Research Note: Comparison of chicken blood chemistry and electrolyte parameters between the portable i-STAT1 clinical analyzer and VetScan VS2 serum biochemistry panel using Hy-Line commercial white-egg laying hens.

The i-STAT1 clinical analyzer has become an increasingly popular tool in clinical production animal medicine as it can provide pen-side results in a cost effective and timely manner when compared to standard benchtop serum biochemistry blood gas and chemistry analyses. This study compares the results of the portable Abbott i-STAT1 analyzer and the Abaxis VetScan VS2 for glucose (Glu, mg/dL), ionized Ca (mmol/L), Na (mmol/L), and K (mmol/L) values. Three genetically distinct commercial varieties (CV) of Hy-Line white-egg laying hens are used in this study (Hy-Line W-36, Hy-Line W-80, and Hy-Line W-80+). Thirty blood samples (n = 10 per CV) were obtained in the production house from the brachial vein and concurrently analyzed by the i-STAT1 portable device. Serum from 22 of these same samples was analyzed via VetScan VS2, a benchtop serum clinical biochemistry analyzer, using VetScan Avian/Reptilian Profile Plus reagent rotors. A paired T-test was used to test for statistical differences in means between the 2 instruments for each of the parameters. Parameters with significant mean differences were then subject to correlation and regression analysis to further evaluate relationships between the results from the 2 methods. Significant differences between means were found for Glu, Na, and K levels. Ca levels were found to be not directly comparable by the 2 analysis instruments. This comparison elucidates the importance of clinical analyzer validations when applying different strategies of diagnostic medicine in poultry.

Accuracy of genomic prediction of shell quality in a White Leghorn line.

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens.

Establishment of Hy-Line commercial laying hen whole blood gas and biochemistry reference intervals utilizing portable i-STAT1 clinical analyzer.

Blood gas and biochemistry reference intervals were established for 3 genetically distinct commercial varieties (CVs) of Hy-Line laying hens: 2 brown-egg layers (Hy-Line Brown, Hy-Line Silver Brown) and a tint-egg layer (Hy-Line Sonia) utilizing the i-STAT1 analyzer. Each respective variety of laying hen was sampled on a replicate cycle of 2 wk for a total of 6 replicates (35 to 46 wk of age). Blood samples were obtained in the production house from the brachial vein, and subsequently analyzed by the i-STAT1 portable device. i-STAT1 clinical analyzer reports blood gas and biochemistry values for the following parameters: pH, partial pressure of carbon dioxide (pvCO2, mm Hg), partial pressure of oxygen (pvO2, mm Hg), bicarbonate (HCO3, mmol/L), base excess (BE, mmol/L), saturation of oxygen on hemoglobin (sO2%), glucose (Glu, mg/dL), sodium (Na, mmol/L), potassium (K, mmol/L), total concentration of carbon dioxide (TCO2, mmol/L), ionized calcium (iCa, mmol/L), hematocrit (Hct % packed cell volume [PCV]), hemoglobin (Hb, g/dL). A total of 1,800 individual hen i-STAT1 records were utilized in the establishment of reference interval values for the 13 parameters between the 3 CVs. Statistical analysis via ANOVA and Tukey's test revealed significant line differences for all 13 blood gas and chemistry parameters measured, with particularly interesting results in iCa. The blood gas and chemistry parameters collected in this study will serve as reference intervals to set the framework for potential future correlations to genetic markers, physiological abnormalities, and production performance.

Investigating the genetic determination of clutch traits in laying hens.

