Behavioral patterns as indicators of resilience after parturition in dairy cows.

During the transition phase, dairy cows are susceptible to develop postpartum diseases. Cows that stay healthy or recover rapidly can be considered to be more resilient in comparison to those that develop postpartum diseases. An indication of loss of resilience will allow for early intervention with preventive and supportive measures before the onset of disease. We investigated which quantitative behavioral characteristics during the dry period could be used as indicators of reduced resilience after calving, using noninvasive Smart Tag neck and Smart Tag leg sensors in dairy cows (Nedap N.V.). We followed 180 cows during 2 wk before until 6 wk after parturition at 4 farms in the Netherlands. Serving as proxy for loss of resilience, as defined by the duration and severity of disease, a clinical assessment was performed twice weekly and blood samples were taken in the first and fifth week after parturition. For each cow, clinical and serum value deviations were aggregated into a total deficit score (TDS total). We also calculated TDS values relating to inflammation, locomotion, or metabolic problems, which were further divided into macro-mineral and liver-related deviations. Smart Tag neck and leg sensors provided continuous behavioral activity signals of which we calculated the average, variance, and autocorrelation during the dry period. Diurnal patterns in the behavioral activity signals were derived by fast Fourier transformation and the calculation of the nonperiodicity. To select significant predictors of resilience, we first performed a univariate analysis with TDS as dependent variable and the behavioral characteristics that were measured during the dry period, as potential predictors with cow as experimental unit. We included parity group as fixed effect and farm as random effect. Next, we performed multivariable analysis with only significant predictors, followed by a variable selection procedure to obtain a final linear mixed model with an optimal subset of predictors with parity group as fixed effect and farm as random effect. The TDS total was best predicted by average inactive time, nonperiodicity ruminating, nonperiodicity of bouts standing up and fast Fourier transformation stand still. Average inactive time was negatively correlated with average eating time, and these 2 predictors could be exchanged with only little difference in model performance. Our best performing model predicted TDS total at a cutoff level of 60 points, with a sensitivity of 79.5% and a specificity of 73.2% with a positive predicted value of 0.69 and a negative predicted value of 0.83. The models to predict the other TDS categories showed a lower predictive performance as compared with the TDS total model, which could be related to the limited sample size and therefore, low occurrence of problems within a specific TDS category. Furthermore, more resilient dairy cows are characterized by high averages of eating time with high regularity in rumination and low averages of inactive time. They reveal high regularity in standing time and transitions from lying to standing, in the dry period. These behaviors can be used as indicators of resilience and allow for preventive intervention during the dry period in vulnerable dairy cattle. However, further examination is still required to find clues for adequate intervention strategies.

Using random regression models to estimate genetic variation in growth pattern and its association with sexual maturity of Thai native chickens.

1. Genetic (co)variances and parameters between body weights (BW) across the growth trajectory were estimated using a univariate random regression (RR) animal model. The effect of growth rates (GH) on age at first egg (AFE) and egg weight at first egg (EWFE) were explored using a series of univariate and bivariate analyses. 2. Body weights were taken from Thai native chickens at hatch day to 168 days of age. The model included interactions between age with hatch nested within year and sex as fixed effects, and random effects of direct additive genetic, direct permanent environmental, maternal genetic and maternal permanent environmental effects. All random effects were fitted as regressions to animals' age via quadratic Legendre polynomials and fitting six classes of residual variances was identified as an optimal variance structure to estimate parameters. 3. Genetic and phenotypic variances for BW increased with increasing age. Estimated heritabilities for direct additive (h2 a) and maternal genetic (h2 m) effects on BW traits ranged from 0.34 to 0.54, and 0.04 to 0.06, respectively. Estimated variance ratios for direct (c2 ape) and maternal permanent environmental (c2 mpe) effects ranged from 0.19 to 0.48 and 0.10 to 0.12, respectively. Estimated correlations between weights at different ages were high for all random effects. 4. Estimated h2 a for six GH traits ranged from 0.06 to 0.28, while for AFE and EWFE these were 0.24 and 0.16, respectively. Estimated h2 m and c2 mpe were low for GH. Estimated genetic correlations between GH and AFE ranged from -0.22 to 0.02 and, between GH and EWFE, ranged from -0.05 to 0.40. These estimates suggested that selecting high GH chickens at 28 days of age can be expected to reduce AFE and to increase EWFE.

