Determination of optimum weighting factors for single-step genetic evaluation via genetic variance partitioning

Title
Determination of optimum weighting factors for single-step genetic evaluation via genetic variance partitioning
Publication Date
2021
Author(s)
Torres-Vázquez, J A
( author )
OrcID: https://orcid.org/0000-0001-6965-6065
Email: torresva@une.edu.au
UNE Id une-id:torresva
Samaraweera, A M
( author )
OrcID: https://orcid.org/0000-0002-8644-8345
Email: asamara2@une.edu.au
UNE Id une-id:asamara2
Jeyaruban, M G
( author )
OrcID: https://orcid.org/0000-0002-0231-0120
Email: gjeyarub@une.edu.au
UNE Id une-id:gjeyarub
Johnston, D J
( author )
OrcID: https://orcid.org/0000-0002-4995-8311
Email: djohnsto@une.edu.au
UNE Id une-id:djohnsto
Boerner, V
Abstract
Paper presented by Gilbert Jeyaruban
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Association for the Advancement of Animal Breeding and Genetics (AAABG)
Place of publication
Armidale, Australia
UNE publication id
une:1959.11/51598
Abstract

It is important in single-step genetic evaluations to use appropriate lambdas (λ) for calculating weighted average of NRM (numerator relationship matrix) and GRM (genomic relationship matrix) in joint relationship matrix. λ is usually estimated using a single-trait cross-validation procedure. However, it can be shown that a univariate single-step model applying a scalar λ is simply a condensed form of an extended model containing two genetic factors, factor H~N(0, H) and factor A~N(0, A), where the partitioning of the total genetic variance reflects λ. For multivariate single-step genetic evaluation, this model condensation implies that all involved genetic variances may yield the same λ, which is highly unlikely. Hence, it is required to estimate λ by accounting for its heterogeneity using the extended model for variance component estimation. This study used an extended single-step model to estimate variances and λs for calving difficulty (CD), gestation length (GL), and birth weight (BW) using Australian Angus data. A total of 129,851 animals with 45,575 genotypes were analysed. Initial variances obtained from a pedigree-only model were then used as starting values for the extended single-step model assigning 90% of the genetic variance to factor A and 10% to factor H. Since CD is a categorical trait with three categories, a threshold model-Gibbs sampling method was used to estimate variances. Heritability estimates for the extended single-step model were very similar to those from the pedigree only model implying that the single-step model was not explaining more variation in the data than the pedigree only model. For CD, GL, and BW, the total heritability estimates were 0.39 ± 0.04, 0.68 ± 0.02, and 0.44 ± 0.01, respectively. For the same traits, the total maternal heritability estimates were 0.17 ± 0.02, 0.11 ± 0.01, and 0.09 ± 0.01, respectively. In contrast, to the Gibbs sampling starting values, the genetic variance was partitioned between A and H such that direct genetic λ estimates for CD, GL, and BW were 0.36 ± 0.05, 0.62 ± 0.03, 0.75 ± 0.03, respectively. Maternal genetic λ estimates ranged from 0.01 ± 0.01 (for BW) to 0.05 ± 0.01 (for CD). The results imply that λ values are heterogeneous in multivariate single-step genomic evaluation. Further studies are needed to investigate the consequences of using heterogenous λ values for direct genetic and maternal genetic components in multivariate single-step evaluation in terms of model dimensions, solver convergence rate, and model forward predictive ability.

Link
Citation
Proceedings of the Association for the Advancement of Animal Breeding and Genetics, v.24, p. 402-405
ISSN
1328-3227
Start page
402
End page
405

Files:

NameSizeformatDescriptionLink