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Genomic prediction across years in a maize doubled haploid breeding program to accelerate 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.

Nan WangHui WangAo ZhangYubo LiuDiansi YuZhuanfang HaoDan IlutJeffrey C. GlaubitzYanxin GaoElizabeth JonesMichael OlsenXinhai LiFelix San VicenteBoddupalli M. PrasannaJose CrossaPaulino Pérez-Rodríguez & Xuecai Zhang

Theoretical and Applied Genetics October 2020; volume 133:pages 2869–2879.

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 first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

 

See https://link.springer.com/article/10.1007/s00122-020-03638-5

Figure 3: The prediction accuracies estimated from the independent validation schemes; when the single-environment model (SM) was applied in the across location analyses, the across years’ predictions were implemented by using TRN and TST from (1) all the DH lines from different years (all blue bars); (2) the full-sib- or half-sib-related DH lines shared between the different years (common red bars); and (3) the DH lines without any shared parental lines between the different years (non-common green bars)

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