Ovulatory response to the first GnRH of Ovsynch is the critical determinant for successful synchronization of ovulation in dairy cows. Our objective in this study was to develop a pre-Ovsynch treatment that increased the percentage of cows that ovulated in response to the first GnRH injection of Ovsynch. To accomplish our goal, we evaluated a hormonal strategy that consisted of PGF2alpha and GnRH before the first GnRH of Ovsynch. Lactating dairy cows (n = 137) were assigned to receive either no treatment before Ovsynch (control) or 25 mg of PGF2alpha (PreP) followed 2 d later by 100 microg of GnRH (PreG), administered 4 (G4G), 5 (G5G), or 6 (G6G) d before initiating the Ovsynch protocol. Transrectal ultrasonography was performed to assess follicular size and resulting ovulation, and blood samples were collected to measure circulating concentrations of progesterone and estradiol immediately before each hormonal injection. Cows were inseminated at a fixed time 16 h after final GnRH of Ovsynch. Pregnancy diagnosis was performed 35 d later by palpation per rectum of uterine contents. Proportion of cows that ovulated to first GnRH of Ovsynch was 56.0, 66.7, 84.6, and 53.8% for G4G, G5G, G6G, and controls, respectively, and was greater for G6G than for control cows. Luteolytic response to PGF2alpha of Ovsynch was greater in all treated than control cows (92.0, 91.7, 96.2, and 69.2% for G4G, G5G, G6G, and control, respectively). Synchronization rate to Ovsynch was greater (92 vs. 69%, respectively) in G6G than in control cows. In addition, cows that ovulated in response to first GnRH of Ovsynch had greater response to PGF2alpha of Ovsynch (92.7 vs. 77.1%, respectively) and greater synchronization rate to the overall protocol (87.9 vs. 62.9%, respectively) than those that did not ovulate. Concentrations of progesterone at PGF2alpha of Ovsynch, and estradiol and follicle size at final GnRH of Ovsynch, were identified as significant predictors of probability of pregnancy 35 d after artificial insemination. In summary, a PGF2alpha-and-GnRH based pre-Ovsynch strategy consisting of a 6-d interval between PreG and first GnRH of Ovsynch resulted in a greater ovulatory and luteolytic response to first GnRH and PGF2alpha of Ovsynch, respectively, compared with control cows. This, in turn, optimized synchronization rate to Ovsynch.

BackgroundThe success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.ResultsAverage r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.ConclusionsOur estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is currently viewed as the most precise technique to quantify levels of messenger RNA. Relative quantification compares the expression of a target gene under two or more experimental conditions normalized to the measured expression of a control gene. The statistical methods and software currently available for the analysis of relative quantification of RT-PCR data lack the flexibility and statistical properties to produce valid inferences in a wide range of experimental situations. In this paper we present a novel method for the analysis of relative quantification of qRT-PCR data, which consists of the analysis of cycles to threshold values (C(T)) for a target and a control gene using a general linear mixed model methodology. Our method allows testing of a broader class of hypotheses than traditional analyses such as the classical comparative C(T). Moreover, a simulation study using plasmode datasets indicated that the estimated fold-change in pairwise comparisons was the same using either linear mixed models or a comparative C(T) method, but the linear mixed model approach was more powerful. In summary, the method presented in this paper is more accurate, powerful and flexible than the traditional methods for analysis of qRT-PCR data. This new method is especially useful for studies involving multiple experimental factors and complex designs.

BackgroundCurrently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects.ResultsThe standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance.ConclusionsThe standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-246) contains supplementary material, which is available to authorized users.

The objective of this study was to identify prostaglandin F(2α) (PG)-induced changes in the transcriptome of bovine corpora lutea (CL) that are specific to mature, PG-responsive (day 11) CL vs. developing (day 4) CL, which do not undergo luteolysis in response to PG administration. CL were collected at 0, 4, and 24 h after PG injection on days 4 and 11 of the estrous cycle (n = 5 per day and time point), and microarray analysis was performed with GeneChip Bovine Genome Arrays. Data normalization was performed with affy package and significance testing with maanova from Bioconductor. Significance (relative to 0 h time point) was declared at fold change >2.0 or <0.5 and false discovery rate of <5%. At 4 and 24 h after PG, 221 (day 4) and 661 (day 11) and 248 (day 4) and 1,421 (day 11) regulated genes, respectively, were identified. The accentuated gene expression response in day 11 CL was accompanied by specific enrichment of PG-regulated genes in distinctive gene ontology categories (immune related and other), particularly at 24 h after injection. Specificity in putative transcription factor binding sites was observed among PG-regulated genes on day 11 vs. day 4, including a potential association of ETS transcription factors with acute PG-induced gene expression specific to day 11 CL. Temporal and PG-induced regulation of abundance of mRNA for ETS transcription factor family members linked to the stage-specific response to PG was not observed. Increased abundance of protein and/or mRNA for six PG-regulated putative ETS-responsive genes was noted in day 11 but not day 4 CL. Results reveal insight into stage-specific gene expression in bovine CL in response to PG and potential transcriptional mediators of luteolysis.