Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/23458
Title: On Breed Composition Estimation of Cross-Bred Animals Using Non-Linear Optimisation
Contributor(s): Boerner, Vinzent  (author)
Publication Date: 2017
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/23458
Open Access Link: http://agbu.une.edu.au/AAABG%202017/22Boerner22097.pdfOpen Access Link
Abstract: Genetically admixed animals are common in most quantitative genetic analysis, and usually are a result of intended crosses between two or more pure breed populations to enhance productivity. Disregarding the genetic heterogeneous architecture of admixed individuals may lead to poor or even wrong inference about the quality, quantity and genome location of genetic factors affecting phenotypes, and it could reduce the accuracy of estimates of genetic merit. In this article a nonlinear optimisation approach (constrained genomic regression, CGR) is presented to describe the marker genotype of a focus animal as a linear function of marker allele frequencies of possible populations of origin. The algorithm was tested on a beef cattle data set consisting of 11639 animals from 11 different breeds with marker genotypes of 4022 single nucleotide polymorphisms, which were used to generate 5000 artificially cross-bred animals. For comparison the data set was also analysed with the ADMIXTURE software (ADM). CGR outperformed ADM with a maximum difference between the true and estimated breed proportion of 0.25 and 0.28 for the 5 and 25 cross-over data set respectively. For ADM this parameter was 0.83 and 0.66. The mean squared estimation error was 15 and 5 times larger for ADM compared to CGR for the 5 and 25 cross-over data set respectively. In addition, CGR always outperformed ADM in terms of speed by factor 20.
Publication Type: Conference Publication
Conference Details: 22nd Conference of the Association for the Advancement of Animal Breeding and Genetics, Townsville, Australia, 2nd - 5th July, 2017
Source of Publication: Proceedings of the Twenty-second (22nd) Conference of the Association for the Advancement of Animal Breeding, v.22, p. 97-100
Publisher: AAABG: Association for the Advancement of Animal Breeding and Genetics
Place of Publication: Fitzroy, Australia
ISSN: 1328-3227
Field of Research (FoR) 2008: 060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
060408 Genomics
070201 Animal Breeding
Field of Research (FoR) 2020: 310506 Gene mapping
310509 Genomics
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: E1 Refereed Scholarly Conference Publication
Other Links: http://www.aaabg.org/aaabghome/
http://agbu.une.edu.au/AAABG_2017.html
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Conference Publication

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