Biomedical subjects

P Madsen

Publications and source records attributed to P Madsen.

At least 19 recordsLinked to original sources

PubMed2006

Bayesian analysis of the linear reaction norm model with unknown covariates.

The reaction norm model is becoming a popular approach for the analysis of genotype x environment interactions. In a classical reaction norm model, the expression of a genotype in different environments is described as a linear function (a reaction norm) of an environmental gradient or value. An environmental value is typically defined as the mean performance of all genotypes in the environment, which is usually unknown. One approximation is to estimate the mean phenotypic performance in each environment and then treat these estimates as known covariates in the model. However, a more satisfactory alternative is to infer environmental values simultaneously with the other parameters of the model. This study describes a method and its Bayesian Markov Chain Monte Carlo implementation that makes this possible. Frequentist properties of the proposed method are tested in a simulation study. Estimates of parameters of interest agree well with the true values. Further, inferences about genetic parameters from the proposed method are similar to those derived from a reaction norm model using true environmental values. On the other hand, using phenotypic means as proxies for environmental values results in poor inferences.

Bayes Theorem↗

PubMed2006

Short communication: genetic correlation between test-day electrical conductivity of milk and mastitis.

Electrical conductivity (EC) of milk is an indicator of mastitis. If EC shows genetic variation and is genetically correlated to mastitis, it could be used in a breeding program that includes selection for improved mastitis resistance. In this study, daily records of EC and mastitis from about 1,500 Holstein cows were analyzed. A bivariate animal model was used for estimation of (co)variance components, including fixed effects of age of calving, herd-test-day, and days in milk, in addition to random additive genetic effects and permanent environmental effects. For EC, the estimated heritability was moderate (0.22 to 0.39), whereas for mastitis, the heritability was low (0.013). The genetic correlation between EC and mastitis was estimated to be 0.75, and genetic improvement of mastitis resistance should be feasible through selection for reduced EC.

Analysis of Variance↗

PubMed2006

Udder health shows inbreeding depression in Danish Holsteins.

The purpose of this study was to estimate inbreeding depression for udder health using records on mastitis incidence and somatic cell count (SCC). Data were selected based on a pedigree completeness index so that inbreeding coefficients were reliable. Average inbreeding was 3.3%. The presence or absence of mastitis was considered in first (CM1), second (CM2), and third (CM3) lactations, and was recorded as a binary trait. Somatic cell count was also included and analyzed on the log scale. A minimum of 140,000 cows with records were included per trait. Linear sire models were used and (co)variance components were estimated. Linear and curvilinear effects of the coefficient of inbreeding were included. Inbreeding significantly affected all traits investigated. Three traits, CM1, CM3, and SCC, showed a nonlinear relationship between phenotype and inbreeding coefficient. A cow with 5% inbreeding compared with a cow with 2% inbreeding had a higher SCC and a higher incidence of mastitis. The SCC increased by 1,500 cells/mL in first lactation and the incidence of mastitis increased by 1.08, 0.55, and 0.98% in first, second, and third lactation, respectively. The corresponding reduction in net return from these traits over 3 lactations amounted to 11.00 US dollars under Danish production circumstances. These results are a step on the way to estimating the total cost of inbreeding depression to determine an acceptable rate of inbreeding.

Animals↗

PubMed2005

SorCS3 does not require propeptide cleavage to bind nerve growth factor.

The functional properties of the Vps10p-domain receptor SorCS3 are undescribed. Here, we examine its processing and sorting in cellular transfectants, and analyze the binding of potential ligands to the purified receptor. We show that SorCS3 is synthesized as a proprotein and converted to its mature form by N-terminal propeptide cleavage in distal Golgi compartments. The propeptide is not a requirement for normal processing of the receptor and does not prevent ligands from binding to the SorCS3 precursor form. Expression of wt and chimeric receptors further suggests that SorCS3 predominates on the plasma membrane, exhibits slow internalization and does not engage in intracellular trafficking. SorCS3 emerges as a new neurotrophin binding Vps10p-domain receptor functionally distinct from its relatives Sortilin and SorLA.

Animals↗

PubMed2005

A case of the birth and death of a high reliability healthcare organisation.

