55 STUDY ON THE INTERSPECIFIC NUCLEAR TRANSFER OF PRZEWALSKI'S GAZELLES AND BOVINES

Przewalski’s gazelle (Procapra przewalskii), also known as Platts antelope, is an endangered species only found in China. It belongs to the Artiodactyla order, Bovidae family, antelope subfamily, and Gazella genus. In this study, 5 experiments were designed to examine the developmental potential of Przewalski’s gazelle somatic cells transplanted into bovine enucleated oocytes. Enucleation was conducted by Hoechst 33342 staining of the oocytes and guided by a fluorescent microscope to ensure the removal of the nuclei. The gazelle cells were then transferred to the enucleated oocytes and electrically fused to reconstructed embryos. The study resulted in 5 major findings. (1) When gazelle-bovine reconstructed embryos were treated with the deacetylase inhibitor valproic acid (VPA), at different concentrations and for different times, treatment of the cloned embryos with VPA at 0.5 mM for 24 h significantly increased the 8- to 16-cell-stage embryo development [61.9% (96/155) v. 33.8% (46/136) control]. However, the morula [1.3% (2/155) v. 1.5% (2/155); P > 0.05] and blastocyst (0.7% v. 1.5%; P > 0.05) development were similar to that of the control. In the intraspecific (bovine-bovine) control group, the cleavage, morula and blastocyst development of 3 cloned embryos were 72.6% (127/175), 28.0% (49/175), and 23.4% (41/175). (2) Octamer-binding transcription factor 4 (Oct-4), as a developmental potential and expression marker, was transfected to gazelle cells. When Oct-4-eGFP-confected cells were transferred, the cloned embryo development did not improve either with or without VPA treatment. (3) When the gazelle-bovine embryos were treated with the deacetylase inhibitor trichostatin A (TSA) for 24 h at 10 ng mL–1, blastocyst development was significantly higher than in the control group [3.6% (6/168) v. 0.8% (1/125); P < 0.05]. (4) When a reverse NT protocol, in which the oocyte nucleus was removed after the cell nucleus was fused to the oocyte, was used for NT, the cloned embryo development did not improve. (5) The gazelle-bovine and bovine-bovine cloned embryos at 8- to 16-cell stages, gazelle cells, bovine cells, and bovine oocytes transcriptomes were analyzed by Affymetrix microarray (Affymetrix Microarray Inc., Santa Clara, CA, USA) and repeated twice. A total of 643 genes were activated in gazelle-cattle embryos compared with oocytes, whereas 1527 genes were activated in bovine-bovine clones. A total of 1010 genes that were exclusively expressed in gazelle somatic cells were still expressed in the interspecies cloned embryos. In conclusion, TSA treatment of Przewalski’s gazelle somatic cells transferred into enucleated bovine oocytes improved development of cloned embryos to the blastocyst stage, although still with low efficiency. Data from microarray analyses of the gazelle-cattle embryos showed that over 1000 gazelle-specific genes were still expressed in the interspecific cloned embryos. This work was supported by the National Basic Research Program of China (no. 2012CB22306).

Comparison of genomic predictions using medium-density (∼54,000) and high-density (∼777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations

This study investigated genomic prediction using medium-density (∼54,000; 54K) and high-density marker panels (∼777,000; 777K), based on data from Nordic Holstein and Red Dairy Cattle (RDC). The Holstein data comprised 4,539 progeny-tested bulls, and the RDC data 4,403 progeny-tested bulls. The data were divided into reference data and test data using October 1, 2001, as a cut-off date (birth date of the bulls). This resulted in about 25% genotyped bulls in the Holstein test data and 20% in the RDC test data. For each breed, 3 data sets of markers were used to predict breeding values: (1) 54K data set with missing genotypes, (2) 54K data set where missing genotypes were imputed, and (3) imputed high-density (HD) marker data set created by imputing the 54K data to the HD data based on 557 bulls genotyped using a 777K single nucleotide polymorphism chip in Holstein, and 706 bulls in RDC. Based on the 3 marker data sets, direct genomic breeding values (DGV) for protein, fertility, and udder health were predicted using a genomic BLUP model (GBLUP) and a Bayesian mixture model with 2 normal distributions. Reliability of DGV was measured as squared correlations between deregressed proofs (DRP) and DGV corrected for reliability of DRP. Unbiasedness was assessed by regression of DRP on DGV, based on the bulls in the test data sets. Averaged over the 3 traits, reliability of DGV based on the HD markers was 0.5% higher than that based on the 54K data in Holstein, and 1.0% higher than that in RDC. In addition, the HD markers led to an improvement of unbiasedness of DGV. The Bayesian mixture model led to 0.5% higher reliability than the GBLUP model in Holstein, but not in RDC. Imputing missing genotypes in the 54K marker data did not improve genomic predictions for most of the traits.

