Prediction of genetic values according to the dimensionality reduction of SNP's markers in complex models

Antônio Carlos da Júnior; Michele Jorge  Silva Siqueira; Waldênia de Melo  Moura; Ivan de Paiva  Barbosa; Francyse Edite de Oliveira  Chagas; Cosme Damião  Cruz; Moyses  Nascimento

doi:10.33158/ASB.r254.v12.2026

Authors

Antônio Carlos da Júnior Federal University of Viçosa
Michele Jorge Silva Siqueira Escola Superior de Agricultura Luiz de Queiroz
Waldênia de Melo Moura Federal University of Viçosa
Ivan de Paiva Barbosa Federal University of Viçosa
Francyse Edite de Oliveira Chagas Federal University of Viçosa
Cosme Damião Cruz Federal University of Viçosa
Moyses Nascimento Federal University of Viçosa

DOI:

https://doi.org/10.33158/ASB.r254.v12.2026

Keywords:

Quantitative genetic, selection accuracy, breeding value, target traits, chromosomal segments, additive genetic variance, dominance variance, epistatic variance

Abstract

The presence of non-informative markers in Genome Wide Selection needs to be evaluated so that the genomic prediction is more efficient in a breeding program. This study proposes to evaluate the efficiency of genomic prediction after reducing the dimensionality of SNP's markers in the presence of different levels of dominance, heritability, and epistatic interactions to demonstrate that the results obtained with reduced information improve prediction and preserve the same biological conclusions when using a larger data set. Ten F₂ populations of a diploid species with an effective size of 1000 individuals were simulated, involving the random combination of 2000 gametes generated from contrasting homozygous parents. Ten linkage groups with a size of 100 cM each and comprised 2010 bi-allelic SNP´s distributed equally and equidistant form. Nine traits were simulated, formed by different degrees of dominance, heritability, and epistatic interactions. The dimensionality reduction was performed randomly in the simulated population and then the efficiency of genomic prediction was tested in two different studies. The parameters square of correlation ( ), root mean squares error (RMSE), and the Akaike Information Criterion (AIC) was used to evaluate the efficiency of the model used in the RR-BLUP. The results obtained from the reduced information predicted by the RR-BLUP were able to improve the prediction and preserve the same biological conclusions when using a larger data set. Non-informational or small effect markers can be removed from the original data set. The inclusion of dominance effects was an efficient strategy to improve predictive capacity.

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