1
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Ou S, Jiang N, Hirsch CN, Hufford MB. Response to Commentary: Accounting for diverse transposable element landscapes is key to developing and evaluating accurate de novo annotation strategies. Genome Biol 2024; 25:6. [PMID: 38169403 PMCID: PMC10762968 DOI: 10.1186/s13059-023-03119-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024] Open
Affiliation(s)
- Shujun Ou
- Department of Molecular Genetics, Ohio State University, Columbus, OH, 43210, USA
| | - Ning Jiang
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA.
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
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2
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Schoemaker DL, Qiu Y, de Leon N, Hirsch CN, Kaeppler SM. Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize. G3 (Bethesda) 2023; 14:jkad256. [PMID: 37950891 PMCID: PMC10755172 DOI: 10.1093/g3journal/jkad256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/13/2023]
Abstract
The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus.
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Affiliation(s)
- Dylan L Schoemaker
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
| | - Yinjie Qiu
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Shawn M Kaeppler
- Department of Plant and Agroecosystem Sciences, University of Wisconsin—Madison, Madison, WI 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin—Madison, Middleton, WI 53562, USA
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3
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Munasinghe M, Read A, Stitzer MC, Song B, Menard CC, Ma KY, Brandvain Y, Hirsch CN, Springer N. Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion. PLoS Genet 2023; 19:e1011086. [PMID: 38134220 PMCID: PMC10773942 DOI: 10.1371/journal.pgen.1011086] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 01/08/2024] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
Structural differences between genomes are a major source of genetic variation that contributes to phenotypic differences. Transposable elements, mobile genetic sequences capable of increasing their copy number and propagating themselves within genomes, can generate structural variation. However, their repetitive nature makes it difficult to characterize fine-scale differences in their presence at specific positions, limiting our understanding of their impact on genome variation. Domesticated maize is a particularly good system for exploring the impact of transposable element proliferation as over 70% of the genome is annotated as transposable elements. High-quality transposable element annotations were recently generated for de novo genome assemblies of 26 diverse inbred maize lines. We generated base-pair resolved pairwise alignments between the B73 maize reference genome and the remaining 25 inbred maize line assemblies. From this data, we classified transposable elements as either shared or polymorphic in a given pairwise comparison. Our analysis uncovered substantial structural variation between lines, representing both simple and complex connections between TEs and structural variants. Putative insertions in SNP depleted regions, which represent recently diverged identity by state blocks, suggest some TE families may still be active. However, our analysis reveals that within these recently diverged genomic regions, deletions of transposable elements likely account for more structural variation events and base pairs than insertions. These deletions are often large structural variants containing multiple transposable elements. Combined, our results highlight how transposable elements contribute to structural variation and demonstrate that deletion events are a major contributor to genomic differences.
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Affiliation(s)
- Manisha Munasinghe
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Andrew Read
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Michelle C. Stitzer
- Institute for Genomic Diversity, Cornell University, Ithaca, New York, United States of America
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America
| | - Baoxing Song
- Peking University Institute of Advanced Agricultural Sciences, Weifang, China
| | - Claire C. Menard
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Kristy Yubo Ma
- Department of Mathematics, Statistics, and Computer Science, Macalester College, St. Paul, Minnesota, United States of America
| | - Yaniv Brandvain
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, United States of America
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Nathan Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, United States of America
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4
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Barreto Ortiz J, Hirsch CN, Ehlke NJ, Watkins E. SpykProps: an imaging pipeline to quantify architecture in unilateral grass inflorescences. Plant Methods 2023; 19:125. [PMID: 37957737 PMCID: PMC10644492 DOI: 10.1186/s13007-023-01104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/06/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. RESULTS Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE < 1), estimate their length (R2 = 0.96), and number of spikelets (R2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R2 = 0.94), rachis weight (R2 = 0.83), and seed shattering (R2 = 0.85). CONCLUSIONS SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.
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Affiliation(s)
- Joan Barreto Ortiz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Horticultural Science, 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
| | - Nancy Jo Ehlke
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Eric Watkins
- Department of Horticultural Science, University of Minnesota, St. Paul, MN, 55108, USA.
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5
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Ellison EL, Zhou P, Hermanson P, Chu YH, Read A, Hirsch CN, Grotewold E, Springer NM. Mutator transposon insertions within maize genes often provide a novel outward reading promoter. Genetics 2023; 225:iyad171. [PMID: 37815810 DOI: 10.1093/genetics/iyad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 09/04/2023] [Indexed: 10/11/2023] Open
Abstract
The highly active family of Mutator (Mu) DNA transposons has been widely used for forward and reverse genetics in maize. There are examples of Mu-suppressible alleles that result in conditional phenotypic effects based on the activity of Mu. Phenotypes from these Mu-suppressible mutations are observed in Mu-active genetic backgrounds, but absent when Mu activity is lost. For some Mu-suppressible alleles, phenotypic suppression likely results from an outward-reading promoter within Mu that is only active when the autonomous Mu element is silenced or lost. We isolated 35 Mu alleles from the UniformMu population that represent insertions in 24 different genes. Most of these mutant alleles are due to insertions within gene coding sequences, but several 5' UTR and intron insertions were included. RNA-seq and de novo transcript assembly were utilized to document the transcripts produced from 33 of these Mu insertion alleles. For 20 of the 33 alleles, there was evidence of transcripts initiating within the Mu sequence reading through the gene. This outward-reading promoter activity was detected in multiple types of Mu elements and does not depend on the orientation of Mu. Expression analyses of Mu-initiated transcripts revealed the Mu promoter often provides gene expression levels and patterns that are similar to the wild-type gene. These results suggest the Mu promoter may represent a minimal promoter that can respond to gene cis-regulatory elements. Findings from this study have implications for maize researchers using the UniformMu population, and more broadly highlight a strategy for transposons to co-exist with their host.
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Affiliation(s)
- Erika L Ellison
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Peter Hermanson
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Yi-Hsuan Chu
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Andrew Read
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
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6
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Della Coletta R, Fernandes SB, Monnahan PJ, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Importance of genetic architecture in marker selection decisions for genomic prediction. Theor Appl Genet 2023; 136:220. [PMID: 37819415 DOI: 10.1007/s00122-023-04469-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023]
Abstract
KEY MESSAGE We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Samuel B Fernandes
- Department of Crop, Soil and Environmental Sciences at University of Arkansas, Fayetteville, AR, 72701, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Mark A Mikel
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Martin O Bohn
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA.
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7
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Lima DC, Aviles AC, Alpers RT, Perkins A, Schoemaker DL, Costa M, Michel KJ, Kaeppler S, Ertl D, Romay MC, Gage JL, Holland J, Beissinger T, Bohn M, Buckler E, Edwards J, Flint-Garcia S, Gore MA, Hirsch CN, Knoll JE, McKay J, Minyo R, Murray SC, Schnable J, Sekhon RS, Singh MP, Sparks EE, Thomison P, Thompson A, Tuinstra M, Wallace J, Washburn JD, Weldekidan T, Xu W, de Leon N. 2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative. BMC Res Notes 2023; 16:219. [PMID: 37710302 PMCID: PMC10502993 DOI: 10.1186/s13104-023-06430-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/17/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.
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Affiliation(s)
- Dayane Cristina Lima
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA.
| | | | - Ryan Timothy Alpers
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Alden Perkins
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Dylan L Schoemaker
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Martin Costa
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Kathryn J Michel
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Shawn Kaeppler
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27606, USA
| | - Timothy Beissinger
- Department of Crop Science, Center for Integrated Breeding Research, University of Göttingen, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany
| | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Jode Edwards
- USDA ARS CICGRU, 716 Farmhouse Ln, Ames, IA, 50011-1051, USA
| | - Sherry Flint-Garcia
- USDA-ARS, Plant Genetics Research Unit, University of Missouri, 205 Curtis Hall, Columbia, MO, 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Joseph E Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA, 31793, USA
| | - John McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Richard Minyo
- Department of Horticulture and Crop Science, College of Food, Agricultural, and Environmental Sciences, Ohio State University, Wooster, OH, 44691, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - James Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Rajandeep S Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29634, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA
| | | | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Mitchell Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, 49707, USA
| | - Jason Wallace
- Department of Crop & Soil Sciences, University of Georgia, Athens, GA, 30602, USA
| | - Jacob D Washburn
- USDA-ARS, Plant Genetics Research Unit, University of Missouri, 205 Curtis Hall, Columbia, MO, 65211, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
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8
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Della Coletta R, Liese SE, Fernandes SB, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Linking genetic and environmental factors through marker effect networks to understand trait plasticity. Genetics 2023; 224:iyad103. [PMID: 37246567 DOI: 10.1093/genetics/iyad103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 05/30/2023] Open
Abstract
Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Sharon E Liese
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samuel B Fernandes
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Mark A Mikel
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Martin O Bohn
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
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9
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Lima DC, Washburn JD, Varela JI, Chen Q, Gage JL, Romay MC, Holland J, Ertl D, Lopez-Cruz M, Aguate FM, de Los Campos G, Kaeppler S, Beissinger T, Bohn M, Buckler E, Edwards J, Flint-Garcia S, Gore MA, Hirsch CN, Knoll JE, McKay J, Minyo R, Murray SC, Ortez OA, Schnable JC, Sekhon RS, Singh MP, Sparks EE, Thompson A, Tuinstra M, Wallace J, Weldekidan T, Xu W, de Leon N. Genomes to Fields 2022 Maize genotype by Environment Prediction Competition. BMC Res Notes 2023; 16:148. [PMID: 37461058 DOI: 10.1186/s13104-023-06421-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/28/2023] [Indexed: 07/20/2023] Open
Abstract
OBJECTIVES The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.
