Literature DB >> 26715032

Genome-wide dissection of the maize ear genetic architecture using multiple populations.

Yingjie Xiao1, Hao Tong1, Xiaohong Yang2, Shizhong Xu3, Qingchun Pan1, Feng Qiao1, Mohammad Sharif Raihan1, Yun Luo1, Haijun Liu1, Xuehai Zhang1, Ning Yang1, Xiaqing Wang1, Min Deng1, Minliang Jin1, Lijun Zhao1, Xin Luo1, Yang Zhou1, Xiang Li1, Jie Liu1, Wei Zhan1, Nannan Liu1, Hong Wang1, Gengshen Chen1, Ye Cai2, Gen Xu2, Weidong Wang2, Debo Zheng2, Jianbing Yan1.   

Abstract

Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.
© 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Entities:  

Keywords:  genome-wide association study (GWAS); joint linkage mapping; maize (Zea mays); multi-parent population; quantitative trait loci (QTLs); yield traits

Mesh:

Year:  2015        PMID: 26715032     DOI: 10.1111/nph.13814

Source DB:  PubMed          Journal:  New Phytol        ISSN: 0028-646X            Impact factor:   10.151


  54 in total

1.  High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.

Authors:  Xuehai Zhang; Chenglong Huang; Di Wu; Feng Qiao; Wenqiang Li; Lingfeng Duan; Ke Wang; Yingjie Xiao; Guoxing Chen; Qian Liu; Lizhong Xiong; Wanneng Yang; Jianbing Yan
Journal:  Plant Physiol       Date:  2017-01-30       Impact factor: 8.340

2.  The Genetic Basis of Plant Architecture in 10 Maize Recombinant Inbred Line Populations.

Authors:  Qingchun Pan; Yuancheng Xu; Kun Li; Yong Peng; Wei Zhan; Wenqiang Li; Lin Li; Jianbing Yan
Journal:  Plant Physiol       Date:  2017-08-24       Impact factor: 8.340

3.  The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice.

Authors:  Jie Liu; Juan Huang; Huan Guo; Liu Lan; Hongze Wang; Yuancheng Xu; Xiaohong Yang; Wenqiang Li; Hao Tong; Yingjie Xiao; Qingchun Pan; Feng Qiao; Mohammad Sharif Raihan; Haijun Liu; Xuehai Zhang; Ning Yang; Xiaqing Wang; Min Deng; Minliang Jin; Lijun Zhao; Xin Luo; Yang Zhou; Xiang Li; Wei Zhan; Nannan Liu; Hong Wang; Gengshen Chen; Qing Li; Jianbing Yan
Journal:  Plant Physiol       Date:  2017-08-15       Impact factor: 8.340

4.  Linkage mapping combined with association analysis reveals QTL and candidate genes for three husk traits in maize.

Authors:  Zhenhai Cui; Aiai Xia; Ao Zhang; Jinhong Luo; Xiaohong Yang; Lijun Zhang; Yanye Ruan; Yan He
Journal:  Theor Appl Genet       Date:  2018-07-24       Impact factor: 5.699

5.  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

6.  Genome-wide association study of maize plant architecture using F1 populations.

Authors:  Yang Zhao; Hengsheng Wang; Chen Bo; Wei Dai; Xingen Zhang; Ronghao Cai; Longjiang Gu; Qing Ma; Haiyang Jiang; Jun Zhu; Beijiu Cheng
Journal:  Plant Mol Biol       Date:  2018-12-05       Impact factor: 4.076

7.  Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping.

Authors:  Chaoshu Zhang; Zhiqiang Zhou; Hongjun Yong; Xiaochong Zhang; Zhuanfang Hao; Fangjun Zhang; Mingshun Li; Degui Zhang; Xinhai Li; Zhenhua Wang; Jianfeng Weng
Journal:  Theor Appl Genet       Date:  2017-02-18       Impact factor: 5.699

8.  A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments.

Authors:  Xiaoxiang Zhang; Zhongrong Guan; Zhaoling Li; Peng Liu; Langlang Ma; Yinchao Zhang; Lang Pan; Shijiang He; Yanling Zhang; Peng Li; Fei Ge; Chaoying Zou; Yongcong He; Shibin Gao; Guangtang Pan; Yaou Shen
Journal:  Theor Appl Genet       Date:  2020-06-27       Impact factor: 5.699

9.  Genome-wide association mapping for agronomic traits in an 8-way Upland cotton MAGIC population by SLAF-seq.

Authors:  Cong Huang; Chao Shen; Tianwang Wen; Bin Gao; Dingguo Li; Zhongxu Lin
Journal:  Theor Appl Genet       Date:  2021-04-28       Impact factor: 5.699

10.  Genome-Wide Association Study for Grain Micronutrient Concentrations in Wheat Advanced Lines Derived From Wild Emmer.

Authors:  Jia Liu; Lin Huang; Tingxuan Li; Yaxi Liu; Zehong Yan; Guan Tang; Youliang Zheng; Dengcai Liu; Bihua Wu
Journal:  Front Plant Sci       Date:  2021-05-14       Impact factor: 5.753

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