Literature DB >> 27170319

Genomic prediction for grain zinc and iron concentrations in spring wheat.

Govindan Velu1, Jose Crossa2, Ravi P Singh2, Yuanfeng Hao2, Susanne Dreisigacker2, Paulino Perez-Rodriguez3, Arun K Joshi4, Ravish Chatrath5, Vikas Gupta5, Arun Balasubramaniam6, Chhavi Tiwari6, Vinod K Mishra6, Virinder Singh Sohu7, Gurvinder Singh Mavi7.   

Abstract

KEY MESSAGE: Predictability estimated through cross-validation approach showed moderate to high level; hence, genomic selection approach holds great potential for biofortification breeding to enhance grain zinc and iron concentrations in wheat. Wheat (Triticum aestivum L.) is a major staple crop, providing 20 % of dietary energy and protein consumption worldwide. It is an important source of mineral micronutrients such as zinc (Zn) and iron (Fe) for resource poor consumers. Genomic selection (GS) approaches have great potential to accelerate development of Fe- and Zn-enriched wheat. Here, we present the results of large-scale genomic and phenotypic data from the HarvestPlus Association Mapping (HPAM) panel consisting of 330 diverse wheat lines to perform genomic predictions for grain Zn (GZnC) and Fe (GFeC) concentrations, thousand-kernel weight (TKW) and days to maturity (DTM) in wheat. The HPAM lines were phenotyped in three different locations in India and Mexico in two successive crop seasons (2011-12 and 2012-13) for GZnC, GFeC, TKW and DTM. The genomic prediction models revealed that the estimated prediction abilities ranged from 0.331 to 0.694 for Zn and from 0.324 to 0.734 for Fe according to different environments, whereas prediction abilities for TKW and DTM were as high as 0.76 and 0.64, respectively, suggesting that GS holds great potential in biofortification breeding to enhance grain Zn and Fe concentrations in bread wheat germplasm.

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Year:  2016        PMID: 27170319     DOI: 10.1007/s00122-016-2726-y

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  18 in total

1.  Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods.

Authors:  Gustavo De los Campos; Daniel Gianola; Guilherme J M Rosa; Kent A Weigel; José Crossa
Journal:  Genet Res (Camb)       Date:  2010-08       Impact factor: 1.588

2.  Predicting quantitative traits with regression models for dense molecular markers and pedigree.

Authors:  Gustavo de los Campos; Hugo Naya; Daniel Gianola; José Crossa; Andrés Legarra; Eduardo Manfredi; Kent Weigel; José Miguel Cotes
Journal:  Genetics       Date:  2009-03-16       Impact factor: 4.562

3.  Efficient methods to compute genomic predictions.

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

4.  Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.

Authors:  Nicolas Heslot; Deniz Akdemir; Mark E Sorrells; Jean-Luc Jannink
Journal:  Theor Appl Genet       Date:  2013-11-22       Impact factor: 5.699

5.  Genome-based prediction of testcross values in maize.

Authors:  Theresa Albrecht; Valentin Wimmer; Hans-Jürgen Auinger; Malena Erbe; Carsten Knaak; Milena Ouzunova; Henner Simianer; Chris-Carolin Schön
Journal:  Theor Appl Genet       Date:  2011-04-20       Impact factor: 5.699

6.  Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R.

Authors:  Paulino Pérez; Gustavo de Los Campos; José Crossa; Daniel Gianola
Journal:  Plant Genome       Date:  2010       Impact factor: 4.089

7.  Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Authors:  José Crossa; Gustavo de Los Campos; Paulino Pérez; Daniel Gianola; Juan Burgueño; José Luis Araus; Dan Makumbi; Ravi P Singh; Susanne Dreisigacker; Jianbing Yan; Vivi Arief; Marianne Banziger; Hans-Joachim Braun
Journal:  Genetics       Date:  2010-09-02       Impact factor: 4.562

8.  A reaction norm model for genomic selection using high-dimensional genomic and environmental data.

Authors:  Diego Jarquín; José Crossa; Xavier Lacaze; Philippe Du Cheyron; Joëlle Daucourt; Josiane Lorgeou; François Piraux; Laurent Guerreiro; Paulino Pérez; Mario Calus; Juan Burgueño; Gustavo de los Campos
Journal:  Theor Appl Genet       Date:  2013-12-12       Impact factor: 5.699

9.  Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.

