Knowledge Center Catalog

Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat (Record no. 65174)

MARC details
000 -LEADER
fixed length control field 03407nab|a22004097a|4500
001 - CONTROL NUMBER
control field 65174
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240919020954.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 20221s2022||||mx |||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0040-5752
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1432-2242 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1007/s00122-022-04085-0
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Atanda, A.S.
Field link and sequence number 001711295
-- 001712571
9 (RLIN) 8531
Miscellaneous information Global Maize Program
-- Formerly Global Wheat Program
245 11 - TITLE STATEMENT
Title Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. 2022.
Place of publication, distribution, etc. Berlin (Germany) :
500 ## - GENERAL NOTE
General note Peer review
520 ## - SUMMARY, ETC.
Summary, etc. Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Genes
Source of heading or term AGROVOC
9 (RLIN) 3563
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Breeding programmes
Source of heading or term AGROVOC
9 (RLIN) 21704
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 Accuracy
Source of heading or term AGROVOC
9 (RLIN) 27100
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environment
Source of heading or term AGROVOC
9 (RLIN) 1098
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Spring wheat
Source of heading or term AGROVOC
9 (RLIN) 1806
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Forecasting
Source of heading or term AGROVOC
9 (RLIN) 2701
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Velu, G.
Field link and sequence number INT2983
9 (RLIN) 880
Miscellaneous information Global Wheat Program
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Singh, R.P.
Field link and sequence number INT0610
9 (RLIN) 825
Miscellaneous information Global Wheat Program
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Robbins, K.
9 (RLIN) 5987
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Crossa, J.
Miscellaneous information Genetic Resources Program
Field link and sequence number CCJL01
9 (RLIN) 59
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Bentley, A.R.
Field link and sequence number 001712492
Miscellaneous information Formerly Global Wheat Program
9 (RLIN) 9599
773 0# - HOST ITEM ENTRY
Title Theoretical and Applied Genetics
Place, publisher, and date of publication Berlin (Germany) : Springer, 2022
International Standard Serial Number 0040-5752
Related parts v 135, no. 6, p. 1939–1950
Record control number G444762
856 ## - ELECTRONIC LOCATION AND ACCESS
Link text Open Access through DSpace
Uniform Resource Identifier https://hdl.handle.net/10883/22045
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
04/10/2022   04/10/2022 Article Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 04/10/2022

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