Biomedical subjects

R Rekaya

Publications and source records attributed to R Rekaya.

At least 19 recordsLinked to original sources

PubMed2006

Combining genetic test information and correlated phenotypic records for breeding value estimation.

The use of marker assisted selection in the beef cattle industry to date has involved using traditional EPD in tandem with molecular test information. In the current study, a multiple-trait simulation was carried out to create a beef cattle data set using genetic parameter estimates from the literature to identify the best procedure for combining both sources of information and to assess the added benefit of the procedure. To reach these objectives, the following simulation/ analysis steps were implemented: (1) varying percentages (100, 5, or 0) of available records for the trait of interest, (2) varying percentages (100, 50, 25, or 0) of animals with molecular information, (3) scenarios where the favorable (F) or the unfavorable (U) allele was more frequent, and (4) analysis of the response due to selection over 5 generations. The data sets included 3 correlated traits in which 2 of them, birth weight and postweaning gain, had complete recording and the availability of records for the third trait (marbling score) varied. It was further assumed that molecular information was available for the third trait for a causative gene that explained 10% of the genetic variation. Estimates of Pearson correlations between true and predicted breeding values for marbling score declined as the amount of information declined, and instances in which the molecular information was recorded were always closer to the true values than in the case in which the molecular information was absent. When the U allele was more frequent, rank correlation estimates were increased among top sires, low accuracy sires, and high accuracy sires by approximately 24.9, 12.1, and 4.7% with limited marbling score records and complete genotyping compared with limited marbling score records and no genotyping. Similar results were seen when the F allele was more frequent. When there was a complete absence of recording for the trait of interest, the same trends in correlations were observed and were lower than when the trait of interest was recorded. Jointly considering molecular and phenotypic information showed a greater long-term response compared with tandem selection, showing that discrimination of candidates for selection based solely on molecular information is not optimal.

Alleles↗

PubMed2006

Success at first insemination in Australian Angus cattle: analysis of uncertain binary responses.

Field data from Australian Angus herds were used to investigate 2 methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included herd, year, and month of mating as fixed effects; unrelated service sire, additive animal, and residual as random effects; and linear and quadratic effects of age at mating as covariates was used to analyze binary data. An average gestation length (GL) derived from artificial insemination data was used to assign an insemination date to females mated to natural service sires. Females that deviated from this average GL led to uncertain binary responses. Two analyses were carried out: 1) a threshold model fitted to uncertain binary data, ignoring uncertainty (M1); and 2) a threshold model fitted to uncertain binary data, accounting for uncertainty via fuzzy logic classification (M2). There was practically no difference between point estimates obtained from M1 and M2 for service sire and herd variance; however, when uncertain binary data were analyzed ignoring uncertainty (M1), additive variance and heritability estimates were greater than with M2. Pearson correlations indicated that no major reranking would be expected for service sire effects and animal breeding values using M1 and M2. Given the results of the current study, a threshold model contemplating uncertainty is suggested for noisy binary data to avoid bias when estimating genetic parameters.

Animals↗

PubMed2006

Random regression models for male and female fertility evaluation using longitudinal binary data.