High reliability organisations (HROs) are those in which errors rarely occur. To accomplish this they conduct relatively error free operations over long periods of time and make consistently good decisions resulting in high quality and reliability. Some organisational processes that characterise HROs are process auditing, implementing appropriate reward systems, avoiding quality degradation, appropriately perceiving that risk exists and developing strategies to deal with it, and command and control. Command and control processes include migrating decision making, redundancy in people or hardware, developing situational awareness, formal rules and procedures, and training. These processes must be tailored to the specific organisation implementing them. These processes were applied to a paediatric intensive care unit (PICU) where care was derived from problem solving methodology rather than protocol. After a leadership change, the unit returned to the hierarchical medical model of care. Important outcome variables such as infant mortality, patient return to the PICU after discharge, days on the PICU, air transports, degraded. Implications for clinical practice include providing caregivers with sufficient flexibility to meet changing situations, encouraging teamwork, and avoiding shaming, naming, and blaming.

Adolescent↗

PubMed2005

A Bayesian threshold-normal mixture model for analysis of a continuous mastitis-related trait.

Mastitis is associated with elevated somatic cell count in milk, inducing a positive correlation between milk somatic cell score (SCS) and the absence or presence of the disease. In most countries, selection against mastitis has focused on selecting parents with genetic evaluations that have low SCS. Univariate or multivariate mixed linear models have been used for statistical description of SCS. However, an observation of SCS can be regarded as drawn from a 2- (or more) component mixture defined by the (usually) unknown health status of a cow at the test-day on which SCS is recorded. A hierarchical 2-component mixture model was developed, assuming that the health status affecting the recorded test-day SCS is completely specified by an underlying liability variable. Based on the observed SCS, inferences can be drawn about disease status and parameters of both SCS and liability to mastitis. The prior probability of putative mastitis was allowed to vary between subgroups (e.g., herds, families), by specifying fixed and random effects affecting both SCS and liability. Using simulation, it was found that a Bayesian model fitted to the data yielded parameter estimates close to their true values. The model provides selection criteria that are more appealing than selection for lower SCS. The proposed model can be extended to handle a wide range of problems related to genetic analyses of mixture traits.

Animals↗

PubMed2005

Prior (co)variances can improve multiple-trait across-country evaluations of weakly linked bull populations.

National genetic evaluation results for fore udder attachment from 9 Ayrshire populations were used to assess the impact of different uses of prior genetic correlations in multiple-trait across-country evaluations (MACE) on predicted international genetic merit. These Ayrshire populations were poorly connected; that is, 2% of the bulls had evaluations in 2 or more countries. Genetic correlations from the Holstein populations in the same countries were used as prior information to improve inferences of location parameters and international genetic merits. Fully Bayesian analyses using Gibbs sampling and computationally less demanding traditional MACE assuming a weighted average of prior and estimated Ayrshire genetic correlations were compared for 3 different prior degrees of belief and for different groups of bulls. Posterior means of genetic correlations estimated by Gibbs sampling were on average higher (+0.2) than those estimated by REML. Posterior heritabilities differed up to 0.2 units from those assumed in national genetic evaluations. Predicted genetic merit and international sire rankings of bulls with daughter information in the country of interest were not affected substantially by method of analysis and even less by varying prior degree of belief. Method of analysis had a larger impact on predicted genetic merit for bulls without daughter information in the country of interest. Here the average correlation between predicted genetic merit in different analyses ranged from 0.62 to 0.99. The predictive ability for young and randomly chosen bulls favored Bayesian MACE. The prior degree of belief did not have much impact on sire rankings and predictive ability, but intermediate prior degree of belief tended to perform best. All MACE analyses yielded nearly unbiased predictions. Traditional MACE assuming a simple weighted average of prior and estimated Ayrshire genetic correlations has been implemented by Interbull for routine international genetic evaluations.

Analysis of Variance↗

PubMed2005

Short communication: Difficulties in estimating across-country genetic correlations for weakly linked bull populations.

Genetic correlations across 9 weakly linked bull populations were estimated with various models, methods, and data sets. Estimates of the same genetic correlation differed noticeably depending on estimation strategy (up to 1.03 units) for country-pairs with no or few direct genetic links. Differences in estimated genetic correlations decreased with an increasing number of genetic ties between countries, and, essentially, no differences were found between estimated genetic correlations based on 61 common bulls, regardless of the estimation strategy used.

Analysis of Variance↗

PubMed2004

Genetic parameters for test-day electrical conductivity of milk for first-lactation cows from random regression models.

Electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis during the last few decades. The correlation of EC to mastitis, easy access to EC data, and the low cost of recording are properties that make EC a good indicator trait for mastitis. In this study, EC was measured daily during the lactation and available from 2101 first-lactation Holstein cows in 8 herds in the United States. Data were analyzed with an animal model that included herd-test-day, age at calving and days in milk (DIM) as fixed effects, and random additive genetic and permanent environmental effects. A repeatability model and 5 random regression (RR) models with increasing order of Legendre polynomials were used. The goodness of fit for the different models was evaluated based on several tests. Our results indicate that the best model was a RR model with a fourth-order Legendre polynomial for both additive genetic and permanent environmental effects. Heritability estimates obtained with this model were from 0.26 to 0.36. Due to the relatively high heritability obtained for EC of milk, EC might be a potential indicator trait to use in a breeding program designed to reduce the incidence of mastitis.

Animals↗

PubMed2003

Genetic parameters of postnatal mortality in Danish Holstein calves.

The objective of this study was to estimate genetic parameters of postnatal mortality (PM) in dairy cattle. Data originated from 841,921 Danish Holstein calves. Four binary traits of mortality were considered: D1-14, D15-60, D61-180, and D1-180 with numbers indicating the period of risk in days after birth. The unadjusted frequency of D1-14, D15-60, D61-180, and D1-180 were 0.027, 0.018, 0.020, and 0.066, respectively. A linear sire-model was fitted to the data, and average information-REML was used to estimate (co)variance components. Estimates of direct heritabilities for the four mortality traits ranged from 0.001 to 0.008 but were all significant. D61-180 and D1-180 had the highest direct heritabilities. Maternal heritabilities were very low, ranging from 0.0002 to 0.0015 and significant for D1-14 and D1-180 only. The direct genetic correlation between D1-14 and D15-60, between D15-60 and D61-180, and between D1-14 and D61-180 was 0.73, 0.54, and 0.34, respectively. It indicates that different genes are responsible for early PM (D1-14) and late PM (D61-180). When D61-180 was treated as a different trait for females, males not transferred, and transferred males, the direct heritability was 0.004, 0.008, and 0.034, respectively, but the direct genetic correlations between these three traits were very high. If transfers of calves are getting more common, the importance of including PM in a breeding program will increase, as the genetic variation of PM was considerably higher for transferred calves than for calves that were not transferred.

Aging↗

PubMed2003

Bayesian estimates of covariance components between lactation curve parameters and disease liability in Danish Holstein cows.

In the present work, covariance components for milk yield and disease liability were estimated with bivariate random regression test-day sire models using a Bayesian approach and implemented via the Gibbs sampler. The data consist of 8075 first-parity Danish Holstein (DH) cows, from 1259 sires, performing in 57 herds from 1992 to 1997. Treatments associated with five different type of diseases were pooled into a single general disease liability for each cow. Two models were fitted to the data. First, using a bivariate model, milk yield is modeled via a random regression, and disease liability via a repeatablility model. Second, using a bivariate model, both milk yield and disease liability are modeled using random regressions. A comparison based on a Bayes factor provides very strong support for the bivariate random regression model. Posterior means of heritabilities for each of the traits were estimated for five different points in time throughout lactation. Across models, heritabilities for milk yield are lowest in the beginning of the lactation (0.19) and highest at the end of the lactation (0.35). Posterior means of heritabilities of disease liability range from 0.04 to 0.10 for test days, and is equal to 0.20 for the whole lactation. Heritability of persistency measures estimated from the two models are 0.20 and 0.21. Estimates of posterior means of genetic correlations between single test-day milk yield and single test-day disease liability are in the range of 0.31 to 0.57. The estimates of posterior mean and of the 95% posterior interval of the genetic correlation between persistency and (total) disease liability using the model with the highest posterior probability are -0.12 and (-0.44; 0.20), respectively. Even though the largest proportion of the posterior probability mass is spread along negative values of the correlation (indicating that individuals with a flatter lactation curve tend to have lower disease liability), a value of zero of the genetic correlation falls comfortably within the 95% posterior interval. Thus the prospects of reducing incidence of disease by manipulating persistency as defined in this work remain inconclusive.

Animals↗

PubMed2003

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling.

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

Animals↗

PubMed2003

Genetic relationship between body condition score, dairy character, mastitis, and diseases other than mastitis in first-parity Danish Holstein cows.

