中文摘要：

迷失的遗传型是用 SNP 薄片技术获得的高密度 SNP 数据集的一个普通特征，这是可能的减少 genomic 的精确性选择。这个问题能被与估计的遗传型把失踪的遗传型归咎于避免。当实现归罪时，标准为 SNP 数据质量控制使用了并且是否在数据质量控制需要前后执行归罪考虑。在这份报纸，我们用不同归罪方法比较了归罪和质量控制的六策略，不同质量控制标准并且由改变归罪和质量控制的顺序，对在中国荷兰的一种乳牛的牛奶生产特点的真实数据集牛。结果表明了那，不管方法和质量控制标准什么归罪被使用，有在质量控制前的归罪的策略以 genomic 的精确性在质量控制以后与归罪比策略更好表现了选择。不同归罪方法和质量控制标准显著地没影响 genomic 选择的精确性。在质量控制能增加 genomic 选择的精确性以前，我们结束了那条表现归罪，特别当失踪的遗传型的率高，参考书人口是小的时。

英文摘要：

Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.