ArticleJDS communications2026
Decreasing the computing time of approximated reliabilities of genomic estimated breeding values in the single-step genomic best linear unbiased predictor using different core sizes for the algorithm for proven and young.
Article in JDS communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The single-step genomic best linear unbiased predictor (ssGBLUP) along with the algorithm for proven and young (APY) are used to compute GEBV in livestock populations with extensive genomic data. Calculating GEBV reliabilities is computationally expensive, particularly with many genotyped animals, because it requires inverting the left-hand side of the mixed model equations. However, reliabilities in ssGBLUP models can be approximated by leveraging the sparse structure of the APY. The primary computational bottleneck of the algorithm lies in a matrix multiplication step, which scales quadratically with the size of the core set. This study aimed to decrease the computing time for approximating GEBV reliabilities in ssGBLUP by reducing the size of the core set in APY without compromising the precision of the reliability approximations. Reliabilities were approximated for a single-trait model for calf respiratory disease in Holsteins (h
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