A longitudinal Bayesian threshold analysis of insemination outcomes was carried out using 2 random regression models with 3 (Model 1) and 5 (Model 2) parameters to model the additive genetic values at the liability scale. All insemination events of first-parity Holstein cows were used. The outcome of an insemination event was treated as a binary response of either a success (1) or a failure (0). Thus, all breeding information for a cow, including all service sires, was included, thereby allowing for a joint evaluation of male and female fertility. An edited data set of 369,353 insemination records from 210,373 first-lactation cows was used. On the liability scale, both models included the systematic effects of herd-year, month of insemination, technician, and regressions on age of service sire and milk yield during the first 100 d of lactation. The random effects in the model were the 3 or 5 random regression coefficients specific to each cow, the permanent effect of the cow, and the service sire effect. Using Model 1, the estimated heritability of an insemination outcome decreased from 0.035 at d 50 to 0.032 at d 140 and then increased continuously with DIM. The genetic correlations for insemination success at different time points ranged from 0.83 to 0.99, and their magnitude decreased with an increase in the interval between inseminations. A similar trend was observed for heritability and genetic correlations using Model 2. However, the average estimate of heritability was much higher (0.058) than those obtained using Model 1 or a repeatability model. In addition, the estimated genetic correlations followed the same trend as Model 1, but were lower and with a higher rate of decrease when the interval between inseminations increased. The posterior mean of service sire variance was 0.01 for both models, and permanent environmental variance was 0.05 and 0.02 for Models 1 and 2, respectively. Model comparison based on the Bayes factor indicated that Model 1 was more plausible, given the data.

Animals↗

PubMed2006

Gene expression in the brain-pituitary adipose tissue axis and luteinising hormone secretion during pubertal development in the gilt.

The occurrence of puberty in the female is due to the interplay of central and peripheral mechanisms in which the hypothalamic-pituitary-ovarian axis regulates growth and gonadal function, as well as adipocyte hormone secretion. Hypothalamic GnRH mRNA expression increased at 3.5 months of age and declined by 6 months of age. Concomitant with the age related reduction in the oestrogen negative feedback on LH secretion was a decline in hypothalamic oestrogen receptor-alpha (ERalpha) expression and increased expression of repressor of ER activity gene (REA) at 210 days of age. Hypothalamic proopiomelanocortin expression increased at 6 months of age followed by increased expression of progesterone receptor (PR) membrane compliment-1 and steroid membrane binding protein gene at 210 days of age. This represents development of the endogenous opioid peptide-progesterone dependent LH inhibitory pathway. Adipose tissue leptin and insulin like growth factor-I (IGF-I) gene expression increased with age and adiposity. Pituitary transcription factors, steroidogenic factor 1 (SF1) and Lhx3, and LHbeta and FSHbeta gene expression increased with age. These results identify key hypothalamic and pituitary genes associated with changes in LH secretion and growth during pubertal development and adipose tissue genes and secreted proteins related to maturation of the neuroendocrine axis and puberty.

Adipose Tissue↗

PubMed2005

Longitudinal multiple-trait versus cumulative single-trait analysis of male and female fertility and hatchability in chickens.

The objective of the current study was to compare the performances of a longitudinal multipletrait (MT) and cumulative single-trait (ST) best linear unbiased prediction (BLUP) analyses for eggs set (E), percentage fertility (F), and percentage hatched of fertile eggs (H) based on rank correlations of hens and roosters (ROO). Longitudinal data consisted of weekly records, and the ST analysis used cumulative records of the 3 traits. Comparisons were carried out using simulated data, in which true values assigned during the simulation were compared with estimates obtained from MT and ST analyses. Two methods were used to calculate breeding values and ROO effects. Weekly E records equal to zero were assumed as valid records, and missing weekly F and H records were predicted in MT analysis. However, missing F and H were not included in the cumulative record. Weekly E, F, and H records were converted to a cumulative record following a procedure used by a commercial broiler company. Further, ST models were developed to evaluate hens and ROO separately as currently practiced in the industry. There was virtually no difference in hen rank correlations between true and predicted breeding values obtained from MT (0.64) and ST (0.62) analyses for E, thus suggesting that hens would rank similarly when weekly or cumulative data were used for genetic evaluation of E. With ST analysis, rank correlations between true and estimated hen breeding values decreased, compared with MT analysis, by 74 and 75% for F and H, respectively. For F (H), the rank correlation between true and estimated ROO effects were 0.64 (0.67) and 0.17 (0.12) with MT and ST analyses, respectively. The results suggested that longitudinal MT BLUP, which handles missing records, was better at estimating true hen and ROO effects than ST BLUP. Therefore, it was recommended that the MT BLUP method be used for genetic evaluation of hens and ROO for E, F, and H.

