Literature DB >> 33852055

Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.

Jacob I Marsh1, Haifei Hu1, Mitchell Gill1, Jacqueline Batley1, David Edwards2.   

Abstract

KEY MESSAGE: Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.

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Mesh:

Year:  2021        PMID: 33852055     DOI: 10.1007/s00122-021-03820-3

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


  127 in total

Review 1.  The application of genomics and bioinformatics to accelerate crop improvement in a changing climate.

Authors:  Jacqueline Batley; David Edwards
Journal:  Curr Opin Plant Biol       Date:  2016-02-27       Impact factor: 7.834

2.  Breeding Top Genotypes and Accelerating Response to Recurrent Selection by Selecting Parents with Greater Gametic Variance.

Authors:  Piter Bijma; Yvonne C J Wientjes; Mario P L Calus
Journal:  Genetics       Date:  2019-11-26       Impact factor: 4.562

3.  PGP repository: a plant phenomics and genomics data publication infrastructure.

Authors:  Daniel Arend; Astrid Junker; Uwe Scholz; Danuta Schüler; Juliane Wylie; Matthias Lange
Journal:  Database (Oxford)       Date:  2016-04-17       Impact factor: 3.451

4.  Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks.

Authors:  Camilo Akimushkin; Diego Raphael Amancio; Osvaldo Novais Oliveira
Journal:  PLoS One       Date:  2017-01-26       Impact factor: 3.240

5.  Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.

Authors:  Christina B Azodi; Emily Bolger; Andrew McCarren; Mark Roantree; Gustavo de Los Campos; Shin-Han Shiu
Journal:  G3 (Bethesda)       Date:  2019-11-05       Impact factor: 3.154

6.  Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection.

Authors:  Antoine Allier; Christina Lehermeier; Alain Charcosset; Laurence Moreau; Simon Teyssèdre
Journal:  Front Genet       Date:  2019-10-29       Impact factor: 4.599

Review 7.  Translating High-Throughput Phenotyping into Genetic Gain.

Authors:  José Luis Araus; Shawn C Kefauver; Mainassara Zaman-Allah; Mike S Olsen; Jill E Cairns
Journal:  Trends Plant Sci       Date:  2018-03-16       Impact factor: 18.313

8.  Haplotype-based genotyping-by-sequencing in oat genome research.

Authors:  Wubishet A Bekele; Charlene P Wight; Shiaoman Chao; Catherine J Howarth; Nicholas A Tinker
Journal:  Plant Biotechnol J       Date:  2018-03-25       Impact factor: 9.803

Review 9.  GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley - A review.

Authors:  Ahmad M Alqudah; Ahmed Sallam; P Stephen Baenziger; Andreas Börner
Journal:  J Adv Res       Date:  2019-11-04       Impact factor: 10.479

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  8 in total

Review 1.  Breeding crops for climate resilience.

Authors:  Peter Langridge; Hans Braun; Brent Hulke; Eric Ober; B M Prasanna
Journal:  Theor Appl Genet       Date:  2021-05-27       Impact factor: 5.699

Review 2.  Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value.

Authors:  Tinashe Zenda; Songtao Liu; Anyi Dong; Jiao Li; Yafei Wang; Xinyue Liu; Nan Wang; Huijun Duan
Journal:  Front Plant Sci       Date:  2021-12-01       Impact factor: 5.753

Review 3.  Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species.

Authors:  Cassandria Geraldine Tay Fernandez; Benjamin John Nestor; Monica Furaste Danilevicz; Mitchell Gill; Jakob Petereit; Philipp Emanuel Bayer; Patrick Michael Finnegan; Jacqueline Batley; David Edwards
Journal:  Int J Mol Sci       Date:  2022-02-28       Impact factor: 5.923

4.  Haplotype mapping uncovers unexplored variation in wild and domesticated soybean at the major protein locus cqProt-003.

Authors:  Jacob I Marsh; Haifei Hu; Jakob Petereit; Philipp E Bayer; Babu Valliyodan; Jacqueline Batley; Henry T Nguyen; David Edwards
Journal:  Theor Appl Genet       Date:  2022-02-09       Impact factor: 5.574

5.  Suitability Evaluation of Crop Variety via Graph Neural Network.

Authors:  Qiusi Zhang; Bo Li; Yong Zhang; Shufeng Wang
Journal:  Comput Intell Neurosci       Date:  2022-08-09

Review 6.  Pangenomics and Crop Genome Adaptation in a Changing Climate.

Authors:  Jakob Petereit; Philipp E Bayer; William J W Thomas; Cassandria G Tay Fernandez; Junrey Amas; Yueqi Zhang; Jacqueline Batley; David Edwards
Journal:  Plants (Basel)       Date:  2022-07-27

Review 7.  Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.

Authors:  Muhammad Hafeez Ullah Khan; Shoudong Wang; Jun Wang; Sunny Ahmar; Sumbul Saeed; Shahid Ullah Khan; Xiaogang Xu; Hongyang Chen; Javaid Akhter Bhat; Xianzhong Feng
Journal:  Int J Mol Sci       Date:  2022-09-22       Impact factor: 6.208

Review 8.  Biotechnological Advances to Improve Abiotic Stress Tolerance in Crops.

Authors:  Miguel Angel Villalobos-López; Analilia Arroyo-Becerra; Anareli Quintero-Jiménez; Gabriel Iturriaga
Journal:  Int J Mol Sci       Date:  2022-10-10       Impact factor: 6.208

  8 in total

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