Indicators of resilience during the transition period in dairy cows: A case study.

The transition period is a demanding phase in the life of dairy cows. Metabolic and infectious disorders frequently occur in the first weeks after calving. To identify cows that are less able to cope with the transition period, physiologic or behavioral signals acquired with sensors might be useful. However, it is not yet clear which signals or combination of signals and which signal properties are most informative with respect to disease severity after calving. Sensor data on activity and behavior measurements as well as rumen and ear temperature data from 22 dairy cows were collected during a period starting 2 wk before expected parturition until 6 wk after parturition. During this period, the health status of each cow was clinically scored daily. A total deficit score (TDS) was calculated based on the clinical assessment, summarizing disease length and intensity for each cow. Different sensor data properties recorded during the period before calving as well as the period after calving were tested as a predictor for TDS using univariate analysis of covariance. To select the model with the best combination of signals and signal properties, we quantified the prediction accuracy for TDS in a multivariate model. Prediction accuracy for TDS increased when sensors were combined, using static and dynamic signal properties. Statistically, the most optimal linear combination of predictors consisted of average eating time, variance of daily ear temperature, and regularity of daily behavior patterns in the dry period. Our research indicates that a combination of static and dynamic sensor data properties could be used as indicators of cow resilience.

Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations.

With the new opportunities from DNA technology, multitier breeding schemes have the potential to become more effective and more integrated. Integrated breeding schemes can also be better adapted to account for potential genotype by environment interactions (G × E) between tiers. In this case, phenotypic and genotype information from lower tiers becomes more valuable as it involves measurement of traits that directly represent the breeding objective. The objective of this study was to compare scenarios that represented different selection strategies and their economic effectiveness in fine-wool commercial sheep operations that exploit multitier breeding structures. Genomic selection (GS) applied in the multiplier and the commercial tier presented the largest additional revenue among all scenarios, as it resulted in the largest amount of genetic progress. The largest benefits from GS were outweighed by the genotyping costs, which made DNA parentage the most feasible strategy for the multiplier tier, resulting in the highest cumulative net present value (CNPV). The benefits of phenotypes and genotype information from the commercial environment were larger in the presence of G × E between the nucleus and the commercial tier. The CNPV was larger with a 50% reduction in genotyping costs, which increased the returns of GS scenarios by 2.7-fold on average. Higher selection intensity when selecting multiplier rams also resulted in larger benefits. In this case, returns for the breeding scheme were 3.5-fold higher when 33% of multiplier males were selected based on commercial information, compared to scenarios selecting 50% of the available multiplier rams. The benefits of collecting commercial phenotypes and genotypes were long term, which means that return on investment often took more than 10 years to be achieved, and were largely dependent on two-stage selection to reduce cost while maintaining selection efficiency and on the cost of a genotype test.

MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.

UNLABELLED: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. AVAILABILITY AND IMPLEMENTATION: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2 CONTACT: hong.lee@une.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic prediction of reproduction traits for Merino sheep.

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations.

The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross-bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on "average information restricted maximum likelihood" using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10-fold cross-validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross-bred population. In the combined cross-bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross-bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross-bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross-bred population could be overestimated if heterosis is not fitted in the model.

Contrast effects and sex influence maternal and self-report dimensional measures of attention-deficit hyperactivity disorder.