Animals↗

PubMed2005

Genetic analysis of age at first service, return rate, litter size, and weaning-to-first service interval of gilts and sows.

The aim of this study was to estimate genetic parameters of seven traits related to sow reproductive performance. Data on all Norwegian Landrace pigs (NL) born in nucleus herds and raised in nucleus or multiplying herds from 1990 to 2000 were extracted from the Norwegian national recording scheme. Reproductive traits investigated were age at first service (AFS), return rate in gilts (RRg), age at first farrowing (AFF), live-born piglets in the first litter (NBA1), interval from weaning to first service after first litter (WTS1), return rate after first litter (RR1), live-born piglets in the second litter (NBA2), and interval from weaning to first service after second litter (WTS2). After editing, the data set comprised 12,583 to 56,042 records, depending on the trait. A mixed linear and a joint linear threshold animal model were used to estimate (co)variance components. A full Bayesian approach via Gibbs sampling was adopted. The statistical model used for analysis included contemporary groups of herd-year (-season), purebred or crossbred litter, single or double insemination, mating type, parity in which the animal was born, a regression on lactation length, and an additive genetic effect. Neither the estimated heritabilities nor the genetic correlations differed much between the two approaches, but there was a tendency for higher genetic correlations using the joint linear threshold model approach. Average heritabilities were as follows: AFS = 0.31; RRg = 0.03; RR1 = 0.02; NBA1 = 0.12; NBA2 = 0.14; WTS1 = 0.08; and WTS2 = 0.03. The highest genetic correlations were estimated between NBA1 and NBA2 (r(g) = 0.95), RR1 and WTS1 (r(g) = 0.93), and between WTS1 and WTS2 (r(g) = 0.78). The estimated genetic correlation between NBA and WTS were close to zero. Selection for increased NBA will slightly increase AFS and reduce the probability of a return. Selection for decreased AFS will have a favorable effect on WTS intervals; however, selection for decreased AFS seems to have an unfavorable effect on return rate both on gilts and sows. Conversely, selection for decreased WTS intervals will reduce the probability of a return. Potential selection candidates to include in a multivariate fertility index are AFS, NBA, and WTS1. Due to the low heritability and low, but favorable, genetic correlations to NBA and WTS, RR is not recommended as a selection candidate.

Age Factors↗

PubMed2004

Male and female fertility and hatchability in chickens: a longitudinal mixed model approach.

The objective of the current study was to investigate different approaches for handling missing records and to develop and implement a multivariate longitudinal mixed model for the genetic evaluation of male and female fertility and hatchability in chickens. The traits recorded on a weekly basis were eggs set (E), percentage fertility (F), and percentage hatch of fertile eggs (H). Three approaches for handling missing records were investigated: 1) all records with zero weekly laid eggs were removed and remaining records with missing F and H were predicted (M1); 2) missing records, including zero weekly laid eggs, were assumed known and equal to zero (M2); and 3) zero weekly laid eggs were assumed as a valid record and missing F and H were predicted (M3). A longitudinal mixed model was used for the multiple trait analysis of E, F, and H. Fixed effects included week-flock, age of service sire (rooster in the pen at data collection), and age of hen. Unrelated service sire, additive hen, permanent environmental, and the residual term were included as random effects. Heritability estimates ranged from 0.06 to 0.13 for E, F, and H. Heritability estimates of F and H were overestimated using M2. Correlations among the 3 traits were highest using M2 and lowest using M1. Pearson correlations indicated that reranking of birds and service sires could occur when using M1. Therefore, M1 should not be used for the analysis of longitudinal data of fertility and hatchability in chickens. Furthermore, M3 seems to be the most ideal method for handling missing records.

Analysis of Variance↗

PubMed2004

Comparison of methods for handling censored records in beef fertility data: simulation study.