Clutch traits were proposed as a more detailed description of egg-laying patterns than simple total egg production. In this study, egg production of 23,809 Rhode Island Red (RIR) and 22,210 White Leghorn (WL) hens was described in terms of number of clutches, average and maximum clutch size, age at first egg, total saleable egg production, and percentage of egg defects. Genetic parameters were estimated using a six-trait animal model. Of the phenotyped birds, 1433 RIR hens and 1515 WL hens were genotyped with line specific 50K Affymetrix Axiom single nucleotide polymorphism chips to perform genome-wide association analyses. Moderate heritabilities were estimated for clutch traits of 0.20 to 0.42 in the RIR line and 0.29 to 0.41 in the WL line. Average and maximum clutch size was positively genetically correlated with total saleable egg number in both lines. Genome-wide association analysis identified seven regions that were associated with egg production in the RIR line and 12 regions in the WL line. The regions identified were line and trait specific, except for one region on chromosome 6 from 28 to 29 Mb that influenced number of clutches and maximum and average clutch size in WL hens. Regions associated with egg production identified here overlapped with 260 genes, with some strong positional candidates based on gene ontology including WASH1, which is involved in oocyte maturation, NPVF, involved in regulation of follicle-stimulating hormone secretion, and FOXO3, involved in oocyte maturation and ovulation from the ovarian follicle. Confirmation of the role of these genes in regulation of egg production pattern will require further studies.

Genome wide association study for heat stress induced mortality in a white egg layer line.

High environmental temperature is a serious stress affecting economic and biological efficiency of poultry production in tropical and subtropical countries that is expected to become more prominent with global climate change. Iowa experienced 3 acute heat waves of 11, 3, and 4 d of heat index above 38°C in the summer of 2012, which led to production losses and increased bird mortality. For the current study, the proportion of daughters that died from heat stress during this period was calculated for 118 sire families of an elite White Leghorn layer line. The number of daughters per sire ranged from 25 to 111 and averaged 68. Average mortality due to heat stress was 8.2%, ranging from 0 to 24.6%. All sires were genotyped using a 600 K Affymetrix chip. After stringent quality filtering (clustering quality, parentage, missing genotypes, MAF) 113,344 SNPs were retained for the analysis. Method BayesB with π equal to 0.999, for the number of markers fitted not to exceed the number of observations, was applied. Markers explained 8% of the phenotypic variance. One 1-Mb window on chromosome 5 explained 1.2% of genetic variance. When the number of daughters was fitted as a weight in the analysis, the proportion of variance explained by markers dropped to 1%, but 9 1-Mb windows explained more than 1% of genetic variance on chromosomes 1, 3, 5 (the same top window as in the unweighted analysis), 9, 17, and 18. Although the support of the genomic regions associated with heat stress resistance identified in this study was not very strong, they overlapped with previously reported quantitative trait loci regions for immune response and physiological traits in chickens and contained genes that have been associated with response to heat stress in other studies. Further research is needed to validate the results.

Identifying the genetic basis for resistance to avian influenza in commercial egg layer chickens.

Two highly pathogenic avian influenza (HPAI) outbreaks have affected commercial egg production flocks in the American continent in recent years; a H7N3 outbreak in Mexico in 2012 that caused 70% to 85% mortality and a H5N2 outbreak in the United States in 2015 with over 99% mortality. Blood samples were obtained from survivors of each outbreak and from age and genetics matched non-affected controls. A total of 485 individuals (survivors and controls) were genotyped with a 600 k single nucleotide polymorphism (SNP) array to detect genomic regions that influenced the outcome of highly pathogenic influenza infection in the two outbreaks. A total of 420458 high quality, segregating SNPs were identified across all samples. Genetic differences between survivors and controls were analyzed using a logistic model, mixed models and a Bayesian variable selection approach. Several genomic regions potentially associated with resistance to HPAI were identified, after performing multidimensional scaling and adjustment for multiple testing. Analysis conducted within each outbreak identified different genomic regions for resistance to the two virus strains. The strongest signals for the Iowa H5N2 survivor samples were detected on chromosomes 1, 7, 9 and 15. Positional candidate genes were mainly coding for plasma membrane proteins with receptor activity and were also involved in immune response. Three regions with the strongest signal for the Mexico H7N3 samples were located on chromosomes 1 and 5. Neuronal cell surface, signal transduction and immune response proteins coding genes were located in the close proximity of these regions.

Repeatability vs. multiple-trait models to evaluate shell dynamic stiffness for layer chickens.