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Affiliation(s)
- Dayane Cristina Lima
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA.
| | - Jacob D Washburn
- USDA-ARS Plant Genetics Research Unit, 205 Curtis Hall, Columbia, MO, 65211, USA
| | - José Ignacio Varela
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Qiuyue Chen
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27606, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Fernando M Aguate
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Gustavo de Los Campos
- Department of Plant, Soil and Microbial Sciences, Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Shawn Kaeppler
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Timothy Beissinger
- Department of Crop Science, Center for Integrated Breeding Research, University of Göttingen, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany
| | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Jode Edwards
- USDA ARS CICGRU, 716 Farmhouse Ln, Ames, IA, 50011-1051, USA
| | - Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit, 205 Curtis Hall, Columbia, MO, 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Joseph E Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA, 31793, USA
| | - John McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Richard Minyo
- Department of Horticulture and Crop Science, College of Food, Agricultural, and Environmental Sciences, Ohio State University, Wooster, OH, 44691, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Osler A Ortez
- Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, 43210, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Rajandeep S Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29634, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Mitchell Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, 49707, USA
| | - Jason Wallace
- Department of Crop & Soil Sciences, University of Georgia, Athens, GA, 30602, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
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10
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Lima DC, Aviles AC, Alpers RT, McFarland BA, Kaeppler S, Ertl D, Romay MC, Gage JL, Holland J, Beissinger T, Bohn M, Buckler E, Edwards J, Flint-Garcia S, Hirsch CN, Hood E, Hooker DC, Knoll JE, Kolkman JM, Liu S, McKay J, Minyo R, Moreta DE, Murray SC, Nelson R, Schnable JC, Sekhon RS, Singh MP, Thomison P, Thompson A, Tuinstra M, Wallace J, Washburn JD, Weldekidan T, Wisser RJ, Xu W, de Leon N. 2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project. BMC Genom Data 2023; 24:29. [PMID: 37231352 DOI: 10.1186/s12863-023-01129-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
OBJECTIVES This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.
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Affiliation(s)
| | | | | | - Bridget A McFarland
- Panama-USA Commission for the Eradication and Prevention of Screwworm (COPEG), USDA-APHIS-IS, Pacora, Panama
| | - Shawn Kaeppler
- Department of Agronomy, University of WI - Madison, Madison, WI, 53706, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27606, USA
| | - Timothy Beissinger
- Department of Crop Science, University of Göttingen Center for Integrated Breeding Research, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany
| | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Jode Edwards
- USDA ARS CICGRU, 716 Farmhouse Ln, Ames, IA, 50011-1051, USA
| | | | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Elizabeth Hood
- College of Agriculture, Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, 72404, USA
| | - David C Hooker
- Department of Plant Agriculture, University of Guelph, Ridgetown Campus, Ridgetown, ON, Canada
| | - Joseph E Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA, 31793, USA
| | - Judith M Kolkman
- School of Integrative Plant Science, Cornell University, Ithaca, NY, 14850, USA
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66503, USA
| | - John McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Richard Minyo
- Department of Horticulture and Crop Science, Ohio State University College of Food, Agricultural, and Environmental Sciences, Wooster, OH, 44691, USA
| | - Danilo E Moreta
- School of Integrative Plant Science, Cornell University, Ithaca, NY, 14850, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | | | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Rajandeep S Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29634, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | | | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Mitchell Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, 49707, USA
| | - Jason Wallace
- Department of Crop & Soil Sciences, University of Georgia, Athens, GA, 30602, USA
| | | | | | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA
- Laboratoire d'Ecophysiologie Des Plantes Sous Stress Environmentaux, INRAE, 34060, Montpellier, France
| | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
| | - Natalia de Leon
- Department of Agronomy, University of WI - Madison, Madison, WI, 53706, USA
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11
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Kick DR, Wallace JG, Schnable JC, Kolkman JM, Alaca B, Beissinger TM, Edwards J, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta D, Singh MP, Thompson A, Weldekidan T, Washburn JD. Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning. G3 (Bethesda) 2023; 13:6982634. [PMID: 36625555 PMCID: PMC10085787 DOI: 10.1093/g3journal/jkad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 07/28/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023]
Abstract
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and BLUP models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield - those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
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Affiliation(s)
- Daniel R Kick
- United States Department of Agriculture - Agricultural Research Service Plant Genetics Research Unit, Columbia, Missouri 65211, USA.,Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211, USA
| | - Jason G Wallace
- Department of Crop & Soil Science, University of Georgia, Athens, Georgia 30602, USA
| | - James C Schnable
- Center for Plant Science Innovation & Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588, USA
| | - Judith M Kolkman
- School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Barış Alaca
- Division of Plant Breeding Methodology, Department of Crop Science University of Goettingen, Goettingen, 37073, Germany.,Center for Integrated Breeding Research University of Goettingen, Goettingen, 37073, Germany
| | - Timothy M Beissinger
- Division of Plant Breeding Methodology, Department of Crop Science University of Goettingen, Goettingen, 37073, Germany.,Center for Integrated Breeding Research University of Goettingen, Goettingen, 37073, Germany
| | - Jode Edwards
- United States Department of Agriculture - Agricultural Research Service, AMES, 50011, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, Iowa 50131, USA
| | - Sherry Flint-Garcia
- United States Department of Agriculture - Agricultural Research Service Plant Genetics Research Unit, Columbia, Missouri 65211, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108, USA
| | - Joseph E Knoll
- United States Department of Agriculture - Agricultural Research Service Crop Genetics and Breeding Research Unit, Tifton, Georgia 31793, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Dayane C Lima
- Plant Breeding and Plant Genetics Program, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Danilo Moreta
- School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Maninder P Singh
- Plant, Soil and Microbial Sciences Dept., Michigan State University, East Lansing, Michigan 48824, USA
| | - Addie Thompson
- Plant, Soil and Microbial Sciences Dept., Michigan State University, East Lansing, Michigan 48824, USA
| | | | - Jacob D Washburn
- United States Department of Agriculture - Agricultural Research Service Plant Genetics Research Unit, Columbia, Missouri 65211, USA.,Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211, USA
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12
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Read A, Weiss T, Crisp PA, Liang Z, Noshay J, Menard CC, Wang C, Song M, Hirsch CN, Springer NM, Zhang F. Genome-wide loss of CHH methylation with limited transcriptome changes in Setaria viridis DOMAINS REARRANGED METHYLTRANSFERASE (DRM) mutants. Plant J 2022; 111:103-116. [PMID: 35436373 PMCID: PMC9541237 DOI: 10.1111/tpj.15781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 04/12/2022] [Indexed: 05/17/2023]
Abstract
The DOMAINS REARRANGED METHYLTRANSFERASEs (DRMs) are crucial for RNA-directed DNA methylation (RdDM) in plant species. Setaria viridis is a model monocot species with a relatively compact genome that has limited transposable element (TE) content. CRISPR-based genome editing approaches were used to create loss-of-function alleles for the two putative functional DRM genes in S. viridis to probe the role of RdDM. Double mutant (drm1ab) plants exhibit some morphological abnormalities but are fully viable. Whole-genome methylation profiling provided evidence for the widespread loss of methylation in CHH sequence contexts, particularly in regions with high CHH methylation in wild-type plants. Evidence was also found for the locus-specific loss of CG and CHG methylation, even in some regions that lack CHH methylation. Transcriptome profiling identified genes with altered expression in the drm1ab mutants. However, the majority of genes with high levels of CHH methylation directly surrounding the transcription start site or in nearby promoter regions in wild-type plants do not have altered expression in the drm1ab mutant, even when this methylation is lost, suggesting limited regulation of gene expression by RdDM. Detailed analysis of the expression of TEs identified several transposons that are transcriptionally activated in drm1ab mutants. These transposons are likely to require active RdDM for the maintenance of transcriptional repression.
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Affiliation(s)
- Andrew Read
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Trevor Weiss
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
- Center for Precision Plant GenomicsUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Peter A. Crisp
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
- School of Agriculture and Food SciencesThe University of QueenslandBrisbaneQueensland4072Australia
| | - Zhikai Liang
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Jaclyn Noshay
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Claire C. Menard
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Chunfang Wang
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
- Center for Precision Plant GenomicsUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Meredith Song
- Department of Genetics, Cell Biology and DevelopmentUniversity of MinnesotaMinneapolisMinnesota55108USA
| | - Candice N. Hirsch
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Nathan M. Springer
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
- Center for Precision Plant GenomicsUniversity of MinnesotaSaint PaulMinnesota55108USA
| | - Feng Zhang
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMinnesota55108USA
- Center for Precision Plant GenomicsUniversity of MinnesotaSaint PaulMinnesota55108USA
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13
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Ou S, Su W, Liao Y, Chougule K, Agda JRA, Hellinga AJ, Lugo CSB, Elliott TA, Ware D, Peterson T, Jiang N, Hirsch CN, Hufford MB. Author Correction: Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biol 2022; 23:76. [PMID: 35260190 PMCID: PMC8903655 DOI: 10.1186/s13059-022-02645-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Weijia Su
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Yi Liao
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, 92697, USA
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Harbor, Cold Spring, NY, 11724, USA
| | - Jireh R A Agda
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Adam J Hellinga
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | | | - Tyler A Elliott
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Harbor, Cold Spring, NY, 11724, USA.,USDA-ARS NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY, 14853, USA
| | - Thomas Peterson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Ning Jiang
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA.
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA.
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA.
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14
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Li Z, Tirado SB, Kadam DC, Coffey L, Miller ND, Spalding EP, Lorenz AJ, de Leon N, Kaeppler SM, Schnable PS, Springer NM, Hirsch CN. Correction to: Characterizing introgression‑by‑environment interactions using maize near isogenic lines. Theor Appl Genet 2021; 134:4077. [PMID: 34668979 DOI: 10.1007/s00122-021-03959-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Sara B Tirado
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
- Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA
| | - Dnyaneshwar C Kadam
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Lisa Coffey
- Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA.