A simulation study was conducted to compare methods for handling censored records for days to calving in beef cattle data. Days to calving was defined as the time, in days, between when a bull is turned out in the pasture and the subsequent parturition. Simulated data were generated to have data structure and genetic relationships similar to an available field data set. Records were simulated for 33,176 daughters of 4,238 sires. Data were simulated using a mixed linear model that included the fixed effects of contemporary group and sex of calf, linear and quadratic covariates for age at mating, and random effects of animal and residual error. Two methods for handling censored records were evaluated, and two censoring rates of 12 and 20% were applied to assess the influence of higher censoring rates on inferences. Censored records were assigned penalty values on a within-contemporary group basis under the first method (DCPEN). Under the second method (DCSIM), censored records were drawn from their respective predictive distributions. A Bayesian approach via Gibbs sampling was used to estimate variance components and predict breeding values. Posterior means (PM) and standard deviations (SD) of additive genetic variance for DCPEN at 12 and 20% censoring were 23.2 (3.7) and 21.0 (3.6), respectively, whereas the same estimates for DCSIM at 12 and 20% censoring were 23.7(3.3) and 21.9 (3.4), respectively. In all cases, the true value of the genetic variance was within the high posterior density (HPD) interval (95%). The PM (SD) of residual variance for DCPEN at 12 and 20% censoring were 415.7 (4.7) and 440.0 (4.8) respectively, whereas the same estimates for DCSIM at 12 and 20% censoring were 371.0 (4.3) and 365.4 (4.4), respectively. The true value of the residual variance was within the HPD (95%) for DCSIM, but it was outside this interval for DCPEN at both censoring rates, indicating a systematic bias for this parameter. Bayes Factor and Deviance Information Criteria were used for model comparisons, and both criteria indicated the superiority of the DCSIM method. However, little difference was observed between the two methods for correlations between true breeding values and posterior means of animal effects for sires, indicating that no major reranking of sires would be expected. This finding suggests that either censored data handling technique can be successfully used in a genetic evaluation for days to calving.

Animals↗

PubMed2004

Comparison of methods for handling censored records in beef fertility data: field data.

The purpose of this study was to compare methods for handling censored days to calving records in beef cattle data, and verify results of an earlier simulation study. Data were records from natural service matings of 33,176 first-calf females in Australian Angus herds. Three methods for handling censored records were evaluated. Censored records (records on noncalving females) were assigned penalty values on a within-contemporary group basis under the first method (DCPEN). Under the second method (DCSIM), censored records were drawn from their respective predictive truncated normal distributions, whereas censored records were deleted under the third method (DCMISS). Data were analyzed using a mixed linear model that included the fixed effects of contemporary group and sex of calf, linear and quadratic covariates for age at mating, and random effects of animal and residual error. A Bayesian approach via Gibbs sampling was used to estimate variance components and predict breeding values. Posterior means (PM) (SD) of additive genetic variance for DCPEN, DCSIM, and DCMISS were 22.6d2 (4.2d2), 26.1d2 (3.6d2), and 13.5d2 (2.9d2), respectively. The PM (SD) of residual variance for DCPEN, DCSIM, and DCMISS were 431.4d2 (5.0d2), 371.4d2 (4.5d2), and 262.2d2 (3.4d2), respectively. The PM (SD) of heritability for DCPEN, DCSIM, and DCMISS were 0.05 (0.01), 0.07 (0.01), and 0.05 (0.01), respectively. Simulating trait records for noncalving females resulted in similar heritability to the penalty method but lower residual variance. Pearson correlations between posterior means of animal effects for sires with more than 20 daughters with records were 0.99 between DCPEN and DCSIM, 0.77 between DCPEN and DCMISS, and 0.81 between DCSIM and DCMISS. Of the 424 sires ranked in the top 10% and bottom 10% of sires in DCPEN, 91% and 89%, respectively, were also ranked in the top 10% and bottom 10% in DCSIM. Little difference was observed between DCPEN and DCSIM for correlations between posterior means of animal effects for sires, indicating that no major reranking of sires would be expected. This finding suggests little difference between these two censored data handling techniques for use in genetic evaluation of days to calving.

