Biomedical subjects

D Gianola

Publications and source records attributed to D Gianola.

At least 19 recordsLinked to original sources

PubMed2007

Genetic analysis of somatic cell scores in US Holsteins with a Bayesian mixture model.

The objective of this study was to apply finite mixture models to field data for somatic cell scores (SCS) for estimation of genetic parameters. Data were approximately 170,000 test-day records for SCS from first-parity Holstein cows in Wisconsin. Five different models of increasing level of complexity were fitted. Model 1 was the standard single-component model, and the others were 2-component Gaussian mixtures consisting of similar but distinct linear models. All mixture models (i.e., 2 to 5) included separate means for the 2 components. Model 2 assumed entirely homogeneous variances for both components. Models 3 and 4 assumed heterogeneous variances for either residual (model 3) or genetic and permanent environmental variances (model 4). Model 5 was the most complex, in which variances of all random effects were allowed to vary across components. A Bayesian approach was applied and Gibbs sampling was used to obtain posterior estimates. Five chains of 205,000 cycles were generated for each model. Estimates of variance components were based on posterior means. Models were compared by use of the deviance information criterion. Based on the deviance information criterion, all mixture models were superior to the linear model for analysis of SCS. The best model was one in which genetic and PE variances were heterogeneous, but residual variances were homogeneous. The genetic analysis suggested that SCS in healthy and infected cattle are different traits, because the genetic correlation between SCS in the 2 components of 0.13 was significantly different from unity.

Animals↗

PubMed2007

Nonparametric analysis of the impact of inbreeding on production in Jersey cows.

Under dominance, the relationship between a quantitative trait and the inbreeding coefficient (F) is expected to be linear. Epistasis involving dominance effects or selection against inbred individuals can produce nonlinear patterns in the form of inbreeding depression. The form of the relationship between F and yield traits was explored via local regression (LOESS). First-lactation milk, fat, and protein records from 59,778 Jersey cows with at least 6 generations of known pedigree were used. The F ranged from 0.6 to 34% and median F was 6.3%. The LOESS regressions of predicted residuals from an animal model (empirical best linear unbiased predictions, EBLUP) on F were calculated for each trait; the EBLUP model included fixed herd-year-season, age at calving and DIM effects, and random additive genetic effects. The relationship between EBLUP residuals and inbreeding was complex and nonlinear. Yields were unaffected for F 20% for fat and protein yield. For SCS, both parametric and LOESS fits were consistent with the absence of sizable dominance effects. Effects of inbreeding on performance seemed to be more complex than suggested by previous studies based on linear regression. Results should be interpreted with caution because the data were scarce at high levels of F.

Animals↗

PubMed2006

A comparison between multivariate Slash, Student's t and probit threshold models for analysis of clinical mastitis in first lactation cows.

Robust threshold models with multivariate Student's t or multivariate Slash link functions were employed to infer genetic parameters of clinical mastitis at different stages of lactation, with each cow defining a cluster of records. The robust fits were compared with that from a multivariate probit model via a pseudo-Bayes factor and an analysis of residuals. Clinical mastitis records on 36 178 first-lactation Norwegian Red cows from 5286 herds, daughters of 245 sires, were analysed. The opportunity for infection interval, going from 30 days pre-calving to 300 days postpartum, was divided into four periods: (i) -30 to 0 days pre-calving; (ii) 1-30 days; (iii) 31-120 days; and (iv) 121-300 days of lactation. Within each period, absence or presence of clinical mastitis was scored as 0 or 1 respectively. Markov chain Monte Carlo methods were used to draw samples from posterior distributions of interest. Pseudo-Bayes factors strongly favoured the multivariate Slash and Student's t models over the probit model. The posterior mean of the degrees of freedom parameter for the Slash model was 2.2, indicating heavy tails of the liability distribution. The posterior mean of the degrees of freedom for the Student's t model was 8.5, also pointing away from a normal liability for clinical mastitis. A residual was the observed phenotype (0 or 1) minus the posterior mean of the probability of mastitis. The Slash and Student's t models tended to have smaller residuals than the probit model in cows that contracted mastitis. Heritability of liability to clinical mastitis was 0.13-0.14 before calving, and ranged from 0.05 to 0.08 after calving in the robust models. Genetic correlations were between 0.50 and 0.73, suggesting that clinical mastitis resistance is not the same trait across periods, corroborating earlier findings with probit models.

