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September 10, 2025

Multifactor Analysis of a Genome-Wide Selection System in

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Prediction accuracy is highest for glucosinolate and lowest for linolenic acid, impacting trait improvement.
Using 5000 markers with a sample population of 400 enhances prediction accuracy for most traits, emphasizing population size in breeding.
The inclusion of trait-specific SNPs significantly boosts prediction accuracy, especially with markers showing strong statistical significance.
Nonadditive effects had minimal influence on high prediction accuracy traits but were effective in improving low accuracy traits.

Authors

WTWanqing TanSouthwest University ZWZhiyuan WangJiangnan University JWJia WangSouthwest University

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Implication

Observational analysis reveals improved prediction accuracy in Brassica napus traits using genome-wide selection models, highlighting the importance of marker densities and trait-specific SNPs.

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Tan et al. (2025) studied this question.

synapsesocial.com/papers/68c1a3f954b1d3bfb60de244 https://doi.org/10.3390/plants14142095

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Implication

Observational analysis reveals improved prediction accuracy in Brassica napus traits using genome-wide selection models, highlighting the importance of marker densities and trait-specific SNPs.

Multifactor Analysis of a Genome-Wide Selection System in

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Implication

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Multifactor Analysis of a Genome-Wide Selection System in

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Discussion

Implication

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