Animals↗

PubMed2004

Genetic evaluation of calving to first insemination using natural and artificial insemination mating data.

Mating and calving records for 51,084 first-parity heifers in Australian Angus herds were used to examine the relationship between probability of calving to first insemination (CFI) in artificial insemination and natural service (NS) mating data. Calving to first insemination was defined as a binary trait for both sources of data. Two Bayesian models were employed: 1) a bivariate threshold model with CFI in AI data regarded as a trait separate from CFI in NS data and 2) a univariate threshold model with CFI regarded as the same trait for both sources of data. Posterior means (SD) of additive variance in the bivariate analysis were similar: 0.049 (0.013) and 0.075 (0.021) for CFI in AI and NS data, respectively, indicating lack of heterogeneity for this parameter. A similar trend was observed for heritability in the bivariate analysis, with posterior means (SD) of 0.025 (0.007) and 0.048 (0.012) for AI and NS data, respectively. The posterior means (SD) of the additive covariance and corresponding genetic correlation between the traits were 0.048 (0.006) and 0.821 (0.138), respectively. Differences were observed between posterior means for herd-year variance: 0.843 vs. 0.280 for AI and NS data, respectively, which may reflect the higher incidence of 100% conception rates within a herd-year class (extreme category problem) in AI data. Parameter estimates under the univariate model were close to the weighted average of the corresponding parameters under the bivariate model. Posterior means (SD) for additive, herd-year, and service sire variance and heritability under the univariate model were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013), and 0.036 (0.007), respectively. These results indicate that, genetically, cows with a higher probability of CFI when mated using AI also have a high probability of CFI when mated via NS. The high correlation between the two traits, along with the lack of heterogeneity for the additive variance, implies that a common additive variance could be used for AI and NS data. A single-trait analysis of CFI with heterogeneous variances for herd-year and service sire could be implemented. The low estimates of heritability indicate that response to selection for probability of calving to first insemination would be expected to be low.

Animals↗

PubMed2004

Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle.

Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.

Animals↗

PubMed2004

Teat scores in first-parity Gelbvieh cows: relationship with suspensory score and calf growth traits.

Teat and udder suspensory scores from 9,418 first-parity Gelbvieh cows and growth records from 19,119 calves were used to estimate genetic and environmental parameters for teat and suspensory score and to investigate the relationship of teat and suspensory score with calf growth traits and maternal genetic growth effects. First-parity cows did not have multiple records within 280 d, gave birth to one calf, were 4 yr of age or younger at first-calving, and were at least 50% Gelbvieh. Producers scored cows within 24 h of parturition. Teat score (T), a subjective measure of teat size, ranged from 0 (very large) to 50 (very small), and suspensory score (S), a subjective score of udder support, ranged from 0 (very pendulous) to 50 (very tight). Unadjusted birth weight (BW), weaning weight, and yearling weight of the calves, born in the first three parities to cows with first-parity T and S records, were used to calculate pre- and postweaning ADG (WG and YG, respectively). A mixed model was used for the multiple trait analysis of T, S, BW, WG, and YG, which included herd-year, month of calving, age of cow at calving, and sex of calf (included only for BW, WG, and YG) as systematic effects; regression on the percentage of Gelbvieh; and additive animal and maternal genetic of dam (included only for BW and WG), maternal permanent environment (included only for BW and WG), and residual as random effects. The genetic correlation between T and S was 0.95, suggesting that T and S are basically the same trait in this dataset. The genetic correlations between T (S) with direct BW, WG, and YG and with maternal BW and WG were -0.18 (-0.06), 0.38 (0.31), 0.09 (-0.01), -0.16 (-0.16), and -0.47 (-0.55), respectively, suggesting that cows with smaller teats and tighter udders produced less milk and raised calves that had higher genetic growth potential for WG. Further, the Pearson correlations between predicted breeding values of T and S with maternal WG indicated that animals with extremely large teats or pendulous udders may produce more milk, but that the calf may have trouble accessing it. Conversely, with extremely small teats or tight udders, smaller amounts of milk would be produced and there may be a problem producing enough milk to maintain the growing calfs maintenance requirements. Therefore, it may be more beneficial for producers to select animals that have intermediate breeding values for T and S.