Shell quality is one of the most important traits for improvement in layer chickens. Proper consideration of repeated records can increase the accuracy of estimated breeding values and thus genetic improvement of shell quality. The objective of this study was to compare different models for genetic evaluation of the collected data. For this study, 81,646 dynamic stiffness records on 21,321 brown egg layers and 93,748 records on 24,678 white egg layers from 4 generations were analyzed. Across generations, data were collected at 2 to 4 ages (at approximately 26, 42, 65, and 86 wk), with repeated records at each age. Seven models were compared, including 5 repeatability models with increasing complexity, a random regression model, and a multitrait model. The models were compared using Akaike Information Criteria with significance testing of nested models with a Log Likelihood Ratio test. Estimates of heritability were 0.31-0.36 for the brown line and 0.23-0.26 for the white line, but repeatability was higher for the model with age-specific permanent environment effects (0.59 for both lines) than for the model with an overall permanent environmental effects (0.47 for the brown and 0.41 for the white line). The model that allowed for permanent environmental effect within age and heterogeneous residual variance between ages resulted in improved fit compared to the traditional model that fits single permanent environment and residual effects, but was inferior in fit and predictive ability to the full multiple-trait model. The random regression model had better fit to the data than repeatability models but slightly worse than the multiple-trait model. For traits with repeated records at different ages, repeatability within and across ages as well as genetic correlations should be considered while choosing the number of records collected per individual as well as the model for genetic evaluation.

Model comparison to evaluate a shell quality bio-complex in layer hens.

Reducing the incidence of egg shell breakage is an important selection goal in egg layer hens breeding. Breaking strength provides an indicator of static shell resistance correlated with shell thickness. Acoustic egg tests combine shell's resonance profile with egg mass to calculate dynamic stiffness (KDyn) a quantitative indicator of integral shell resistance, and a novel direct detection of both cracks and micro-cracks (MCr) making it possible for use in selection programs aiming improvement of shell quality. A shell quality bio-complex was defined to improve overall shell quality, including: breaking strength at equator (BSe) and poles (BSp), KDyn, and MCr, on multiple eggs/hen-age. A total of 81,667; 101,113; and 72,462 records from 4 generations of three pure lines were evaluated. Two models were tested in the brown-egg line: I) four-trait linear repeatability model and II) three-trait linear (BS, KDyn)-threshold (MCr) in the three lines. Models were implemented with AIREMLF90 and THRGIBBS1F90. Heritability and repeatability (Model I) estimates were: h2 = 0.14, 0.18, 0.33, and 0.02; r = 0.16, 0.28, 0.43, and 0.03 for BSe, BSp, KDyn, and MCr, respectively. Corresponding values in White Plymouth Rock were h2 = 0.14, 0.17, 0.33, and 0.02; r = 0.21, 0.33, 0.44, and 0.04, and in White Leghorn were h2 = 0.14, 0.23, 0.36, and 0.02; r = 0.24, 0.38, 0.52, and 0.02. Genetic correlations between BSe and BSp were between 0.51 and 0.68. The BS traits were moderately correlated with KDyn (+0.23 to +0.51), and tended to be negatively correlated with MCr. KDyn, and MCr (-0.46 to -0.62). Model II had similar results; except for increased h2 = 0.06 and r = 0.09 for MCr. Results indicate that BSe and BSp are different traits; while incidence of MCr is low in heritable but showed negative genetic correlations with the other traits. This makes MCr unsuitable for direct selection; but favors indirect selection against MCr via BSe, BSp, and KDyn for a holistic selection to improve shell quality, in particular to achieve the ultimate goal, reduction of egg breakage.

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.

Commercial Hy-Line W-36 pullet and laying hen venous blood gas and chemistry profiles utilizing the portable i-STAT®1 analyzer.

