Title |
Design of a Bovine Low-Density SNP Array Optimized for Imputation
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Published in |
PLOS ONE, March 2012
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DOI | 10.1371/journal.pone.0034130 |
Pubmed ID | |
Authors |
Didier Boichard, Hoyoung Chung, Romain Dassonneville, Xavier David, André Eggen, Sébastien Fritz, Kimberly J. Gietzen, Ben J. Hayes, Cynthia T. Lawley, Tad S. Sonstegard, Curtis P. Van Tassell, Paul M. VanRaden, Karine A. Viaud-Martinez, George R. Wiggans |
Abstract |
The Illumina BovineLD BeadChip was designed to support imputation to higher density genotypes in dairy and beef breeds by including single-nucleotide polymorphisms (SNPs) that had a high minor allele frequency as well as uniform spacing across the genome except at the ends of the chromosome where densities were increased. The chip also includes SNPs on the Y chromosome and mitochondrial DNA loci that are useful for determining subspecies classification and certain paternal and maternal breed lineages. The total number of SNPs was 6,909. Accuracy of imputation to Illumina BovineSNP50 genotypes using the BovineLD chip was over 97% for most dairy and beef populations. The BovineLD imputations were about 3 percentage points more accurate than those from the Illumina GoldenGate Bovine3K BeadChip across multiple populations. The improvement was greatest when neither parent was genotyped. The minor allele frequencies were similar across taurine beef and dairy breeds as was the proportion of SNPs that were polymorphic. The new BovineLD chip should facilitate low-cost genomic selection in taurine beef and dairy cattle. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 5 | 3% |
Brazil | 2 | 1% |
France | 1 | <1% |
Australia | 1 | <1% |
Finland | 1 | <1% |
Colombia | 1 | <1% |
New Zealand | 1 | <1% |
Poland | 1 | <1% |
Unknown | 169 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 44 | 24% |
Student > Ph. D. Student | 39 | 21% |
Student > Master | 21 | 12% |
Student > Doctoral Student | 13 | 7% |
Student > Postgraduate | 8 | 4% |
Other | 28 | 15% |
Unknown | 29 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 108 | 59% |
Biochemistry, Genetics and Molecular Biology | 20 | 11% |
Veterinary Science and Veterinary Medicine | 8 | 4% |
Medicine and Dentistry | 5 | 3% |
Computer Science | 4 | 2% |
Other | 6 | 3% |
Unknown | 31 | 17% |