Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs

Genetic selection against boar taint, which is caused by high skatole and androstenone concentrations in fat, is a more acceptable alternative than is the current practice of castration. Genomic predictors offer an opportunity to overcome the limitations of such selection caused by the phenotype being expressed only in males at slaughter, and this study evaluated different approaches to obtain such predictors. Samples from 1000 pigs were included in a design which was dominated by 421 sib pairs, each pair having one animal with high and one with low skatole concentration (≥0.3 μg/g). All samples were measured for both skatole and androstenone and genotyped using the Illumina SNP60 porcine BeadChip for 62 153 single nucleotide polymorphisms. The accuracy of predicting phenotypes was assessed by cross‐validation using six different genomic evaluation methods: genomic best linear unbiased prediction (GBLUP) and five Bayesian regression methods. In addition, this was compared to the accuracy of predictions using only QTL that showed genome‐wide significance. The range of accuracies obtained by different prediction methods was narrow for androstenone, between 0.29 (Bayes Lasso) and 0.31 (Bayes B), and wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies, corrected for h², were 0.54–0.56 and 0.75–0.94 for androstenone and skatole respectively. The whole‐genome evaluation methods gave greater accuracy than using only the QTL detected in the data. The results demonstrate that GBLUP for androstenone is the simplest genomic technology to implement and was also close to the most accurate method. More specialised models may be preferable for skatole.     

Shrinkage Estimators for Covariance Matrices

Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big. A standard approach to more stably estimating the matrix in small samples is to compute the ML or REML estimator under some simple structure that involves estimation of fewer parameters, such as compound symmetry or independence. However, these estimators will not be consistent unless the hypothesized structure is correct. If interest focuses on estimation of regression coefficients with correlated (or longitudinal) data, a sandwich estimator of the covariance matrix may be used to provide standard errors for the estimated coefficients that are robust in the sense that they remain consistent under misspecification of the covariance structure. With large matrices, however, the inefficiency of the sandwich estimator becomes worrisome. We consider here two general shrinkage approaches to estimating the covariance matrix and regression coefficients. The first involves shrinking the eigenvalues of the unstructured ML or REML estimator. The second involves shrinking an unstructured estimator toward a structured estimator. For both cases, the data determine the amount of shrinkage. These estimators are consistent and give consistent and asymptotically efficient estimates for regression coefficients. Simulations show the improved operating characteristics of the shrinkage estimators of the covariance matrix and the regression coefficients in finite samples. The final estimator chosen includes a combination of both shrinkage approaches, i.e., shrinking the eigenvalues and then shrinking toward structure. We illustrate our approach on a sleep EEG study that requires estimation of a 24 x 24 covariance matrix and for which inferences on mean parameters critically depend on the covariance estimator chosen. We recommend making inference using a particular shrinkage estimator that provides a reasonable compromise between structured and unstructured estimators.     

恶性卵巢肿瘤的Bayes预测

文献〔1〕通过逐步回归确定了与卵巢肿瘤有关的五个主要因素:年龄(X_1)、生育关系(x_2)、肿瘤大小(x_3)、肿瘤硬度(X_4)、并发症(X_5),并将这五个主要因素的指标分级如表1,给出最大似然诊断法。本文在此基础上,根据延边医学院1981年1月至1985年12月底经手术治疗的237例肿瘤病人的统计数据,对〔1〕的结果进一步研究与改进,给出了恶性卵巢肿瘤的Bayes诊     

Model fitting and evaluation in climate reconstruction of tree-ring data: A comment on Steinschneider et al. (2017): Hierarchical regression models for dendroclimatic standardization and climate reconstruction

Steinschneider et al. (2017) investigate model choices made in the hierarchical climate reconstruction approach of Schofield et al. (2016). We identify two flaws in their approach. The first is the use of an unusual approximation to Bayesian inference that unnecessarily discards important information. The second is that they mischaracterize the robustness of their reconstructions due to overlooking important features of the out-of-sample predictions. We demonstrate how full Bayesian inference can be conducted with no additional effort, providing R/JAGS code. We also show how graphical visualization of the out-of-sample predictions can lead to better understanding and comparison of the models fitted.     

Accounting for multiplicity in the evaluation of "signals" obtained by data mining from spontaneous report adverse event databases

Surveillance of drug products in the marketplace continues after approval, to identify rare potential toxicities that are unlikely to have been observed in the clinical trials carried out before approval. This surveillance accumulates large numbers of spontaneous reports of adverse events along with other information in spontaneous report databases. Recently developed empirical Bayes and Bayes methods provide a way to summarize the data in these databases, including a quantitative measure of the strength of the reporting association between the drugs and the events. Determining which of the particular drug-event associations, of which there may be many tens of thousands, are real reporting associations and which random noise presents a substantial problem of multiplicity because the resources available for medical and epidemiologic followup are limited. The issues are similar to those encountered with the evaluation of microarrays, but there are important differences. This report compares the application of a standard empirical Bayes approach with micorarray-inspired methods for controlling the False Discovery Rate, and a new Bayesian method for the resolution of the multiplicity problem to a relatively small database containing about 48,000 reports. The Bayesian approach appears to have attractive diagnostic properties in addition to being easy to interpret and implement computationally.     

Extrapolation in Risk Assessment: Improving the Quantification of Uncertainty,and Improving Information to Reduce the Uncertainty

Risk assessments inevitably extrapolate from the known to the unknown. The resulting calculation of risk involves two fundamental kinds of uncertainty: uncertainty owing to intrinsically unpredictable (random) components of the future events, and uncertainty owing to imperfect prediction formulas (parameter uncertainty and error in model structure) that are used to predict the component that we think is predictable. Both types of uncertainty weigh heavily both in health and ecological risk assessments. Our first responsibility in conducting risk assessments is to ensure that the reported risks correctly reflect our actual level of uncertainty (of both types). The statistical methods that lend themselves to correct quantification of the uncertainty are also effective for combining different sources of information. One way to reduce uncertainty is to use all the available data. To further sharpen future risk assessments, it is useful to partition the uncertainty between the random component and the component due to parameter uncertainty, so that we can quantify the expected reduction in uncertainty that can be achieved by investing in a given amount of future data. An example is developed to illustrate the potential for use of comparative data, from toxicity testing on other species or other chemicals, to improve the estimates of low-effect concentration in a particular case with sparse case-specific data.     

The role of game management in wildlife populations: uncertainty analysis of expert knowledge

Uncertainties about future states of wildlife populations make it difficult to pre-adapt to possible threats and ensure sustainability of resources and harvesting over the long term. This uncertainty is partly due to the unknown impact and future states of many factors that explain population sizes and variation. In this paper, the effect of local game management activities on the uncertainty of future population sizes of groups of Finnish wildlife species (ungulates, forest grouse, large predators, small predators and mountain hare) was analysed using expert knowledge and the Bayesian belief networks (BBNs) modelling techniques. As a result, the current knowledge and agreement of the relationships between wildlife population sizes and the game management activities explaining their variation as well as trends are evaluated. Information given to hunters and the number of hunters were seen as the most effective factors for the management of game populations. However, there were great uncertainties in the expectations regarding future trends in the management activities, especially in feeding, and there was disagreement in the direction of the trend in the length of the hunting season. The trends in the size of forest grouse populations were viewed as the most uncertain trend among species groups. At the same time, forest grouse were seen as the most regulated species group by local game management. Among interest variables, experts were very uncertain and they disagreed about the direction of the trend in the recreational value of hunting.