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| 1 | SNP芯片基因型填充至测序数据的策略显示文摘根据畜禽基因组信息,模拟了1条染色体测序数据、中密度和高密度SNP芯片数据。利用Beagle和FImpute将SNP芯片填充至测序数据,提出了一步法和两步法2种填充策略。结果表明,直接将中密度芯片填充至测序数据的一步法策略填充准确性较低,当公畜为参考群体时,两步法策略将后裔填充准确性由一步法时的0.691提高至0.863;当不同规模母畜为参考群体时,一步法策略下半同胞填充准确性为0.553~0.664,而两步法策略则大幅度提高到0.832~0.866。随着参考群体的增加,SNP芯片到测序数据的填充准确性可进一步提高,同样亲缘关系也有助于填充准确性提高。一步法填充策略下,Beagle填充准确性低于FImpute,但随着参考群体的扩大,差距逐步缩小,而在两步法策略下,Beagle与FImpute基因型填充准确性没有显著差异。 | 李乐义 邵东东 丁向东 张勤 | 2016 | 中国科技论文2016，11，12： | 3 |
| 2 | Statistical considerations for genomic selection显示文摘Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator.Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either trainingtesting approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future. | Huimin KANG Lei ZHOU Jianfeng LIU | 2017 | Frontiers of Agricultural Science and Engineering2017，4，3： | 0 |
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