Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms
Genomic prediction has been widely applied to the pig industry and has greatly accelerated the progress of genetic improvement in pigs. With the development of sequencing technology and price reduction, more and more genotype imputation panels of pig have been investigated, providing an effective an...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Animal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731124003240 |
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| author | J. Sun J. Wei Y. Pan M. Cao X. Li J. Xiao G. Yang T. Yu |
| author_facet | J. Sun J. Wei Y. Pan M. Cao X. Li J. Xiao G. Yang T. Yu |
| author_sort | J. Sun |
| collection | DOAJ |
| description | Genomic prediction has been widely applied to the pig industry and has greatly accelerated the progress of genetic improvement in pigs. With the development of sequencing technology and price reduction, more and more genotype imputation panels of pig have been investigated, providing an effective and economical method to further study the genetic variation of pig economic traits. In this study, the imputation from 80 k Single Nucleotide Polymorphism chip data of 832 Large White pigs to whole-genome sequencing genotypes was performed by Swine Imputation Server, Pig Haplotypes Reference Panel (PHARP), Animal Genotype Imputation Database and 1k-pig-genomes four thousand-pig imputation panels. Then, linkage disequilibrium (LD) pruning and genome-wide association study (GWAS) preselected markers strategies were utilised to compare the genomic prediction accuracy of the different imputation data for reproductive traits, respectively. Our results showed that the PHARP panel exhibited the best genomic prediction accuracy among the four imputation panels. Meanwhile, the genomic prediction accuracy of the imputation data can be further improved by utilising the LD pruning and GWAS preselected marker strategies. In conclusion, our study provides insights into imputation data for pig genetic breeding. |
| format | Article |
| id | doaj-art-dab22a99aa5a43ec949079b1d0f14240 |
| institution | Kabale University |
| issn | 1751-7311 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Animal |
| spelling | doaj-art-dab22a99aa5a43ec949079b1d0f142402025-01-19T06:24:49ZengElsevierAnimal1751-73112025-01-01191101387Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platformsJ. Sun0J. Wei1Y. Pan2M. Cao3X. Li4J. Xiao5G. Yang6T. Yu7Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agroecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, Hunan 410125, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition & Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China; Corresponding author.Genomic prediction has been widely applied to the pig industry and has greatly accelerated the progress of genetic improvement in pigs. With the development of sequencing technology and price reduction, more and more genotype imputation panels of pig have been investigated, providing an effective and economical method to further study the genetic variation of pig economic traits. In this study, the imputation from 80 k Single Nucleotide Polymorphism chip data of 832 Large White pigs to whole-genome sequencing genotypes was performed by Swine Imputation Server, Pig Haplotypes Reference Panel (PHARP), Animal Genotype Imputation Database and 1k-pig-genomes four thousand-pig imputation panels. Then, linkage disequilibrium (LD) pruning and genome-wide association study (GWAS) preselected markers strategies were utilised to compare the genomic prediction accuracy of the different imputation data for reproductive traits, respectively. Our results showed that the PHARP panel exhibited the best genomic prediction accuracy among the four imputation panels. Meanwhile, the genomic prediction accuracy of the imputation data can be further improved by utilising the LD pruning and GWAS preselected marker strategies. In conclusion, our study provides insights into imputation data for pig genetic breeding.http://www.sciencedirect.com/science/article/pii/S1751731124003240Genomic estimated breeding valueImputation accuracyLarge White pigMarker preselectionReproduction |
| spellingShingle | J. Sun J. Wei Y. Pan M. Cao X. Li J. Xiao G. Yang T. Yu Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms Animal Genomic estimated breeding value Imputation accuracy Large White pig Marker preselection Reproduction |
| title | Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms |
| title_full | Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms |
| title_fullStr | Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms |
| title_full_unstemmed | Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms |
| title_short | Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms |
| title_sort | improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms |
| topic | Genomic estimated breeding value Imputation accuracy Large White pig Marker preselection Reproduction |
| url | http://www.sciencedirect.com/science/article/pii/S1751731124003240 |
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