Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models

Minna Koivula, Ismo Strandén, Gert P. Aamand and Esa A. Mäntysaari
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Minna Koivula: Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
Ismo Strandén: Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
Gert P. Aamand: NAV Nordic Cattle Genetic Evaluation, 8200 Aarhus, Denmark
Esa A. Mäntysaari: Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland

Agriculture, 2022, vol. 12, issue 3, 1-10

Abstract: Genomic selection is widely used in dairy cattle breeding, but still, single-step models are rarely used in national dairy cattle evaluations. New computing methods have allowed the utilization of very large genomic data sets. However, an unsolved model problem is how to build genomic- ( G ) and pedigree- ( A 22 ) relationship matrices that satisfy the theoretical assumptions about the same scale and equal base populations. Incompatibility issues have also been observed in the manner in which the genetic groups are included in the model. In this study, we compared three approaches for accounting for missing pedigree information: (1) GT_H used the full Quaas and Pollak (QP) transformation for the genetic groups, including both the pedigree-based and the genomic-relationship matrices, (2) GT_A 22 used the partial QP transformation that omitted QP transformation in G −1 , and (3) GT_MF used the metafounder approach. In addition to the genomic models, (4) an official animal model with a unknown parent groups (UPG) from the QP transformation and (5) an animal model with the metafounder approach were used for comparison. These models were tested with Nordic Holstein test-day production data and models. The test-day data included 8.5 million cows with a total of 173.7 million records and 10.9 million animals in the pedigree, and there were 274,145 genotyped animals. All models used VanRaden method 1 in G and had a 30% residual polygenic proportion (RPG). The G matrices in GT_H and GT_A 22 were scaled to have an average diagonal equal to that of A 22 . Comparisons between the models were based on Mendelian sampling terms and forward prediction validation using linear regression with solutions from the full- and reduced-data evaluations. Models GT_H and GT_A 22 gave very similar results in terms of overprediction. The MF approach showed the lowest bias.

Keywords: ssGBLUP; ssGTBLUP; genomic evaluation; single-step; Holstein; genetic groups; metafounder (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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