Different methods to calculate genomic predictions--comparisons of BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP)

Several strategies to use genomic data in predictions have been proposed. The aim of this study was to compare different genomic prediction methods. The response variables used in the genomic predictions were deregressed proofs, which were derived from 2 estimated breeding value (EBV) data sets. The full EBV data set from March 2010 included the EBV for production and mastitis traits for all Nordic red bulls. The reduced data set included the same animals as the full data set, but the EBV were predicted from a data set that excluded the last 5 yr of observations. Genomic predictions were obtained using different BLUP models: BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP). For the selection candidate bulls, the SNP-BLUP and G-BLUP models gave the same direct genomic breeding values (e.g., correlation of direct genomic breeding values between SNP-BLUP and G-BLUP for protein was 0.99), but slightly different from genomic EBV obtained from H-BLUP (correlations of SNP-BLUP or G-BLUP with H-BLUP were about 0.96). For all traits, SNP-BLUP and G-BLUP gave the same validation reliability, whereas H-BLUP led to slightly higher reliability. Therefore, the results support a slight advantage of using H-BLUP for genomic evaluation.

Short communication: genotype imputation within and across Nordic cattle breeds

This study investigated the accuracies of imputation from 50K genotypes to high-density genotypes for animals from the Danish, Swedish, or Finnish Red dairy cattle populations, using either a national, combined Red, or combined Red and Holstein reference population. Combining the Red populations increased the imputation accuracy for all 3 populations compared with using single-nationality references. Including Holstein animals in the reference further increased the imputation accuracy for Danish Red.

Effect of marker-data editing on the accuracy of genomic prediction

Genomic selection is a method to predict breeding values using genome-wide single-nucleotide polymorphism (SNP) markers. High-quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker-editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48,222 SNP for Holstein and 44,305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy-Weinberg proportions (HWP) with thresholds of no limit, chi-squared p-values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De-regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p-value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.

IQGAP1 is necessary for pulmonary vascular barrier protection in murine acute lung injury and pneumonia

We recently reported that integrin α(v)β(3) is necessary for vascular barrier protection in mouse models of acute lung injury and peritonitis. Here, we used mass spectrometric sequencing of integrin complexes to isolate the novel β(3)-integrin binding partner IQGAP1. Like integrin β(3), IQGAP1 localized to the endothelial cell-cell junction after sphingosine-1-phosphate (S1P) treatment, and IQGAP1 knockdown prevented cortical actin formation and barrier enhancement in response to S1P. Furthermore, knockdown of IQGAP1 prevented localization of integrin α(v)β(3) to the cell-cell junction. Similar to β(3)-null animals, IQGAP1-null mice had increased pulmonary vascular leak compared with wild-type controls 3 days after intratracheal LPS. In an Escherichia coli pneumonia model, IQGAP1 knockout mice had increased lung weights, lung water, and lung extravascular plasma equivalents of (125)I-labeled albumin compared with wild-type controls. Taken together, these experiments indicate that IQGAP1 is necessary for S1P-mediated vascular barrier protection during acute lung injury and is required for junctional localization of the barrier-protective integrin α(v)β(3).

Post hoc analysis of a single IV infusion of zoledronic acid versus daily oral risedronate on lumbar spine bone mineral density in different subgroups with glucocorticoid-induced osteoporosis