The heritability of attention-deficit/hyperactivity disorder (ADHD) is higher for children than adults. This may be due to increasing importance of environment in symptom variation, measurement inaccuracy when two raters report behavior of a twin-pair, a contrast effect resulting from parental comparison of siblings and/or dimensionality of measures. We examine rater contrast and sex effects in ADHD subtypes using a dimensional scale and compare the aetiology of self, versus maternal-report. Data were collected using the Strengths and Weaknesses of ADHD and Normal Behaviour Scale (SWAN): maternal-report for 3,223 twins and siblings (mean age 21.2, SD = 6.3) and self-report for 1,617 twins and siblings (mean age 25.5, SD = 3.2). Contrast effects and magnitude of genetic and environmental contributions to variance of ADHD phenotypes (inattention, hyperactivity-impulsivity, combined behaviours) were examined using structural equation modeling. Contrast effects were evident for maternal-report hyperactivity-impulsivity (b = -0.04) and self-report inattention (-0.09) and combined ADHD (-0.08). Dominant genetic effects were shared by raters for inattention, hyperactivity-impulsivity and combined ADHD. Broad-sense heritability was equal across sex for maternal-report inattention, hyperactivity-impulsivity and combined ADHD (0.72, 0.83, 0.80). Heritability for corresponding subtypes in self-reported data were best represented by sex (0.46, 0.30, 0.39 for males; 0.69, 0.41, 0.65 for females). Heritability difference between maternal and self-report ADHD was due to greater variance of male specific environment in self-report data. Self-reported ADHD differed across sex by magnitude of specific environment and genetic effects.

Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy.

Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.

Random regression test day models to estimate genetic parameters for milk yield and milk components in Philippine dairy buffaloes.

Heritabilities and genetic correlations for milk production traits were estimated from first-parity test day records on 1022 Philippine dairy buffalo cows. Traits analysed included milk (MY), fat (FY) and protein (PY) yields, and fat (Fat%) and protein (Prot%) concentrations. Varying orders of Legendre polynomials (Leg(m)) as well as the Wilmink function (Wil) were used in random regression models. These various models were compared based on log likelihood, Akaike's information criterion, Bayesian information criterion and genetic variance estimates. Six residual variance classes were sufficient for MY, FY, PY and Fat%, while seven residual classes for Prot%. Multivariate analysis gave higher estimates of genetic variance and heritability compared with univariate analysis for all traits. Heritability estimates ranged from 0.25 to 0.44, 0.13 to 0.31 and 0.21 to 0.36 for MY, FY and PY, respectively. Wilmink's function was the better fitting function for additive genetic effects for all traits. It was also the preferred function for permanent environment effects for Fat% and Prot%, but for MY, FY and PY, the Legm was the appropriate function. Genetic correlations of MY with FY and PY were high and they were moderately negative with Fat% and Prot%. To prevent deterioration in Fat% and Prot% and improve milk quality, more weight should be applied to milk component traits.

Genetic parameters for egg production traits in purebred and hybrid chicken in a tropical environment.

Genetic parameters were estimated for 5 economically important egg production traits using records collected over 9 years in chickens reared under tropical conditions in Thailand. The data were from two purebred lines and two hybrid lines of layer parent stocks. The two purebred lines were Rhode Island Red (RIR) and White Plymouth Rock (WPR) and the hybrid lines were formed by crossing a commercial brown egg laying strain to Rhode Island Red (RC) and White Plymouth Rock (WC), respectively. Five egg production traits were analysed, including age at first egg (AFE), body weight at first egg (BWT), egg weight at first egg (EWFE), number of eggs from the first 17 weeks of lay (EN) and average egg weight over the 17th week of lay (EW). Fixed effects of year and hatch within year were significant for all 5 traits and were included in the model. Maternal genetic and permanent environmental effects of the dam were not significant, except for EN and EW in RIR and BWT and EW in WPR. Estimated heritability of AFE, BWT, EWFE, EN and EW were 0.45, 0.50, 0.29, 0.19 and 0.43 in RIR; 0.44, 0.38, 0.33, 0.20 and 0.38 in WPR; 0.37, 0.41, 0.38, 0.18 and 0.36 in RC; and 0.46, 0.53, 0.36, 0.38 and 0.45 in WC lines, respectively. The EN was negatively correlated with other traits, except for BWT in RC and AFE and BWT in WC. It was concluded that selection for increased EN will reduce other egg production traits in purebred and hybrid chicken and therefore EN needs to be combined with other egg production traits in a multi-trait selection index to improve all traits optimally according to a defined breeding objective.

