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

dc.creatorNan Wang
dc.creatorHui Wang
dc.creatorAo Zhang
dc.creatorYubo Liu
dc.creatorDiansi Yu
dc.creatorZhuanfang Hao
dc.creatorIlut, D.C.
dc.creatorGlaubitz, J.C.
dc.creatorYanxin Gao
dc.creatorJones, E.
dc.creatorOlsen, M.
dc.creatorXinhai Li
dc.creatorSan Vicente, F.M.
dc.creatorPrasanna, B.M.
dc.creatorCrossa, J.
dc.creatorPerez-Rodriguez, P.
dc.creatorZhang, X.
dc.date2020-11-28T01:05:15Z
dc.date2020-11-28T01:05:15Z
dc.date2020
dc.date.accessioned2023-07-17T20:06:23Z
dc.date.available2023-07-17T20:06:23Z
dc.identifierhttps://hdl.handle.net/10883/21015
dc.identifier10.1007/s00122-020-03638-5
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512801
dc.descriptionWith 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.
dc.description2869-2879
dc.languageEnglish
dc.publisherSpringer
dc.relationhttp://hdl.handle.net/11529/10201
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source10
dc.source133
dc.source0040-5752
dc.sourceTheoretical and Applied Genetics
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGENOMICS
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectPLANT BREEDING
dc.subjectMAIZE
dc.titleGenomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing
dc.typeArticle
dc.typePublished Version
dc.coverageBerlin (Germany)


Este ítem pertenece a la siguiente institución