Luke
 

A computationally feasible multi-trait single-step genomic prediction model with trait-specific marker weights

Stranden_Jenko_2024_GenSelEvol_A_computationally_feasible_multi-trait.pdf
Stranden_Jenko_2024_GenSelEvol_A_computationally_feasible_multi-trait.pdf - Publisher's version - 1.04 MB
How to cite: Strandén, I., Jenko, J. A computationally feasible multi-trait single-step genomic prediction model with trait-specific marker weights. Genet Sel Evol 56, 58 (2024). https://doi.org/10.1186/s12711-024-00926-2

Tiivistelmä

Background Regions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights. Results In this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy. Conclusions We investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights.

ISBN

OKM-julkaisutyyppi

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisusarja

Genetics selection evolution

Volyymi

56

Numero

1

Sivut

Sivut

10 p.

ISSN

0999-193X
1297-9686