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15
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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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16
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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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17
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Bornowski N, Michel KJ, Hamilton JP, Ou S, Seetharam AS, Jenkins J, Grimwood J, Plott C, Shu S, Talag J, Kennedy M, Hundley H, Singan VR, Barry K, Daum C, Yoshinaga Y, Schmutz J, Hirsch CN, Hufford MB, de Leon N, Kaeppler SM, Buell CR. Genomic variation within the maize stiff-stalk heterotic germplasm pool. Plant Genome 2021; 14:e20114. [PMID: 34275202 DOI: 10.1002/tpg2.20114] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/06/2021] [Indexed: 05/28/2023]
Abstract
The stiff-stalk heterotic group in Maize (Zea mays L.) is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and, to a lesser extent, B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four expired Plant Variety Protection (ex-PVP) lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40, which is a Pioneer Hi-Bred International (PHI) early stiff-stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13-2.18 Gbp with N50 scaffold lengths >200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas, whereas transposable element (TE) annotation was conducted using de novo and homology-directed methodologies. Compared with the reference inbred B73, synteny analyses revealed extensive collinearity across the five stiff-stalk genomes, although unique components of the maize pangenome were detected. Comparison of this set of stiff-stalk inbreds with the original Iowa Stiff Stalk Synthetic breeding population revealed that these inbreds represent only a proportion of variation in the original stiff-stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original stiff-stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent stiff-stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pangenome.
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Affiliation(s)
- Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
| | - Kathryn J Michel
- Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
| | - John P Hamilton
- Dep. of Plant Biology, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
| | - Shujun Ou
- Dep. of Ecology, Evolution, and Organismal Biology, Iowa State Univ., 2200 Osborn Drive, Ames, IA, 50011, USA
| | - Arun S Seetharam
- Dep. of Ecology, Evolution, and Organismal Biology, Iowa State Univ., 2200 Osborn Drive, Ames, IA, 50011, USA
| | - Jerry Jenkins
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
| | - Jane Grimwood
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
| | - Chris Plott
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
| | - Shengqiang Shu
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Jayson Talag
- Arizona Genomics Institute, School of Plant Sciences, Univ. of Arizona, 1657 E Helen Street, Tucson, AZ, 85721, USA
| | - Megan Kennedy
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Hope Hundley
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Vasanth R Singan
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Kerrie Barry
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Chris Daum
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Yuko Yoshinaga
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Jeremy Schmutz
- HudsonAlpha Institute for Biotechnology, 601 Genome Way Northwest, Huntsville, AL, 35806, USA
- U.S. Dep. of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Candice N Hirsch
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Matthew B Hufford
- Dep. of Ecology, Evolution, and Organismal Biology, Iowa State Univ., 2200 Osborn Drive, Ames, IA, 50011, USA
| | - Natalia de Leon
- Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Dep. of Energy, Great Lakes Bioenergy Research Center, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Dep. of Energy, Great Lakes Bioenergy Research Center, Univ. of Wisconsin - Madison, 1575 Linden Drive, Madison, WI, 53706, USA
- Wisconsin Crop Innovation Center, Univ. of Wisconsin - Madison, 8520 University Green, Middleton, WI, 53562, USA
| | - C Robin Buell
- Dep. of Plant Biology, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
- Dep. of Energy, Great Lakes Bioenergy Research Center, Michigan State Univ., 612 Wilson Road, East Lansing, MI, 48824, USA
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18
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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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19
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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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20
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Haas M, Kono T, Macchietto M, Millas R, McGilp L, Shao M, Duquette J, Qiu Y, Hirsch CN, Kimball J. Whole-genome assembly and annotation of northern wild rice, Zizania palustris L., supports a whole-genome duplication in the Zizania genus. Plant J 2021; 107:1802-1818. [PMID: 34310794 DOI: 10.1111/tpj.15419] [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] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 06/16/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Zizania palustris L. (northern wild rice, NWR) is an aquatic grass native to North America that is notable for its nutritious grain. This is an important species with ecological, cultural and agricultural significance, specifically in the Great Lakes region of the USA. Using flow cytometry, we first estimated the NWR genome size to be 1.8 Gb. Using long- and short-range sequencing, Hi-C scaffolding and RNA-seq data from eight tissues, we generated an annotated whole-genome de novo assembly of NWR. The assembly was 1.29 Gb in length, highly repetitive (approx. 76.0%) and contained 46 421 putative protein-coding genes. The expansion of retrotransposons within the genome and a whole-genome duplication (WGD) after the Zizania-Oryza speciation event have both led to an increase in the genome size of NWR in comparison with Oryza sativa L. and Zizania latifolia. Both events depict a genome rapidly undergoing change over a short evolutionary time. Comparative analyses revealed the conservation of large syntenic blocks between NWR and O. sativa, which were used to identify putative seed-shattering genes. Estimates of divergence times revealed that the Zizania genus diverged from Oryza approximately 26-30 million years ago (26-30 MYA), whereas NWR and Z. latifolia diverged from one another approximately 6-8 MYA. Comparative genomics confirmed evidence of a WGD in the Zizania genus and provided support that the event occurred prior to the NWR-Z. latifolia speciation event. This genome assembly and annotation provides a valuable resource for comparative genomics in the Oryzeae tribe and provides an important resource for future conservation and breeding efforts of NWR.
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Affiliation(s)
- Matthew Haas
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Thomas Kono
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Marissa Macchietto
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Reneth Millas
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Lillian McGilp
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Mingqin Shao
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Jacques Duquette
- North Central Research and Outreach Center, University of Minnesota, Grand Rapids, MN, 55744, USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, 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
| | - Jennifer Kimball
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
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21
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Noshay JM, Marand AP, Anderson SN, Zhou P, Mejia Guerra MK, Lu Z, O'Connor CH, Crisp PA, Hirsch CN, Schmitz RJ, Springer NM. Assessing the regulatory potential of transposable elements using chromatin accessibility profiles of maize transposons. Genetics 2021; 217:1-13. [PMID: 33683350 DOI: 10.1093/genetics/iyaa003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/02/2020] [Indexed: 11/14/2022] Open
Abstract
Transposable elements (TEs) have the potential to create regulatory variation both through the disruption of existing DNA regulatory elements and through the creation of novel DNA regulatory elements. In a species with a large genome, such as maize, many TEs interspersed with genes create opportunities for significant allelic variation due to TE presence/absence polymorphisms among individuals. We used information on putative regulatory elements in combination with knowledge about TE polymorphisms in maize to identify TE insertions that interrupt existing accessible chromatin regions (ACRs) in B73 as well as examples of polymorphic TEs that contain ACRs among four inbred lines of maize including B73, Mo17, W22, and PH207. The TE insertions in three other assembled maize genomes (Mo17, W22, or PH207) that interrupt ACRs that are present in the B73 genome can trigger changes to the chromatin, suggesting the potential for both genetic and epigenetic influences of these insertions. Nearly 20% of the ACRs located over 2 kb from the nearest gene are located within an annotated TE. These are regions of unmethylated DNA that show evidence for functional importance similar to ACRs that are not present within TEs. Using a large panel of maize genotypes, we tested if there is an association between the presence of TE insertions that interrupt, or carry, an ACR and the expression of nearby genes. While most TE polymorphisms are not associated with expression for nearby genes, the TEs that carry ACRs exhibit enrichment for being associated with higher expression of nearby genes, suggesting that these TEs may contribute novel regulatory elements. These analyses highlight the potential for a subset of TEs to rewire transcriptional responses in eukaryotic genomes.
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Affiliation(s)
- Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, 120 W Green St, Athens, GA 30602, USA
| | - Sarah N Anderson
- Department of Genetics, Development, and Cell Biology, Iowa State University, 2437 Pammel Dr, Ames, IA 50011, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108, USA
| | | | - Zefu Lu
- Department of Genetics, University of Georgia, 120 W Green St, Athens, GA 30602, USA
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, 1994 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN 55108, USA
| | - Peter A Crisp
- School of Agriculture and Food Sciences, The University of Queensland, Harley Teakle Building, Keyhold Rd, St Lucia QLD 4067, Australia
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, 1994 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN 55108, USA
| | - Robert J Schmitz
- Department of Genetics, University of Georgia, 120 W Green St, Athens, GA 30602, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108, USA
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22
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Noshay JM, Liang Z, Zhou P, Crisp PA, Marand AP, Hirsch CN, Schmitz RJ, Springer NM. Stability of DNA methylation and chromatin accessibility in structurally diverse maize genomes. G3 (Bethesda) 2021; 11:6288454. [PMID: 34849810 PMCID: PMC8496265 DOI: 10.1093/g3journal/jkab190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/27/2021] [Indexed: 06/13/2023]
Abstract
Accessible chromatin and unmethylated DNA are associated with many genes and cis-regulatory elements. Attempts to understand natural variation for accessible chromatin regions (ACRs) and unmethylated regions (UMRs) often rely upon alignments to a single reference genome. This limits the ability to assess regions that are absent in the reference genome assembly and monitor how nearby structural variants influence variation in chromatin state. In this study, de novo genome assemblies for four maize inbreds (B73, Mo17, Oh43, and W22) are utilized to assess chromatin accessibility and DNA methylation patterns in a pan-genome context. A more complete set of UMRs and ACRs can be identified when chromatin data are aligned to the matched genome rather than a single reference genome. While there are UMRs and ACRs present within genomic regions that are not shared between genotypes, these features are 6- to 12-fold enriched within regions between genomes. Characterization of UMRs present within shared genomic regions reveals that most UMRs maintain the unmethylated state in other genotypes with only ∼5% being polymorphic between genotypes. However, the majority (71%) of UMRs that are shared between genotypes only exhibit partial overlaps suggesting that the boundaries between methylated and unmethylated DNA are dynamic. This instability is not solely due to sequence variation as these partially overlapping UMRs are frequently found within genomic regions that lack sequence variation. The ability to compare chromatin properties among individuals with structural variation enables pan-epigenome analyses to study the sources of variation for accessible chromatin and unmethylated DNA.
