Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/30744
Title: Partitioning of variance between multiple relationship matrices in BLUP analyses
Contributor(s): Gurman, Phillip M  (author)orcid ; Li, Li  (author)orcid ; Swan, Andrew A  (author)orcid ; Moghaddar, Nasir  (author)orcid ; van der Werf, Julius H J  (author)orcid 
Publication Date: 2020
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/30744
Open Access Link: https://icqg6.org/icqg6-abstracts-book/Open Access Link
Abstract: GWAS analyses have resulted in SNP sets that are more predictive for specific traits. Combining these SNPs in a genomic relationship matrix (GRM) with non-selected SNPs may dilute their predictive ability. Instead, predictive SNPs could be treated separately with their own variance. This study examines the partitioning of variance between multiple genetic effects defined by multiple relationship matrices. Univariate REML analyses were performed using GCTA for intramuscular fat (imf), carcase eye muscle depth (cemd), and carcase fat (ccfat) measured on approximately 9.5k genotyped sheep from multiple breeds. Genetic relationship matrices fitted included numerator relationship matrix (NRM) and two GRMs, one based on a standard SNP array (GRMC, 48.5k) and one based on SNPs selected from whole-genome sequence (GRMP, 2.7k). GRMs were constructed with either breed-specific allele frequencies, or population allele frequencies. Breed structure was accommodated by fitting random genetic groups. For GRMs constructed with population allele frequencies, the proportion of genetic variance attributed to GRMC was between 0.14 for ccfat and 0.38 for imf, while for GRMP it was between 0.36 for imf and 0.73 for cemd. The remaining genetic variance was explained by the NRM (range 0.02- 0.38). Similar proportions were observed for the multi-breed GRM. Proportions of genetic variances estimated for the NRM and GRMs can be used in singlestep models to increase prediction accuracy, but questions remain regarding the impact of co-linearity between effects. For example, using a breed-adjusted GRM resulted in an increase in genetic group variance relative to the other genetic effects.
Publication Type: Conference Publication
Conference Details: ICQG 6: 6th International Conference on Quantitative Genetics, Online Event, 3rd - 13th November, 2020
Source of Publication: ICQG 6, Abstracts 2020, p. 150-150
Publisher: International Conference on Quantitative Genetics
Place of Publication: Australia
Fields of Research (FoR) 2008: 070201 Animal Breeding
Fields of Research (FoR) 2020: 300305 Animal reproduction and breeding
Socio-Economic Objective (SEO) 2008: 830301 Beef Cattle
Socio-Economic Objective (SEO) 2020: 100401 Beef cattle
Peer Reviewed: Yes
HERDC Category Description: E3 Extract of Scholarly Conference Publication
Publisher/associated links: https://icqg6.org/
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Conference Publication
School of Environmental and Rural Science

Files in This Item:
3 files
File Description SizeFormat 
Show full item record

Page view(s)

1,660
checked on Mar 8, 2023

Download(s)

12
checked on Mar 8, 2023
Google Media

Google ScholarTM

Check


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.