Solberg T R, Sonesson A K, Woolliams J A, Meuwissen T H E
Department of Animal and Aquacultural Sciences, University of Life Sciences, N-1432 As, Norway.
J Anim Sci. 2008 Oct;86(10):2447-54. doi: 10.2527/jas.2007-0010. Epub 2008 Apr 11.
With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the use of marker haplotypes versus marker genotypes alone. Different densities were tested with marker densities equivalent to 2, 1, 0.5, and 0.25N(e) markers/morgan using microsatellites and 8, 4, 2, and 1N(e) markers/morgan using SNP, where 1N(e) markers/morgan means 100 markers per morgan, if effective size (N(e)) is 100. Marker characteristics and linkage disequilibria were obtained by simulating a population over 1,000 generations to achieve a mutation drift balance. The marker designs were evaluated for their accuracy of predicting breeding values from either estimating marker effects or estimating effects of haplotypes based upon combining 2 markers. Using microsatellites as direct marker effects, the accuracy of selection increased from 0.63 to 0.83 as the density increased from 0.25N(e)/morgan to 2N(e)/morgan. Using SNP markers as direct marker effects, the accuracy of selection increased from 0.69 to 0.86 as the density increased from 1N(e)/morgan to 8N(e)/morgan. The SNP markers required a 2 to 3 times greater density compared with using microsatellites to achieve a similar accuracy. The biases that genomic selection EBV often show are due to the prediction of marker effects instead of QTL effects, and hence, genomic selection EBV may need rescaling for practical use. Using haplotypes resulted in similar or reduced accuracies compared with using direct marker effects. In practical situations, this means that it is advantageous to use direct marker effects, because this avoids the estimation of marker phases with the associated errors. In general, the results showed that the accuracy remained responsive with small bias to increasing marker density at least up to 8N(e) SNP/morgan, where the effective population size was 100 and with the genomic model assumed. For a 30-morgan genome and N(e) = 100, this implies that about approximately 24,000 SNP are needed.
随着高密度标记图谱的出现和经济高效的基因分型技术的发展，基因组选择方法可能比目前基于表型和系谱的选择方法能带来更快的遗传进展。在此，我们研究了一些影响基因组选择准确性的因素，即标记密度和标记类型（即微卫星和单核苷酸多态性（SNP）标记），以及单独使用标记单倍型与标记基因型的情况。使用微卫星时，分别测试了相当于2、1、0.5和0.25N(e)个标记/厘摩的不同密度，使用SNP时分别测试了相当于8、4、2和1N(e)个标记/厘摩的不同密度，其中1N(e)个标记/厘摩表示如果有效群体大小（N(e)）为100，则每厘摩有100个标记。通过模拟一个经过1000代的群体以达到突变 - 漂变平衡来获得标记特征和连锁不平衡。基于估计标记效应或基于组合两个标记估计单倍型效应，对标记设计预测育种值的准确性进行了评估。以微卫星作为直接标记效应，当密度从0.25N(e)/厘摩增加到2N(e)/厘摩时，选择准确性从0.63提高到0.83。以SNP标记作为直接标记效应，当密度从1N(e)/厘摩增加到8N(e)/厘摩时，选择准确性从0.69提高到0.86。与使用微卫星相比，SNP标记需要高2至3倍的密度才能达到相似的准确性。基因组选择估计育种值（EBV）经常显示的偏差是由于对标记效应而非数量性状基因座（QTL）效应的预测，因此，基因组选择EBV在实际应用中可能需要重新校准。与使用直接标记效应相比，使用单倍型导致的准确性相似或降低。在实际情况中，这意味着使用直接标记效应是有利的，因为这避免了带有相关误差的标记相位估计。总体而言，结果表明，至少在有效群体大小为100且假设基因组模型的情况下，对于高达8N(e) SNP/厘摩的标记密度增加，准确性仍然保持响应且偏差较小。对于一个30厘摩的基因组和N(e) = 100，这意味着大约需要24,000个SNP。
