Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders
In pig production, the production animals are generally three- or four-way crossbreeds. Reliable information regarding the breed of origin of slaughtered pigs is useful, even a prerequisite, for a number of purposes, e.g., evaluating potential breed effects on carcass grading. Genetic data from slau...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1289130/full |
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| author | H. Vinje H. K. Brustad A. Heggli A. Heggli C. A. Sevillano M. Van Son L. E. Gangsei L. E. Gangsei |
| author_facet | H. Vinje H. K. Brustad A. Heggli A. Heggli C. A. Sevillano M. Van Son L. E. Gangsei L. E. Gangsei |
| author_sort | H. Vinje |
| collection | DOAJ |
| description | In pig production, the production animals are generally three- or four-way crossbreeds. Reliable information regarding the breed of origin of slaughtered pigs is useful, even a prerequisite, for a number of purposes, e.g., evaluating potential breed effects on carcass grading. Genetic data from slaughtered pigs can easily be extracted and used for crossbreed classification. In the current study, four classification methods, namely, random forest (RF), ADMIXTURE, partial least squares regression (PLSR), and partial least squares together with quadratic discriminant analysis (PLS-QDA) were evaluated on simulated (n = 7,500) genomic data of crossbreeds. The derivation of the theory behind PLS-QDA is a major part of the current study, whereas RF and ADMIXTURE are known and well-described in the literature. Classification success (CS) rate, square loss (SL), and Kullback–Leibler (KL) divergence loss for the simulated data were used to compare methods. Overall, PLS-QDA performed best with 99%/0.0018/0.002 (CS/SL/KL) vs. 97%/0.0084/0.051, 97%/0.0087/0.0623, and 17%/0.068/0.39 for PLSR, ADMIXTURE, and RF, respectively. PLS-QDA and ADMIXTURE, as the most relevant methods, were used on a real dataset (n = 1,013) from Norway where the two largest classes contained 532 and 192 (PLS-QDA), and 531 and 193 (ADMIXTURE) individuals, respectively. These two classes were expected to be dominating a priori. The Bayesian nature of PLS-QDA enables inclusion of desirable features such as a separate class “unknown breed combination” and informative priors for crossbreeds, making this a preferable method for the classification of breed combination in the industry. |
| first_indexed | 2024-03-09T03:06:06Z |
| format | Article |
| id | doaj.art-8d9ae9eebbb74e1d97a313bfeae58c42 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-09T03:06:06Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-8d9ae9eebbb74e1d97a313bfeae58c422023-12-04T07:00:35ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-12-011410.3389/fgene.2023.12891301289130Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred foundersH. Vinje0H. K. Brustad1A. Heggli2A. Heggli3C. A. Sevillano4M. Van Son5L. E. Gangsei6L. E. Gangsei7Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, NorwayOslo Center of Biostatistics and Epidemiology, Oslo University Hospital, Oslo, NorwayFaculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, NorwayAnimalia AS, Oslo, NorwayTopigs Norsvin Research Center, Beuningen, NetherlandsNorsvin SA, Hamar, NorwayFaculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, NorwayAnimalia AS, Oslo, NorwayIn pig production, the production animals are generally three- or four-way crossbreeds. Reliable information regarding the breed of origin of slaughtered pigs is useful, even a prerequisite, for a number of purposes, e.g., evaluating potential breed effects on carcass grading. Genetic data from slaughtered pigs can easily be extracted and used for crossbreed classification. In the current study, four classification methods, namely, random forest (RF), ADMIXTURE, partial least squares regression (PLSR), and partial least squares together with quadratic discriminant analysis (PLS-QDA) were evaluated on simulated (n = 7,500) genomic data of crossbreeds. The derivation of the theory behind PLS-QDA is a major part of the current study, whereas RF and ADMIXTURE are known and well-described in the literature. Classification success (CS) rate, square loss (SL), and Kullback–Leibler (KL) divergence loss for the simulated data were used to compare methods. Overall, PLS-QDA performed best with 99%/0.0018/0.002 (CS/SL/KL) vs. 97%/0.0084/0.051, 97%/0.0087/0.0623, and 17%/0.068/0.39 for PLSR, ADMIXTURE, and RF, respectively. PLS-QDA and ADMIXTURE, as the most relevant methods, were used on a real dataset (n = 1,013) from Norway where the two largest classes contained 532 and 192 (PLS-QDA), and 531 and 193 (ADMIXTURE) individuals, respectively. These two classes were expected to be dominating a priori. The Bayesian nature of PLS-QDA enables inclusion of desirable features such as a separate class “unknown breed combination” and informative priors for crossbreeds, making this a preferable method for the classification of breed combination in the industry.https://www.frontiersin.org/articles/10.3389/fgene.2023.1289130/fullslaughter pigsbreed classificationcrossbreedsfuzzy classificationsingle-nucleotide polymorphismpartial least squares |
| spellingShingle | H. Vinje H. K. Brustad A. Heggli A. Heggli C. A. Sevillano M. Van Son L. E. Gangsei L. E. Gangsei Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders Frontiers in Genetics slaughter pigs breed classification crossbreeds fuzzy classification single-nucleotide polymorphism partial least squares |
| title | Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders |
| title_full | Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders |
| title_fullStr | Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders |
| title_full_unstemmed | Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders |
| title_short | Classification of breed combinations for slaughter pigs based on genotypes—modeling DNA samples of crossbreeds as fuzzy sets from purebred founders |
| title_sort | classification of breed combinations for slaughter pigs based on genotypes modeling dna samples of crossbreeds as fuzzy sets from purebred founders |
| topic | slaughter pigs breed classification crossbreeds fuzzy classification single-nucleotide polymorphism partial least squares |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1289130/full |
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