Age Factors↗

PubMed2004

Genetic analysis of litter size, parturition length, and birth assistance requirements in primiparous sows using a joint linear-threshold animal model.

The aim of this study was to investigate whether selection for number of live born piglets has led to prolonged parturition and increased requirement for birth assistance, resulting in increased numbers of stillborn piglets. Data were collected from 6,718 primiparous Norwegian Landrace sows farrowing between 2001 and 2003. The need for birth assistance was recorded as a binary response. Physical intervention in the birth of piglets and/or hormonal treatment by the farmer was recorded as birth assistance. The duration of the parturition was analyzed as a binary trait (<4 h and >4 h). The statistical model used for analysis included contemporary groups of herd-year, litter breed, season of farrowing, parity in which the sow was born, a regression on the age of sow at farrowing, an additive genetic effect, and a service sire effect. A full Bayesian approach via Gibbs sampling was adopted to estimate the genetic relationships between these four traits. A total chain length of 100,000 iterations was run. The first 10,000 samples were discarded as burn-in, and the remaining 90,000 iterations were retained without thinning for post-Gibbs analysis. The highest direct heritability was estimated for the number of live-born piglets (h2 = 0.07), followed by the duration of farrowing (h2 = 0.05), the need for birth assistance (h2 = 0.05), and the number of stillborn piglets (h2 = 0.04). The genetic correlations revealed that the number of live and stillborn piglets was uncorrelated; however, the number of live piglets born had a moderate genetic correlation to the need for birth assistance (rg = 0.24 +/- 0.01) and duration of farrowing (rg = -0.20 +/- 0.01), whereas the number of stillborn piglets was highly correlated to the need for birth assistance (rg = 0.74 +/- 0.01) and the duration of parturition (rg = 0.66 +/- 0.01). The duration of farrowing and the need for birth assistance were genetically highly correlated (rg = 0.89 +/- 0.00). For all traits, the service sire variance was approximately one quarter in magnitude compared with its respective genetic variance. The results showed that selection for the number of live born piglets is not expected to influence the number of stillborn piglets. Increasing the number of live piglets born through selection should have a slight negative effect on farrowing duration and a minor increase in the need for birth assistance. Sows with a high genetic potential for birth assistance and prolonged parturition were more likely to give birth to greater numbers of stillborn piglets.

Animals↗

PubMed2004

Genetic components of days open under heat stress.

A reaction norm approach was used to estimate the genetic parameters of days open (DO) with a model that accounted for heat stress. Data included DO records for Georgia, Tennessee, and North Carolina in the Southeastern United States. A fixed effect model included herd-year, month of calving (MOC), age of cow, and a regression on 305-d milk yield. The reaction norm model additionally included the effect of animal with random regression on a heat stress index (HI), calculated as the standardized solutions to MOC derived from the fixed effect model; the residual variance was assumed to be a function of the HI. The shape of the distribution of the HI was close to a sinusoidal function with the highest value in March/April and the lowest value in September. Genetic and residual variances and heritabilities were highest for spring calvings and lowest for fall calvings. The variance associated with the random regression of the highest level of HI was 33% of the genetic variance of the regular animal genetic effect. Genetic correlation between these effects was 0.67. As a validation, DO data were grouped into 4 seasons of calving and treated as different traits. A 4-trait mixed linear model that included the fixed effects listed above except MOC, was used to analyze the grouped data. In general, the estimates of genetic and residual variances of the multiple trait analyses followed those of the reaction norm model. Genetic correlations of spring with summer, and fall with winter were both 0.90. Genetic correlations between spring/summer and fall/winter were around 0.80. The reaction norm model for DO allows inexpensive genetic evaluation of fertility under heat stress. Results of such an evaluation may strongly depend on editing criteria and model specifications.

