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Tytuł pozycji:

Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing.

Tytuł:
Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing.
Autorzy:
Wang N; Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Wang H; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.; CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai, China.; Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China.
Zhang A; College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China.
Liu Y; College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China.
Yu D; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.; CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai, China.; Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China.
Hao Z; Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Ilut D; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.
Glaubitz JC; Institute of Biotechnology, Cornell University, Ithaca, NY, USA.
Gao Y; Institute of Biotechnology, Cornell University, Ithaca, NY, USA.
Jones E; Institute of Biotechnology, Cornell University, Ithaca, NY, USA.
Olsen M; International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Nairobi, Kenya.
Li X; Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
San Vicente F; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Prasanna BM; International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Nairobi, Kenya.
Crossa J; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Pérez-Rodríguez P; Colegio de Postgraduados, Texcoco, Estado De México, Mexico. .
Zhang X; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico. .
Źródło:
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik [Theor Appl Genet] 2020 Oct; Vol. 133 (10), pp. 2869-2879. Date of Electronic Publication: 2020 Jun 30.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: Berlin, New York, Springer
MeSH Terms:
Genome, Plant*
Haploidy*
Plant Breeding*
Selection, Genetic*
Zea mays/*genetics
Crosses, Genetic ; Genotype ; Models, Genetic ; Phenotype
References:
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Grant Information:
OPP1093167 Bill and Melinda Gates Foundation; 31661143010 National Natural Science Foundation of China; 31801442 National Natural Science Foundation of China
Entry Date(s):
Date Created: 20200702 Date Completed: 20210120 Latest Revision: 20220427
Update Code:
20240105
PubMed Central ID:
PMC7782462
DOI:
10.1007/s00122-020-03638-5
PMID:
32607592
Czasopismo naukowe
Key Message: Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

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