The use of frozen semen to minimize inbreeding in small populations

In this study, we compared the average coancestry and inbreeding levels for two genetic conservation schemes in which frozen semen from a gene bank is used to reduce the inbreeding in a live population. For a simple scheme in which only semen of generation-0 (G0) sires is used, the level of inbreeding asymptotes to 1/(2N), where N is the number of newborn sires in the base generation and rate of inbreeding goes to zero. However, when only sires of G0 are selected, all genes will eventually descend from the founder sires and all genes from the founder dams are lost. We propose an alternative scheme in which N sires from generation 1 (G1), as well as the N sires from G0, have semen conserved, and the semen of G0 and G1 sires is used for dams of odd and even generation numbers, respectively. With this scheme, the level of inbreeding asymptotes to 1/(3N) and the genes of founder dams are also conserved, because 50% of the genes of sires of G1 are derived from the founder dams. A computer simulation study shows that this is the optimum design to minimize inbreeding, even if semen from later generations is available

Intracerebral hemorrhage in a neonate with an intragenic COL4A2 duplication

Intracerebral hemorrhage is rare in term born neonates. Besides several non-genetic risk factors, pathogenic variants in COL4A1 and COL4A2 have been described to play a role in the pathophysiology of neonatal intracerebral hemorrhage. To the best of our knowledge, no intragenic COL4A2 duplications have been reported in humans to date. We report a neonate with intracerebral hemorrhage and a de novo intragenic COL4A2 duplication. Although it is not clear yet whether this genetic factor fully explains the clinical phenotype, it may have contributed at least as a risk factor for cerebral hemorrhage. Screening for intragenic COL4A1 and COL4A2 duplications as part of collagen IV diagnostics should be considered as part of the fetal and neonatal work-up for unexplained cerebral hemorrhages and to collect more evidence of the pathogenicity of this genetic mechanism.Genetics of disease, diagnosis and treatmen

Optimization of progeny tests with prior information on young bulls

Algorithms are derived to optimize the number of progeny records per young bull (or boar) in a progeny test when prior EBVs are available. The prior EBV may come from a pedigree index, own record or indicator traits. Progeny testing of the highest ranking young bulls on prior EBV, with an equal number of progeny records per bull is close to optimal even when: (1) some bulls are more promising than others based on prior EBV; (2) the bulls are related; and (3) the cost prices of the bulls differ. The latter depends on the assumed distribution of the cost prices, and the algorithm that was derived to account for the differences in cost prices may still prove a useful safeguard against buying too expensive bulls. The algorithm was extended to the situation where inbreeding was reduced by putting a cost on the average relationship among the selected bulls, i.e., G - kĀ was maximized, where G and Ā are the genetic merit and the average relationship of the selected bulls and k is the cost factor. In an example, the algorithm yielded 11% higher G - kĀ than simply progeny testing the highest ranking bulls based on prior EBV

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

The objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated-measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd-scaling factors, which were calculated by accounting for heterogeneity of phenotypic within-herd variances. Models were selected based on cross-validation and prediction accuracy results. Scaling factors were re-estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree-based relationship matrix. Results indicated that the model fitted for scaled data showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re-estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97