4.3 Article

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

Journal

G3-GENES GENOMES GENETICS
Volume 11, Issue 11, Pages -

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/g3journal/jkab206

Keywords

genomic prediction; Alpine Merino sheep; wool traits; GBLUP; Bayesian alphabet; marker density; GenPred; shared data resource

Funding

  1. Agricultural Science and Technology Innovation Program of China [CAASASTIP-2015-LIHPS, CAAS-ZDXT2018006]
  2. Modern China Wool Cashmere Technology Research System [CARS-39-02]

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The accuracy of genomic prediction (GP) or selection (GS) depends on marker density, heritability level, and statistical models used. Increasing marker density improves GP accuracy, and different models are more suitable for traits with different heritability levels. The study highlights the importance of incorporating these factors into real data to optimize GP.
The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesCir, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n=821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

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