Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population
Abstract Background Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approac...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-03-01
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| Series: | Journal of Animal Science and Biotechnology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40104-018-0241-5 |
| _version_ | 1818929106007359488 |
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| author | Shaopan Ye Xiaolong Yuan Xiran Lin Ning Gao Yuanyu Luo Zanmou Chen Jiaqi Li Xiquan Zhang Zhe Zhang |
| author_facet | Shaopan Ye Xiaolong Yuan Xiran Lin Ning Gao Yuanyu Luo Zanmou Chen Jiaqi Li Xiquan Zhang Zhe Zhang |
| author_sort | Shaopan Ye |
| collection | DOAJ |
| description | Abstract Background Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation. Results We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study. Conclusions In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations. |
| first_indexed | 2024-12-20T03:39:31Z |
| format | Article |
| id | doaj.art-cc6ca961553c4c91bc6c36ef71da031d |
| institution | Directory Open Access Journal |
| issn | 2049-1891 |
| language | English |
| last_indexed | 2024-12-20T03:39:31Z |
| publishDate | 2018-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Animal Science and Biotechnology |
| spelling | doaj.art-cc6ca961553c4c91bc6c36ef71da031d2022-12-21T19:54:46ZengBMCJournal of Animal Science and Biotechnology2049-18912018-03-019111210.1186/s40104-018-0241-5Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken populationShaopan Ye0Xiaolong Yuan1Xiran Lin2Ning Gao3Yuanyu Luo4Zanmou Chen5Jiaqi Li6Xiquan Zhang7Zhe Zhang8Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityGuangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural UniversityAbstract Background Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation. Results We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study. Conclusions In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.http://link.springer.com/article/10.1186/s40104-018-0241-5ChickensImputationRe-sequencingSNP |
| spellingShingle | Shaopan Ye Xiaolong Yuan Xiran Lin Ning Gao Yuanyu Luo Zanmou Chen Jiaqi Li Xiquan Zhang Zhe Zhang Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population Journal of Animal Science and Biotechnology Chickens Imputation Re-sequencing SNP |
| title | Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population |
| title_full | Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population |
| title_fullStr | Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population |
| title_full_unstemmed | Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population |
| title_short | Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population |
| title_sort | imputation from snp chip to sequence a case study in a chinese indigenous chicken population |
| topic | Chickens Imputation Re-sequencing SNP |
| url | http://link.springer.com/article/10.1186/s40104-018-0241-5 |
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