Literature DB >> 28875394

Genomic-based-breeding tools for tropical maize improvement.

Thammineni Chakradhar1, Vemuri Hindu2, Palakolanu Sudhakar Reddy3.   

Abstract

Maize has traditionally been the main staple diet in the Southern Asia and Sub-Saharan Africa and widely grown by millions of resource poor small scale farmers. Approximately, 35.4 million hectares are sown to tropical maize, constituting around 59% of the developing worlds. Tropical maize encounters tremendous challenges besides poor agro-climatic situations with average yields recorded <3 tones/hectare that is far less than the average of developed countries. On the contrary to poor yields, the demand for maize as food, feed, and fuel is continuously increasing in these regions. Heterosis breeding introduced in early 90 s improved maize yields significantly, but genetic gains is still a mirage, particularly for crop growing under marginal environments. Application of molecular markers has accelerated the pace of maize breeding to some extent. The availability of array of sequencing and genotyping technologies offers unrivalled service to improve precision in maize-breeding programs through modern approaches such as genomic selection, genome-wide association studies, bulk segregant analysis-based sequencing approaches, etc. Superior alleles underlying complex traits can easily be identified and introgressed efficiently using these sequence-based approaches. Integration of genomic tools and techniques with advanced genetic resources such as nested association mapping and backcross nested association mapping could certainly address the genetic issues in maize improvement programs in developing countries. Huge diversity in tropical maize and its inherent capacity for doubled haploid technology offers advantage to apply the next generation genomic tools for accelerating production in marginal environments of tropical and subtropical world. Precision in phenotyping is the key for success of any molecular-breeding approach. This article reviews genomic technologies and their application to improve agronomic traits in tropical maize breeding has been reviewed in detail.

Entities:  

Keywords:  Genome-wide association studies (GWAS); Genomic selection (GS); Informatics tools; Maize; Next generation sequencing (NGS); Phenotyping; QTL-seq

Mesh:

Year:  2017        PMID: 28875394     DOI: 10.1007/s10709-017-9981-y

Source DB:  PubMed          Journal:  Genetica        ISSN: 0016-6707            Impact factor:   1.082


  76 in total

1.  Improvement of hybrid yield by advanced backcross QTL analysis in elite maize.

Authors:  C. Ho; R. McCouch; E. Smith
Journal:  Theor Appl Genet       Date:  2002-06-19       Impact factor: 5.699

2.  Genome-wide association and genomic selection in animal breeding.

Authors:  Ben Hayes; Mike Goddard
Journal:  Genome       Date:  2010-11       Impact factor: 2.166

3.  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

Review 4.  Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product.

Authors:  Mark Cooper; Carla Gho; Roger Leafgren; Tom Tang; Carlos Messina
Journal:  J Exp Bot       Date:  2014-03-04       Impact factor: 6.992

5.  Phenotyping for drought tolerance of crops in the genomics era.

Authors:  Roberto Tuberosa
Journal:  Front Physiol       Date:  2012-09-19       Impact factor: 4.566

6.  An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.

Authors:  Vincent Segura; Bjarni J Vilhjálmsson; Alexander Platt; Arthur Korte; Ümit Seren; Quan Long; Magnus Nordborg
Journal:  Nat Genet       Date:  2012-06-17       Impact factor: 38.330

7.  Genetic diversity among INERA maize inbred lines with single nucleotide polymorphism (SNP) markers and their relationship with CIMMYT, IITA, and temperate lines.

Authors:  Abdalla Dao; Jacob Sanou; Sharon E Mitchell; Vernon Gracen; Eric Y Danquah
Journal:  BMC Genet       Date:  2014-11-25       Impact factor: 2.797

8.  A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array.

Authors:  Sandra Unterseer; Eva Bauer; Georg Haberer; Michael Seidel; Carsten Knaak; Milena Ouzunova; Thomas Meitinger; Tim M Strom; Ruedi Fries; Hubert Pausch; Christofer Bertani; Alessandro Davassi; Klaus Fx Mayer; Chris-Carolin Schön
Journal:  BMC Genomics       Date:  2014-09-29       Impact factor: 3.969

9.  Genomic Regions Associated with Root Traits under Drought Stress in Tropical Maize (Zea mays L.).

Authors:  P H Zaidi; K Seetharam; Girish Krishna; L Krishnamurthy; S Gajanan; Raman Babu; M Zerka; M T Vinayan; B S Vivek
Journal:  PLoS One       Date:  2016-10-21       Impact factor: 3.240

10.  Dynamic QTL analysis and candidate gene mapping for waterlogging tolerance at maize seedling stage.

Authors:  Khalid A Osman; Bin Tang; Yaping Wang; Juanhua Chen; Feng Yu; Liu Li; Xuesong Han; Zuxin Zhang; Jianbin Yan; Yonglian Zheng; Bing Yue; Fazhan Qiu
Journal:  PLoS One       Date:  2013-11-14       Impact factor: 3.240

View more
  3 in total

1.  Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization.

Authors:  A Badji; D B Kwemoi; L Machida; D Okii; N Mwila; S Agbahoungba; F Kumi; A Ibanda; A Bararyenya; M Solemanegy; T Odong; P Wasswa; M Otim; G Asea; M Ochwo-Ssemakula; H Talwana; S Kyamanywa; P Rubaihayo
Journal:  Genes (Basel)       Date:  2020-06-24       Impact factor: 4.096

2.  Fall-armyworm invasion, control practices and resistance breeding in Sub-Saharan Africa.

Authors:  Prince M Matova; Casper N Kamutando; Cosmos Magorokosho; Dumisani Kutywayo; Freeman Gutsa; Maryke Labuschagne
Journal:  Crop Sci       Date:  2020-11-11       Impact factor: 2.319

3.  LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks.

Authors:  Bruna Piereck; Marx Oliveira-Lima; Ana Maria Benko-Iseppon; Sarah Diehl; Reinhard Schneider; Ana Christina Brasileiro-Vidal; Adriano Barbosa-Silva
Journal:  BMC Bioinformatics       Date:  2020-08-24       Impact factor: 3.169

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.