Welcome To Website IAS

Hot news
Achievement

Independence Award

- First Rank - Second Rank - Third Rank

Labour Award

- First Rank - Second Rank -Third Rank

National Award

 - Study on food stuff for animal(2005)

 - Study on rice breeding for export and domestic consumption(2005)

VIFOTEC Award

- Hybrid Maize by Single Cross V2002 (2003)

- Tomato Grafting to Manage Ralstonia Disease(2005)

- Cassava variety KM140(2010)

Centres
Website links
Vietnamese calendar
Library
Visitors summary
 Curently online :  13
 Total visitors :  7359904

Calibration and validation of predicted genomic breeding values in an advanced cycle maize population

The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers.

Hans-Jürgen AuingerChristina LehermeierDaniel GianolaManfred MayerAlbrecht E. MelchingerSofia da SilvaCarsten KnaakMilena Ouzunova & Chris-Carolin Schön

Theoretical and Applied Genetics September 2021; vol. 134: 3069–3081

Key message

Model training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets.

Abstract

The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.

 

See: https://link.springer.com/article/10.1007/s00122-021-03880-5

 

Figure: Principal coordinate analysis of pairwise realised kinship coefficients of 5968 DH lines. DH lines are coloured according to their grouping in data sets. Axis labels show the percentage of variance explained by the coordinate.

Trở lại      In      Số lần xem: 201

[ Tin tức liên quan ]___________________________________________________

 

Designed & Powered by WEBSO CO.,LTD