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Affiliation(s)
- Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Zhikai Liang
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Peter A Crisp
- School of Agriculture and Food Sciences, University of Queensland, St Lucia, QLD 4072, Australia
| | | | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108, USA
| | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
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23
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Hufford MB, Seetharam AS, Woodhouse MR, Chougule KM, Ou S, Liu J, Ricci WA, Guo T, Olson A, Qiu Y, Della Coletta R, Tittes S, Hudson AI, Marand AP, Wei S, Lu Z, Wang B, Tello-Ruiz MK, Piri RD, Wang N, Kim DW, Zeng Y, O'Connor CH, Li X, Gilbert AM, Baggs E, Krasileva KV, Portwood JL, Cannon EKS, Andorf CM, Manchanda N, Snodgrass SJ, Hufnagel DE, Jiang Q, Pedersen S, Syring ML, Kudrna DA, Llaca V, Fengler K, Schmitz RJ, Ross-Ibarra J, Yu J, Gent JI, Hirsch CN, Ware D, Dawe RK. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science 2021; 373:655-662. [PMID: 34353948 PMCID: PMC8733867 DOI: 10.1126/science.abg5289] [Citation(s) in RCA: 195] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 06/24/2021] [Indexed: 12/24/2022]
Abstract
We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.
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Affiliation(s)
- Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Arun S Seetharam
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
- Genome Informatics Facility, Iowa State University, Ames, IA 50011, USA
| | - Margaret R Woodhouse
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | | | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Jianing Liu
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - William A Ricci
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Andrew Olson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, 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
| | - Silas Tittes
- Center for Population Biology, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Center for Population Biology, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | | | - Sharon Wei
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Zhenyuan Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Bo Wang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | | | - Rebecca D Piri
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | - Na Wang
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Dong Won Kim
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Yibing Zeng
- Department of Genetics, University of Georgia, Athens, GA 30602, 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
| | - Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Erin Baggs
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Ksenia V Krasileva
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - John L Portwood
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - Ethalinda K S Cannon
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - Carson M Andorf
- USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - Nancy Manchanda
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Samantha J Snodgrass
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - David E Hufnagel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| | - Qiuhan Jiang
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Sarah Pedersen
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - Michael L Syring
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
| | - David A Kudrna
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
| | | | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, GA 30602, USA
| | - Jeffrey Ross-Ibarra
- Center for Population Biology, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
- Genome Center, University of California, Davis, CA 95616, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Jonathan I Gent
- Department of Plant Biology, University of Georgia, Athens, GA 30602, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Doreen Ware
- USDA-ARS NAA Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, NY 14853, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - R Kelly Dawe
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA.
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24
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Rogers AR, Dunne JC, Romay C, Bohn M, Buckler ES, Ciampitti IA, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Hood E, Hooker DC, Knoll J, Lee EC, Lorenz A, Lynch JP, McKay J, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Sekhon R, Singh M, Smith M, Springer N, Thelen K, Thomison P, Thompson A, Tuinstra M, Wallace J, Wisser RJ, Xu W, Gilmour AR, Kaeppler SM, De Leon N, Holland JB. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. G3 (Bethesda) 2021; 11:6062399. [PMID: 33585867 DOI: 10.1093/g3journal/jkaa050] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/12/2022]
Abstract
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA
| | - Jeffrey C Dunne
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,USDA-ARS Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY 14853, USA
| | | | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA 50131, USA
| | - Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit, University of Missouri, Columbia, MO 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD 57769, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Elizabeth Hood
- College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA
| | - David C Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON N0P 2C0, Canada
| | - Joseph Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph N1G 2W1, Canada
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA 16802, USA
| | - John McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,Plant Sciences Institute, Iowa State University, Ames, IA 50011, USA
| | - Rajandeep Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
| | - Maninder Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Margaret Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nathan Springer
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Kurt Thelen
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210, USA
| | - Addie Thompson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Jason Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens GA 30602, USA
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
| | - Wenwei Xu
- Texas A& M AgriLife Research, Texas A& M University, Lubbock, TX 79403, USA
| | | | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Natalia De Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA.,Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA.,USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695-7620, USA
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25
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Jarquin D, de Leon N, Romay C, Bohn M, Buckler ES, Ciampitti I, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Holland JB, Hooker D, Kaeppler SM, Knoll J, Lee EC, Lawrence-Dill CJ, Lynch JP, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Smith M, Springer N, Thomison P, Tuinstra M, Wisser RJ, Xu W, Yu J, Lorenz A. Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project. Front Genet 2021; 11:592769. [PMID: 33763106 PMCID: PMC7982677 DOI: 10.3389/fgene.2020.592769] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 12/21/2020] [Indexed: 11/29/2022] Open
Abstract
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
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Affiliation(s)
- Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, United States
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL, United States
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States.,U.S. Department of Agriculture - Agricultural Research Service Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY, United States
| | - Ignacio Ciampitti
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA, United States.,U.S. Department of Agriculture - Agricultural Research Service Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, United States
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, United States
| | - Sherry Flint-Garcia
- U.S. Department of Agriculture - Agricultural Research Service Plant Genetics Research Unit, University of Missouri, Columbia, MO, United States
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD, United States
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - James B Holland
- U.S. Department of Agriculture - Agricultural Research Service Plant Science Research Unit, North Carolina State University, Raleigh, NC, United States
| | - David Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON, Canada
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, United States
| | - Joseph Knoll
- U.S. Department of Agriculture - Agricultural Research Service Crop Genetics and Breeding Research Unit, Tifton, GA, United States
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Carolyn J Lawrence-Dill
- Department of Agronomy, Iowa State University, Ames, IA, United States.,Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, United States.,Plant Sciences Institute, Iowa State University, Ames, IA, United States
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA, United States
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL, United States
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Patrick S Schnable
- U.S. Department of Agriculture - Agricultural Research Service Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, United States.,Plant Sciences Institute, Iowa State University, Ames, IA, United States
| | - Margaret Smith
- U.S. Department of Agriculture - Agricultural Research Service Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY, United States
| | - Nathan Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, United States
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, United States
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, United States
| | - Wenwei Xu
- Texas A&M AgriLife Research, Texas A&M University, Lubbock, TX, United States
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
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26
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Abstract
Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108 USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108 USA
| | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
| | - Matthew B. Hufford
- Department of Ecology, Evolution, and Organismal 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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27
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Della Coletta R, Qiu Y, Ou S, Hufford MB, Hirsch CN. How the pan-genome is changing crop genomics and improvement. Genome Biol 2021. [PMID: 33397434 DOI: 10.1186/s13059-020-02224-2228] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023] Open
Abstract
Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal 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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28
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Li Z, Zhou P, Della Coletta R, Zhang T, Brohammer AB, H O'Connor C, Vaillancourt B, Lipzen A, Daum C, Barry K, de Leon N, Hirsch CD, Buell CR, Kaeppler SM, Springer NM, Hirsch CN. Single-parent expression drives dynamic gene expression complementation in maize hybrids. Plant J 2021; 105:93-107. [PMID: 33098691 DOI: 10.1111/tpj.15042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 09/27/2020] [Accepted: 10/02/2020] [Indexed: 06/11/2023]
Abstract
Single-parent expression (SPE) is defined as gene expression in only one of the two parents. SPE can arise from differential expression between parental alleles, termed non-presence/absence (non-PAV) SPE, or from the physical absence of a gene in one parent, termed PAV SPE. We used transcriptome data of diverse Zea mays (maize) inbreds and hybrids, including 401 samples from five different tissues, to test for differences between these types of SPE genes. Although commonly observed, SPE is highly genotype and tissue specific. A positive correlation was observed between the genetic distance of the two inbred parents and the number of SPE genes identified. Regulatory analysis showed that PAV SPE and non-PAV SPE genes are mainly regulated by cis effects, with a small fraction under trans regulation. Polymorphic transposable element insertions in promoter sequences contributed to the high level of cis regulation for PAV SPE and non-PAV SPE genes. PAV SPE genes were more frequently expressed in hybrids than non-PAV SPE genes. The expression of parentally silent alleles in hybrids of non-PAV SPE genes was relatively rare but occurred in most hybrids. Non-PAV SPE genes with expression of the silent allele in hybrids are more likely to exhibit above high parent expression level than hybrids that do not express the silent allele, leading to non-additive expression. This study provides a comprehensive understanding of the nature of non-PAV SPE and PAV SPE genes and their roles in gene expression complementation in maize hybrids.
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Affiliation(s)
- Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Tifu Zhang
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Alex B Brohammer
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Brieanne Vaillancourt
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Anna Lipzen
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Chris Daum
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Kerrie Barry
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
| | - Cory D Hirsch
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN, 55108, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA
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29
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Li Z, Tirado SB, Kadam DC, Coffey L, Miller ND, Spalding EP, Lorenz AJ, de Leon N, Kaeppler SM, Schnable PS, Springer NM, Hirsch CN. Characterizing introgression-by-environment interactions using maize near isogenic lines. Theor Appl Genet 2020; 133:2761-2773. [PMID: 32572549 DOI: 10.1007/s00122-020-03630-z] [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: 08/15/2019] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
Significant introgression-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome. Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to introgression-by-environment interactions. To study potential patterns of introgression-by-environment interactions, fifteen near-isogenic lines (NILs) with > 90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess introgression-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the introgression-by-environment interaction varies across traits. Some introgressed segments were more prone to introgression-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of introgression-by-environment interaction in NILs is useful in consideration of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.
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Affiliation(s)
- Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Sara B Tirado
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
- Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA
| | - Dnyaneshwar C Kadam
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Lisa Coffey
- Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI, 53706, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, 1111 WOI Rd, Ames, IA, 50011, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, 1479 Gortner Avenue, Saint Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, Saint Paul, MN, 55108, USA.
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30
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Tirado SB, Hirsch CN, Springer NM. UAV-based imaging platform for monitoring maize growth throughout development. Plant Direct 2020; 4:e00230. [PMID: 32524060 PMCID: PMC7278367 DOI: 10.1002/pld3.230] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 05/06/2023]
Abstract
Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV-based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV-based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.