This study summarizes the treatment effect of zoledronic acid infusion on lumbar spine bone mineral density in different subgroups with glucocorticoid-induced osteoporosis. Zoledronic acid is significantly more effective than risedronate in increasing lumbar spine (LS) bone mineral density (BMD) in both prevention and treatment of glucocorticoid-induced osteoporosis. Introduction In patients on glucocorticoids, a single zoledronic acid infusion significantly increased BMD versus daily oral risedronate. We assessed treatment effect on LS BMD in different patient subgroups at month 12 that contributed to the risk of osteoporosis in addition to glucocorticoids. Methods Patients randomized to a single IV infusion of zoledronic acid 5 mg or risedronate (5 mg/day) and stratified based on glucocorticoids duration [treatment (>3 months) and prevention (≤ 3 months) subpopulations]were subgrouped by age; gender; menopausal status in women; dose and duration of prednisone during the trial; and baseline serum 25-OH vitamin D, LS BMD T-score, creatinine clearance, and concomitant medication use. Results At month 12, zoledronic acid significantly increased LS BMD versus risedronate in patients ≤ 74 years (P<0.05) in the treatment and 65-74 years (P = 0.0008) in the prevention subpopulation. At month 12, zoledronic acid significantly increased LS BMD versus risedronate in both subpopulations irrespective of gender (all P<0.05), cumulative prednisone dose (all P<0.01), and postmenopausal status (all P<0.05). In premenopausal women, in both subpopulations, zoledronic acid significantly increased total hip BMD (all P<0.05) versus risedronate at month 12 but not LS BMD. Osteoporotic patients in the prevention (P=0.0189) and osteopenic patients in the treatment subpopulation (P=0.0305) showed significant LS BMD increases with zoledronic acid versus risedronate at month 12. Conclusions This post hoc analysis suggests that zoledronic acid is significantly more effective than risedronate in increasing LS BMD in prevention and treatment of glucocorticoid-induced osteoporosis across a wide range of patients.

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

This study investigated the accuracy of direct genomic breeding values (DGV) using a genomic BLUP model, genomic enhanced breeding values (GEBV) using a one-step blending approach, and GEBV using a selection index blending approach for 15 traits of Nordic Red Cattle. The data comprised 6,631 bulls of which 4,408 bulls were genotyped using Illumina Bovine SNP50 BeadChip (Illumina, San Diego, CA). To validate reliability of genomic predictions, about 20% of the youngest genotyped bulls were taken as test data set. Deregressed proofs (DRP) were used as response variables for genomic predictions. Reliabilities of genomic predictions in the validation analyses were measured as squared correlations between DRP and genomic predictions corrected for reliability of DRP, based on the bulls in the test data sets. A set of weighting (scaling) factors was used to construct the combined relationship matrix among genotyped and nongenotyped bulls for one-step blending, and to scale DGV and its expected reliability in the selection index blending. Weighting (scaling) factors had a small influence on reliabilities of GEBV, but a large influence on the variation of GEBV. Based on the validation analyses, averaged over the 15 traits, the reliability of DGV for bulls without daughter records was 11.0 percentage points higher than the reliability of conventional pedigree index. Further gain of 0.9 percentage points was achieved by combining information from conventional pedigree index using the selection index blending, and gain of 1.3 percentage points was achieved by combining information of genotyped and nongenotyped bulls simultaneously applying the one-step blending. These results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls in Nordic Red population, and the one-step blending approach is a good alternative to predict GEBV in practical genetic evaluation program.

Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50,000-marker SNP panel. Data consisted of genotypes of 15,966 European Holstein bulls from the combined EuroGenomics reference population. Genotypes with the low-density chip were created by erasing markers from 50,000-marker data. The studies were performed in the Nordic countries (Denmark, Finland, and Sweden) using a BLUP model for prediction of DGV and in France using a genomic marker-assisted selection approach for prediction of GEBV. Imputation in both studies was done using a combination of the DAGPHASE 1.1 and Beagle 2.1.3 software. Traits considered were protein yield, fertility, somatic cell count, and udder depth. Imputation of missing markers and prediction of breeding values were performed using 2 different reference populations in each country: either a national reference population or a combined EuroGenomics reference population. Validation for accuracy of imputation and genomic prediction was done based on national test data. Mean imputation error rates when using national reference animals was 5.5 and 3.9% in the Nordic countries and France, respectively, whereas imputation based on the EuroGenomics reference data set gave mean error rates of 4.0 and 2.1%, respectively. Prediction of GEBV based on genotypes imputed with a national reference data set gave an absolute loss of 0.05 in mean reliability of GEBV in the French study, whereas a loss of 0.03 was obtained for reliability of DGV in the Nordic study. When genotypes were imputed using the EuroGenomics reference, a loss of 0.02 in mean reliability of GEBV was detected in the French study, and a loss of 0.06 was observed for the mean reliability of DGV in the Nordic study. Consequently, the reliability of DGV using the imputed SNP data was 0.38 based on national reference data, and 0.48 based on EuroGenomics reference data in the Nordic validation, and the reliability of GEBV using the imputed SNP data was 0.41 based on national reference data, and 0.44 based on EuroGenomics reference data in the French validation.