The aim of this study was to explore the possibilities of using body condition score (BCS) or dairy character (DC) as indicators of mastitis and diseases other than mastitis in first-parity Danish Holsteins. The dataset included 28,948 observations on conformation scores and 365,136 disease observations. The analysis was performed using a multitrait linear sire model. Heritability estimates for BCS and DC were moderate (0.25 and 0.22), and heritability estimates for mastitis and diseases other than mastitis were low (0.038 and 0.022). Between BCS and diseases other than mastitis, the genetic correlation was -0.22, whereas the genetic correlation was -0.16 between BCS and mastitis. The genetic correlation between DC and diseases other than mastitis was 0.43, and between DC and mastitis it was 0.27. The genetic correlation between BCS and DC was -0.61. Residual correlations were close to 0, except between BCS and DC (-0.37). Including DC as an indicator of diseases other than mastitis will increase the accuracy of the predicted breeding value for diseases, especially when the progeny group is small. Using BCS as an additional indicator of diseases did not increase the accuracy. Breeding for less DC will increase resistance to diseases.

Animals↗

PubMed2003

Genetic analysis of somatic cell score in Norwegian cattle using random regression test-day models.

The dataset used in this analysis contained a total of 341,736 test-day observations of somatic cell scores from 77,110 primiparous daughters of 1965 Norwegian Cattle sires. Initial analyses, using simple random regression models without genetic effects, indicated that use of homogeneous residual variance was appropriate. Further analyses were carried out by use of a repeatability model and 12 random regression sire models. Legendre polynomials of varying order were used to model both permanent environmental and sire effects, as did the Wilmink function, the Lidauer-Mäntysaari function, and the Ali-Schaeffer function. For all these models, heritability estimates were lowest at the beginning (0.05 to 0.07) and higher at the end (0.09 to 0.12) of lactation. Genetic correlations between somatic cell scores early and late in lactation were moderate to high (0.38 to 0.71), whereas genetic correlations for adjacent DIM were near unity. Models were compared based on likelihood ratio tests, Bayesian information criterion, Akaike information criterion, residual variance, and predictive ability. Based on prediction of randomly excluded observations, models with 4 coefficients for permanent environmental effect were preferred over simpler models. More highly parameterized models did not substantially increase predictive ability. Evaluation of the different model selection criteria indicated that a reduced order of fit for sire effects was desireable. Models with zeroth- or first-order of fit for sire effects and higher order of fit for permanent environmental effects probably underestimated sire variance. The chosen model had Legendre polynomials with 3 coefficients for sire, and 4 coefficients for permanent environmental effects. For this model, trajectories of sire variance and heritability were similar assuming either homogeneous or heterogeneous residual variance structure.

Animals↗

PubMed2003

Genetic analysis of body condition score in first-parity Danish Holstein cows.

The aim of this study was to test whether genetic components for body condition score (BCS) changed during lactation in first-parity Danish Holsteins. Data were extracted from the national conformation scoring system and consisted of 28,948 records from 3894 herds. Cows were scored once during lactation for BCS on a scale from 1 to 9 with increments of 1. The majority of records were made from d 30 to 150 of lactation. Mean BCS was 4.28 +/- 0.98. Body condition score was lowest in wk 8 to 10 from calving. A multivariate sire model with BCS recordings in six lactation stages treated as different traits was used to analyze the data. In addition, a random regression sire model was used to evaluate the changes in BCS as continuous functions of lactation stage. Estimates of heritability from the multivariate approach ranged from 0.14 to 0.29, and the estimated genetic correlations between BCS at different lactation stages were all higher than 0.82. The random regression model was based on Legendre polynomials (LP) specified on days in milk at scoring. To evaluate the change in mean BCS during lactation, the fixed part of the model included a fifth-order LP on the effect of days in milk at scoring. The highest order of fit used for the sire effect was a third-order LP, but based on likelihood ratio tests this could be reduced to a 0 order, i.e., a model with only the intercept term for the sire effect. This means that the genetic variation is constant over the investigated part of the lactation. Therefore, BCS can be considered the same trait during lactation, and a simple sire model can be used for prediction of breeding values.

Adipose Tissue↗

PubMed2002

Identification of two novel and potent competitive inhibitors of the glucose-6-phosphatase catalytic protein.

