利用牛小肠液冻干粉法研究不同加工处理玉米瘤胃非降解残渣中干物质和淀粉的小肠消化率

牛小肠液冻干粉(BIF)法是一种常用的体外法,该法利用小肠中的各种酶研究营养物质的小肠消化情况,具有较好的稳定性和可重复性。本试验进行了BIF的制备、酶活性的检测及底物用量与培养时间的优化筛选,利用BIF研究不同加工处理(膨化、制粒、压扁、烘炒)玉米在泌乳奶牛瘤胃非降解残渣中干物质(DM)和淀粉的小肠消化率。结果表明,BIF的用量为0.5g/0.5g饲料,最佳培养时间为8h。膨化玉米组的DM和淀粉的小肠消化率显著高于颗粒玉米、烘炒玉米、压扁玉米及对照玉米组(P<0.05),其他组之间差异均不显著(P>0.05),这与瘤胃中的降解情况不同,可能与玉米的不同加工处理有关;各组DM和淀粉的小肠消化率之间比较,差异均不显著(P>0.05)。从BIF培养的结果来看,小肠消化率低于文献报道的小肠淀粉消化率,可能是BIF培养的底物为瘤胃未降解的残渣,与直接通过十二指肠灌注淀粉的组成有很大的差异。     

Efficient approximation of reliabilities for single-step genomic best linear unbiased predictor models with the Algorithm for Proven and Young

The objectives of this study were to develop an efficient algorithm for calculating prediction error variances (PEVs) for genomic best linear unbiased prediction (GBLUP) models using the Algorithm for Proven and Young (APY), extend it to single-step GBLUP (ssGBLUP), and apply this algorithm for approximating the theoretical reliabilities for single- and multiple-trait models in ssGBLUP. The PEV with APY was calculated by block sparse inversion, efficiently exploiting the sparse structure of the inverse of the genomic relationship matrix with APY. Single-step GBLUP reliabilities were approximated by combining reliabilities with and without genomic information in terms of effective record contributions. Multi-trait reliabilities relied on single-trait results adjusted using the genetic and residual covariance matrices among traits. Tests involved two datasets provided by the American Angus Association. A small dataset (Data1) was used for comparing the approximated reliabilities with the reliabilities obtained by the inversion of the left-hand side of the mixed model equations. A large dataset (Data2) was used for evaluating the computational performance of the algorithm. Analyses with both datasets used single-trait and three-trait models. The number of animals in the pedigree ranged from 167,951 in Data1 to 10,213,401 in Data2, with 50,000 and 20,000 genotyped animals for single-trait and multiple-trait analysis, respectively, in Data1 and 335,325 in Data2. Correlations between estimated and exact reliabilities obtained by inversion ranged from 0.97 to 0.99, whereas the intercept and slope of the regression of the exact on the approximated reliabilities ranged from 0.00 to 0.04 and from 0.93 to 1.05, respectively. For the three-trait model with the largest dataset (Data2), the elapsed time for the reliability estimation was 11 min. The computational complexity of the proposed algorithm increased linearly with the number of genotyped animals and with the number of traits in the model. This algorithm can efficiently approximate the theoretical reliability of genomic estimated breeding values in ssGBLUP with APY for large numbers of genotyped animals at a low cost.