Identifying factors hampering physical activity in longstanding rheumatoid arthritis: what is the role of glucocorticoid therapy?

OBJECTIVES: To identify factors hampering the level of physical activity in longstanding rheumatoid arthritis (RA) patients, and to evaluate the effects of glucocorticoid therapy on physical activity. METHODS: Patient characteristics, disease characteristics and cardiovascular parameters were recorded in 170 patients, who participated in a study about glucose metabolism in longstanding RA treated with or without glucocorticoids. Disease activity scores (DAS28) were calculated and x-rays of hands and feet were taken and scored according to the Sharp van der Heijde score (SHS). Participants completed the health assessment questionnaire and short questionnaire to assess health-enhancing physical activity (SQUASH), which reflect physical disability and physical activity, respectively. Adherence rates to recommendations on physical activity were calculated, and patients were categorised as fully adhering, insufficiently adhering (adherence on less than the recommended number of days per week) or inactive (adherence on none of the days). RESULTS: Forty-four percent of the patients showed adherence to the recommended minimum level of physical activity, and 22% were classified as inactive. Higher DAS28 and SHS, glucocorticoid therapy, and presence of cardiovascular risk factors were associated with lower total SQUASH physical activity scores univariately. In a multivariate model, higher age, higher body mass index (BMI), higher DAS28, and higher SHS negatively influenced the score significantly; cardiovascular risk factors and glucocorticoid therapy were no longer significantly influencing physical activity. CONCLUSIONS: Physical activity in longstanding RA is hampered by higher age, higher BMI, higher disease activity, and more radiographic joint damage. Glucocorticoid therapy was not identified as independent risk factor in multivariate analyses.

Reorganization of oscillatory activity in human parietal cortex during spatial updating.

Single-neuron recordings have shown that the posterior parietal cortex (PPC) processes spatial information in many frames of reference, including gaze-centered, head-centered, body-centered, and intermediate coding frames. At the population level, rhythmic neuronal synchronization may provide a mechanism by which PPC could selectively emphasize the task-relevant reference frame in spatial processing. Using magnetoencephalography, we tested this hypothesis by studying the modulations in oscillatory activity in a spatial updating task. Human subjects had to remember the location of a target, briefly flashed left or right of central fixation. Next, they refixated and then, after a further memory delay, made a saccade to the memorized target location. We observed gamma-band (>40 Hz) synchronization and alpha-band (8-12 Hz) desychronization in contralateral occipital and parietal areas, both showing updating in a gaze-centered reference frame but with fast and slow time courses, respectively. Furthermore, after updating, ipsilateral areas showed less alpha desynchronization when they had been contralateral to the target before updating. Taken together, our results suggest that power in the gamma band is instantly reorganized to encode task-relevant visuomotor space in a gaze-centered reference frame, while power in the alpha band reflects a regulatory mechanism actively facilitating the gating of the saccade target and inhibiting the original stimulus representation.

Macro- and micro-genetic environmental sensitivity of yearling weight in Angus beef cattle.