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Affiliation(s)
- Sara B. Tirado
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSaint PaulMNUSA
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMNUSA
| | - Candice N. Hirsch
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSaint PaulMNUSA
| | - Nathan M. Springer
- Department of Plant and Microbial BiologyUniversity of MinnesotaSaint PaulMNUSA
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31
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Tirado SB, Hirsch CN, Springer NM. UAV-based imaging platform for monitoring maize growth throughout development. Plant Direct 2020; 4:e00230. [PMID: 32524060 DOI: 10.1101/794057] [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] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 05/23/2023]
Abstract
Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV-based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV-based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.
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Affiliation(s)
- Sara B Tirado
- Department of Agronomy and Plant Genetics University of Minnesota Saint Paul MN USA
- Department of Plant and Microbial Biology University of Minnesota Saint Paul MN USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics University of Minnesota Saint Paul MN USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology University of Minnesota Saint Paul MN USA
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32
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Liu J, Seetharam AS, Chougule K, Ou S, Swentowsky KW, Gent JI, Llaca V, Woodhouse MR, Manchanda N, Presting GG, Kudrna DA, Alabady M, Hirsch CN, Fengler KA, Ware D, Michael TP, Hufford MB, Dawe RK. Gapless assembly of maize chromosomes using long-read technologies. Genome Biol 2020; 21:121. [PMID: 32434565 PMCID: PMC7238635 DOI: 10.1186/s13059-020-02029-9] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 04/23/2020] [Indexed: 12/16/2022] Open
Abstract
Creating gapless telomere-to-telomere assemblies of complex genomes is one of the ultimate challenges in genomics. We use two independent assemblies and an optical map-based merging pipeline to produce a maize genome (B73-Ab10) composed of 63 contigs and a contig N50 of 162 Mb. This genome includes gapless assemblies of chromosome 3 (236 Mb) and chromosome 9 (162 Mb), and 53 Mb of the Ab10 meiotic drive haplotype. The data also reveal the internal structure of seven centromeres and five heterochromatic knobs, showing that the major tandem repeat arrays (CentC, knob180, and TR-1) are discontinuous and frequently interspersed with retroelements.
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Affiliation(s)
- Jianing Liu
- Department of Genetics, University of Georgia, Athens, GA, 30602, USA
| | - Arun S Seetharam
- Genome Informatics Facility, Iowa State University, Ames, IA, 50011, USA
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Kyle W Swentowsky
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
| | - Jonathan I Gent
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
| | - Victor Llaca
- Corteva Agriscience™, 8325 NW 62nd Ave, Johnston, IA, 50131, USA
| | | | - Nancy Manchanda
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Gernot G Presting
- Molecular Biosciences and Bioengineering, University of Hawaii, Honolulu, HI, 96822, USA
| | - David A Kudrna
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Magdy Alabady
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
- Georgia Genomics and Bioinformatics Core Laboratory, University of Georgia, Athens, GA, 30602, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Kevin A Fengler
- Corteva Agriscience™, 8325 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, NY, 14853, USA
| | - Todd P Michael
- Informatics Department, J. Craig Venter Institute, La Jolla, CA, USA
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - R Kelly Dawe
- Department of Genetics, University of Georgia, Athens, GA, 30602, USA.
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA.
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33
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Liu J, Seetharam AS, Chougule K, Ou S, Swentowsky KW, Gent JI, Llaca V, Woodhouse MR, Manchanda N, Presting GG, Kudrna DA, Alabady M, Hirsch CN, Fengler KA, Ware D, Michael TP, Hufford MB, Dawe RK. Gapless assembly of maize chromosomes using long-read technologies. Genome Biol 2020. [PMID: 32434565 DOI: 10.1101/2020.01.14.906230v1.full] [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] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Creating gapless telomere-to-telomere assemblies of complex genomes is one of the ultimate challenges in genomics. We use two independent assemblies and an optical map-based merging pipeline to produce a maize genome (B73-Ab10) composed of 63 contigs and a contig N50 of 162 Mb. This genome includes gapless assemblies of chromosome 3 (236 Mb) and chromosome 9 (162 Mb), and 53 Mb of the Ab10 meiotic drive haplotype. The data also reveal the internal structure of seven centromeres and five heterochromatic knobs, showing that the major tandem repeat arrays (CentC, knob180, and TR-1) are discontinuous and frequently interspersed with retroelements.
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Affiliation(s)
- Jianing Liu
- Department of Genetics, University of Georgia, Athens, GA, 30602, USA
| | - Arun S Seetharam
- Genome Informatics Facility, Iowa State University, Ames, IA, 50011, USA
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Kyle W Swentowsky
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
| | - Jonathan I Gent
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
| | - Victor Llaca
- Corteva Agriscience™, 8325 NW 62nd Ave, Johnston, IA, 50131, USA
| | | | - Nancy Manchanda
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Gernot G Presting
- Molecular Biosciences and Bioengineering, University of Hawaii, Honolulu, HI, 96822, USA
| | - David A Kudrna
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Magdy Alabady
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
- Georgia Genomics and Bioinformatics Core Laboratory, University of Georgia, Athens, GA, 30602, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Kevin A Fengler
- Corteva Agriscience™, 8325 NW 62nd Ave, Johnston, IA, 50131, USA
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, NY, 14853, USA
| | - Todd P Michael
- Informatics Department, J. Craig Venter Institute, La Jolla, CA, USA
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - R Kelly Dawe
- Department of Genetics, University of Georgia, Athens, GA, 30602, USA.
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA.
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34
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Ou S, Liu J, Chougule KM, Fungtammasan A, Seetharam AS, Stein JC, Llaca V, Manchanda N, Gilbert AM, Wei S, Chin CS, Hufnagel DE, Pedersen S, Snodgrass SJ, Fengler K, Woodhouse M, Walenz BP, Koren S, Phillippy AM, Hannigan BT, Dawe RK, Hirsch CN, Hufford MB, Ware D. Effect of sequence depth and length in long-read assembly of the maize inbred NC358. Nat Commun 2020; 11:2288. [PMID: 32385271 PMCID: PMC7211024 DOI: 10.1038/s41467-020-16037-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 04/09/2020] [Indexed: 01/23/2023] Open
Abstract
Improvements in long-read data and scaffolding technologies have enabled rapid generation of reference-quality assemblies for complex genomes. Still, an assessment of critical sequence depth and read length is important for allocating limited resources. To this end, we have generated eight assemblies for the complex genome of the maize inbred line NC358 using PacBio datasets ranging from 20 to 75 × genomic depth and with N50 subread lengths of 11-21 kb. Assemblies with ≤30 × depth and N50 subread length of 11 kb are highly fragmented, with even low-copy genic regions showing degradation at 20 × depth. Distinct sequence-quality thresholds are observed for complete assembly of genes, transposable elements, and highly repetitive genomic features such as telomeres, heterochromatic knobs, and centromeres. In addition, we show high-quality optical maps can dramatically improve contiguity in even our most fragmented base assembly. This study provides a useful resource allocation reference to the community as long-read technologies continue to mature.
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Affiliation(s)
- Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA
| | - Jianing Liu
- Department of Genetics, University of Georgia, Athens, Georgia, 30602, USA
| | - Kapeel M Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | | | - Arun S Seetharam
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA
- Genome Informatics Facility, Iowa State University, Ames, Iowa, 50011, USA
| | - Joshua C Stein
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | - Victor Llaca
- Genomics Technologies, Applied Science and Technology, Corteva Agriscience TM, Johnston, Iowa, 50131, USA
| | - Nancy Manchanda
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA
| | - Amanda M Gilbert
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota, 55108, USA
| | - Sharon Wei
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | - Chen-Shan Chin
- DNAnexus, Inc., Mountain View, San Francisco, California, 94040, USA
| | - David E Hufnagel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA
| | - Sarah Pedersen
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA
| | - Samantha J Snodgrass
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA
| | - Kevin Fengler
- Genomics Technologies, Applied Science and Technology, Corteva Agriscience TM, Johnston, Iowa, 50131, USA
| | - Margaret Woodhouse
- USDA ARS Corn Insects and Crop Genetics Research Unit, Ames, Iowa, 50011, USA
| | - Brian P Walenz
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Brett T Hannigan
- DNAnexus, Inc., Mountain View, San Francisco, California, 94040, USA
| | - R Kelly Dawe
- Department of Genetics, University of Georgia, Athens, Georgia, 30602, USA.
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota, 55108, USA.
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, 50011, USA.
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA.
- USDA ARS Robert W. Holley Center for Agriculture and Health, Agricultural Research Service, Ithaca, New York, 14853, USA.
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35
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Zhou P, Li Z, Magnusson E, Gomez Cano F, Crisp PA, Noshay JM, Grotewold E, Hirsch CN, Briggs SP, Springer NM. Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions. Plant Cell 2020; 32:1377-1396. [PMID: 32184350 PMCID: PMC7203921 DOI: 10.1105/tpc.20.00080] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/06/2020] [Accepted: 03/16/2020] [Indexed: 05/22/2023]
Abstract
The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.
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Affiliation(s)
- Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Erika Magnusson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Fabio Gomez Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Peter A Crisp
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Steven P Briggs
- Division of Biological Sciences, University of California, San Diego, La Jolla, California 92093
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
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36
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Monnahan PJ, Michno JM, O'Connor C, Brohammer AB, Springer NM, McGaugh SE, Hirsch CN. Using multiple reference genomes to identify and resolve annotation inconsistencies. BMC Genomics 2020; 21:281. [PMID: 32264824 DOI: 10.1101/651984] [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] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 03/24/2020] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Advances in sequencing technologies have led to the release of reference genomes and annotations for multiple individuals within more well-studied systems. While each of these new genome assemblies shares significant portions of synteny between each other, the annotated structure of gene models within these regions can differ. Of particular concern are split-gene misannotations, in which a single gene is incorrectly annotated as two distinct genes or two genes are incorrectly annotated as a single gene. These misannotations can have major impacts on functional prediction, estimates of expression, and many downstream analyses. RESULTS We developed a high-throughput method based on pairwise comparisons of annotations that detect potential split-gene misannotations and quantifies support for whether the genes should be merged into a single gene model. We demonstrated the utility of our method using gene annotations of three reference genomes from maize (B73, PH207, and W22), a difficult system from an annotation perspective due to the size and complexity of the genome. On average, we found several hundred of these potential split-gene misannotations in each pairwise comparison, corresponding to 3-5% of gene models across annotations. To determine which state (i.e. one gene or multiple genes) is biologically supported, we utilized RNAseq data from 10 tissues throughout development along with a novel metric and simulation framework. The methods we have developed require minimal human interaction and can be applied to future assemblies to aid in annotation efforts. CONCLUSIONS Split-gene misannotations occur at appreciable frequency in maize annotations. We have developed a method to easily identify and correct these misannotations. Importantly, this method is generic in that it can utilize any type of short-read expression data. Failure to account for split-gene misannotations has serious consequences for biological inference, particularly for expression-based analyses.
