生物
选择(遗传算法)
遗传学
单核苷酸多态性
基因型
标记辅助选择
基因组
基因组选择
SNP公司
遗传标记
最佳线性无偏预测
DNA测序
计算生物学
人口
基因
机器学习
计算机科学
社会学
人口学
作者
T.H.E. Meuwissen,Ben J. Hayes,Michael E. Goddard
出处
期刊:Annual Review of Animal Biosciences
[Annual Reviews]
日期:2013-01-01
卷期号:1 (1): 221-237
被引量:294
标识
DOI:10.1146/annurev-animal-031412-103705
摘要
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.
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