight on variation in TEs in maize and factors that contribute to this variation.
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Affiliation(s)
- Yinjie Qiu
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Christine H O’Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN 55108, USA
| | - Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Jonathan S Renk
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN 55108, USA
| | - Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Zhikai Liang
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Amanda Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Sarah N Anderson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA 50011, USA
| | - Suzanne E McGaugh
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN 55108, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
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