Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy
Abstract Background Accurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in catt...
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | BMC Genetics |
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| Online Access: | http://link.springer.com/article/10.1186/s12863-017-0588-1 |
| _version_ | 1829112371636862976 |
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| author | Steven G. Larmer Mehdi Sargolzaei Luiz F. Brito Ricardo V. Ventura Flávio S. Schenkel |
| author_facet | Steven G. Larmer Mehdi Sargolzaei Luiz F. Brito Ricardo V. Ventura Flávio S. Schenkel |
| author_sort | Steven G. Larmer |
| collection | DOAJ |
| description | Abstract Background Accurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in cattle. Methods Methods for clustering of animals for imputation were explored, using 1000 Bull Genomes Project sequence data on 1146 animals from a variety of beef and dairy breeds. Imputation from 50 K to 777 K was first carried out to choose an ideal clustering method, using ADMIXTURE or PLINK clustering algorithms with either genotypes or reconstructed haplotypes. Results Due to efficiency, accuracy and ease of use, clustering with PLINK using haplotypes as quasi-genotypes was chosen as the most advantageous grouping method. It was found that using a clustered population slightly decreased computing time, while maintaining accuracy across the population. Although overall accuracy remained the same, a slight increase in accuracy was observed for groups of animals in some breeds (primarily purebred beef cattle from breeds with fewer sequenced animals) and for other groups, primarily crossbreed animals, a slight decrease in accuracy was observed. However, it was noted that some animals in each breed were poorly imputed across all methods. When imputed sequences were included in the reference population to aid imputation of poorly imputed animals, a small increase in overall accuracy was observed for nearly every individual in the population. Two models were created to predict imputation accuracy, a complete model using all information available including Euclidean distances from genotypes and haplotypes, pedigree information, and clustering groups and a simple model using only breed and an Euclidean distance matrix as predictors. Both models were successful in predicting imputation accuracy, with correlations between predicted and true imputation accuracy as measured by concordance rate of 0.87 and 0.83, respectively. Conclusions A clustering methodology can be very useful to subgroup cattle for efficient genotype imputation. In addition, accuracy of genotype imputation from medium to high-density Single Nucleotide Polymorphisms (SNP) chip panels to whole-genome sequence can be predicted well using a simple linear model defined in this study. |
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| id | doaj.art-e195df0d2c114b6bab65aa9df87b34c4 |
| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-12-12T15:16:06Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genetics |
| spelling | doaj.art-e195df0d2c114b6bab65aa9df87b34c42022-12-22T00:20:29ZengBMCBMC Genetics1471-21562017-12-0118111210.1186/s12863-017-0588-1Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracySteven G. Larmer0Mehdi Sargolzaei1Luiz F. Brito2Ricardo V. Ventura3Flávio S. Schenkel4Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of GuelphCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of GuelphCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of GuelphCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of GuelphCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of GuelphAbstract Background Accurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in cattle. Methods Methods for clustering of animals for imputation were explored, using 1000 Bull Genomes Project sequence data on 1146 animals from a variety of beef and dairy breeds. Imputation from 50 K to 777 K was first carried out to choose an ideal clustering method, using ADMIXTURE or PLINK clustering algorithms with either genotypes or reconstructed haplotypes. Results Due to efficiency, accuracy and ease of use, clustering with PLINK using haplotypes as quasi-genotypes was chosen as the most advantageous grouping method. It was found that using a clustered population slightly decreased computing time, while maintaining accuracy across the population. Although overall accuracy remained the same, a slight increase in accuracy was observed for groups of animals in some breeds (primarily purebred beef cattle from breeds with fewer sequenced animals) and for other groups, primarily crossbreed animals, a slight decrease in accuracy was observed. However, it was noted that some animals in each breed were poorly imputed across all methods. When imputed sequences were included in the reference population to aid imputation of poorly imputed animals, a small increase in overall accuracy was observed for nearly every individual in the population. Two models were created to predict imputation accuracy, a complete model using all information available including Euclidean distances from genotypes and haplotypes, pedigree information, and clustering groups and a simple model using only breed and an Euclidean distance matrix as predictors. Both models were successful in predicting imputation accuracy, with correlations between predicted and true imputation accuracy as measured by concordance rate of 0.87 and 0.83, respectively. Conclusions A clustering methodology can be very useful to subgroup cattle for efficient genotype imputation. In addition, accuracy of genotype imputation from medium to high-density Single Nucleotide Polymorphisms (SNP) chip panels to whole-genome sequence can be predicted well using a simple linear model defined in this study.http://link.springer.com/article/10.1186/s12863-017-0588-1Cattle genomicsGenomic clusteringGenotype imputationSequencing data |
| spellingShingle | Steven G. Larmer Mehdi Sargolzaei Luiz F. Brito Ricardo V. Ventura Flávio S. Schenkel Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy BMC Genetics Cattle genomics Genomic clustering Genotype imputation Sequencing data |
| title | Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy |
| title_full | Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy |
| title_fullStr | Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy |
| title_full_unstemmed | Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy |
| title_short | Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy |
| title_sort | novel methods for genotype imputation to whole genome sequence and a simple linear model to predict imputation accuracy |
| topic | Cattle genomics Genomic clustering Genotype imputation Sequencing data |
| url | http://link.springer.com/article/10.1186/s12863-017-0588-1 |
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