Animals↗

PubMed2006

A structural equation model for describing relationships between somatic cell score and milk yield in dairy goats.

The relationship between milk yield and somatic cell score (log-transformed somatic cell count) in dairy goats may involve complex pathways with recursive or simultaneous effects. Structural equation models were fitted to longitudinal data on milk yield and on somatic cell scores. Data consisted of 4 repeated records of milk production and of somatic cell score from left and right halves of the udder in each of 47 dairy goats; infection status of each of the halves at each test day was also available. Results strongly suggest the existence of a within-half, first-order autoregressive process and of simultaneity of effects between somatic cell scores from the left and right halves of the udder. This indicates that the immune response to an infection is not restricted to the half of the udder in which the infection takes place and that it tends to propagate over time. The existence of a negative effect of somatic cell score on milk yield was also supported by the results; however, evidence in favor of an effect in the opposite direction, a dilution effect, was not strong.

Animals↗

PubMed2006

Bivariate analysis of number of services to conception and days open in Norwegian red using a censored threshold-linear model.

A bivariate censored threshold-linear model was used to study genetic parameters of number of services to conception (STC) and days open (DO) in first-lactation Norwegian Red (NRF) cows. Records of 1,454,916 NRF cows, with a first insemination from 1980 to 2004, were analyzed. It was assumed that every cow had at least a first insemination. The number of inseminations was recorded until a cow conceived or was culled, whichever occurred first. If a cow was culled before conception, it was considered censored at the number of services until culling was recorded. Twenty-one percent of cows were censored for both STC and DO. Using an univariate probit link function for STC, unobserved liabilities to STC and DO were modeled jointly as a linear function of age at first calving, month-year at first calving, herd-5-yr period, sire of cow and residual effects. Heritability of liability to STC and DO was 4% for each trait. The genetic and residual correlations between STC and DO were 0.77 and 0.68, respectively. There has been little or no genetic change for DO, whereas STC had favorable genetic and phenotypic trends.

Analysis of Variance↗

PubMed2006

Genetic associations between clinical mastitis and somatic cell score in early first-lactation cows.

The objectives of this study were to examine genetic associations between clinical mastitis and somatic cell score (SCS) in early first-lactation cows, to estimate genetic correlations between SCS of cows with and without clinical mastitis, and to compare genetic evaluations of sires based on SCS or clinical mastitis. Clinical mastitis records from 15 d before to 30 d after calving and first test-day SCS records (from 6 to 30 d after calving) from 499,878 first-lactation daughters of 2,043 sires were analyzed. Results from a bivariate linear sire model analysis of SCS in cows with and without clinical mastitis suggest that SCS is a heterogeneous trait. Heritability of SCS was 0.03 for mastitic cows and 0.08 for healthy cows, and the genetic correlation between the 2 traits was 0.78. The difference in rank between sire evaluations based on SCS of cows with and without clinical mastitis varied from -994 to 1,125, with mean 0. A bivariate analysis with a threshold-liability model for clinical mastitis and a linear Gaussian model for SCS indicated that heritability of liability to clinical mastitis is at least as large as that of SCS in early lactation. The mean (standard deviation) of the posterior distribution of heritability was 0.085 (0.006) for liability to clinical mastitis and 0.070 (0.003) for SCS. The posterior mean (standard deviation) of the genetic correlation between liability to clinical mastitis and SCS was 0.62 (0.03). A comparison of sire evaluations showed that genetic evaluation based on SCS was not able to identify the best sires for liability to clinical mastitis. The association between sire posterior means for liability to clinical mastitis and sire predicted transmitting ability for SCS was far from perfect.

