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Background: We adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common SNP improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients.Methods: Adapted Bayesian sequential threshold models in combination with LASSO were applied to consider the time-to-event and the censoring nature of data. We studied 822 NMIBC patients followed-up >10 years. The study outcomes were time-to-first-recurrence and time-to-progression. The predictive ability of the models including up to 171,304 SNP and/or 6 clinico-pathological prognosticators was evaluated using AUC-ROC and determination coefficient.Results: Clinico-pathological prognosticators explained a larger proportion of the time-to-first-recurrence (3.1 %) and time-to-progression (5.4 %) phenotypic variances than SNPs (1 and 0.01 %, respectively). Adding SNPs to the clinico-pathological-parameters model slightly improved the prediction of time-to-first-recurrence (up to 4 %). The prediction of time-to-progression using both clinico-pathological prognosticators and SNP did not improve. Heritability (ĥ (2)) of both outcomes was <1 % in NMIBC.Conclusions: We adapted a Bayesian statistical learning method to deal with a large number of parameters in prognostic studies. Common SNPs showed a limited role in predicting NMIBC outcomes yielding a very low heritability for both outcomes. We report for the first time a heritability estimate for a disease outcome. Our method can be extended to other disease models. [ABSTRACT FROM AUTHOR]

We developed and implemented change-point recursive models and compared them with a linear recursive model and a standard mixed model (SMM), in the scope of the relationship between litter size (LS) and number of stillborns (NSB) in pigs. The proposed approach allows us to estimate the point of change in multiple-segment modeling of a nonlinear relationship between phenotypes. We applied the procedure to a data set provided by a commercial Large White selection nucleus. The data file consisted of LS and NSB records of 4,462 parities. The results of the analysis clearly identified the location of the change points between different structural regression coefficients. The magnitude of these coefficients increased with LS, indicating an increasing incidence of LS on the NSB ratio. However, posterior distributions of correlations were similar across subpopulations (defined by the change points on LS), except for those between residuals. The heritability estimates of NSB did not present differences between recursive models. Nevertheless, these heritabilities were greater than those obtained for SMM (0.05) with a posterior probability of 85%. These results suggest a nonlinear relationship between LS and NSB, which supports the adequacy of a change-point recursive model for its analysis. Furthermore, the results from model comparisons support the use of recursive models. However, the adequacy of the different recursive models depended on the criteria used: the linear recursive model was preferred on account of its smallest deviance value, whereas nonlinear recursive models provided a better fit and predictive ability based on the cross-validation approach. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the impact of calving ease (CE) on functional longevity of Basque Holsteins, using a Weibull proportional hazards model. The data considered for the analysis were 53,353 calving records from 25,810 Holstein cows distributed across 781 herds and sired by 746 bulls. The effects included in the statistical model were age at first calving, stage of lactation, interaction between year and season of calving, 305-d adjusted milk yield, CE, herd, and sire. Calving ease was considered as a time-dependent covariate and, as was the case for the rest of covariates included in the model, had a significant effect on functional longevity. Calvings needing assistance or surgery increased culling risk by 18%, when compared with unassisted calvings. The effect of CE on length of productive life in primiparous and multiparous cows was also investigated. A second analysis was performed replacing the CE effect with the interaction between parity and CE to evaluate the effect of CE in primiparous and multiparous cows. An increase in calving difficulty had a greater impact on culling during first lactations than in subsequent ones. Therefore, difficult calvings, mainly at first parities, had a high impact on herd amortization costs, increasing them by 10% in relation to easy calvings. Therefore, calving difficulty should be avoided as much as possible, especially in primiparous cows, to avoid reduction of profitability. [ABSTRACT FROM AUTHOR]

The impact of different breeding goals on the genetic response for calving ease (CE) and yield traits was studied in the Basque Holstein cattle population. The economic value for CE was estimated with a bioeconomic model, using Basque production and market circumstances and taking into account the categorical nature of CE. The economic value for CE was -€18.03/cow per calving interval per liability unit. This value was relatively insensitive to changes in the market price of animals but was more sensitive to changes in the incidence of dystocia. Records from parities between 1995 and 2002 were used for the estimation of genetic parameters for yield (actual milk, fat, and protein yield) and CE using a multivariate model. Linear sire models for yield traits and a threshold sire-maternal grandsire model for CE were used. A Holstein population was simulated to determine the consequences of including CE in the breeding goal. Three selection strategies were considered: 1) selection only on yield traits, 2) selection on yield and direct CE (DCE), and 3) selection on yield, DCE, and maternal CE (MCE). Selection on yield traits only resulted in a slight reduction of dystocia. Selection strategies in which DCE or DCE and MCE were included in the breeding goal did not improve the genetic response for DCE and MCE obtained with the first selection strategy. Genetic responses were also calculated using the 2.5th, 50th, and 97.5th percentiles of posterior densities of genetic correlations between DCE and MCE and yield traits. Because responses in CE were sensitive to deviations in estimates of genetic parameters, the inclusion of CE in the monitoring scheme is recommended. Genetic evaluation of bulls for CE is of considerable value because it provides farmers with the opportunity to use assortative matings of sires with favorable estimated breeding values for DCE to primiparous COWS. [ABSTRACT FROM AUTHOR]