Literature DB >> 33585867

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

Anna R Rogers1, Jeffrey C Dunne2, Cinta Romay3, Martin Bohn4, Edward S Buckler3,5, Ignacio A Ciampitti6, Jode Edwards7,8, David Ertl9, Sherry Flint-Garcia10, Michael A Gore11, Christopher Graham12, Candice N Hirsch13, Elizabeth Hood14, David C Hooker15, Joseph Knoll16, Elizabeth C Lee17, Aaron Lorenz13, Jonathan P Lynch18, John McKay19, Stephen P Moose4, Seth C Murray20, Rebecca Nelson21, Torbert Rocheford22, James C Schnable23, Patrick S Schnable7,24, Rajandeep Sekhon25, Maninder Singh26, Margaret Smith11, Nathan Springer23, Kurt Thelen27, Peter Thomison28, Addie Thompson27, Mitch Tuinstra22, Jason Wallace29, Randall J Wisser30, Wenwei Xu31, A R Gilmour32, Shawn M Kaeppler33, Natalia De Leon33, James B Holland1,2,34.   

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. Published by Oxford University Press on behalf of Genetics Society of America 2021. This work is written by US Government employees and is in the public domain in the US.

Entities:  

Keywords:  Genotype-by-environment interaction; dominance genetic variance; environmental covariates; multienvironment

Mesh:

Year:  2021        PMID: 33585867      PMCID: PMC8022981          DOI: 10.1093/g3journal/jkaa050

Source DB:  PubMed          Journal:  G3 (Bethesda)        ISSN: 2160-1836            Impact factor:   3.154


  48 in total

Review 1.  Reconciling the analysis of IBD and IBS in complex trait studies.

Authors:  Joseph E Powell; Peter M Visscher; Michael E Goddard
Journal:  Nat Rev Genet       Date:  2010-09-28       Impact factor: 53.242

2.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

3.  On the additive and dominant variance and covariance of individuals within the genomic selection scope.

Authors:  Zulma G Vitezica; Luis Varona; Andres Legarra
Journal:  Genetics       Date:  2013-10-11       Impact factor: 4.562

Review 4.  From association to prediction: statistical methods for the dissection and selection of complex traits in plants.

Authors:  Alexander E Lipka; Catherine B Kandianis; Matthew E Hudson; Jianming Yu; Jenny Drnevich; Peter J Bradbury; Michael A Gore
Journal:  Curr Opin Plant Biol       Date:  2015-03-17       Impact factor: 7.834

5.  The genetic architecture of maize flowering time.

Authors:  Edward S Buckler; James B Holland; Peter J Bradbury; Charlotte B Acharya; Patrick J Brown; Chris Browne; Elhan Ersoz; Sherry Flint-Garcia; Arturo Garcia; Jeffrey C Glaubitz; Major M Goodman; Carlos Harjes; Kate Guill; Dallas E Kroon; Sara Larsson; Nicholas K Lepak; Huihui Li; Sharon E Mitchell; Gael Pressoir; Jason A Peiffer; Marco Oropeza Rosas; Torbert R Rocheford; M Cinta Romay; Susan Romero; Stella Salvo; Hector Sanchez Villeda; H Sofia da Silva; Qi Sun; Feng Tian; Narasimham Upadyayula; Doreen Ware; Heather Yates; Jianming Yu; Zhiwu Zhang; Stephen Kresovich; Michael D McMullen
Journal:  Science       Date:  2009-08-07       Impact factor: 47.728

6.  Population resequencing reveals local adaptation of Arabidopsis lyrata to serpentine soils.

Authors:  Thomas L Turner; Elizabeth C Bourne; Eric J Von Wettberg; Tina T Hu; Sergey V Nuzhdin
Journal:  Nat Genet       Date:  2010-01-24       Impact factor: 38.330

7.  Identification of mega-environments and rice genotypes for general and specific adaptation to saline and alkaline stresses in India.

Authors:  S L Krishnamurthy; P C Sharma; D K Sharma; K T Ravikiran; Y P Singh; V K Mishra; D Burman; B Maji; S Mandal; S K Sarangi; R K Gautam; P K Singh; K K Manohara; B C Marandi; G Padmavathi; P B Vanve; K D Patil; S Thirumeni; O P Verma; A H Khan; S Tiwari; S Geetha; M Shakila; R Gill; V K Yadav; S K B Roy; M Prakash; J Bonifacio; Abdelbagi Ismail; G B Gregorio; Rakesh Kumar Singh
Journal:  Sci Rep       Date:  2017-08-11       Impact factor: 4.379

8.  Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets.

