TY  - JA
AU  - Nan Wang
AU  - Hui Wang
AU  - Ao Zhang
AU  - Yubo Liu
AU  - Diansi Yu
AU  - Zhuanfang Hao
AU  - Ilut,D.C.
AU  - Glaubitz,J.C.
AU  - Yanxin Gao
AU  - Jones,E.
AU  - Olsen,M.
AU  - Xinhai Li
AU  - San Vicente,F.M.
AU  - Prasanna,B.M.
AU  - Crossa,J.
AU  - Perez-Rodriguez,P.
AU  - Xuecai Zhang
TI  - Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing
SN  - 0040-5752
PY  - 2020///
CY  - Berlin (Germany)
PB  - Springer
KW  - Genomics
KW  - AGROVOC
KW  - Marker-assisted selection
KW  - Plant breeding
KW  - Maize
N1  - Peer review; Open Access; MCRP; FP2; FP3
N2  - 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
UR  - https://hdl.handle.net/10883/21015
T2  - Theoretical and Applied Genetics
DO  - https://doi.org/10.1007/s00122-020-03638-5
ER  -