Bayesian inference in genetic parameter estimation of visual scores in Nellore beef-cattle

The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits.     

A periodic analysis of longitudinal binary responses: a case study of clinical mastitis in Norwegian Red cows

A Bayesian procedure for analyzing longitudinal binary responses using a periodic cosine function was developed. It was assumed that, after adjustment for "seasonal" effects, the oscillation of the underlying latent variables for longitudinal binary responses was a stationary series. Based on this assumption, a single dimension sinusoidal analysis of longitudinal binary responses using the Gibbs sampling and Metropolis algorithms was implemented in a study of clinical mastitis records of Norwegian Red cows taken over five lactations.     

Exploration of lagged relationships between mastitis and milk yield in dairycows using a Bayesian structural equation Gaussian-threshold model

A Gaussian-threshold model is described under the general framework of structural equation models for inferring simultaneous and recursive relationships between binary and Gaussian characters, and estimating genetic parameters. Relationships between clinical mastitis (CM) and test-day milk yield (MY) in first-lactation Norwegian Red cows were examined using a recursive Gaussian-threshold model. For comparison, the data were also analyzed using a standard Gaussian-threshold, a multivariate linear model, and a recursive multivariate linear model. The first 180 days of lactation were arbitrarily divided into three periods of equal length, in order to investigate how these relationships evolve in the course of lactation. The recursive model showed negative within-period effects from (liability to) CM to test-day MY in all three lactation periods, and positive between-period effects from test-day MY to (liability to) CM in the following period. Estimates of recursive effects and of genetic parameters were time-dependent. The results suggested unfavorable effects of production on liability to mastitis, and dynamic relationships between mastitis and test-dayMYin the course of lactation. Fitting recursive effects had little influence on the estimation of genetic parameters. However, some differences were found in the estimates of heritability, genetic, and residual correlations, using different types of models (Gaussian-threshold vs. multivariate linear).     

Do complex population histories drive higher estimates of substitution rate in phylogenetic reconstructions?

Our curiosity about biodiversity compels us to reconstruct the evolutionary past of species. Molecular evolutionary theory now allows parameterization of mathematically sophisticated and detailed models of DNA evolution, which have resulted in a wealth of phylogenetic histories. But reconstructing how species and population histories have played out is critically dependent on the assumptions we make, such as the clock-like accumulation of genetic differences over time and the rate of accumulation of such differences. An important stumbling block in the reconstruction of evolutionary history has been the discordance in estimates of substitution rate between phylogenetic and pedigree-based studies. Ancient genetic data recovered directly from the past are intermediate in time scale between phylogenetics-based and pedigree-based calibrations of substitution rate. Recent analyses of such ancient genetic data suggest that substitution rates are closer to the higher, pedigree-based estimates. In this issue, Navascués & Emerson (2009) model genetic data from contemporary and ancient populations that deviate from a simple demographic history (including changes in population size and structure) using serial coalescent simulations. Furthermore, they show that when these data are used for calibration, we are likely to arrive at upwardly biased estimates of mutation rate.     

Genetic management of infectious diseases: a heterogeneous epidemio-genetic model illustrated with S. aureus mastitis

Given that individuals are genetically heterogeneous in their degree of resistance to infection, a model is proposed to formulate appropriate choices that will limit the spread of an infectious disease. The model is illustrated with data on S. aureus mastitis and is based on parameters characterizing the spread of the disease (contact rate, probability of infection after contact, and rate of recovery after infection), the demography (replacement and culling rates) and the genetic composition (degree of relationship and heritability of the disease trait) of the animal population. To decrease infection pressure, it is possible to apply non-genetic procedures that increase the culling (e.g., culling of chronically infected cows) and recovery (e.g., antibiotic therapy) rates of infected cows. But the contribution of the paper is to show that genetic management of infectious disease is also theoretically possible as a control measure complementary to non-genetic actions. Indeed, the probability for an uninfected individual to become infected after contact with an infected one is partially related to their degree of kinship: the more closely they are related, the more likely they are to share identical genes like those associated to the non-resistance to infection. Different prospective genetic management procedures are proposed to decrease the contact rate between infected and uninfected relatives and keep the number of secondary cases generated by one infected animal below 1.     