Venous blood gas and chemistry reference ranges were determined for commercial Hy-Line W-36 pullets and laying hens utilizing the portable i-STAT®1 analyzer and CG8+ cartridges. A total of 632 samples were analyzed from birds between 4 and 110 wk of age. Reference ranges were established for pullets (4 to 15 wk), first cycle laying hens (20 to 68 wk), and second cycle (post molt) laying hens (70 to 110 wk) for the following traits: sodium (Na mmol/L), potassium (K mmol/L), ionized calcium (iCa mmol/L), glucose (Glu mg/dl), hematocrit (Hct% Packed Cell Volume [PCV]), pH, partial pressure carbon dioxide (PCO2 mm Hg), partial pressure oxygen (PO2 mm Hg), total concentration carbon dioxide (TCO2 mmol/L), bicarbonate (HCO3 mmol/L), base excess (BE mmol/L), oxygen saturation (sO2%), and hemoglobin (Hb g/dl). Data were analyzed using ANOVA to investigate the effect of production status as categorized by bird age. Trait relationships were evaluated by linear correlation and their spectral decomposition. All traits differed significantly among pullets and mature laying hens in both first and second lay cycles. Levels for K, iCa, Hct, pH, TCO2, HCO3, BE, sO2, and Hb differed significantly between first cycle and second cycle laying hens. Many venous blood gas and chemistry parameters were significantly correlated. The first 3 eigenvalues explained ∼2/3 of total variation. The first 2 principal components (PC) explained 51% of the total variation and indicated acid-balance and relationship between blood O2 and CO2. The third PC explained 16% of variation and seems to be related to blood iCa. Establishing reference ranges for pullet and laying hen blood gas and chemistry with the i-STAT®1 handheld unit provides a mechanism to further investigate pullet and layer physiology, evaluate metabolic disturbances, and may potentially serve as a means to select breeder candidates with optimal blood gas or chemistry levels on-farm.

The influence of the time-of-day administration of the drug on the pharmacokinetics of sunitinib in rabbits.

OBJECTIVES: At present it is known that the adjustment of the anticancer therapy to the circadian rhythms in tissues reduces the toxicity of the treatment. Chronotherapy also increases the efficacy of the anticancer treatment, which has been proved for many drugs. Sunitinib is a tyrosine kinase inhibitor, which is broadly used for the treatment of numerous cancers. The aim of the study was a comparison of the concentrations and pharmacokinetics of sunitinib after a single administration to rabbits at 08:00 (control group) and 20:00. Additionally, the effect of sunitinib on glucose levels was investigated. MATERIALS AND METHODS: The research was carried out on two groups of rabbits: I08:00, a group with the drug administered at 08:00 (n=8) and II20:00, a group with the drug administered at 20:00 (n=8). The rabbits were treated with sunitinib at an oral dose of 25 mg. Plasma concentrations of sunitinib and its metabolite (SU12662) were measured with a validated HPLC method with UV detection. RESULTS: The comparison of the sunitinib Cmax and AUC0-t in the group with sunitinib administered at 20:00 with the control group gave the ratios of 2.20 (90% confidence interval (CI) (2.17; 2.22) and 1.64 (1.61; 1.68), respectively. Statistically significant differences between the groups under analysis were revealed for Cmax (p < 0.0001), AUC0-t (p = 0.0079), AUC0-∞ (p = 0.0149), and tmax (p = 0.0085). The mean glycemia drop was higher in group I08:00. than in group II20:00 (22.7% vs. 14.3%; p = 0.0622). The glycemia values returned to the initial values in 24 h after the administration of the drug in both groups. CONCLUSIONS: The research proved a significant influence of the time-of-day administration on the pharmacokinetics of sunitinib.

Genome-wide association study for egg production and quality in layer chickens.

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome-wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13,000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. (2012). The Bayesian whole genome prediction model--BayesB (Meuwissen et al. 2001) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non-zero effect with posterior probability > 0.9 for one or more traits.

Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens.