Zoledronic acid results in better health-related quality of life following hip fracture: the HORIZON-Recurrent Fracture Trial

This study evaluated the benefits of ZOL versus placebo on health-related quality of life (HRQoL) among patients from HORIZON-RFT. At month 24 and end of the study visit, ZOL significantly improved patients' overall health state compared to placebo as assessed by the EQ-5D VAS.

INTRODUCTION

To evaluate the benefits of zoledronic acid (ZOL) versus placebo on health-related quality of life (HRQoL) among patients from The Health Outcomes and Reduced Incidence With Zoledronic Acid Once Yearly Recurrent Fracture Trial (HORIZON-RFT).

METHODS

In this randomized, double-blind, placebo-controlled trial, 2,127 patients were randomized to receive annual infusion of ZOL 5 mg (n = 1,065) or placebo (n = 1,062) within 90 days after surgical repair of low-trauma hip fracture. HRQoL was measured using EQ-5D Visual Analogue Scale (VAS) and utility scores (EuroQol instrument) at months 6, 12, 24, 36, and end of the study visit. Analysis of covariance model included baseline EQ-5D value, region, and treatment as explanatory variables.

RESULTS

At baseline, patients (mean age 75 years; 24% men and 76% women) were well matched between treatment groups with mean EQ-5D VAS of 65.82 in ZOL and 65.70 in placebo group. At the end of the study, mean change from baseline in EQ-5D VAS was greater for ZOL vs. placebo in all patients (7.67 ± 0.56 vs. 5.42 ± 0.56), and in subgroups of patients experiencing clinical vertebral fractures (8.86 ± 4.91 vs. -1.69 ± 3.42), non-vertebral fractures (5.03 ± 2.48 vs. -1.07 ± 2.16), and clinical fractures (5.19 ± 2.25 vs. -0.72 ± 1.82) with treatment difference significantly in favor of ZOL. EQ-5D utility scores were comparable for ZOL and placebo groups, but more patients on placebo consistently had extreme difficulty in mobility (1.74% for ZOL vs. 2.13% for placebo; p = 0.6238), self-care (4.92% vs. 6.69%; p = 0.1013), and usual activities (10.28% vs. 12.91%; p = 0.0775).

CONCLUSION

ZOL significantly improves HRQoL in patients with low-trauma hip fracture.

Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables

This study compared genomic predictions using conventional estimated breeding values (EBV) and daughter yield deviations (DYD) as response variables based on simulated data. Eight scenarios were simulated in regard to heritability (0.05 and 0.30), number of daughters per sire (30, 100, and unequal numbers with an average of 100 per sire) and numbers of genotyped sires (all or half of sires were genotyped). The simulated genome had a length of 1200 cM with 15,000 equally spaced Single-nucleotide polymorphism (SNP) markers and 500 randomly distributed Quantitative trait locus (QTL). In the simulated scenarios, the EBV approach was as effective as or slightly better than the DYD approach at predicting breeding value, dependent on simulated scenarios and statistical models. Applying a Bayesian common prior model (the same prior distribution of marker effect variance) and a linear mixed model (GBLUP), the EBV and DYD approaches provided similar genomic estimated breeding value (GEBV) reliabilities, except for scenarios with unequal numbers of daughters and half of sires without genotype, for which the EBV approach was superior to the DYD approach (by 1.2 and 2.4%). Using a Bayesian mixture prior model (mixture prior distribution of marker effect variance), the EBV approach resulted in slightly higher reliabilities of GEBV than the DYD approach (by 0.3-3.6% with an average of 1.9%), and more obvious in scenarios with low heritability, small or unequal numbers of daughters, and half of sires without genotype. Moreover, the results showed that the correlation between GEBV and conventional parent average (PA) was lower (corresponding to a relatively larger gain by including PA) when using the DYD approach than when using the EBV approach. Consequently, the two approaches led to similar reliability of an index combining GEBV and PA in most scenarios. These results indicate that EBV can be used as an alternative response variable for genomic prediction.

Overlapping chromosomal regions for fertility traits and production traits in the Danish Holstein population

