Estimation & Prediction

SNP weighting improves accuracy of single-step genomic predictions for calving difficulty traits

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Abstract

The single-step genomic BLUP (ssGBLUP) method assumes normally distributed SNP effects with a common variance, whereas Bayesian methods allow more flexible priors and improved detection of large-effect loci. Despite these advantages, ssGBLUP remains the preferred approach for routine genomic evaluations due to its computational efficiency. This is particularly relevant for large-scale national evaluation systems, such as those in Ireland, where extensive genotyping data are routinely processed. To combine the strengths of both approaches, SNP weighting can be applied to give greater emphasis to markers identified as important through GWAS. This study therefore evaluated the impact of SNP weighting on ssGBLUP prediction accuracy for calving difficulty traits. Calving difficulty in Irish dairy and beef herds is scored on a four-point scale (1 = no assistance; 4 = veterinary assistance). Four traits were defined based on the dam and breed group: dairy heifers (DH), dairy cows (DC), beef heifers (BH), and beef cows (BC). Birth weight (BWT) and birth size (BSize; 1-5 scale) were included as correlated predictor traits in a multi-trait evaluation. The pedigree contained 27,651,328 animals, including 2,258,091 genotyped animlas. GEBVs were estimated using ssGBLUP (ssGTBLUP), which integrates genomic, pedigree, and phenotypic information and provides SNP effect estimates (Vandenplas et al., 2023). SNP weights for the weighted ssGBLUP (ssWGBLUP) were calculated using the VanRaden (2008) formula: , where and is the SNP k effect of traits i. SNP effects were obtained either from the routine ssGBLUP analysis or from single-trait GWAS. Genomic prediction accuracy was assessed at both animal and sire levels using correlations between GEBVs from full and reduced datasets, as well as between reduced-dataset GEBVs and sires' daughter yield deviations (DYD).Across all six traits (DH, DC, BH, BC, BSize, BWT), incorporating SNP weighting improved prediction accuracy. The weighted ssGBLUP using SNP effects from the routine analysis (ssWGBLUP) consistently outperformed both the unweighted ssGBLUP and the version using GWAS-derived weights (ssGWGBLUP). For example, accuracy for DC increased from 0.82 (ssGBLUP) to 0.85 (ssGWGBLUP) and to 0.90 (ssWGBLUP); for BWT, accuracy increased from 0.78 to 0.81 and to 0.85, respectively. These results demonstrate that SNP weighting enhances predictive performance, with ssWGBLUP performing best; likely because multi-trait ssGBLUP-derived SNP effects better capture shared genetic variance than single-trait GWAS estimates.

Keywords: 2026

How to Cite:

Naderi, S., Evans, R. & Strandén, I., (2026) “SNP weighting improves accuracy of single-step genomic predictions for calving difficulty traits”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286400. doi: https://doi.org/10.31274/wcgalp.23984

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