Australian beef cattle experience variable conditions, which may give rise to genotype-by-environment interactions depending on the genotypes' macro- and/or micro-genetic environmental sensitivity (GES). Macro-GES gives rise to genotype-by-environment interactions across definable and shared environments, while micro-GES causes heritable variation of phenotypes, e.g., the performance of progeny from one sire may be more variable than other sires. Yearling weight (YW) is a key trait in Australian Angus cattle that may be impacted by both macro- and micro-GES. Current models for genetic evaluation of YW attempt to account for macro-GES by fitting sire-by-herd interactions (S × H). Variation in micro-GES had not yet been estimated for YW in Australian Angus. The aim of this study was to estimate genetic variation due to macro- and micro-GES in YW of Australian Angus cattle. A reaction norm with contemporary group effects as the environmental covariate was fitted either as an alternative to or in combination with a random S × H effect to account for macro-GES. Double hierarchical generalised linear models (DHGLM), fitted as sire models, were used to estimate the genetic variance of the dispersion as a measure of micro-GES. Variation due to both macro- and micro-GES were found in YW. The variance of the slope of the reaction norm was 0.02-0.03 (SEs 0.00), while the S × H variance accounted for 7% of the phenotypic variance in all models. Results showed that both a random S × H effect and a reaction norm should be included to account for both macro-GES and the additional variation captured by an S × H effect. The heritability of the dispersion on the measurement scale ranged from 0.06 to 0.10 (SEs 0.00) depending on which model was used. It should therefore be possible to alter both macro- and micro-GES of YW in Australian Angus through selection. However, care should be taken to ensure an appropriate data structure when including sire-by-herd interactions in the mean part of a DHGLM; otherwise, it can cause biased estimates of micro-GES.

Short communication: Accuracy of whole-genome sequence imputation in Angus cattle using within-breed and multi breed reference populations.

Genotype imputation is a standard approach used in the field of genetics. It can be used to fill in missing genotypes or to increase genotype density. Accurate imputed genotypes are required for downstream analyses. In this study, the accuracy of whole-genome sequence imputation for Angus beef cattle was examined using two different ways to form the reference panel, a within-breed reference population and a multi breed reference population. A stepwise imputation was conducted by imputing medium-density (50k) genotypes to high-density, and then to the whole genome sequence (WGS). The reference population consisted of animals with WGS information from the 1 000 Bull Genomes project. The within-breed reference panel comprised 396 Angus cattle, while an additional 2 380 Taurine cattle were added to the reference population for the multi breed reference scenario. Imputation accuracies were variant-wise average accuracies from a 10-fold cross-validation and expressed as concordance rates (CR) and Pearson's correlations (PR). The two imputation scenarios achieved moderate to high imputation accuracies ranging from 0.896 to 0.966 for CR and from 0.779 to 0.834 for PR. The accuracies from two different scenarios were similar, except for PR from WGS imputation, where the within-breed scenario outperformed the multi breed scenario. The result indicated that including a large number of animals from other breeds in the reference panel to impute purebred Angus did not improve the accuracy and may negatively impact the results. In conclusion, the imputed WGS in Angus cattle can be obtained with high accuracy using a within-breed reference panel.

Are changes in bone mineral density different between groups of early rheumatoid arthritis patients treated according to a tight control strategy with or without prednisone if osteoporosis prophylaxis is applied?

UNLABELLED: Addition of 10 mg prednisone daily to a methotrexate-based tight control strategy does not lead to bone loss in early rheumatoid arthritis (RA) patients receiving preventive treatment for osteoporosis. A small increase in lumbar bone mineral density (BMD) during the first year of treatment was recorded, regardless of use of glucocorticoids. INTRODUCTION: This study aims to describe effects on BMD of treatment according to EULAR guidelines with a methotrexate-based tight control strategy including 10 mg prednisone daily versus the same strategy without prednisone in early RA patients who received preventive therapy for osteoporosis. METHODS: Early RA patients were included in the CAMERA-II trial: a randomized, placebo-controlled, double-blind 2-year trial, in which effects of addition of 10 mg prednisone daily to a methotrexate-based tight control strategy were studied. All patients received calcium, vitamin D and bisphosphonates. Disease activity was assessed every 4 weeks. Radiographs of hands and feet and dual-energy X-ray absorptiometry of lumbar spine and left hip were performed at baseline and after 1 and 2 years of treatment. RESULTS: BMD increased significantly over time in both treatment groups at the lumbar spine with a mean of 2.6% during the first year (p<0.001), but not at the hip; at none of the time points did BMD differ significantly between the prednisone and placebo group. Higher age and lower weight at baseline and higher disease activity scores during the trial, but not glucocorticoid therapy, were associated with lower BMD at both the lumbar spine and the hip in mixed-model analyses. CONCLUSION: Addition of 10 mg prednisone daily to a methotrexate-based tight control strategy does not lead to bone loss in early RA patients on bisphosphonates. A small increase in lumbar BMD during the first year of treatment was found, regardless of use of glucocorticoids.

Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows.

Lameness is a major problem in modern dairy husbandry and has welfare implications and other negative consequences. The behavior of dairy cows is influenced by lameness. Automated lameness detection can, among other methods, be based on day-to-day variation in animal behavior. Activity sensors that measure lying time, number of lying bouts, and other parameters were used to record behavior per cow per day. The objective of this research was to develop and validate a lameness detection model based on daily activity data. Besides the activity data, milking data and data from the computerized concentrate feeders were available as input data. Locomotion scores were available as reference data. Data from up to 100 cows collected at an experimental farm during 23 mo in 2010 and 2011 were available for model development. Behavior is cow-dependent, and therefore quadratic trend models were fitted with a dynamic linear model on-line per cow for 7 activity variables and 2 other variables (milk yield per day and concentrate leftovers per day). It is assumed that lameness develops gradually; therefore, a lameness alert was given when the linear trend in 2 or more of the 9 models differed significantly from zero in a direction that corresponded with lameness symptoms. The developed model was validated during the first 4 mo of 2012 with almost 100 cows on the same farm by generating lameness alerts each week. Performance on the model validation data set was comparable with performance on the model development data set. The overall sensitivity (percentage of detected lameness cases) was 85.5% combined with specificity (percentage of nonlame cow-days that were not alerted) of 88.8%. All variables contributed to this performance. These results indicate that automated lameness detection based on day-to-day variation in behavior is a useful tool for dairy management.

Genetic correlations for foal and studbook traits with racing traits and implications for selection strategies in the Finnhorse and Standardbred trotter.

Genetic correlations for body measurements and conformation and functional traits in foals and studbook horses with racing traits were estimated in the Finnhorse and Standardbred. Genetic response and accuracy were estimated using records of animal, half-sibs and parents in selection scenarios for racing traits, for foal and racing traits, for studbook and racing traits, and using records of animal, half-sibs and parents for foal traits and racing traits of parents. Racing time and earnings were the breeding objective. Low-to-moderate genetic correlations for body measurements and racing traits indicated that selection favours bigger horses at all ages. Being mainly favourable for the breeding objective, genetic correlations for conformation and functional traits with racing traits were highest for the foal traits of type, trot and overall grade and for the studbook traits of character and movements. Genetic correlations for foal and studbook conformation with racing traits were low in the Finnhorse and moderate to high in the Standardbred. In foals, the highest genetic correlations were for trot with racing time (-0.54) and with earnings (0.52) in the Finnhorse, and for overall grade with racing time (-0.54) and with earnings (0.54) in the Standardbred. In studbook horses, genetic correlations were high for character with racing time and earnings in the Finnhorse (-0.68, 0.61) and in the Standardbred (-0.63, 0.70), and for movements with racing time and earnings in the Finnhorse (-0.70, 0.69) and in the Standardbred (-0.90, 0.88). To increase accuracy of conformation and functional traits, foal traits would be more useful in the index with racing traits, as being less preselected than studbook traits. The foal traits (type, trot, overall grade) having moderate heritability and genetic correlations with racing traits would be useful in multi-trait index before a racing career, where the greatest gain is because of a shorter generation interval. It would be feasible to implement for AI stallions.

Genomic evaluations using similarity between haplotypes.

Long-range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off-diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of individuals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype-based prediction methods was not higher than those based on unphased genotype information.