One approach for cost-effective implementation of genomic selection is to genotype training individuals with a high-density (HD) panel and selection candidates with an evenly spaced, low-density (ELD) panel. The purpose of this study was to evaluate the extent to which the ELD approach reduces the accuracy of genomic estimated breeding values (GEBV) in a broiler line, in which 1,091 breeders from 3 generations were used for training and 160 progeny of the third generation for validation. All birds were genotyped with an Illumina Infinium platform HD panel that included 20,541 segregating markers. Two subsets of HD markers, with 377 (ELD-1) or 766 (ELD-2) markers, were selected as ELD panels. The ELD-1 panel was genotyped using KBiosciences KASPar SNP genotyping chemistry, whereas the ELD-2 panel was simulated by adding markers from the HD panel to the ELD-1 panel. The training data set was used for 2 traits: BW at 35 d on both sexes and hen house production (HHP) between wk 28 and 54. Methods Bayes-A, -B, -C and genomic best linear unbiased prediction were used to estimate HD-marker effects. Two scenarios were used: (1) the 160 progeny were ELD-genotyped, and (2) the 160 progeny and their dams (117 birds) were ELD-genotyped. The missing HD genotypes in ELD-genotyped birds were imputed by a Gibbs sampler, capitalizing on linkage within families. In scenario (1), the correlation of GEBV for BW (HHP) of the 160 progeny based on observed HD versus imputed genotypes was greater than 0.94 (0.98) with the ELD-1 panel and greater than 0.97 (0.99) with the ELD-2 panel. In scenario (2), the correlation of GEBV for BW (HHP) was greater than 0.92 (0.96) with the ELD-1 panel and greater than 0.95 (0.98) with the ELD-2 panel. Hence, in a pedigreed population, genomic selection can be implemented by genotyping selection candidates with about 400 ELD markers with less than 6% loss in accuracy. This leads to substantial savings in genotyping costs, with little sacrifice in accuracy.

Analysis of egg production in layer chickens using a random regression model with genomic relationships.

Random regression models allow for analysis of longitudinal data, which together with the use of genomic information are expected to increase accuracy of selection, when compared with analyzing average or total production with pedigree information. The objective of this study was to estimate variance components for egg production over time in a commercial brown egg layer population using genomic relationship information. A random regression reduced animal model with a marker-based relationship matrix was used to estimate genomic breeding values of 3,908 genotyped animals from 6 generations. The first 5 generations were used for training, and predictions were validated in generation 6. Daily egg production up to 46 wk in lay was accumulated into 85,462 biweekly (every 2 wk) records for training, of which 17,570 were recorded on genotyped hens and the remaining on their nongenotyped progeny. The effect of adding additional egg production data of 2,167 nongenotyped sibs of selection candidates [16,037 biweekly (every 2 wk) records] to the training data was also investigated. The model included a 5th order Legendre polynomial nested within hatch-week as fixed effects and random terms for coefficients of quadratic polynomials for genetic and permanent environmental components. Residual variance was assumed heterogeneous among 2-wk periods. Models using pedigree and genomic relationships were compared. Estimates of residual variance were very similar under both models, but the model with genomic relationships resulted in a larger estimate of genetic variance. Heritability estimates increased with age up to mid production and decreased afterward, resulting in an average heritability of 0.20 and 0.33 for pedigree and genomic models. Prediction of total egg number was more accurate with the genomic than with the pedigree-based random regression model (correlation in validation 0.26 vs. 0.16). The genomic model outperformed the pedigree model in most of the 2-wk periods. Thus, results of this study show that random regression reduced animal models can be used in breeding programs using genomic information and can result in substantial improvements in the accuracy of selection for trajectory traits.

Pharmacokinetics and pharmacodynamics of ciprofloxacin in critically ill patients after the first intravenous administration of 400 mg.

