Title |
Accelerating Improvement of Livestock with Genomic Selection
|
---|---|
Published in |
Annual Review of Animal Biosciences, January 2013
|
DOI | 10.1146/annurev-animal-031412-103705 |
Pubmed ID | |
Authors |
Theo Meuwissen, Ben Hayes, Mike Goddard |
Abstract |
Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 1% |
Brazil | 3 | <1% |
Colombia | 2 | <1% |
Germany | 1 | <1% |
United Kingdom | 1 | <1% |
India | 1 | <1% |
Mexico | 1 | <1% |
Canada | 1 | <1% |
Unknown | 290 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 64 | 21% |
Student > Ph. D. Student | 53 | 17% |
Student > Master | 37 | 12% |
Student > Doctoral Student | 19 | 6% |
Student > Bachelor | 18 | 6% |
Other | 54 | 18% |
Unknown | 59 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 172 | 57% |
Biochemistry, Genetics and Molecular Biology | 28 | 9% |
Veterinary Science and Veterinary Medicine | 8 | 3% |
Computer Science | 7 | 2% |
Engineering | 4 | 1% |
Other | 12 | 4% |
Unknown | 73 | 24% |