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Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Agriculture

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

M. Lopez-cruz, Y. Beyene, et al.

Discover how innovative genomic prediction models can enhance crop improvement strategies! This research, led by Marco Lopez-Cruz, Yoseph Beyene, Manje Gowda, Jose Crossa, Paulino Pérez-Rodríguez, and Gustavo de los Campos, reveals that combining sparse selection indices with kernel methods significantly boosts prediction accuracy in maize data.

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Playback language: English
Abstract
Genomic prediction models are often calibrated using multi-generation data, which can lead to heterogeneity in allele frequency and linkage disequilibrium patterns. This study investigated whether combining sparse selection indices (SSI) and kernel methods could improve prediction accuracy. Using four years of doubled haploid maize data, the results showed that kernel methods (KBLUP) outperformed genomic BLUP (GBLUP), and SSI with additive relationships (GSSI) increased accuracy by 5–17% relative to GBLUP. However, differences between KBLUP and kernel-based SSI were smaller.
Publisher
Heredity
Published On
Sep 25, 2021
Authors
Marco Lopez-Cruz, Yoseph Beyene, Manje Gowda, Jose Crossa, Paulino Pérez-Rodríguez, Gustavo de los Campos
Tags
genomic prediction
maize
kernel methods
sparse selection indices
prediction accuracy
allele frequency
linkage disequilibrium
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