Authors:  Naser AlKhalifah; Darwin A Campbell; Celeste M Falcon; Jack M Gardiner; Nathan D Miller; Maria Cinta Romay; Ramona Walls; Renee Walton; Cheng-Ting Yeh; Martin Bohn; Jessica Bubert; Edward S Buckler; Ignacio Ciampitti; Sherry Flint-Garcia; Michael A Gore; Christopher Graham; Candice Hirsch; James B Holland; David Hooker; Shawn Kaeppler; Joseph Knoll; Nick Lauter; Elizabeth C Lee; Aaron Lorenz; Jonathan P Lynch; Stephen P Moose; Seth C Murray; Rebecca Nelson; Torbert Rocheford; Oscar Rodriguez; James C Schnable; Brian Scully; Margaret Smith; Nathan Springer; Peter Thomison; Mitchell Tuinstra; Randall J Wisser; Wenwei Xu; David Ertl; Patrick S Schnable; Natalia De Leon; Edgar P Spalding; Jode Edwards; Carolyn J Lawrence-Dill
Journal:  BMC Res Notes       Date:  2018-07-09

9.  Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

Authors:  Bridget A McFarland; Naser AlKhalifah; Martin Bohn; Jessica Bubert; Edward S Buckler; Ignacio Ciampitti; Jode Edwards; David Ertl; Joseph L Gage; Celeste M Falcon; Sherry Flint-Garcia; Michael A Gore; Christopher Graham; Candice N Hirsch; James B Holland; Elizabeth Hood; David Hooker; Diego Jarquin; Shawn M Kaeppler; Joseph Knoll; Greg Kruger; Nick Lauter; Elizabeth C Lee; Dayane C Lima; Aaron Lorenz; Jonathan P Lynch; John McKay; Nathan D Miller; Stephen P Moose; Seth C Murray; Rebecca Nelson; Christina Poudyal; Torbert Rocheford; Oscar Rodriguez; Maria Cinta Romay; James C Schnable; Patrick S Schnable; Brian Scully; Rajandeep Sekhon; Kevin Silverstein; Maninder Singh; Margaret Smith; Edgar P Spalding; Nathan Springer; Kurt Thelen; Peter Thomison; Mitchell Tuinstra; Jason Wallace; Ramona Walls; David Wills; Randall J Wisser; Wenwei Xu; Cheng-Ting Yeh; Natalia de Leon
Journal:  BMC Res Notes       Date:  2020-02-12

10.  Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies.

Authors:  Daniela Bustos-Korts; Martin P Boer; Marcos Malosetti; Scott Chapman; Karine Chenu; Bangyou Zheng; Fred A van Eeuwijk
Journal:  Front Plant Sci       Date:  2019-11-27       Impact factor: 5.753

View more
  8 in total

1.  Genetic mapping and prediction of flowering time and plant height in a maize Stiff Stalk MAGIC population.

Authors:  Kathryn J Michel; Dayane C Lima; Hope Hundley; Vasanth Singan; Yuko Yoshinaga; Chris Daum; Kerrie Barry; Karl W Broman; C Robin Buell; Natalia de Leon; Shawn M Kaeppler
Journal:  Genetics       Date:  2022-05-31       Impact factor: 4.402

2.  Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.

Authors:  Anna R Rogers; James B Holland
Journal:  G3 (Bethesda)       Date:  2022-02-04       Impact factor: 3.542

3.  The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data.

Authors:  Jose Crossa; Roberto Fritsche-Neto; Osval A Montesinos-Lopez; Germano Costa-Neto; Susanne Dreisigacker; Abelardo Montesinos-Lopez; Alison R Bentley
Journal:  Front Plant Sci       Date:  2021-04-16       Impact factor: 5.753

4.  Analysis of genotype-by-environment interactions in a maize mapping population.

Authors:  Asher I Hudson; Sarah G Odell; Pierre Dubreuil; Marie-Helene Tixier; Sebastien Praud; Daniel E Runcie; Jeffrey Ross-Ibarra
Journal:  G3 (Bethesda)       Date:  2022-03-04       Impact factor: 3.154

5.  Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods.

Authors:  Zitong Li; Shiming Liu; Warren Conaty; Qian-Hao Zhu; Philippe Moncuquet; Warwick Stiller; Iain Wilson
Journal:  Heredity (Edinb)       Date:  2022-05-06       Impact factor: 3.832

6.  Optimizing predictions in IRRI's rice drought breeding program by leveraging 17 years of historical data and pedigree information.

Authors:  Apurva Khanna; Mahender Anumalla; Margaret Catolos; Sankalp Bhosale; Diego Jarquin; Waseem Hussain
Journal:  Front Plant Sci       Date:  2022-09-20       Impact factor: 6.627

7.  Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat.

Authors:  Yusheng Zhao; Patrick Thorwarth; Yong Jiang; Norman Philipp; Albert W Schulthess; Mario Gils; Philipp H G Boeven; C Friedrich H Longin; Johannes Schacht; Erhard Ebmeyer; Viktor Korzun; Vilson Mirdita; Jost Dörnte; Ulrike Avenhaus; Ralf Horbach; Hilmar Cöster; Josef Holzapfel; Ludwig Ramgraber; Simon Kühnle; Pierrick Varenne; Anne Starke; Friederike Schürmann; Sebastian Beier; Uwe Scholz; Fang Liu; Renate H Schmidt; Jochen C Reif
Journal:  Sci Adv       Date:  2021-06-11       Impact factor: 14.136

8.  Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.

Authors:  Roberto Fritsche-Neto; Giovanni Galli; Karina Lima Reis Borges; Germano Costa-Neto; Filipe Couto Alves; Felipe Sabadin; Danilo Hottis Lyra; Pedro Patric Pinho Morais; Luciano Rogério Braatz de Andrade; Italo Granato; Jose Crossa
Journal:  Front Plant Sci       Date:  2021-07-01       Impact factor: 5.753

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.