PURPOSE: To investigate the pharmacokinetics and pharmacodynamics of ciprofloxacin in critically ill patients after the first intravenous administration of 400 mg. MATERIAL/METHODS: Plasma concentrations were measured in 20 critically ill patients (mean [SD]; age, 55.5 [16.5] years; weight, 80.3 [16.9] kg; and creatinine clearance, 110.0 [71.5] mL/min). Four blood samples were drawn at the following time points 0, 0.5, 6 , 8 hours after infusion. Ciprofloxacin concentrations were determined by high-performance liquid chromatography. RESULTS: In the cases where ciprofloxacin was applied in targeted antibiotic therapy the minimum inhibitory concentrations (MIC) were ≤0.5 mg/l. The maximum and minimum plasma concentrations of ciprofloxacin were 1.74 (0.58-7.90) and 0.45 (0.16-2.96) mg/l, respectively. The main pharmacokinetic parameters for ciprofloxacin in the analyzed patients were as follows: k(el), 0.21 h-1; t(1/2kel), 3.37 h; AUC(0-inf), 10.10 mg×h/l; AUMC(0-last), 15.36 mg×h(2)/l; MRT, 1.71 h; V(d), 214.8 l; Cl, 39.70 l/h. Considering the maximum value of MIC (0.5 mg/l) only 30% and 25% of analyzed patients had desired values of the PK/PD indexes AUIC>125 and C(max) /MIC>10, respectively. CONCLUSIONS: The target plasma concentrations after the first dose of ciprofloxacin were reached only in a few critically ill patients. Considerable inter-subject variability for PK/PD parameters in ICU patients requires systematic monitoring.

Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle.

Objectives of the current study were to estimate genetic parameters in Holstein cows for energy balance (EB) and related traits including dry matter intake (DMI), body weight (BW), body condition score (BCS), energy-corrected milk (ECM) production, and gross feed efficiency (GFE), defined as the ratio of total ECM yield to total DMI over the first 150 d of lactation. Data were recorded for the first half of lactation on 227 and 175 cows in their first or later lactation, respectively. Random regression models were fitted to longitudinal data. Also, each trait was averaged over monthly intervals and analyzed by single and multivariate animal models. Heritability estimates ranged from 0.27 to 0.63, 0.12 to 0.62, 0.12 to 0.49, 0.63 to 0.72, and 0.49 to 0.53 for DMI, ECM yield, EB, BW, and BCS, respectively, averaged over monthly intervals. Daily heritability estimates ranged from 0.18 to 0.30, 0.10 to 0.26, 0.07 to 0.22, 0.43 to 0.67, and 0.25 to 0.38 for DMI, ECM yield, EB, BW, and BCS, respectively. Estimated heritability for GFE was 0.32. The genetic correlation of EB at 10d in milk (DIM) with EB at 150 DIM was -0.19, suggesting the genetic regulation of this trait differs by stage of lactation. Positive genetic correlations were found among DMI, ECM yield, and BW averaged over monthly intervals, whereas correlations of these traits with BCS depended upon stage of lactation. Total ECM yield for the lactation was positively correlated with DMI, but a negative genetic correlation between total ECM yield and EB was found. However, the genetic correlation between total ECM yield and EB in the first month of lactation was -0.02, indicating that total production is not genetically correlated with EB during the first month of lactation, when negative EB is most closely associated with diminished fitness. The genetic correlation between GFE and EB ranged from -0.73 to -0.99, indicating that selection for more efficient cows would favor a lower energy status. However, the genetic correlation between EB in the first month of lactation and GFE calculated from 75 to 150 DIM was not significant, indicating that the unfavorable correlation between GFE and EB in early lactation may be minimized with alternative definitions of efficiency. Thus, EB, GFE and related traits will likely respond to genetic selection in Holstein cows. However, the impact of selection for improved feed efficiency on EB must be carefully considered to avoid potential negative consequences of further reductions in EB at the onset of lactation.

Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens.