Analysis of Variance↗

PubMed2004

Genetic analysis of male and female fertility using longitudinal binary data.

A longitudinal Bayesian threshold analysis of insemination events during the first 250 d after calving of first-parity Holsteins was carried out. The outcome of an insemination event was treated as a binary response of either a success (1) or a failure (0). Thus, all breeding information for a cow, including all service sires, was included, thereby allowing for a joint evaluation of male and female fertility. An edited data set of 297,823 insemination records from 151,758 first lactation cows was used. On the liability scale, the model included the systematic effects of herd-year of insemination, technician, month of insemination, and regressions on age of service sire, 3 test days in the first 100 d of lactation (early milk yield), and days in milk at insemination. The random effects in the model were the additive breeding value, the permanent effect of the cow, and the service sire effect. Posterior mean (standard deviation) of the dispersion parameters in the model were 0.034 (0.006), 0.009 (0.001), and 0.171 (0.013) for the additive, service sire, and permanent environmental variances, respectively. The residual variance was fixed at 1, as a result of the nonidentifiability of the threshold model. The posterior mean (standard deviation) of heritability was 0.028 (0.005). This point estimate of heritability is well within the range of available estimates for the trait. Thus, these estimates suggest that some genetic variation exists that can potentially be used to improve reproductive performance or at least avoid its further deterioration. The estimate of the regression coefficient on age of service sire was 0.001, indicating better fertility among older bulls. However, this result has to be interpreted with caution given the preferential use of proven bulls on well-managed cows (as opposed to problem breeders). The estimate of the regression coefficient was negative (-0.005) for early milk yield, as expected, and positive (0.003) for days in milk at insemination. This suggests that high-producing cows are less likely to conceive at the beginning of lactation.

Age Factors↗

PubMed2004

Genetic parameters for days open and pregnancy rates in US Holsteins using different editing criteria.

The influence of various editing criteria for days open (DO) records on genetic parameter estimates of DO and pregnancy rates (PR) in US Holsteins was investigated. Data included first parity 305-d milk yield and DO records from 8 states: Georgia (GA), Florida (FL), North Carolina (NC), Texas (TX), Arizona (AZ), California (CA), New York (NY), and Wisconsin (WI). The pregnancy rate was computed as 1/[(DO - VWP)/HI + 1)], where VWP was the approximate voluntary waiting period and HI was the heat interval set as 21 d. The upper limit for PR was set to 1.0. A bivariate animal model for DO (or PR) and 305-d milk yield was fit separately for each state. The model included fixed effects of herd-year, month of calving, and age of cow, as well as random animal and residual effects. In separate analyses, maximum DO records were limited to 150, 200, 250, 300, and 365 d. Analyses for PR used values of 50, 80, and 120 d for the VWP. Genetic and residual variances for DO were strongly dependent on the upper limit; both variances were 8 times larger as the upper bound increased from 150 to 365 d. Estimates of heritability for DO varied between 0.03 and 0.06. There was a 30% increase in the heritability estimate as the upper limit increased from 150 to 250 d for FL and NC, and small or no increases for the other states. The increase of the upper limit from 250 to 365 d resulted in little change. The genetic correlation between milk and DO was the highest for FL (0.6) and the lowest for GA (0.12 to 0.23). For PR with VWP=50, the heritability was higher than the corresponding estimate for DO in GA, equal to that in AZ, and lower in the remaining states. Heritabilities of PR also varied by the length of VWP; highest heritabilities were obtained at VWP=50 d for GA and AZ; at VWP=80 d for NY and WI; at VWP=120 d for FL, NC, and CA. Increase of genetic variation for records of DO < 250 d was small. Days open and PR are strongly influenced by differences in management protocols among states.

