Literature DB >> 27012534

Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population.

Yong-Xiang Li1, Chunhui Li1, Peter J Bradbury2, Xiaolei Liu2, Fei Lu2, Cinta M Romay2, Jeffrey C Glaubitz2, Xun Wu1, Bo Peng1, Yunsu Shi1, Yanchun Song1, Dengfeng Zhang1, Edward S Buckler2,3, Zhiwu Zhang4,5, Yu Li1, Tianyu Wang1.   

Abstract

Flowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi- genetic background population that contained more than 8000 lines under multiple Sino-United States environments. The population included two nested association mapping (NAM) panels and a natural association panel. Nearly 1 million single-nucleotide polymorphisms (SNPs) were used in the analyses. Through the parallel linkage analysis of the two NAM panels, both common and unique flowering time regions were detected. Genome wide, a total of 90 flowering time regions were identified. One-third of these regions were connected to traits associated with the environmental sensitivity of maize flowering time. The genome-wide association study of the three panels identified nearly 1000 flowering time-associated SNPs, mainly distributed around 220 candidate genes (within a distance of 1 Mb). Interestingly, two types of regions were significantly enriched for these associated SNPs - one was the candidate gene regions and the other was the approximately 5 kb regions away from the candidate genes. Moreover, the associated SNPs exhibited high accuracy for predicting flowering time.
© 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

Entities:  

Keywords:  flowering time; genome-wide association study (GWAS); linkage analysis; maize (Zea mays L.); nested association mapping (NAM)

Mesh:

Year:  2016        PMID: 27012534     DOI: 10.1111/tpj.13174

Source DB:  PubMed          Journal:  Plant J        ISSN: 0960-7412            Impact factor:   6.417


  38 in total

1.  Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize.

Authors:  Rajandeep S Sekhon; Christopher Saski; Rohit Kumar; Barry S Flinn; Feng Luo; Timothy M Beissinger; Arlyn J Ackerman; Matthew W Breitzman; William C Bridges; Natalia de Leon; Shawn M Kaeppler
Journal:  Plant Cell       Date:  2019-06-25       Impact factor: 11.277

Review 2.  Ten Years of the Maize Nested Association Mapping Population: Impact, Limitations, and Future Directions.

Authors:  Joseph L Gage; Brandon Monier; Anju Giri; Edward S Buckler
Journal:  Plant Cell       Date:  2020-05-12       Impact factor: 11.277

3.  Genome-wide analysis of deletions in maize population reveals abundant genetic diversity and functional impact.

Authors:  Xiao Zhang; Yonghui Zhu; Karl A G Kremling; M Cinta Romay; Robert Bukowski; Qi Sun; Shibin Gao; Edward S Buckler; Fei Lu
Journal:  Theor Appl Genet       Date:  2021-10-18       Impact factor: 5.699

4.  Parent-of-Origin-Effect rough endosperm Mutants in Maize.

Authors:  Fang Bai; Mary Daliberti; Alyssa Bagadion; Miaoyun Xu; Yubing Li; John Baier; Chi-Wah Tseung; Matthew M S Evans; A Mark Settles
Journal:  Genetics       Date:  2016-07-20       Impact factor: 4.562

5.  Distinct genetic architectures for phenotype means and plasticities in Zea mays.

Authors:  Aaron Kusmec; Srikant Srinivasan; Dan Nettleton; Patrick S Schnable
Journal:  Nat Plants       Date:  2017-09-04       Impact factor: 15.793

6.  Different genetic basis for alcohol dehydrogenase activity and plasticity in a novel alcohol environment for Drosophila melanogaster.

Authors:  Sheng Pei Wang; David M Althoff
Journal:  Heredity (Edinb)       Date:  2020-06-01       Impact factor: 3.821

7.  Dissecting the genetics of cold tolerance in a multiparental maize population.

Authors:  Q Yi; R A Malvar; L Álvarez-Iglesias; B Ordás; Pedro Revilla
Journal:  Theor Appl Genet       Date:  2019-11-18       Impact factor: 5.699

8.  Characterization and fine mapping of qkrnw4, a major QTL controlling kernel row number in maize.

Authors:  Ningning Nie; Xiaoyu Ding; Lin Chen; Xun Wu; Yixin An; Chunhui Li; Yanchun Song; Dengfeng Zhang; Zhizhai Liu; Tianyu Wang; Yu Li; Yong-Xiang Li; Yunsu Shi
Journal:  Theor Appl Genet       Date:  2019-09-25       Impact factor: 5.699

9.  De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes.

Authors:  Matthew B Hufford; Arun S Seetharam; Margaret R Woodhouse; Kapeel M Chougule; Shujun Ou; Jianing Liu; William A Ricci; Tingting Guo; Andrew Olson; Yinjie Qiu; Rafael Della Coletta; Silas Tittes; Asher I Hudson; Alexandre P Marand; Sharon Wei; Zhenyuan Lu; Bo Wang; Marcela K Tello-Ruiz; Rebecca D Piri; Na Wang; Dong Won Kim; Yibing Zeng; Christine H O'Connor; Xianran Li; Amanda M Gilbert; Erin Baggs; Ksenia V Krasileva; John L Portwood; Ethalinda K S Cannon; Carson M Andorf; Nancy Manchanda; Samantha J Snodgrass; David E Hufnagel; Qiuhan Jiang; Sarah Pedersen; Michael L Syring; David A Kudrna; Victor Llaca; Kevin Fengler; Robert J Schmitz; Jeffrey Ross-Ibarra; Jianming Yu; Jonathan I Gent; Candice N Hirsch; Doreen Ware; R Kelly Dawe
Journal:  Science       Date:  2021-08-06       Impact factor: 47.728

Review 10.  Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.

Authors:  Marcin Grzybowski; Nuwan K Wijewardane; Abbas Atefi; Yufeng Ge; James C Schnable
Journal:  Plant Commun       Date:  2021-05-27
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.