Title |
Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle
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Published in |
BMC Genomics, August 2014
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DOI | 10.1186/1471-2164-15-728 |
Pubmed ID | |
Authors |
Rasmus Froberg Brøndum, Bernt Guldbrandtsen, Goutam Sahana, Mogens Sandø Lund, Guosheng Su |
Abstract |
The advent of low cost next generation sequencing has made it possible to sequence a large number of dairy and beef bulls which can be used as a reference for imputation of whole genome sequence data. The aim of this study was to investigate the accuracy and speed of imputation from a high density SNP marker panel to whole genome sequence level. Data contained 132 Holstein, 42 Jersey, 52 Nordic Red and 16 Brown Swiss bulls with whole genome sequence data; 16 Holstein, 27 Jersey and 29 Nordic Reds had previously been typed with the bovine high density SNP panel and were used for validation. We investigated the effect of enlarging the reference population by combining data across breeds on the accuracy of imputation, and the accuracy and speed of both IMPUTE2 and BEAGLE using either genotype probability reference data or pre-phased reference data. All analyses were done on Bovine autosome 29 using 387,436 bi-allelic variants and 13,612 SNP markers from the bovine HD panel. |
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Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 20% |
India | 1 | 20% |
United States | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 60% |
Members of the public | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 3 | 3% |
New Zealand | 1 | <1% |
Finland | 1 | <1% |
Spain | 1 | <1% |
Belgium | 1 | <1% |
Unknown | 102 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 28 | 26% |
Researcher | 23 | 21% |
Student > Master | 14 | 13% |
Student > Doctoral Student | 8 | 7% |
Professor > Associate Professor | 5 | 5% |
Other | 13 | 12% |
Unknown | 18 | 17% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 73 | 67% |
Veterinary Science and Veterinary Medicine | 6 | 6% |
Biochemistry, Genetics and Molecular Biology | 6 | 6% |
Medicine and Dentistry | 3 | 3% |
Nursing and Health Professions | 1 | <1% |
Other | 1 | <1% |
Unknown | 19 | 17% |