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Progressive motility – a potential predictive parameter for semen fertilization capacity in bovines
- Y. Li, D. Kalo, Y. Zeron, Z. Roth
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We examined the association between progressive motility of spermatozoa and in vitro fertilization (IVF) competence of bovine ejaculates. Fresh semen was evaluated using a computerized sperm quality analyzer for bulls using progressive motility as the primary parameter. Ejaculates with high progressive motility (HPM; >81%) were compared with those with low progressive motility (LPM; <62%). Semen concentration and sperm velocity were lower (P < 0.05) in HPM versus LPM ejaculates. Volume and motile sperm concentration did not differ between groups (P > 0.05). Examination of sperm morphology revealed a higher proportion of spermatozoa with abnormal morphology (P < 0.01) in LPM versus HPM ejaculates, the predominant abnormal feature being a bent tail (P < 0.05). Sperm viability, acrosome integrity and DNA fragmentation did not differ between HPM and LPM samples. Mitochondrial membrane potential was higher (P < 0.01) in HPM versus LPM semen. Zinc concentrations in the seminal plasma correlated with progressive motility (R2 = 0.463, P = 0.03). In addition, representative ejaculates from HPM and LPM groups were cryopreserved in straws and used for IVF. The proportions of embryos cleaved to 2- and 4-cell stages (88.1 ± 1.1 versus 80.5 ± 1.7, P = 0.001) and developed to blastocysts (33.5 ± 1.6 versus 23.5 ± 2.2, P = 0.026) were higher for HPM than LPM semen. The total cell number of embryos and blastocyst apoptotic index did not differ between groups. Although sperm progressive motility is associated with IVF competence, further examination is required to determine whether progressive motility can serve as a predictor of semen fertilization capacity in vivo.
Predictive ability of selected subsets of single nucleotide polymorphisms (SNPs) in a moderately sized dairy cattle population
- J. I. Weller, G. Glick, A. Shirak, E. Ezra, E. Seroussi, M. Shemesh, Y. Zeron, M. Ron
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Several studies have shown that computation of genomic estimated breeding values (GEBV) with accuracies significantly greater than parent average (PA) estimated breeding values (EBVs) requires genotyping of at least several thousand progeny-tested bulls. For all published analyses, GEBV computed from the selected samples of markers have lower or equal accuracy than GEBV derived on the basis of all valid single nucleotide polymorphisms (SNPs). In the current study, we report on four new methods for selection of markers. Milk, fat, protein, somatic cell score, fertility, persistency, herd life and the Israeli selection index were analyzed. The 972 Israeli Holstein bulls genotyped with EBV for milk production traits computed from daughter records in 2012 were assigned into a training set of 844 bulls with progeny test EBV in 2008, and a validation set of 128 young bulls. Numbers of bulls in the two sets varied slightly among the nonproduction traits. In EFF12, SNPs were first selected for each trait based on the effects of each marker on the bulls’ 2012 EBV corrected for effective relationships, as determined by the SNP matrix. EFF08 was the same as EFF12, except that the SNPs were selected on the basis of the 2008 EBV. In DIFmax, the SNPs with the greatest differences in allelic frequency between the bulls in the training and validation sets were selected, whereas in DIFmin the SNPs with the smallest differences were selected. For all methods, the numbers of SNPs retained varied over the range of 300 to 6000. For each trait, except fertility, an optimum number of markers between 800 and 5000 was obtained for EFF12, based on the correlation between the GEBV and current EBV of the validation bulls. For all traits, the difference between the correlation of GEBV and current EBV and the correlation of the PA and current EBV was >0.25. EFF08 was inferior to EFF12, and was generally no better than PA EBV. DIFmax always outperformed DIFmin and generally outperformed EFF08 and PA. Furthermore, GEBV based on DIFmax were generally less biased than PA. It is likely that other methods of SNP selection could improve upon these results.
A simple method for genomic selection of moderately sized dairy cattle populations
- J. I. Weller, M. Ron, G. Glick, A. Shirak, Y. Zeron, E. Ezra
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An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a ‘cow model,’ estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.