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

Esa A. Mäntysaari,Gert P. Aamand,Ismo Strandén,Minna Koivula

doi:10.3390/agriculture12030388

Esa A. Mäntysaari, Gert P. Aamand + Show 2 more

Open Access

https://doi.org/10.3390/agriculture12030388

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Journal: Agriculture	Publication Date: Mar 10, 2022
Citations: 3	License type: CC BY 4.0

Affiliation: Natural Resources Institute Finland

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- (A22) 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_A22 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_A22 were scaled to have an average diagonal equal to that of A22. 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_A22 gave very similar results in terms of overprediction. The MF approach showed the lowest bias.

Highlights

Meuwissen et al [1] introduced the genome-wide marker-assisted prediction model called genomic selection
Even though a decade has passed since the introduction of single-step genomic BLUP (ssGBLUP), it is still not widely implemented in national dairy cattle evaluations
Some of the computational challenges related to very large genomic data sets can be overcome by using an alternative expression of the inverse genomicrelationship matrix or a model equivalent to ssGBLUP [1,5,6]

Summary

Introduction

Meuwissen et al [1] introduced the genome-wide marker-assisted prediction model called genomic selection. Many alternative prediction models have been developed to use genomic selection in dairy cattle breeding [2]. The best model is single-step genomic BLUP (ssGBLUP) when phenotypes are available from both genotyped and nongenotyped individuals. Even though a decade has passed since the introduction of ssGBLUP, it is still not widely implemented in national dairy cattle evaluations. Practical implementation of ssGBLUP has encountered different computational and modeling challenges. Some of the computational challenges related to very large genomic data sets can be overcome by using an alternative expression of the inverse genomicrelationship matrix or a model equivalent to ssGBLUP [1,5,6]. An unsolved modeling problem is how to build genomic- (G) and pedigree- (A22) relationship matrices that satisfy the theoretical assumptions about the same scale and equal base populations [7]. Similar incompatibility issues were observed when genetic groups were included in the model [10]

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