Knowledge Center Catalog

Metabolic marker-assisted genomic prediction improves hybrid breeding (Record no. 68613)

MARC details
000 -LEADER
fixed length control field 04653nab|a22006017a|4500
001 - CONTROL NUMBER
control field 68613
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251221164717.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 20251ss2025||||xxu||ppoop|||00||0|eengdd
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2590-3462 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1016/j.xplc.2024.101199
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Yang Xu
9 (RLIN) 17252
245 10 - TITLE STATEMENT
Title Metabolic marker-assisted genomic prediction improves hybrid breeding
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. United States of America :
Name of publisher, distributor, etc. Cell Press ;
-- Plant Communications Shanghai Editorial,
Date of publication, distribution, etc. 2025.
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open access
520 ## - SUMMARY, ETC.
Summary, etc. Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or eXtreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.
546 ## - LANGUAGE NOTE
Language note Text in English
591 ## - CATALOGING NOTES
Affiliation Yunbi Xu : Not CIMMYT Affiliation
597 ## - CGIAR Initiative
Donor or Funder National Key Research and Development Program
-- National Natural Science Foundation of China
-- Jiangsu Province Agricultural Science and Technology Independent Innovation
-- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
-- Seed Industry Revitalization Project of Jiangsu Province
-- Jiangsu Provincial Key Research and Development Program
-- Shenzhen Science and Technology Innovation Program
-- Hebei University of Science and Technology
-- Shanghai Agricultural Science and Technology Innovation Program
-- Qing Lan Project of Jiangsu Province
-- Yangzhou University High-end Talent Support Program
Program & Accelerators Breeding for Tomorrow
CGSpace handle https://hdl.handle.net/10568/179152
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Genomics
9 (RLIN) 1132
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Forecasting
9 (RLIN) 2701
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Hybrids
9 (RLIN) 1151
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Breeding
Source of heading or term AGROVOC
9 (RLIN) 1029
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Metabolome
Source of heading or term AGROVOC
9 (RLIN) 38192
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Association mapping
9 (RLIN) 1512
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Marker-assisted selection
9 (RLIN) 10737
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Maize
Source of heading or term AGROVOC
9 (RLIN) 1173
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Genome-wide association studies
9 (RLIN) 31443
Source of heading or term AGROVOC
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Wenyan Yang
9 (RLIN) 29404
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Jie Qiu
9 (RLIN) 38186
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Kai Zhou
9 (RLIN) 29403
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Guangning Yu
9 (RLIN) 29400
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yuxiang Zhang
9 (RLIN) 36502
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Xin Wang
9 (RLIN) 6995
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Yuxin Jiao
9 (RLIN) 29402
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Xinyi Wang
9 (RLIN) 38187
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Shujun Hu
9 (RLIN) 38188
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Xuecai Zhang
Miscellaneous information Global Maize Program
Field link and sequence number INT3400
9 (RLIN) 951
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Pengcheng Li
9 (RLIN) 17447
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yue Lu
9 (RLIN) 38189
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Rujia Chen
9 (RLIN) 38190
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Tianyun Tao
9 (RLIN) 38191
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Zefeng Yang
9 (RLIN) 17448
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Yunbi Xu
Miscellaneous information Global Maize Program
Field link and sequence number INT2735
9 (RLIN) 857
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Chenwu Xu
9 (RLIN) 17254
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication United States of America : Cell Press ; Plant Communications Shanghai Editorial, 2025.
Related parts v. 6, no. 3, art. 101199
Title Plant Communications
International Standard Serial Number 2590-3462
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Open Access through DSpace
Uniform Resource Identifier https://hdl.handle.net/10883/35473
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Article
Suppress in OPAC No
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
02/05/2025   02/05/2025 Article Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 02/05/2025

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