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Affiliation(s)
- 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
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Jean-Michel Michno
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Christine 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
| | - Alex B Brohammer
- Department of Agronomy and Plant Genetics, 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
| | - Suzanne E McGaugh
- Department of Ecology, Evolution, and Behavior, 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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37
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Monnahan PJ, Michno JM, O'Connor C, Brohammer AB, Springer NM, McGaugh SE, Hirsch CN. Using multiple reference genomes to identify and resolve annotation inconsistencies. BMC Genomics 2020; 21:281. [PMID: 32264824 PMCID: PMC7140576 DOI: 10.1186/s12864-020-6696-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 03/24/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Advances in sequencing technologies have led to the release of reference genomes and annotations for multiple individuals within more well-studied systems. While each of these new genome assemblies shares significant portions of synteny between each other, the annotated structure of gene models within these regions can differ. Of particular concern are split-gene misannotations, in which a single gene is incorrectly annotated as two distinct genes or two genes are incorrectly annotated as a single gene. These misannotations can have major impacts on functional prediction, estimates of expression, and many downstream analyses. RESULTS We developed a high-throughput method based on pairwise comparisons of annotations that detect potential split-gene misannotations and quantifies support for whether the genes should be merged into a single gene model. We demonstrated the utility of our method using gene annotations of three reference genomes from maize (B73, PH207, and W22), a difficult system from an annotation perspective due to the size and complexity of the genome. On average, we found several hundred of these potential split-gene misannotations in each pairwise comparison, corresponding to 3-5% of gene models across annotations. To determine which state (i.e. one gene or multiple genes) is biologically supported, we utilized RNAseq data from 10 tissues throughout development along with a novel metric and simulation framework. The methods we have developed require minimal human interaction and can be applied to future assemblies to aid in annotation efforts. CONCLUSIONS Split-gene misannotations occur at appreciable frequency in maize annotations. We have developed a method to easily identify and correct these misannotations. Importantly, this method is generic in that it can utilize any type of short-read expression data. Failure to account for split-gene misannotations has serious consequences for biological inference, particularly for expression-based analyses.
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Affiliation(s)
- 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
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Jean-Michel Michno
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Christine 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
| | - Alex B Brohammer
- Department of Agronomy and Plant Genetics, 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
| | - Suzanne E McGaugh
- Department of Ecology, Evolution, and Behavior, 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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38
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McFarland BA, AlKhalifah N, Bohn M, Bubert J, Buckler ES, Ciampitti I, Edwards J, Ertl D, Gage JL, Falcon CM, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Holland JB, Hood E, Hooker D, Jarquin D, Kaeppler SM, Knoll J, Kruger G, Lauter N, Lee EC, Lima DC, Lorenz A, Lynch JP, McKay J, Miller ND, Moose SP, Murray SC, Nelson R, Poudyal C, Rocheford T, Rodriguez O, Romay MC, Schnable JC, Schnable PS, Scully B, Sekhon R, Silverstein K, Singh M, Smith M, Spalding EP, Springer N, Thelen K, Thomison P, Tuinstra M, Wallace J, Walls R, Wills D, Wisser RJ, Xu W, Yeh CT, de Leon N. Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets. BMC Res Notes 2020; 13:71. [PMID: 32051026 PMCID: PMC7017475 DOI: 10.1186/s13104-020-4922-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/27/2020] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.
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Affiliation(s)
| | | | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jessica Bubert
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Edward S Buckler
- Cornell University, Ithaca, NY, 14853, USA.,USDA-ARS, Beltsville, MD, USA
| | | | - Jode Edwards
- USDA-ARS, Beltsville, MD, USA.,Iowa State University, Ames, IA, 50011, USA
| | - David Ertl
- Iowa Corn Growers Association, Johnston, IA, 50131, USA
| | | | | | - Sherry Flint-Garcia
- USDA-ARS, Beltsville, MD, USA.,University of Missouri, Columbia, MO, 65211, USA
| | | | | | | | - James B Holland
- USDA-ARS, Beltsville, MD, USA.,North Carolina State University, Raleigh, NC, 27695, USA
| | | | | | | | | | | | - Greg Kruger
- University of Nebraska, Lincoln, NE, 68583, USA
| | - Nick Lauter
- USDA-ARS, Beltsville, MD, USA.,Iowa State University, Ames, IA, 50011, USA
| | | | | | - Aaron Lorenz
- University of Minnesota, St. Paul, MN, 55108, USA
| | | | - John McKay
- Colorado State University, Fort Collins, CO, 80523, USA
| | | | - Stephen P Moose
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Seth C Murray
- Texas A&M University, College Station, TX, 77843, USA
| | | | | | | | | | | | | | | | - Brian Scully
- USDA-ARS, Beltsville, MD, USA.,University of Florida, Gainesville, FL, 32611, USA
| | | | | | | | | | | | | | - Kurt Thelen
- Michigan State University, East Lansing, MI, 48824, USA
| | | | | | | | | | - David Wills
- University of Missouri, Columbia, MO, 65211, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
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39
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Crisp PA, Hammond R, Zhou P, Vaillancourt B, Lipzen A, Daum C, Barry K, de Leon N, Buell CR, Kaeppler SM, Meyers BC, Hirsch CN, Springer NM. Variation and Inheritance of Small RNAs in Maize Inbreds and F1 Hybrids. Plant Physiol 2020; 182:318-331. [PMID: 31575624 PMCID: PMC6945832 DOI: 10.1104/pp.19.00817] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 09/23/2019] [Indexed: 05/20/2023]
Abstract
Small RNAs (sRNAs) regulate gene expression, play important roles in epigenetic pathways, and are hypothesized to contribute to hybrid vigor in plants. Prior investigations have provided valuable insights into associations between sRNAs and heterosis, often using a single hybrid genotype or tissue, but our understanding of the role of sRNAs and their potential value to plant breeding are limited by an incomplete picture of sRNA variation between diverse genotypes and development stages. Here, we provide a deep exploration of sRNA variation and inheritance among a panel of 108 maize (Zea mays) samples spanning five tissues from eight inbred parents and 12 hybrid genotypes, covering a spectrum of heterotic groups, genetic variation, and levels of heterosis for various traits. We document substantial developmental and genotypic influences on sRNA expression, with varying patterns for 21-nucleotide (nt), 22-nt, and 24-nt sRNAs. We provide a detailed view of the distribution of sRNAs in the maize genome, revealing a complex makeup that also shows developmental plasticity, particularly for 22-nt sRNAs. sRNAs exhibited substantially more variation between inbreds as compared with observed variation for gene expression. In hybrids, we identify locus-specific examples of nonadditive inheritance, mostly characterized as partial or complete dominance, but rarely outside the parental range. However, the global abundance of 21-nt, 22-nt, and 24-nt sRNAs varies very little between inbreds and hybrids, suggesting that hybridization affects sRNA expression principally at specific loci rather than on a global scale. This study provides a valuable resource for understanding the potential role of sRNAs in hybrid vigor.
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Affiliation(s)
- Peter A Crisp
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Reza Hammond
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware 19711
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
| | - Brieanne Vaillancourt
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Anna Lipzen
- United States Department of Energy Joint Genome Institute, Walnut Creek, California 94598
| | - Chris Daum
- United States Department of Energy Joint Genome Institute, Walnut Creek, California 94598
| | - Kerrie Barry
- United States Department of Energy Joint Genome Institute, Walnut Creek, California 94598
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin 53706
| | - Blake C Meyers
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota 55108
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Ou S, Su W, Liao Y, Chougule K, Agda JRA, Hellinga AJ, Lugo CSB, Elliott TA, Ware D, Peterson T, Jiang N, Hirsch CN, Hufford MB. Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biol 2019. [PMID: 31843001 DOI: 10.1101/657890v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Sequencing technology and assembly algorithms have matured to the point that high-quality de novo assembly is possible for large, repetitive genomes. Current assemblies traverse transposable elements (TEs) and provide an opportunity for comprehensive annotation of TEs. Numerous methods exist for annotation of each class of TEs, but their relative performances have not been systematically compared. Moreover, a comprehensive pipeline is needed to produce a non-redundant library of TEs for species lacking this resource to generate whole-genome TE annotations. RESULTS We benchmark existing programs based on a carefully curated library of rice TEs. We evaluate the performance of methods annotating long terminal repeat (LTR) retrotransposons, terminal inverted repeat (TIR) transposons, short TIR transposons known as miniature inverted transposable elements (MITEs), and Helitrons. Performance metrics include sensitivity, specificity, accuracy, precision, FDR, and F1. Using the most robust programs, we create a comprehensive pipeline called Extensive de-novo TE Annotator (EDTA) that produces a filtered non-redundant TE library for annotation of structurally intact and fragmented elements. EDTA also deconvolutes nested TE insertions frequently found in highly repetitive genomic regions. Using other model species with curated TE libraries (maize and Drosophila), EDTA is shown to be robust across both plant and animal species. CONCLUSIONS The benchmarking results and pipeline developed here will greatly facilitate TE annotation in eukaryotic genomes. These annotations will promote a much more in-depth understanding of the diversity and evolution of TEs at both intra- and inter-species levels. EDTA is open-source and freely available: https://github.com/oushujun/EDTA.