Analysis of Variance↗

PubMed2006

Genetic analysis of number of mastitis cases and number of services to conception using a censored threshold model.

The objective of this study was to infer genetic parameters and genetic change for number of clinical mastitis cases (NCM) and number of services to conception (STC) in first-lactation Norwegian Red (NRF) cows. Records on 620,492 daughters of 3,064 NRF sires, with first calving from 1980 through 2004, were analyzed with a bivariate threshold liability model that takes censoring into account. Posterior mean (SD) of heritability of liability was 0.08 (0.004) for NCM and 0.03 (0.002) for STC. The mean (SD) of the posterior distribution of the genetic correlation between the 2 traits was 0.21 (0.04). Posterior means of the correlation between herd-5-yr effects, and between residuals for NCM and STC were 0.17 and 0.05, respectively. To evaluate effects of taking censoring into account, the data were also analyzed with a bivariate ordered threshold model ignoring censoring. The genetic correlation between NCM and STC was lower than in the censored threshold model (0.09 vs. 0.21). Heritability of liability to NCM and STC from this model was also slightly lower, whereas the point estimates of herd-5-yr and residual correlations were 0.15, and -0.01, respectively. These results suggest that genetic (co)variance may be understated in models ignoring censoring. For comparison purposes, the data were analyzed with a bivariate linear sire model and standard REML-BLUP procedures. The correlation (rank correlation) between sire evaluations from the censored threshold model and sire predicted transmitting abilities from the linear model was 0.90 (0.90) for NCM and 0.87 (0.86) for STC. The evolution of average sire posterior means by birth year of daughters was used to assess genetic change, and results indicated genetic reduction (i.e., genetic improvement) of NCM and little or no genetic change for STC in the NRF population.

Animals↗

PubMed2006

Short communication: Genetic analysis of nonreturn rate and mastitis in first-lactation Norwegian Red cows.

Associations between clinical mastitis (CM) and nonreturn rate within 56 d after first insemination (NR56) were examined in Norwegian Red (NRF) cows. Records on absence or presence of CM within each of the intervals, -30 to 30, 31 to 150, and 151 to 300 d after first calving, and records on NR56 for 620,492 first-lactation daughters of 3,064 NRF sires were analyzed with a Bayesian multivariate threshold liability model. Point estimates of genetic correlations between NR56 and the 3 CM traits were between -0.05 and -0.02. Residual correlations were close to zero, and correlations between herd-5-yr effects on NR56 and CM in the 3 lactation intervals ranged from -0.15 to -0.17. It appears that CM and NR56 in first lactation are independent traits.

Animals↗

PubMed2006

Selection for female fertility using censored fertility traits and investigation of the relationship with milk production.

Bivariate models (censored linear-linear and censored threshold-linear) were used to estimate genetic parameters for production and fertility traits in the Spanish Holstein population. Records on 71,217 lactations from 41,515 cows were used: 30 and 36% of lactations were censored for days open (DO) and number of inseminations to conception (INS), respectively. Heritability estimates for production traits (milk, fat, protein) ranged between 0.18 and 0.25. Heritability of days to first service (DFS) and DO was 0.05; heritability of INS on the liability scale was 0.04. Genetic correlations between fertility traits were 0.41, 0.71, and 0.87 for DFS-INS, DO-INS, and DO-DFS, respectively. Days open had a larger genetic correlation (ranging from 0.63 to 0.76) with production traits than did DFS (0.47 to 0.59) or INS (0.16 to 0.23). Greater antagonism between production and DO may be due to voluntary management decisions for high-yielding cows, resulting in longer lactation lengths. Inseminations to conception appeared to be less correlated with milk production than were the other 2 female fertility traits. Including INS in a total merit index would be expected to increase genetic gain in terms of profit, but profit would decrease if either DO or DO and DFS were included in the index. Thus, INS is the trait to be preferred when selecting for female fertility. The genetic correlation between actual milk yield and 305-d standardized milk yield was 0.96 in the present study, suggesting that some reranking of sires could occur. Because the target of attaining a 12-mo calving interval, as implied by a 305-d standardized lactation length, is changing in the dairy industry, routine genetic evaluation of actual total lactation milk yield should be considered.

