Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/27652
Title: Use of genetic variance to determine weighting factors for genomic information used in single step genomic evaluation
Contributor(s): Zhang, Y  (author); Swan, A A  (author); Banks, R G  (author)
Publication Date: 2018
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/27652
Open Access Link: http://www.wcgalp.org/proceedings/2018/use-genetic-variance-determine-weighting-factors-genomic-information-used-singleOpen Access Link
Abstract: Single step genomic BLUP (SS-GBLUP) has recently been implemented in the Australian beef cattle and sheep industries. Selection of the weighting factor (λ) to combine the genomic and pedigree relationship matrixes could affect the accuracies of EBV. Cross validation has been used as a method to determine the optimal λ. This study estimated genetic variances using models fitting the genomic relationship matrix (G, Vg), the pedigree relationship matrix (A, Va), or both (Vt), and used the ratio Vg/Vt as an alternative approach to identify the optimal λ. Results showed that the ratio of genetic variances (Vg/Vt) aligned reasonably well with the optimal values of λ for most traits. Compared with cross validation, this approach was computationally fast, and can be used as an alternative to identify the optimal values of λ in SS-GBLUP.
Publication Type: Conference Publication
Conference Details: World Congress on Genetics Applied to Livestock Production, Auckland, New Zealand, 10-16 February
Source of Publication: Proceedings of the World Congress on Genetics Applied to Livestock Production, v.11, p. 1-5
Field of Research (FOR): 070201 Animal Breeding
Socio-Economic Objective (SEO): 830399 Livestock Raising not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Other Links: http://www.wcgalp.org/proceedings/2018
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Conference Publication
School of Environmental and Rural Science
UNE Business School

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