Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe-Reference-Cited by-同舟云学术

Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe

Published:2016-12-19 Issue:4 Volume:130 Page:635-647
ISSN:0040-5752
Container-title:Theoretical and Applied Genetics
language:en
Short-container-title:Theor Appl Genet

Author:

He Sang,Reif Jochen C.,Korzun Viktor,Bothe Reiner,Ebmeyer Erhard,Jiang Yong

Publisher

Springer Science and Business Media LLC

Subject

Genetics,Agronomy and Crop Science,General Medicine,Biotechnology

Link

http://link.springer.com/content/pdf/10.1007/s00122-016-2840-x.pdf

Reference75 articles.

1. Akdemir D, Jannink J-L (2015) Locally epistatic genomic relationship matrices for genomic association and prediction. Genetics 199:857–871

2. Azadi A, Mardi M, Hervan EM, Mohammadi SA, Moradi F, Tabatabaee MT, Pirseyedi SM, Ebrahimi M, Fayaz F, Kazemi M, Ashkani S, Nakhoda B, Mohammadi-Nejad G (2015) QTL mapping of yield and yield components under normal and salt-stress conditions in bread wheat (Triticum aestivum L.). Plant Molecular Biology Reporter 33:102–120

3. Bellucci A, Torp AM, Bruun S, Magid J, Andersen SB, Rasmussen SK (2015) Association mapping in scandinavian winter wheat for yield, plant height, and traits important for second-generation bioethanol production. Front Plant Sci 6:1046

4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57:289–300

5. Bennett D, Reynolds M, Mullan D, Izanloo A, Kuchel H, Langridge P, Schnurbusch T (2012) Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet 125:1473–1485

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice;Rice;2023-06-07

2. Genomic prediction using composite training sets is an effective method for exploiting germplasm conserved in rice gene banks;The Crop Journal;2022-08

3. A SIMD algorithm for the detection of epistatic interactions of any order;Future Generation Computer Systems;2022-07

4. Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice;Frontiers in Genetics;2022-06-22

5. NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction;Frontiers in Plant Science;2022-04-29