National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese academy of Agricultural Sciences, Beijing 100193, China.
BMC Genet. 2014 Mar 4;15:30. doi: 10.1186/1471-2156-15-30.
In this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.
The results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model.
These results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM.
在这项研究中，使用常规的单性状和多性状线性混合模型计算的常规估计育种值（EBV）作为响应变量，比较了单性状基因组模型（STGM）和多性状基因组模型（MTGM）在基因组预测中的表现。模拟了三种有无缺失数据的情况：无缺失数据、高遗传力性状缺失数据 90%、低遗传力性状缺失数据 90%。模拟的基因组长度为 500cM，有 5000 个等间隔的单核苷酸多态性标记和 300 个随机分布的数量性状基因座（QTL）。每个性状的真实育种值使用 200 个 QTL 确定，其余 100 个 QTL 假设同时影响高（遗传力为 0.3 的性状 I）和低（遗传力为 0.05 的性状 II）遗传力性状。性状 I 和 II 之间的遗传相关系数为 0.5，剩余相关系数为零。
结果表明，当不存在缺失记录时，MTGM 和 STGM 对性状 I 的基因组预测具有相同的可靠性，而对于性状 II，MTGM 的表现优于 STGM。当两个性状之一存在缺失记录时，MTGM 的表现明显优于 STGM。一般来说，对于无缺失数据的性状，使用多性状或单性状模型估计的 EBV 响应变量预测的基因组 EBV 可靠性差异相对较小。然而，对于存在缺失数据的性状，多性状模型获得的 EBV 响应变量比单性状模型获得的 EBV 响应变量更可靠的基因组预测。
这些结果表明，MTGM 对低遗传力性状和记录数量有限的性状的表现优于 STGM。即使使用多性状模型获得 EBV 响应变量，使用 MTGM 进行基因组预测也比使用 STGM 进行预测更可靠。