Animals↗

PubMed2006

A structural equation model for describing relationships between somatic cell score and milk yield in first-lactation dairy cows.

Relationships between production and diseases may involve recursive or simultaneous effects between traits. Four structural equation models (SEqM) for somatic cell score and milk yield, with varying specifications for the effects relating the 2 traits, were compared. Data consisted of repeated records of milk yield and somatic cell score of 33,453 first-lactation daughters of 245 Norwegian Red sires that had their first progeny test in 1991 and 1992. All models included random effects of the sire and of the cow and were fitted using the LISREL software. The Bayesian information criterion clearly favored a model with a recursive effect from somatic cell score on milk yield over the 3 other models fitted (absence of recursive effects; an effect from milk yield on somatic cell score; simultaneity of effects between the 2 traits). This provides evidence that the negative association between milk yield and somatic cell score is more likely due to an effect of infection (measured indirectly by the somatic cell score) on production than to a dilution effect. Estimates indicated that a mastitis event would reduce milk yield in the following 15 d by about 900 g/d. The estimated genetic (co)variances did not change sizably when the specification of recursive or simultaneous effects was varied. However, estimates of the phenotypic covariance were altered when a recursive effect from somatic cell score on milk yield was included in the model.

Age Factors↗

PubMed2006

Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms.

The fertility of lactating dairy cows is economically important, but the mean reproductive performance of Holstein cows has declined during the past 3 decades. Traits such as first-service conception rate and pregnancy status at 150 d in milk (DIM) are influenced by numerous explanatory factors common to specific farms or individual cows on these farms. Machine learning algorithms offer great flexibility with regard to problems of multicollinearity, missing values, or complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the reproductive performance of lactating Holstein cows on large dairy farms. This study used data from farms in the Alta Genetics Advantage progeny-testing program. Production and reproductive records from 153 farms were obtained from on-farm DHI-Plus, Dairy Comp 305, or PCDART herd management software. A survey regarding management, facilities, labor, nutrition, reproduction, genetic selection, climate, and milk production was completed by managers of 103 farms; body condition scores were measured by a single evaluator on 63 farms; and temperature data were obtained from nearby weather stations. The edited data consisted of 31,076 lactation records, 14,804 cows, and 317 explanatory variables for first-service conception rate and 17,587 lactation records, 9,516 cows, and 341 explanatory variables for pregnancy status at 150 DIM. An alternating decision tree algorithm for first-service conception rate classified 75.6% of records correctly and identified the frequency of hoof trimming maintenance, type of bedding in the dry cow pen, type of cow restraint system, and duration of the voluntary waiting period as key explanatory variables. An alternating decision tree algorithm for pregnancy status at 150 DIM classified 71.4% of records correctly and identified bunk space per cow, temperature for thawing semen, percentage of cows with low body condition scores, number of cows in the maternity pen, strategy for using a clean-up bull, and milk yield at first service as key factors.

Algorithms↗

PubMed2005

Prediction of random effects in finite mixture models with Gaussian components.

Prediction of random effects in finite mixture models with Gaussian distributions is discussed from a non-Bayesian perspective, assuming that location and dispersion parameters are known. The focus is on calculating the best predictor, that is, the statistic with minimum expected squared prediction error, for several models. Coverage includes mixture sampling models, as well as mixtures for the distribution of the random effects. Longitudinal and cross-sectional specifications with correlated random effects, such as those arising in animal breeding and genetics, are examined. The best linear predictor and the best linear unbiased predictor are derived for these models as well.

Analysis of Variance↗

PubMed2005

Genetic association between susceptibility to clinical mastitis and protein yield in norwegian dairy cattle.