Using phylochronology to reveal cryptic population histories: review and synthesis of 29 ancient DNA studies

The evolutionary history of a population involves changes in size, movements and selection pressures through time. Reconstruction of population history based on modern genetic data tends to be averaged over time or to be biased by generally reflecting only recent or extreme events, leaving many population historic processes undetected. Temporal genetic data present opportunities to reveal more complex population histories and provide important insights into what processes have influenced modern genetic diversity. Here we provide a synopsis of methods available for the analysis of ancient genetic data. We review 29 ancient DNA studies, summarizing the analytical methods and general conclusions for each study. Using the serial coalescent and a model-testing approach, we then re-analyse data from two species represented by these data sets in a common interpretive framework. Our analyses show that phylochronologic data can reveal more about population history than modern data alone, thus revealing 'cryptic' population processes, and enable us to determine whether simple or complex models best explain the data. Our re-analyses point to the need for novel methods that consider gene flow, multiple populations and population size in reconstruction of population history. We conclude that population genetic samples over large temporal and geographical scales, when analysed using more complex models and the serial coalescent, are critical to understand past population dynamics and provide important tools for reconstructing the evolutionary process.     

A general approach to mixed effects modeling of residual variances in generalized linear mixed models

We propose a general Bayesian approach to heteroskedastic error modeling for generalized linear mixed models (GLMM) in which linked functions of conditional means and residual variances are specified as separate linear combinations of fixed and random effects. We focus on the linear mixed model (LMM) analysis of birth weight (BW) and the cumulative probit mixed model (CPMM) analysis of calving ease (CE). The deviance information criterion (DIC) was demonstrated to be useful in correctly choosing between homoskedastic and heteroskedastic error GLMM for both traits when data was generated according to a mixed model specification for both location parameters and residual variances. Heteroskedastic error LMM and CPMM were fitted, respectively, to BW and CE data on 8847 Italian Piemontese first parity dams in which residual variances were modeled as functions of fixed calf sex and random herd effects. The posterior mean residual variance for male calves was over 40% greater than that for female calves for both traits. Also, the posterior means of the standard deviation of the herd-specific variance ratios (relative to a unitary baseline) were estimated to be 0.60 ± 0.09 for BW and 0.74 ± 0.14 for CE. For both traits, the heteroskedastic error LMM and CPMM were chosen over their homoskedastic error counterparts based on DIC values.     

Investigation of three strategies for an international genetic evaluation of beef cattle weaning weight

Weaning weights from 83 389 Limousin calves born between 1993 and 2002 in France and the Trans-Tasman block (Australia/New Zealand) were analysed to compare different strategies for running an international genetic evaluation for the breed. These records were a subset of the complete data for both countries and comprised a sample of herds that had recorded progeny of sires used across both countries. Genetic and phenotypic parameters for weaning weight were estimated within the countries. The estimates of direct genetic heritabilities were higher in France than in the Trans-Tasman block (0.31 vs. 0.22), while direct-maternal genetic correlations were less negative in the Trans-Tasman block (-0.10) than in France (-0.21). Different strategies for an international evaluation were studied, and the correlations between the estimated breeding values (EBV) of national evaluations and these strategies were derived. The international evaluation strategies were a) an animal model on raw performance data with non unity genetic correlations and heterogeneous residual and genetic variances across countries; b) the same animal model applied to pre-corrected (for fixed effects) performance data; and c) a sire model on de-regressed proofs (MACE). Estimates of the genetic correlations between weaning weight in both countries were 0.86 (0.80) for direct (maternal) genetic effects for the first strategy. Estimation of variance components by MACE appeared to be very sensitive to the sample of bulls and their reliability approximations. Variance component estimates obtained using pre-corrected data were inconsistent with estimates on raw data. However, the EBV predicted using pre-corrected data and parameters estimated from the raw data were similar to those predicted from raw data. Correlations between national and international EBV were always high (> 0.90) for sires, whichever genetic effect (direct or maternal) or international evaluation model was considered. The ranking of the bulls in the top 100 is of primary interest in terms of international genetic evaluation. In this study, some re-ranking of sires was observed for the top 100 bulls between countries and between the three international evaluation models. Thus, the origin of top sires may vary according to the implemented international evaluation strategy.