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Affiliation(s)
- Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Weija Su
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Yi Liao
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, 92697, USA
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Jireh R A Agda
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Adam J Hellinga
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | | | - Tyler A Elliott
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- USDA-ARS NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY, 14853, USA
| | - Thomas Peterson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Ning Jiang
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA.
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA.
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA.
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Ou S, Su W, Liao Y, Chougule K, Agda JRA, Hellinga AJ, Lugo CSB, Elliott TA, Ware D, Peterson T, Jiang N, Hirsch CN, Hufford MB. Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biol 2019; 20:275. [PMID: 31843001 PMCID: PMC6913007 DOI: 10.1186/s13059-019-1905-y] [Citation(s) in RCA: 404] [Impact Index Per Article: 80.8] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 11/28/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sequencing technology and assembly algorithms have matured to the point that high-quality de novo assembly is possible for large, repetitive genomes. Current assemblies traverse transposable elements (TEs) and provide an opportunity for comprehensive annotation of TEs. Numerous methods exist for annotation of each class of TEs, but their relative performances have not been systematically compared. Moreover, a comprehensive pipeline is needed to produce a non-redundant library of TEs for species lacking this resource to generate whole-genome TE annotations. RESULTS We benchmark existing programs based on a carefully curated library of rice TEs. We evaluate the performance of methods annotating long terminal repeat (LTR) retrotransposons, terminal inverted repeat (TIR) transposons, short TIR transposons known as miniature inverted transposable elements (MITEs), and Helitrons. Performance metrics include sensitivity, specificity, accuracy, precision, FDR, and F1. Using the most robust programs, we create a comprehensive pipeline called Extensive de-novo TE Annotator (EDTA) that produces a filtered non-redundant TE library for annotation of structurally intact and fragmented elements. EDTA also deconvolutes nested TE insertions frequently found in highly repetitive genomic regions. Using other model species with curated TE libraries (maize and Drosophila), EDTA is shown to be robust across both plant and animal species. CONCLUSIONS The benchmarking results and pipeline developed here will greatly facilitate TE annotation in eukaryotic genomes. These annotations will promote a much more in-depth understanding of the diversity and evolution of TEs at both intra- and inter-species levels. EDTA is open-source and freely available: https://github.com/oushujun/EDTA.
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Affiliation(s)
- Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
| | - Weija Su
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA 50011 USA
| | - Yi Liao
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697 USA
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724 USA
| | - Jireh R. A. Agda
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario N1G 2W1 Canada
| | - Adam J. Hellinga
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario N1G 2W1 Canada
| | | | - Tyler A. Elliott
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario N1G 2W1 Canada
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724 USA
- USDA-ARS NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853 USA
| | - Thomas Peterson
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA 50011 USA
| | - Ning Jiang
- Department of Horticulture, Michigan State University, East Lansing, MI 48824 USA
| | - Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108 USA
| | - Matthew B. Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
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Anderson SN, Stitzer MC, Brohammer AB, Zhou P, Noshay JM, O'Connor CH, Hirsch CD, Ross-Ibarra J, Hirsch CN, Springer NM. Transposable elements contribute to dynamic genome content in maize. Plant J 2019; 100:1052-1065. [PMID: 31381222 DOI: 10.1111/tpj.14489] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/15/2019] [Accepted: 07/26/2019] [Indexed: 05/05/2023]
Abstract
Transposable elements (TEs) are ubiquitous components of eukaryotic genomes and can create variation in genome organization and content. Most maize genomes are composed of TEs. We developed an approach to define shared and variable TE insertions across genome assemblies and applied this method to four maize genomes (B73, W22, Mo17 and PH207) with uniform structural annotations of TEs. Among these genomes we identified approximately 400 000 TEs that are polymorphic, encompassing 1.6 Gb of variable TE sequence. These polymorphic TEs include a combination of recent transposition events as well as deletions of older TEs. There are examples of polymorphic TEs within each of the superfamilies of TEs and they are found distributed across the genome, including in regions of recent shared ancestry among individuals. There are many examples of polymorphic TEs within or near maize genes. In addition, there are 2380 gene annotations in the B73 genome that are located within variable TEs, providing evidence for the role of TEs in contributing to the substantial differences in annotated gene content among these genotypes. TEs are highly variable in our survey of four temperate maize genomes, highlighting the major contribution of TEs in driving variation in genome organization and gene content. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://github.com/SNAnderson/maizeTE_variation; https://mcstitzer.github.io/maize_TEs.
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Affiliation(s)
- Sarah N Anderson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Michelle C Stitzer
- Department of Plant Sciences and Center for Population Biology, University of California, Davis, CA, 95616, USA
| | - Alex B Brohammer
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, 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
| | - Christine H O'Connor
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Cory D Hirsch
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Jeffrey Ross-Ibarra
- Department of Plant Sciences and Center for Population Biology, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, 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
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Hemshrot A, Poets AM, Tyagi P, Lei L, Carter CK, Hirsch CN, Li L, Brown-Guedira G, Morrell PL, Muehlbauer GJ, Smith KP. Development of a Multiparent Population for Genetic Mapping and Allele Discovery in Six-Row Barley. Genetics 2019; 213:595-613. [PMID: 31358533 PMCID: PMC6781892 DOI: 10.1534/genetics.119.302046] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 07/16/2019] [Indexed: 11/18/2022] Open
Abstract
Germplasm collections hold valuable allelic diversity for crop improvement and genetic mapping of complex traits. To gain access to the genetic diversity within the USDA National Small Grain Collection (NSGC), we developed the Barley Recombinant Inbred Diverse Germplasm Population (BRIDG6), a six-row spring barley multiparent population (MPP) with 88 cultivated accessions crossed to a common parent (Rasmusson). The parents were randomly selected from a core subset of the NSGC that represents the genetic diversity of landrace and breeding accessions. In total, we generated 6160 F5 recombinant inbred lines (RILs), with an average of 69 and a range of 37-168 RILs per family, that were genotyped with 7773 SNPs, with an average of 3889 SNPs segregating per family. We detected 23 quantitative trait loci (QTL) associated with flowering time with five QTL found coincident with previously described flowering time genes. A major QTL was detected near the flowering time gene, HvPpd-H1 which affects photoperiod. Haplotype-based analysis of HvPpd-H1 identified private alleles to families of Asian origin conferring both positive and negative effects, providing the first observation of flowering time-related alleles private to Asian accessions. We evaluated several subsampling strategies to determine the effect of sample size on the power of QTL detection, and found that, for flowering time in barley, a sample size >50 families or 3000 individuals results in the highest power for QTL detection. This MPP will be useful for uncovering large and small effect QTL for traits of interest, and identifying and utilizing valuable alleles from the NSGC for barley improvement.
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Affiliation(s)
- Alex Hemshrot
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Ana M Poets
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Priyanka Tyagi
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - Li Lei
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Corey K Carter
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Lin Li
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
- HuaZhong Agricultural University, WuHan, 430070, China, and
| | - Gina Brown-Guedira
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
- USDA-ARS Plant Science Research, Raleigh, North Carolina 27695
| | - Peter L Morrell
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Gary J Muehlbauer
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Kevin P Smith
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
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Noshay JM, Anderson SN, Zhou P, Ji L, Ricci W, Lu Z, Stitzer MC, Crisp PA, Hirsch CN, Zhang X, Schmitz RJ, Springer NM. Monitoring the interplay between transposable element families and DNA methylation in maize. PLoS Genet 2019; 15:e1008291. [PMID: 31498837 PMCID: PMC6752859 DOI: 10.1371/journal.pgen.1008291] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 09/19/2019] [Accepted: 07/05/2019] [Indexed: 12/15/2022] Open
Abstract
DNA methylation and epigenetic silencing play important roles in the regulation of transposable elements (TEs) in many eukaryotic genomes. A majority of the maize genome is derived from TEs that can be classified into different orders and families based on their mechanism of transposition and sequence similarity, respectively. TEs themselves are highly methylated and it can be tempting to view them as a single uniform group. However, the analysis of DNA methylation profiles in flanking regions provides evidence for distinct groups of chromatin properties at different TE families. These differences among TE families are reproducible in different tissues and different inbred lines. TE families with varying levels of DNA methylation in flanking regions also show distinct patterns of chromatin accessibility and modifications within the TEs. The differences in the patterns of DNA methylation flanking TE families arise from a combination of non-random insertion preferences of TE families, changes in DNA methylation triggered by the insertion of the TE and subsequent selection pressure. A set of nearly 70,000 TE polymorphisms among four assembled maize genomes were used to monitor the level of DNA methylation at haplotypes with and without the TE insertions. In many cases, TE families with high levels of DNA methylation in flanking sequence are enriched for insertions into highly methylated regions. The majority of the >2,500 TE insertions into unmethylated regions result in changes in DNA methylation in haplotypes with the TE, suggesting the widespread potential for TE insertions to condition altered methylation in conserved regions of the genome. This study highlights the interplay between TEs and the methylome of a major crop species.