The objective of this study was to examine associations between susceptibility to clinical mastitis and protein yield in first-lactation Norwegian Dairy Cattle (NRF) cows. Records from 372,227 first-lactation daughters of 2411 NRF sires were analyzed bivariately, using a threshold-liability model for clinical mastitis and a linear Gaussian model for 305-d protein yield. The mean (SD) of the posterior distribution of heritability was 0.08 (0.004) for susceptibility to clinical mastitis and 0.19 (0.007) for 305-d protein yield. The posterior mean (SD) of the genetic correlation between susceptibility to clinical mastitis and 305-d protein yield was 0.43 (0.03). Posterior means of the correlations between herd-5-yr effects, and between model residuals were 0.19 and -0.008, respectively. Corresponding estimates of genetic, herd-5-yr, and residual correlations from a bivariate linear model analysis were 0.42, 0.18, and -0.008, respectively. An antagonistic genetic relationship between clinical mastitis and protein yield was corroborated.

Animals↗

PubMed2005

Comparison between bivariate models for 56-day nonreturn and interval from calving to first insemination in Norwegian red.

A bivariate threshold-linear (TL) and a bivariate linear-linear (LL) model were assessed for the genetic analysis of 56-d nonreturn (NR56) and interval from calving to first insemination (CFI) in first-lactation Norwegian Red (former Norwegian Dairy Cattle) (NRF). Three different datasets were used to infer genetic parameters and to predict transmitting abilities for NRF sires. Mean progeny group sizes were 147.8, 102.7, and 56.5 daughters, and the corresponding number of sires were 746, 743, and 742 in the 3 datasets. Otherwise, the structures of the 3 datasets were similar. When the TL model was used, heritability of liability to NR56 was 2.8% in the 2 larger datasets and 3.8% in the smallest dataset. In the LL model, the heritability of NR56 in the largest dataset and in the 2 smaller datasets was 1.2 and 0.9%, respectively. For CFI, the heritability was similar in TL and LL models, ranging from 2.4 to 2.7%. The small heritability of the 2 reproductive traits implies that most of the variation is environmental and that large progeny groups are required to get accurate sire PTA. The point estimates of the genetic correlation between NR56 and CFI were near zero in both models. The 2 bivariate models were compared in terms of predictive ability using logistic regression and a chi2 statistic based on differences between observed and predicted outcomes for NR56 in a separate dataset. Comparison was also with respect to ranking of sires and correlations between sire posterior means (TL model) and PTA (LL model). We found very small differences in ability to predict NR56 between the 2 bivariate models, regardless of the dataset used. Correlations between sire posterior means (TL) and sire PTA (LL) and rank correlations between sire evaluations were all >0.98 in the 3 datasets. At present, the LL model is preferred for sire evaluations of NR56 and CFI in NRF. This is because the LL model is less computationally demanding and more robust with respect to the structure of the data than TL.

Analysis of Variance↗

PubMed2005

Application of a finite mixture model to somatic cell scores of Italian goats.

The objectives of this study were to apply a finite mixture model (FMM) to data for somatic cell count in goats and to compare the fit of the FMM with that of a standard linear mixed effects model. Bacteriological information was used to assess the ability of the model to classify records from healthy or infected goats. Data were 4518 observations of somatic cell score (SCS) and bacterial infection from both udder halves of 310 goats from 5 herds in Northern Italy. The records were from a complete production season, and were taken monthly from February to November 2000. Explanatory factors in both models included a 3-parameter regression on days in milk (DIM); fixed class effects of herd-test-day, parity group, and udder side (left or right); and random effects of goat and udder half within goat. In addition, the 2-component FMM included a fixed mean for the second component of the model (theoretically corresponding to infected udder halves), as well as an unknown probability of membership to a given putative infection status. A Bayesian statistical approach was used for the analysis with Gibbs sampling used to obtain draws from posterior distributions of parameters of interest. Two sampling chains of 200,000 cycles each were generated for each model. The FMM yielded a much lower estimate of residual variance than the standard model (1.28 vs. 3.02 SCS2), and a slightly higher estimate for the between-goat variance (1.79 vs. 1.48). The deviance information criterion (DIC) was used to compare the fit of the 2 models. The DIC was much lower for the FMM, indicating a better fit to the data. The FMM was able to classify correctly 60 and 48% of the healthy and infected observations, respectively. This was slightly higher than what would be expected from random classification, but not high enough for useful mastitis diagnosis. Nevertheless, increased precision of genetic evaluation is the goal of applying the FMM, rather than timely and accurate mastitis diagnosis. The results suggest that more research on FMM for SCS is merited and necessary for proper application.