The pioneering work by Professor Soller et al., among others, on the use of genetic markers to analyze quantitative traits has provided opportunities to discover their genetic architecture in livestock by identifying quantitative trait loci (QTL). The recent availability of high-density single nucleotide polymorphism (SNP) panels has advanced such studies by capitalizing on population-wide linkage disequilibrium at positions across the genome. In this study, genomic prediction model Bayes-B was used to identify genomic regions associated with the mean and standard deviation of egg weight at three ages in a commercial brown egg layer line. A total of 24,425 segregating SNPs were evaluated simultaneously using over 2900 genotyped individuals or families. The corresponding phenotypic records were represented as individual measurements or family means from full-sib progeny. A novel approach using the posterior distribution of window variances from the Monte Carlo Markov Chain samples was used to describe genetic architecture and to make statistical inferences about regions with the largest effects. A QTL region on chromosome 4 was found to explain a large proportion of the genetic variance for the mean (30%) and standard deviation (up to 16%) of the weight of eggs laid at specific ages. Additional regions with smaller effects on chromosomes 2, 5, 6, 8, 20, 23, 28 and Z showed suggestive associations with mean egg weight and a region on chromosome 13 with the standard deviation of egg weight at 26-28 weeks of age. The genetic architecture of the analyzed traits was characterized by a limited number of genes or genomic regions with large effects and many regions with small polygenic effects. The region on chromosome 4 can be used to improve both the mean and standard deviation of egg weight by marker-assisted selection.

Genetic parameters of egg defects and egg quality in layer chickens.

Genetic parameters were estimated for egg defects, egg production, and egg quality traits. Eggs from 11,738 purebred brown-egg laying hens were classified as salable or as having one of the following defects: bloody, broken, calcium deposit, dirty, double yolk, misshapen, pee-wee, shell-less, and soft shelled. Egg quality included albumen height, egg weight, yolk weight, and puncture score. Body weight, age at sexual maturity, and egg production were also recorded. Heritability estimates of liability to defects using a threshold animal model were less than 0.1 for bloody and dirty; between 0.1 and 0.2 for pee-wee, broken, misshapen, soft shelled, and shell-less; and above 0.2 for calcium deposit and double yolk. Quality and production traits were more heritable, with estimates ranging from 0.29 (puncture score) to 0.74 (egg weight). High-producing hens had a lower frequency of egg defects. High egg weight and BW were associated with an increased frequency of double yolks, and to a lesser extent, with more shell quality defects. Estimates of genetic correlations among defect traits that were related to shell quality were positive and moderate to strong (0.24-0.73), suggesting that these could be grouped into one category or selection could be based on the trait with the highest heritability or that is easiest to measure. Selection against defective eggs would be more efficient by including egg defect traits in the selection criterion, along with egg production rate of salable eggs and egg quality traits.

Genetic heterogeneity of variance in production traits of laying hens.

Genetic parameters for mean and for environmental variation in egg weight, body weight, age at first egg, and egg production were estimated in eight layer lines. The data were recorded from years 1999-2007, with on average of 6500 birds per line. An iterative mean-variance REML method was applied with a sire-plus-dam model for mean, a sire model for variance, and a fixed effect of hatch within generation for both mean and variance. The estimated heritability of environmental variation averaged about 5% for body weight, 3% for egg weight, and 11% for age at first egg, but was inconsistent among the lines for egg production (0-15%). Correlations between means and variances were slightly positive for body weight, egg weight, and age at first egg; and strongly negative for egg production. Age at first egg had the highest heritability of environmental variation; and favourable correlations with egg production and with variation in other production traits indicate that reducing variation in this trait may have potential benefits for the breeding industry.

Evaluation of egg production in layers using random regression models.

The objectives of this study were to estimate genetic parameters for egg production over the age trajectory in 3 layer lines, which represent different biotypes for egg production, and to validate the use of breeding values for slope as a measure of persistency to be used in the selection program. Egg production of more than 26,000 layers per line from 6 consecutive generations were analyzed with a random regression model with a within-hatch-nested fifth-order fixed-regression polynomial and linear polynomials for random additive genetic and permanent environmental effects. Daily records were cumulated into biweekly periods. In all lines, a nonzero genetic variance for mean and slope and a positive genetic correlation between mean and slope were estimated. Genetic variance of egg production by 2-wk period was low at the beginning of lay and increased as the birds aged for all 3 lines, which resulted in heritability estimates increasing with age. Breeding values for slope reflected the shape of the egg production curve well and can be used to directly select for persistency of egg production.