Animal Husbandry↗

PubMed2003

Simulation study of teat score in first-parity Gelbvieh cows: parameter estimation.

Teat scores from 9,598 first-parity Gelbvieh cows were used to investigate the adequacy of grouping approaches to decrease score misclassifications or inconsistencies as well as to simplify the data collection process. The procedure was tested using simulated data and then validated using teat score records of Gelbvieh cattle. First-parity cows were considered to be 4 yr of age or younger at first calving, did not have multiple records within 280 d, and were at least 50% Gelbvieh. Producers scored cows within 24 h of parturition. Teat score, a subjective measure of teat size, ranged from 0 (very large) to 50 (very small). A linear mixed model that included herd-year, month of calving, and age at calving as systematic effects; regression on the percentage of Gelbvieh; and additive breeding values (BV) and residual as random effects was used to generate the data. Simulated data were analyzed using one of three scoring methods: all values (S50), 10 classes (S10), and five classes (S5). The 10 classes were formed by subdividing every five scores into a single class starting at score zero. Similarly, the five classes were formed by combining every 10 scores into one class. The average Pearson correlations, based on five replicates, between the true and estimated BV (systematic effects) were 0.36 (0.85), 0.35 (0.89), and 0.32 (0.87) using S50, S10, and S5, respectively. Average correlations between estimated BV (systematic effects) were 0.97 (0.95), 0.89 (0.92), and 0.92 (0.97) based on S50 and S10, S50 and S5, and S10 and S5, respectively. Field data were used to validate the simulation procedure. The field data were categorized into 10 classes (F10) and five classes (F5) as described for the simulated data. Pearson correlations between estimated BV (systematic effects) were 0.99 (0.93), 0.93 (0.88), and 0.93 (0.96), based on F50 with F10, F50 with F5, and F10 with F5, respectively. The extremely high correlations between predicted BV based on S50, S10, S5, F50, F10, with F5 suggest that a simplified score classification method could be adopted without compromising the expected genetic progress for the trait under consideration. Furthermore, the difference in corresponding Pearson correlations across the field and simulated data might suggest the presence of some inconsistencies or misclassifications of the actual scoring system.

Animals↗

PubMed2003

Genetic change for clinical mastitis in Norwegian cattle: a threshold model analysis.

Records of clinical mastitis on 1.6 million first-lactation daughters of 2,411 Norwegian Cattle sires that were progeny tested from 1978 through 1998 were analyzed with a threshold model. The main objective was to infer genetic change for the disease in the population. A Bayesian approach via Gibbs sampling was used. The model for the underlying liability had age at first calving, month x year of calving, herd x 3-year-period, and sire of the cow as explanatory variables. Posterior mean (SD) of heritability of liability to clinical mastitis was 0.066 (0.003). Genetic evaluations (posterior means) of sires both in the liability and observable scales were computed. Annual genetic change of liability to clinical mastitis for progeny tested bulls born from 1973 to 1993 was assessed. The linear regression of mean sire effect on year of birth had a posterior mean (SD) of -0.00018 (0.0004), suggesting a nearly constant genetic level for clinical mastitis. However, an analysis of sire posterior means by birth-year of daughters indicated an approximately constant genetic level in the cow population from 1976 to 1990 (-0.02%/yr), and a genetic improvement thereafter (-0.27%/yr). This reflects more emphasis on mastitis in selection of bulls in recent years. Corresponding results obtained with a standard linear model analysis were -0.01% and -0.23% per year, respectively (regression of sire predicted transmitting ability on birth-year of daughters). Genetic change seems to be slightly understated with the linear model, assuming the threshold model holds true.

Animals↗