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Affiliation(s)
- Jaclyn M. Noshay
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul MN, United States of America
| | - Sarah N. Anderson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul MN, United States of America
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul MN, United States of America
| | - Lexiang Ji
- Institute of Bioinformatics, University of Georgia, Athens GA, United States of America
| | - William Ricci
- Department of Plant Biology, University of Georgia, Athens GA, United States of America
| | - Zefu Lu
- Department of Genetics, University of Georgia, Athens GA, United States of America
| | - Michelle C. Stitzer
- Department of Plant Sciences, University of California Davis, Davis CA, United States of America
| | - Peter A. Crisp
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul MN, United States of America
| | - Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul MN, United States of America
| | - Xiaoyu Zhang
- Department of Plant Biology, University of Georgia, Athens GA, United States of America
| | - Robert J. Schmitz
- Department of Genetics, University of Georgia, Athens GA, United States of America
| | - Nathan M. Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul MN, United States of America
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45
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Li Z, Coffey L, Garfin J, Miller ND, White MR, Spalding EP, Leon ND, Kaeppler SM, Schnable PS, Springer NM, Hirsch CN. Correction: Genotype-by-environment interactions affecting heterosis in maize. PLoS One 2019; 14:e0219528. [PMID: 31381609 PMCID: PMC6681948 DOI: 10.1371/journal.pone.0219528] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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46
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Della Coletta R, Hirsch CN, Rouse MN, Lorenz A, Garvin DF. Genomic Dissection of Nonhost Resistance to Wheat Stem Rust in Brachypodium distachyon. Mol Plant Microbe Interact 2019; 32:392-400. [PMID: 30261155 DOI: 10.1094/mpmi-08-18-0220-r] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The emergence of new races of Puccinia graminis f. sp. tritici, the causal pathogen of wheat stem rust, has spurred interest in developing durable resistance to this disease in wheat. Nonhost resistance holds promise to help control this and other diseases because it is durable against nonadapted pathogens. However, the genetic and molecular basis of nonhost resistance to wheat stem rust is poorly understood. In this study, the model grass Brachypodium distachyon, a nonhost of P. graminis f. sp. tritici, was used to genetically dissect nonhost resistance to wheat stem rust. A recombinant inbred line (RIL) population segregating for response to wheat stem rust was evaluated for resistance. Evaluation of genome-wide cumulative single nucleotide polymorphism allele frequency differences between contrasting pools of resistant and susceptible RILs followed by molecular marker analysis identified six quantitative trait loci (QTL) that cumulatively explained 72.5% of the variation in stem rust resistance. Two of the QTLs explained 31.7% of the variation, and their interaction explained another 4.6%. Thus, nonhost resistance to wheat stem rust in B. distachyon is genetically complex, with both major and minor QTLs acting additively and, in some cases, interacting. These findings will guide future research to identify genes essential to nonhost resistance to wheat stem rust.
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Affiliation(s)
- Rafael Della Coletta
- 1 Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, U.S.A
- 2 CAPES Foundation, Ministry of Education of Brazil, Brasilia, DF, Brazil
| | - Candice N Hirsch
- 1 Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, U.S.A
| | - Matthew N Rouse
- 3 USDA-ARS Cereal Disease Laboratory, St. Paul, MN, U.S.A
- 4 Department of Plant Pathology, University of Minnesota; and
| | - Aaron Lorenz
- 1 Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, U.S.A
| | - David F Garvin
- 1 Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, U.S.A
- 5 USDA-ARS Plant Science Research Unit, St. Paul, MN, U.S.A
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Zhou P, Hirsch CN, Briggs SP, Springer NM. Dynamic Patterns of Gene Expression Additivity and Regulatory Variation throughout Maize Development. Mol Plant 2019; 12:410-425. [PMID: 30593858 DOI: 10.1016/j.molp.2018.12.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 12/14/2018] [Accepted: 12/18/2018] [Indexed: 05/26/2023]
Abstract
Gene expression variation is a key component underlying phenotypic variation and heterosis. Transcriptome profiling was performed on 23 different tissues or developmental stages of two maize inbreds, B73 and Mo17, as well as their F1 hybrid. The obtained large-scale datasets provided opportunities to monitor the developmental dynamics of differential expression, additivity for gene expression, and regulatory variation. The transcriptome can be divided into ∼30 000 genes that are expressed in at least one tissue of one inbred and an additional ∼10 000 ″silent" genes that are not expressed in any tissue of any genotype, 90% of which are non-syntenic relative to other grasses. Many (∼74%) of the expressed genes exhibit differential expression in at least one tissue. However, the majority of genes with differential expression do not exhibit consistent differential expression in different tissues. These genes often exhibit tissue-specific differential expression with equivalent expression in other tissues, and in many cases they switch the directionality of differential expression in different tissues. This suggests widespread variation for tissue-specific regulation of gene expression between the two maize inbreds B73 and Mo17. Nearly 5000 genes are expressed in only one parent in at least one tissue (single parent expression) and 97% of these genes are expressed at mid-parent levels or higher in the hybrid, providing extensive opportunities for hybrid complementation in heterosis. In general, additive expression patterns are much more common than non-additive patterns, and this trend is more pronounced for genes with strong differential expression or single parent expression. There is relatively little evidence for non-additive expression patterns that are maintained in multiple tissues. The analysis of allele-specific expression allowed classification of cis- and trans-regulatory variation. Genes with cis-regulatory variation often exhibit additive expression and tend to have more consistent regulatory variation throughout development. In contrast, genes with trans-regulatory variation are enriched for non-additive patterns and often show tissue-specific differential expression. Taken together, this study provides a deeper understanding of regulatory variation and the degree of additive gene expression throughout maize development. The dynamic nature of differential expression, additivity, and regulatory variation imply abundant variability for tissue-specific regulatory mechanisms and suggest that connections between transcriptome and phenome will require expression data from multiple tissues.
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Affiliation(s)
- Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108, USA
| | - Steven P Briggs
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA.
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48
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Mazaheri M, Heckwolf M, Vaillancourt B, Gage JL, Burdo B, Heckwolf S, Barry K, Lipzen A, Ribeiro CB, Kono TJY, Kaeppler HF, Spalding EP, Hirsch CN, Robin Buell C, de Leon N, Kaeppler SM. Genome-wide association analysis of stalk biomass and anatomical traits in maize. BMC Plant Biol 2019; 19:45. [PMID: 30704393 PMCID: PMC6357476 DOI: 10.1186/s12870-019-1653-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 01/14/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Maize stover is an important source of crop residues and a promising sustainable energy source in the United States. Stalk is the main component of stover, representing about half of stover dry weight. Characterization of genetic determinants of stalk traits provide a foundation to optimize maize stover as a biofuel feedstock. We investigated maize natural genetic variation in genome-wide association studies (GWAS) to detect candidate genes associated with traits related to stalk biomass (stalk diameter and plant height) and stalk anatomy (rind thickness, vascular bundle density and area). RESULTS Using a panel of 942 diverse inbred lines, 899,784 RNA-Seq derived single nucleotide polymorphism (SNP) markers were identified. Stalk traits were measured on 800 members of the panel in replicated field trials across years. GWAS revealed 16 candidate genes associated with four stalk traits. Most of the detected candidate genes were involved in fundamental cellular functions, such as regulation of gene expression and cell cycle progression. Two of the regulatory genes (Zmm22 and an ortholog of Fpa) that were associated with plant height were previously shown to be involved in regulating the vegetative to floral transition. The association of Zmm22 with plant height was confirmed using a transgenic approach. Transgenic lines with increased expression of Zmm22 showed a significant decrease in plant height as well as tassel branch number, indicating a pleiotropic effect of Zmm22. CONCLUSION Substantial heritable variation was observed in the association panel for stalk traits, indicating a large potential for improving useful stalk traits in breeding programs. Genome-wide association analyses detected several candidate genes associated with multiple traits, suggesting common regulatory elements underlie various stalk traits. Results of this study provide insights into the genetic control of maize stalk anatomy and biomass.
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Affiliation(s)
- Mona Mazaheri
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
- Department of Energy, Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI 53706 USA
| | - Marlies Heckwolf
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
- Department of Energy, Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI 53706 USA
| | - Brieanne Vaillancourt
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824 USA
- Department of Energy, Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824 USA
| | - Joseph L. Gage
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
| | - Brett Burdo
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
| | - Sven Heckwolf
- Department of Botany, University of Wisconsin, Madison, WI 53706 USA
| | - Kerrie Barry
- Department of Energy, Joint Genome Institute, Walnut Creek, California, 94598 USA
| | - Anna Lipzen
- Department of Energy, Joint Genome Institute, Walnut Creek, California, 94598 USA
| | - Camila Bastos Ribeiro
- Genótika Super Sementes. Colonizador Ênio Pipino - St. Industrial Sul, Sinop, MT 78550-098 Brazil
| | - Thomas J. Y. Kono
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, St Paul, MN 55108 USA
- Present address: Minnesota Supercomputing Institute, 117 Pleasant Street SE, Minneapolis, MN 55455 USA
| | - Heidi F. Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
- Department of Energy, Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI 53706 USA
| | - Edgar P. Spalding
- Department of Botany, University of Wisconsin, Madison, WI 53706 USA
| | - Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, St Paul, MN 55108 USA
| | - C. Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824 USA
- Department of Energy, Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824 USA
- Plant Resilience Institute, Michigan State University, East Lansing, MI 48824 USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
- Department of Energy, Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI 53706 USA
| | - Shawn M. Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI 53706 USA
- Department of Energy, Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, WI 53706 USA
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Salvo S, Cook J, Carlson AR, Hirsch CN, Kaeppler SM, Kaeppler HF. Genetic Fine-Mapping of a Quantitative Trait Locus (QTL) Associated with Embryogenic Tissue Culture Response and Plant Regeneration Ability in Maize ( Zea mays L.). Plant Genome 2018; 11:170111. [PMID: 30025019 DOI: 10.3835/plantgenome2017.12.0111] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Embryogenic and regenerable tissue cultures are widely utilized in plant transformation, clonal propagation, and biological research applications. Germplasm utilized in those applications are limited, however, due to genotype-dependent culture response. The goal of this study was to identify genomic regions controlling embryogenic and regenerable tissue culture response in the globally important crop, maize ( L.), toward the long-term objective of developing approaches for genotype-independent plant genetic engineering and clonal propagation systems. An inbred maize line, WCIC2, nearly-isogenic to reference inbred B73, was developed by phenotypic selection and molecular marker analysis. WCIC2 has over 50x increase in tissue culture response relative to the recurrent parent, B73. This line was used to genetically fine-map a region on chromosome 3 controlling embryogenic and regenerable tissue culture response to a 23.9 Mb region. WCIC2 and derivatives will be useful materials to enable maize research in a genetic background similar to B73, and our genetic mapping results will advance research to identify causal genes controlling somatic embryo formation and plant regeneration in maize.
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Affiliation(s)
- Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, USA.
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, USA.
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