Analysis of Variance↗

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

Genetic analysis of clinical mastitis, milk fever, ketosis, and retained placenta in three lactations of Norwegian red cows.

The objectives were to infer heritability and genetic correlations between clinical mastitis (CM), milk fever (MF), ketosis (KET), and retained placenta (RP) within and between the first 3 lactations and to estimate genetic change over time for these traits. Records of 372,227 daughters of 2411 Norwegian Red (NRF) sires were analyzed with a 12-variate (4 diseases x 3 lactations) threshold model. Within each lactation, absence or presence of each of the 4 diseases was scored based on the cow's health recordings. Each disease was assumed to be a different trait in each of the 3 lactations. The model for liability had trait-specific effects of year-season of calving and age of calving (first lactation) or month-year of calving and calving interval (second and third lactations), herd-5-yr, sire of the cow, and a residual. Posterior means of heritability of liability in first, second, and third lactations were 0.08, 0.07, and 0.07, respectively, for CM; 0.09, 0.11, and 0.13 for MF; 0.14, 0.16, and 0.15 for KET, and 0.08 in all 3 lactations for RP. Posterior means of genetic correlations between liability to CM, MF, KET, and RP, within disease between lactations, ranged from 0.19 to 0.86, and were highest between KET in different lactations. Correlations involving first lactation MF were low and had higher standard deviations. Genetic correlations between diseases were low or moderate (from -0.10 to 0.40), within as well as between lactations; the largest estimates were for MF and KET, and the lowest involved MF or KET and RP. Positive genetic correlations between diseases suggest that some general disease resistance factor with a genetic component exists. Trends of average sire posterior means by birth-year of daughters were used to assess genetic change, and the results indicated genetic improvement of resistance to CM and KET and no genetic change for MF and RP in the NRF population.

Animals↗

PubMed2005

Number of inseminations to conception in Holstein cows using censored records and time-dependent covariates.

Three methodologies that accommodate censoring or time-dependent covariates were used to estimate variance components for number of inseminations to conception. Data included 80,071 lactation records and 143,927 artificial inseminations in 47,509 Spanish Holstein cows. Up to 4 inseminations to conception, along with their respective censoring information, were analyzed. An ordinal-censored threshold model (CTM), a sequential threshold model (STM), and a grouped survival analysis via a discrete proportional hazards model (DPH) were implemented. Sire variance estimates on the liability scale were 0.016 and 0.010 for CTM and STM, respectively, and 0.012 for DPH on the logarithmic scale. Heritability estimates on the liability scale were 0.050 and 0.038 with CTM and STM, respectively. All models led to similar rankings of sires, and the strong correlations (0.97 to 0.98) between methodologies suggested robustness in ranking of sires of cows. Service sire variance estimates were 0.021 for both CTM and STM; DPH led to an approximate service sire variance of 0.020. Rankings for service sires between methodologies ranged from 0.76 to 0.90. These lower values are most likely due to differences in the treatment of time-dependent covariates. The STM had greater predictive ability of daughter fertility at first insemination than the other methodologies. However, the CTM predicted daughter fertility more accurately in subsequent inseminations. The DPH and STM had a similar predictive ability of daughter fertility in second and subsequent inseminations.

Algorithms↗