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  4. Selection of parental lines for plant breeding via genomic prediction
 
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Selection of parental lines for plant breeding via genomic prediction

Journal
Frontiers in plant science
Journal Volume
13
Date Issued
2022
Author(s)
Chung, Ping-Yuan
CHEN-TUO LIAO  
DOI
10.3389/fpls.2022.934767
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148339588&doi=10.1007%2fs10522-023-10023-4&partnerID=40&md5=f27ee279273e10e8d35637b61fc57b7a
https://scholars.lib.ntu.edu.tw/handle/123456789/630156
URL
https://api.elsevier.com/content/abstract/scopus_id/85135838384
Abstract
A set of superior parental lines is imperative for the development of high-performing inbred lines in any biparental crossing program for crops. The main objectives of this study are to (a) develop a genomic prediction approach to identify superior parental lines for multi-trait selection, and (b) generate a software package for users to execute the proposed approach before conducting field experiments. According to different breeding goals of the target traits, a novel selection index integrating information from genomic-estimated breeding values (GEBVs) of candidate accessions was proposed to evaluate the composite performance of simulated progeny populations. Two rice (Oryza sativa L.) genome datasets were analyzed to illustrate the potential applications of the proposed approach. One dataset applied to the parental selection for producing inbred lines with satisfactory performance in primary and secondary traits simultaneously. The other one applied to demonstrate the application of producing inbred lines with high adaptability to different environments. Overall, the results showed that incorporating GEBV and genomic diversity into a selection strategy based on the proposed selection index could assist in selecting superior parents to meet the desired breeding goals and increasing long-term genetic gain. An R package, called IPLGP, was generated to facilitate the widespread application of the approach.
Subjects
genetic gain; genome-wide markers; mixed models; multiple-trait selection; selection index
SDGs

[SDGs]SDG3

Publisher
FRONTIERS MEDIA SA
Type
journal article

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