Before implementing selection based on quantitative trait loci (QTL) for fertility, it is important to determine the existence of correlated effects between the fertility QTL and QTL with effects on production traits. When a QTL is detected for a trait that is a composite of subtraits, it is of interest to validate which of the subtraits are affected by the QTL. Phenotypic and marker data were collected from 34 grandsire families from the Danish Holstein population. First, the trait data for "fertility treatments" were separated into their underlying subtraits: uterine infections, antibiotics placed in the placenta, and abortions. In addition, retained placenta was selected for analysis because it is related to uterine infections. A genome scan was performed using 416 microsatellite markers for the fertility treatment subtraits and retained placenta, and an additional genome scan for milk production traits conditional on the QTL regions for the subtraits and retained placenta was conducted. Second, we selected 24 genomic regions harboring QTL for fertility traits from a previous study. A QTL scan for milk production traits conditional on the selected regions was conducted. We found that 16 selected genomic regions containing a QTL for fertility (including the fertility treatment subtraits and retained placenta) also harbored QTL for milk yield or milk composition traits. Furthermore, 12 QTL regions corresponding to 9 different fertility traits (including the fertility treatment subtraits) did not harbor a QTL for milk production or milk composition traits; that is, the region was specific for the fertility trait. The genome scan for the fertility treatment subtraits did not correspond to the QTL found for fertility treatments. No QTL were detected for the subtrait abortion, however genome scans for retained placenta revealed 4 different QTL.

Heterogeneity of gene expression in stromal fibroblasts of human breast carcinomas and normal breast

Breast carcinoma invasion is associated with prominent alterations in stromal fibroblasts. Carcinoma-associated fibroblasts (CAF) support and promote tumorigenesis, whereas normal mammary fibroblasts (NF) are thought to suppress tumor progression. Little is known about the difference in gene expression between CAF and NF or the patient-to-patient variability in gene expression. Paired CAF and NF were isolated from six primary human breast carcinoma specimens. RNA was extracted from low-passage cultures of CAF and NF and analyzed with Affymetrix Human Genome U133 Plus 2.0 arrays. The array data were examined with an empirical Bayes model and filtered according to the posterior probability of equivalent expression and fold difference in expression. Twenty-one genes (27 probe sets) were up-regulated in CAF, as compared to NF. Known functions of these genes relate to paracrine or intracellular signaling, transcriptional regulation, extracellular matrix and cell adhesion/migration. Ten genes (14 probe sets) were down-regulated in CAF, including the pluripotency transcription factor KLF4. Quantitative RT-PCR analysis of 10 genes validated the array results. Immunohistochemical staining for three gene products confirmed stromal expression in terms of location and relative quantity. Surprisingly, the variability of gene expression was slightly higher in NF than in CAF, suggesting inter-individual heterogeneity of normal stroma.

Improvement in genetic evaluation of female fertility in dairy cattle using multiple-trait models including milk production traits

This study investigated the improvement in genetic evaluation of fertility traits by using production traits as secondary traits (MILK = 305-d milk yield, FAT = 305-d fat yield, and PROT = 305-d protein yield). Data including 471,742 records from first lactations of Denmark Holstein cows, covering the years of inseminations during first lactations from 1995 to 2004, were analyzed. Six fertility traits (i.e., interval in days from calving to first insemination, calving interval, days open, interval in days from first to last insemination, numbers of inseminations per conception, and nonreturn rate within 56 d after first service) were analyzed using single- and multiple-trait sire models including 1 or 3 production traits. Model stability was evaluated by correlation between EBV from 2 sub-data sets (DATA(A) and DATA(B)). Model predictive ability was assessed by the correlation between EBV from training data (DATA(A) or DATA(B)) and daughter performance (yield deviation, defined as average of daughter-records adjusted for nongenetic effects) from test data (DATA(B) or DATA(A)) in a cross-validation procedure, and correlation between EBV obtained from the whole data set (DATA(T)) and from a reduced data set (DATA(C1), which only contained the first crop daughters) for proven bulls. In addition, the superiority of the models was evaluated by expected reliability of EBV, calculated from the prediction error variance of EBV. Based on these criteria, the models combining milk production traits showed better model stability and predictive ability than single-trait models for all the fertility traits, except for nonreturn rate within 56 d after first service. The stability and predictive ability for the model including MILK or PROT were similar to the model including all 3 milk production traits and better than the model including FAT. In addition, it was found that single-trait models underestimated genetic trend of fertility traits. These results suggested that genetic evaluation of fertility traits would be improved using a multiple-trait model including MILK or PROT.

