Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (Brassica napus L.)

被引:18
|
作者
Luo, Xiang [1 ]
Ding, Yi [2 ]
Zhang, Linzhong [3 ]
Yue, Yao [1 ]
Snyder, John H. [2 ]
Ma, Chaozhi [1 ]
Zhu, Jun [2 ]
机构
[1] Huazhong Agr Univ, Natl Ctr Rapeseed Improvement Wuhan, Natl Key Lab Crop Genet Improvement, Wuhan, Peoples R China
[2] Zhejiang Univ, Inst Bioinformat, Hangzhou, Zhejiang, Peoples R China
[3] Anhui Agr Univ, Econ & Tech Coll, Hefei, Peoples R China
基金
国家高技术研究发展计划(863计划); 美国国家科学基金会;
关键词
genomic prediction; genotypic effects; epistasis; agronomic traits; B; napus; FLOWERING TIME GENES; WIDE ASSOCIATION; MISSING HERITABILITY; COMPLEX TRAITS; PLANT HEIGHT; MODEL APPROACH; ARCHITECTURE; IDENTIFICATION; RICE; QTLS;
D O I
10.3389/fgene.2017.00015
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Oilseed rape (Brassica napus) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.
引用
收藏
页数:9
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