Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models
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- (<b>G&l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/3/388 |
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| author | Minna Koivula Ismo Strandén Gert P. Aamand Esa A. Mäntysaari |
| author_facet | Minna Koivula Ismo Strandén Gert P. Aamand Esa A. Mäntysaari |
| author_sort | Minna Koivula |
| collection | DOAJ |
| description | 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- (<b>G</b>) and pedigree- (<b>A</b><sub>22</sub>) 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<sub>22</sub> used the partial QP transformation that omitted QP transformation in <b>G</b><sup>−1</sup>, 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 <b>G</b> and had a 30% residual polygenic proportion (RPG). The <b>G</b> matrices in GT_H and GT_A<sub>22</sub> were scaled to have an average diagonal equal to that of <b>A</b><sub>22</sub>. 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<sub>22</sub> gave very similar results in terms of overprediction. The MF approach showed the lowest bias. |
| first_indexed | 2024-03-09T20:14:29Z |
| format | Article |
| id | doaj.art-55a44e2026984851994834d51a12856d |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T20:14:29Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-55a44e2026984851994834d51a12856d2023-11-24T00:05:36ZengMDPI AGAgriculture2077-04722022-03-0112338810.3390/agriculture12030388Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day ModelsMinna Koivula0Ismo Strandén1Gert P. Aamand2Esa A. Mäntysaari3Natural Resources Institute Finland (Luke), 31600 Jokioinen, FinlandNatural Resources Institute Finland (Luke), 31600 Jokioinen, FinlandNAV Nordic Cattle Genetic Evaluation, 8200 Aarhus, DenmarkNatural Resources Institute Finland (Luke), 31600 Jokioinen, FinlandGenomic 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- (<b>G</b>) and pedigree- (<b>A</b><sub>22</sub>) 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<sub>22</sub> used the partial QP transformation that omitted QP transformation in <b>G</b><sup>−1</sup>, 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 <b>G</b> and had a 30% residual polygenic proportion (RPG). The <b>G</b> matrices in GT_H and GT_A<sub>22</sub> were scaled to have an average diagonal equal to that of <b>A</b><sub>22</sub>. 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<sub>22</sub> gave very similar results in terms of overprediction. The MF approach showed the lowest bias.https://www.mdpi.com/2077-0472/12/3/388ssGBLUPssGTBLUPgenomic evaluationsingle-stepHolsteingenetic groups |
| spellingShingle | Minna Koivula Ismo Strandén Gert P. Aamand Esa A. Mäntysaari Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models Agriculture ssGBLUP ssGTBLUP genomic evaluation single-step Holstein genetic groups |
| title | Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models |
| title_full | Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models |
| title_fullStr | Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models |
| title_full_unstemmed | Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models |
| title_short | Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models |
| title_sort | accounting for missing pedigree information with single step random regression test day models |
| topic | ssGBLUP ssGTBLUP genomic evaluation single-step Holstein genetic groups |
| url | https://www.mdpi.com/2077-0472/12/3/388 |
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