AIM: In this study, we show that inhibitors of the glucose-6-phosphatase (G-6-Pase) catalytic protein could be an alternative approach to the recent G-6-Pase T1-translocase inhibitors to target this enzyme for the treatment of type 2 diabetes. METHOD: The active enantiomers of 4-methoxyphenyl-[4-(4-methoxyphenyl)-4,5,6,7-tetrahydrothieno[3,2-c]pyridin-5-yl]methanone (Compound A-1) and 4-methoxyphenyl-[4-(4-trifluoromethoxyphenyl)-4,5,6,7-tetrahydrothieno[3,2-c]pyridin-5-yl]methanone (Compound B-1) were characterized as inhibitors of the G-6-Pase catalytic protein using pig and rat liver microsomes and cultured rat hepatocytes. RESULTS: Both compounds were found to be potent competitive inhibitors of the G-6-Pase catalytic protein obtained from pig and rat liver microsomes. The K(i) values (microM) were calculated to be 0.61 +/- 0.02 and 0.63 +/- 0.08 for compound A-1 and B-1 on intact pig microsomes, and 0.27 +/- 0.02 and 0.29 +/- 0.06 on disrupted pig microsomes. The corresponding values for rat liver microsomes were found to be 3.3 +/- 0.6 and 4.0 +/- 1.2 for compound A-1 and B-1 on intact microsomes, and 1.54 +/- 0.1 and 1.21 +/- 0.1 on disrupted microsomes. Compound A-1 was also able to inhibit pyrophosphatase activities from both intact and disrupted microsomes with equal potency (IC50; 0.43-0.55 microm). Using cultured rat hepatocytes and glycerol as the substrate, these compounds were able to prevent glucose production up to 60% with a concomitant increase in the G-6-P content (2.3-fold) using compound A-1. No increase in glycogen levels was seen. CONCLUSION: These data demonstrated that these compounds were more potent inhibitors on G-6-Pase obtained from pig microsomes and were able to penetrate the microsomal membrane. The hepatocyte data further support the kinetic data, and are also consistent with the evoked mechanism of action.

Animals↗

PubMed2002

Genetic parameter estimation for milk yield over multiple parities and various lengths of lactation in Danish Jerseys by random regression models.

The objectives of this study were to test for heterogeneity of genetic and environmental variance among completed and extended records from different lactations or different days in milk (DIM) and to build a model that accounts for this heterogeneity. A total of 147,457 305-d milk yield records from Danish Jersey cows calving between 1984 and early 1999 from two regions of Denmark were used in this study. Results showed that DIM and parity influenced parameters estimated from an animal model with repeated records. Therefore, the data were analyzed using random-regression models that allow the covariance between measurements to change gradually with DIM and parity. Random regressions were fitted for additive genetic effects and permanent environmental effects using second- or third-order normalized Legendre polynomials for DIM and parity. Variances of random-regression coefficients associated with all orders of the polynomials were significant. Based on these parameter estimates, a covariance function (CF) was defined. The CF showed that the heritability decreases over parities, but within each parity heritability increases with DIM, whereas variance of permanent environmental effects increases over parities and decreases with DIM. Generally, genetic correlations were higher between records with similar DIM and parity. The results indicate that there are problems with the extension procedure used to predict 305-d milk yields. Using the covariance functions estimated in this study, breeding values could be predicted that take into account the covariance structure between records from different parities and different DIM.

Analysis of Variance↗

PubMed2002

Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML.

(Co)variance components for milk, fat, and protein yield of 8075 first-parity Danish Holsteins (DH) were estimated in random regression models by REML. For all analyses, the fixed part of the model was held constant, whereas four different functions were applied to model the additive genetic effect and the permanent environment effect. Homogeneous residual variance was assumed throughout lactation. Univariate models were compared using a minimum of -2 ln(restricted likelihood) as the criterion for best fit. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Independent of the function applied and the trait in question, heritabilities were lowest in the beginning of the lactation. Heritabilities for persistency of fat yield were slightly higher than heritabilities for persistency of milk and protein yield. Genetic correlations between persistency and 305-d production were higher for protein and milk yield than for fat yield. Bivariate analyses between the production traits were carried out in sire models using the models with the best 3-parameter curve fit in the univariate analyses. Correlations between traits were calculated from covariance components for curve parameters estimated in bivariate analyses. Genetic correlations between milk and protein yield were higher than between milk and fat yield.

Algorithms↗