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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Animal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1751731124003240 |
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| Summary: | 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. |
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| ISSN: | 1751-7311 |