Authors:  Bettina Lado; Ivan Matus; Alejandra Rodríguez; Luis Inostroza; Jesse Poland; François Belzile; Alejandro del Pozo; Martín Quincke; Marina Castro; Jarislav von Zitzewitz
Journal:  G3 (Bethesda)       Date:  2013-12-09       Impact factor: 3.154

10.  Genomic prediction in CIMMYT maize and wheat breeding programs.

Authors:  J Crossa; P Pérez; J Hickey; J Burgueño; L Ornella; J Cerón-Rojas; X Zhang; S Dreisigacker; R Babu; Y Li; D Bonnett; K Mathews
Journal:  Heredity (Edinb)       Date:  2013-04-10       Impact factor: 3.821

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  28 in total

Review 1.  Applying genomic resources to accelerate wheat biofortification.

Authors:  Muhammad Waqas Ali; Philippa Borrill
Journal:  Heredity (Edinb)       Date:  2020-06-11       Impact factor: 3.821

2.  High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.

Authors:  Jin Sun; Jesse A Poland; Suchismita Mondal; José Crossa; Philomin Juliana; Ravi P Singh; Jessica E Rutkoski; Jean-Luc Jannink; Leonardo Crespo-Herrera; Govindan Velu; Julio Huerta-Espino; Mark E Sorrells
Journal:  Theor Appl Genet       Date:  2019-02-18       Impact factor: 5.699

3.  Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.

Authors:  Sivakumar Sukumaran; Jose Crossa; Diego Jarquin; Marta Lopes; Matthew P Reynolds
Journal:  G3 (Bethesda)       Date:  2017-02-09       Impact factor: 3.154

Review 4.  Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

Authors:  C Anilkumar; N C Sunitha; Narayana Bhat Devate; S Ramesh
Journal:  Planta       Date:  2022-09-23       Impact factor: 4.540

5.  Genome-Wide Association Mapping Identifies Key Genomic Regions for Grain Zinc and Iron Biofortification in Bread Wheat.

Authors:  Philomin Juliana; Velu Govindan; Leonardo Crespo-Herrera; Suchismita Mondal; Julio Huerta-Espino; Sandesh Shrestha; Jesse Poland; Ravi P Singh
Journal:  Front Plant Sci       Date:  2022-06-30       Impact factor: 6.627

Review 6.  From markers to genome-based breeding in wheat.

Authors:  Awais Rasheed; Xianchun Xia
Journal:  Theor Appl Genet       Date:  2019-01-23       Impact factor: 5.699

Review 7.  Revisiting the versatile buckwheat: reinvigorating genetic gains through integrated breeding and genomics approach.

Authors:  D C Joshi; Ganesh V Chaudhari; Salej Sood; Lakshmi Kant; A Pattanayak; Kaixuan Zhang; Yu Fan; Dagmar Janovská; Vladimir Meglič; Meiliang Zhou
Journal:  Planta       Date:  2019-01-08       Impact factor: 4.116

8.  Genomic selection can accelerate the biofortification of spring wheat.

Authors:  Reem Joukhadar; Rebecca Thistlethwaite; Richard M Trethowan; Matthew J Hayden; James Stangoulis; Suong Cu; Hans D Daetwyler
Journal:  Theor Appl Genet       Date:  2021-07-12       Impact factor: 5.699

Review 9.  From zero to hero: the past, present and future of grain amaranth breeding.

Authors:  Dinesh C Joshi; Salej Sood; Rajashekara Hosahatti; Lakshmi Kant; A Pattanayak; Anil Kumar; Dinesh Yadav; Markus G Stetter
Journal:  Theor Appl Genet       Date:  2018-07-10       Impact factor: 5.699

Review 10.  Harnessing Diversity in Wheat to Enhance Grain Yield, Climate Resilience, Disease and Insect Pest Resistance and Nutrition Through Conventional and Modern Breeding Approaches.

Authors:  Suchismita Mondal; Jessica E Rutkoski; Govindan Velu; Pawan K Singh; Leonardo A Crespo-Herrera; Carlos Guzmán; Sridhar Bhavani; Caixia Lan; Xinyao He; Ravi P Singh
Journal:  Front Plant Sci       Date:  2016-07-06       Impact factor: 5.753

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