Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing
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ORIGINAL ARTICLE
Genomic prediction across years in a maize doubled haploid breeding program to accelerate early‑stage testcross testing Nan Wang1,2 · Hui Wang2,3,4 · Ao Zhang5 · Yubo Liu5 · Diansi Yu2,3,4 · Zhuanfang Hao1 · Dan Ilut6 · Jeffrey C. Glaubitz7 · Yanxin Gao7 · Elizabeth Jones7 · Michael Olsen8 · Xinhai Li1 · Felix San Vicente2 · Boddupalli M. Prasanna8 · Jose Crossa2 · Paulino Pérez‑Rodríguez9 · Xuecai Zhang2 Received: 20 June 2019 / Accepted: 16 June 2020 © The Author(s) 2020
Abstract 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. Abstract 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 firststage yield testing, which significantly saves the time and cost of early-stage testcross testing.
Communicated by Hiroyoshi Iwata. Nan Wang and Hui Wang contributed equally to this work. * Paulino Pérez‑Rodríguez [email protected]
5
College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
* Xuecai Zhang [email protected]
6
Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
7
Institute of Biotechnology, Cornell University, Ithaca, NY, USA
8
International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Nairobi, Kenya
9
Colegio de Postgraduados, Texcoco, Estado De México, Mexico
1
Institute o
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