Version 1
: Received: 9 November 2023 / Approved: 10 November 2023 / Online: 10 November 2023 (06:52:53 CET)
How to cite:
Zhai, D. Application of Various Genomic Selection Models in Cotton Fiber Quality. Preprints2023, 2023110677. https://doi.org/10.20944/preprints202311.0677.v1
Zhai, D. Application of Various Genomic Selection Models in Cotton Fiber Quality. Preprints 2023, 2023110677. https://doi.org/10.20944/preprints202311.0677.v1
Zhai, D. Application of Various Genomic Selection Models in Cotton Fiber Quality. Preprints2023, 2023110677. https://doi.org/10.20944/preprints202311.0677.v1
APA Style
Zhai, D. (2023). Application of Various Genomic Selection Models in Cotton Fiber Quality. Preprints. https://doi.org/10.20944/preprints202311.0677.v1
Chicago/Turabian Style
Zhai, D. 2023 "Application of Various Genomic Selection Models in Cotton Fiber Quality" Preprints. https://doi.org/10.20944/preprints202311.0677.v1
Abstract
Cotton is the most important natural fiber cash crop, which has high commodity economic benefits and provides an important material foundation for China's construction. With the improvement of textile technology and living standard, higher requirements are put forward for raw cotton quality. Traditional cotton breeding methods need typing and selection. With the development of biotechnology and the research of genomics, genomic selection has been widely used in cotton breeding. Genomic selection is a new breeding method, which can be selected and bred by constructing a prediction model and using high-density molecular markers covering the whole genome. In this study, the application of various genomic selection models in cotton fiber quality was explored, which provided more reliable information for genetic breeding improvement in the future, thus helping to improve the efficiency of actual cotton breeding.
Keywords
Genomic selection; Cotton; Fiber quality
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
