A common dataset for genomic analysis of livestock populations.
A common dataset for genomic analysis of livestock populations.
复制标题
DOI：
10.1534/g3.111.001453
复制
发表时间：
2012-04
期刊：
影响因子：
--
通讯作者：
Forni S
中科院分区：
文献类型：
--
作者：
Cleveland MA;Hickey JM;Forni S
虽然通用数据集是科学界的重要资源，可用于解决重要问题，但在牲畜物种中通常没有有意义大小的基因组数据集。我们描述了一个猪的数据集，PIC（属公司）已经比较基因组预测方法。我们还描述了使用PIC认为预测和验证基因组育种值的最佳实践的方法对数据进行基因组评估，并讨论了数据结构对准确性的影响。该数据集包含3534个具有高密度基因型、表型和估计育种值的个体。基因组育种值使用BayesB计算，表型和去回归育种值，并使用一步基因组BLUP方法，该方法结合了基因分型和未基因分型动物的信息。基因组育种值的准确性随着性状遗传力的增加和训练与验证之间的关系的增加而增加。在几乎所有情况下，贝叶斯B使用去回归育种值优于其他方法，但单步评估的表现仅略差。该数据集可用于比较使用真实的数据进行基因组预测的方法。我们的研究结果表明，验证方法占人口之间的相关性可以纠正潜在的高估基因组育种值的准确性，与基因分型策略进行基因组选择计划的影响。
Although common datasets are an important resource for the scientific community and can be used to address important questions, genomic datasets of a meaningful size have not generally been available in livestock species. We describe a pig dataset that PIC (a Genus company) has made available for comparing genomic prediction methods. We also describe genomic evaluation of the data using methods that PIC considers best practice for predicting and validating genomic breeding values, and we discuss the impact of data structure on accuracy. The dataset contains 3534 individuals with high-density genotypes, phenotypes, and estimated breeding values for five traits. Genomic breeding values were calculated using BayesB, with phenotypes and de-regressed breeding values, and using a single-step genomic BLUP approach that combines information from genotyped and un-genotyped animals. The genomic breeding value accuracy increased with increased trait heritability and with increased relationship between training and validation. In nearly all cases, BayesB using de-regressed breeding values outperformed the other approaches, but the single-step evaluation performed only slightly worse. This dataset was useful for comparing methods for genomic prediction using real data. Our results indicate that validation approaches accounting for relatedness between populations can correct for potential overestimation of genomic breeding value accuracies, with implications for genotyping strategies to carry out genomic selection programs.