Comparison between a sire model and an animal model for genetic evaluation of fertility traits in Danish Holstein population

Comparisons between a sire model, a sire-dam model, and an animal model were carried out to evaluate the ability of the models to predict breeding values of fertility traits, based on data including 471,742 records from the first lactation of Danish Holstein cows, covering insemination years from 1995 to 2004. The traits in the analysis were days from calving to first insemination, calving interval, days open, days from first to last insemination, number of inseminations per conception, and nonreturn rate within 56 d after first service. The correlations between sire estimated breeding value (EBV) from the animal model and the sire-dam model were close to 1 for all the traits, and those between the animal model and the sire model ranged from 0.95 to 0.97. Model ability to predict sire breeding value was assessed using 4 criteria: 1) the correlation between sire EBV from 2 data subsets (DATA(A) and DATA(B)); 2) the correlation between sire EBV from training data (DATA(A) or DATA(B)) and yield deviation from test data (DATA(B) or DATA(A)) in a cross-validation procedure; 3) the correlation between the EBV of proven bulls, obtained from the whole data set (DATA(T)) and from a reduced set of data (DATA(C1)) that contained only the first-crop daughters of sires; and 4) the reliability of sire EBV, calculated from the prediction error variance of EBV. All criteria used showed that the animal model was superior to the sire model for all the traits. The sire-dam model performed as well as the animal model and had a slightly smaller computational demand. Averaged over the 6 traits, the correlations between sire EBV from DATA(A) and DATA(B) were 0.61 (sire model) versus 0.64 (animal model), the correlations between EBV from DATA(T) and DATA(C1) for proven bulls were 0.59 versus 0.67, the correlations between EBV and yield deviation in the cross-validation were 0.21 versus 0.24, and the reliabilities of sire EBV were 0.42 versus 0.46. Model ability to predict cow breeding value was measured by the reliability of cow EBV, which increased from 0.21 using the sire model to 0.27 using the animal model. All the results suggest that the animal model, rather than the sire model, should be used for genetic evaluation of fertility traits.

Genome scan detects quantitative trait loci affecting female fertility traits in Danish and Swedish Holstein cattle

Data from the joint Nordic breeding value prediction for Danish and Swedish Holstein grandsire families were used to locate quantitative trait loci (QTL) for female fertility traits in Danish and Swedish Holstein cattle. Up to 36 Holstein grandsires with over 2,000 sons were genotyped for 416 microsatellite markers. Single trait breeding values were used for 12 traits relating to female fertility and female reproductive disorders. Data were analyzed by least squares regression analysis within and across families. Twenty-six QTL were detected on 17 different chromosomes. The best evidence was found for QTL segregating on Bos taurus chromosome (BTA)1, BTA7, BTA10, and BTA26. On each of these chromosomes, several QTL were detected affecting more than one of the fertility traits investigated in this study. Evidence for segregation of additional QTL on BTA2, BTA9, and BTA24 was found.

Genetic analysis of days from calving to first insemination and days open in Danish Holsteins using different models and censoring scenarios

The objectives of this study were to estimate genetic parameters and evaluate models for genetic evaluation of days from calving to first insemination (ICF) and days open (DO). Data including 509,512 first-parity records of Danish Holstein cows were analyzed using 5 alternative sire models that dealt with censored records in different ways: 1) a conventional linear model (LM) in which a penalty of 21 d was added to censored records; 2) a bivariate threshold-linear model (TLM), which included a threshold model for censoring status (0, 1) of the observations, and a linear model for ICF or DO without any penalty on censored records; 3) a right-censored linear model (CLM); 4) a Weibull proportional hazard model (SMW); and 5) a Cox proportional hazard model (SMC) constructed with piecewise constant baseline hazard function. The variance components for ICF and DO estimated from LM and TLM were similar, whereas CLM gave higher estimates of both additive genetic and residual components. Estimates of heritability from models LM, TLM, and CLM were very similar (0.102 to 0.108 for ICF, and 0.066 to 0.069 for DO). Heritabilities estimated using model SMW were 0.213 for ICF and 0.121 for DO in logarithmic scale. Using SMC, the estimates of heritability, defined as the log-hazard proportional factor for ICF and DO, were 0.013 and 0.009, respectively. Correlations between predicted transmitting ability from different models for sires with records from at least 20 daughters were far from unity, indicating that different models could lead to different rankings. The largest reranking was found between SMW and SMC, whereas negligible reranking was found among LM, TLM, and CLM. The 5 models were evaluated by comparing correlations between predicted transmitting ability from different data sets (the whole data set and 2 subsets, each containing half of the whole data set), for sires with records from at least 20 daughters, and chi(2) statistics based on predicted and observed daughter frequencies using a cross validation. The model comparisons showed that SMC had the best performance in predicting breeding values of the 2 traits. No significant difference was found among models LM, TLM, and CLM. The SMW model had a relatively poor performance, probably because the data are far from a Weibull distribution. The results from the present study suggest that SMC could be a good alternative for predicting breeding values of ICF and DO in the Danish Holstein population.