A zero-inflated poisson model for genetic analysis of the number of mastitis cases in Norwegian Red cows

Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota

Background

The rabbit cecum hosts and interacts with a complex microbial ecosystem that contributes to the variation of traits of economic interest. Although the influence of host genetics on microbial diversity and specific microbial taxa has been studied in several species (e.g., humans, pigs, or cattle), it has not been investigated in rabbits. Using a Bayes factor approach, the aim of this study was to dissect the effects of host genetics, litter and cage on 984 microbial traits that are representative of the rabbit microbiota.

Results

Analysis of 16S rDNA sequences of cecal microbiota from 425 rabbits resulted in the relative abundances of 29 genera, 951 operational taxonomic units (OTU), and four microbial alpha-diversity indices. Each of these microbial traits was adjusted with mixed linear and zero-inflated Poisson (ZIP) models, which all included additive genetic, litter and cage effects, and body weight at weaning and batch as systematic factors. The marginal posterior distributions of the model parameters were estimated using MCMC Bayesian procedures. The deviance information criterion (DIC) was used for model comparison regarding the statistical distribution of the data (normal or ZIP), and the Bayes factor was computed as a measure of the strength of evidence in favor of the host genetics, litter, and cage effects on microbial traits. According to DIC, all microbial traits were better adjusted with the linear model except for the OTU present in less than 10% of the animals, and for 25 of the 43 OTU with a frequency between 10 and 25%. On a global scale, the Bayes factor revealed substantial evidence in favor of the genetic control of the number of observed OTU and Shannon indices. At the taxon-specific level, significant proportions of the OTU and relative abundances of genera were influenced by additive genetic, litter, and cage effects. Several members of the genera Bacteroides and Parabacteroides were strongly influenced by the host genetics and nursing environment, whereas the family S24-7 and the genus Ruminococcus were strongly influenced by cage effects.

Conclusions

This study demonstrates that host genetics shapes the overall rabbit cecal microbial diversity and that a significant proportion of the taxa is influenced either by host genetics or environmental factors, such as litter and/or cage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00738-2.

Invited review: Incidence, risk factors, and effects of clinical mastitis recurrence in dairy cows

Clinical mastitis (CM) is one of the most frequent and costly diseases in dairy cows. A frustrating aspect of CM is its recurrent nature. This review was conducted to synthesize knowledge on risk of repeated cases of CM, effects of recurrent CM cases, and risk factors for CM recurrence. A systematic review methodology was used to identify articles for this narrative review. Searches were performed to identify relevant scientific literature published after 1989 in English or French from 2 databases (PubMed and CAB Abstracts) and 1 search platform (Web of Science). Fifty-seven manuscripts were selected for qualitative synthesis according to the inclusion criteria. Among the 57 manuscripts selected in this review, a description of CM recurrence, its risk factors, and effects were investigated and reported in 33, 37, and 19 selected manuscripts, respectively. Meta-analysis and meta-regression analyses were used to compute risk ratio comparing risk of CM in cows that already had 1 CM event in the current lactation with risk of CM in healthy cows. For these analyses, 9 manuscripts that reported the total number of lactations followed and the number of lactations with ≤1 and ≤2 CM cases were used. When summarizing results from studies requiring ≥5 d between CM events to consider a CM event as a new case, we observed no significant change in CM susceptibility following a first CM case (risk ratio: 0.99; 95% confidence interval: 0.86-1.14). However, for studies using a more liberal CM recurrence definition (i.e., only 24 h between CM events to consider new CM cases), we observed a 1.54 times greater CM risk (95% confidence interval: 1.20-1.97) for cows that already had 1 CM event in the current lactation compared with healthy cows. The most important risk factors for CM recurrence were parity (i.e., higher risk in older cows), a higher milk production, pathogen species involved in the preceding case, and whether a bacteriological cure was observed following the preceding case. The most important effects of recurrent CM were the milk yield reduction following a recurrent CM case, which was reported to be similar to that of the first CM case, and the increased risk of culling and mortality, which were reported to surpass those of first CM cases.

Estimating the prevalence and intensity of Schistosoma mansoni infection among rural communities in Western Tanzania: The influence of sampling strategy and statistical approach

BACKGROUND

Schistosoma mansoni is a parasite of major public health importance in developing countries, where it causes a neglected tropical disease known as intestinal schistosomiasis. However, the distribution of the parasite within many endemic regions is currently unknown, which hinders effective control. The purpose of this study was to characterize the prevalence and intensity of infection of S. mansoni in a remote area of western Tanzania.

METHODOLOGY/PRINCIPAL FINDINGS

Stool samples were collected from 192 children and 147 adults residing in Gombe National Park and four nearby villages. Children were actively sampled in local schools, and adults were sampled passively by voluntary presentation at the local health clinics. The two datasets were therefore analysed separately. Faecal worm egg count (FWEC) data were analysed using negative binomial and zero-inflated negative binomial (ZINB) models with explanatory variables of site, sex, and age. The ZINB models indicated that a substantial proportion of the observed zero FWEC reflected a failure to detect eggs in truly infected individuals, meaning that the estimated true prevalence was much higher than the apparent prevalence as calculated based on the simple proportion of non-zero FWEC. For the passively sampled data from adults, the data were consistent with close to 100% true prevalence of infection. Both the prevalence and intensity of infection differed significantly between sites, but there were no significant associations with sex or age.

CONCLUSIONS/SIGNIFICANCE

Overall, our data suggest a more widespread distribution of S. mansoni in this part of Tanzania than was previously thought. The apparent prevalence estimates substantially under-estimated the true prevalence as determined by the ZINB models, and the two types of sampling strategies also resulted in differing conclusions regarding prevalence of infection. We therefore recommend that future surveillance programmes designed to assess risk factors should use active sampling whenever possible, in order to avoid the self-selection bias associated with passive sampling.

A comparison of nonlinear mixed models and response to selection of tick-infestation on lambs

Tick-borne fever (TBF) is stated as one of the main disease challenges in Norwegian sheep farming during the grazing season. TBF is caused by the bacterium Anaplasma phagocytophilum that is transmitted by the tick Ixodes ricinus. A sustainable strategy to control tick-infestation is to breed for genetically robust animals. In order to use selection to genetically improve traits we need reliable estimates of genetic parameters. The standard procedures for estimating variance components assume a Gaussian distribution of the data. However, tick-count data is a discrete variable and, thus, standard procedures using linear models may not be appropriate. Thus, the objectives of this study were twofold: 1) to compare four alternative non-linear models: Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial based on their goodness of fit for quantifying genetic variation, as well as heritability for tick-count and 2) to investigate potential response to selection against tick-count based on truncation selection given the estimated genetic parameters from the best fit model. Our results showed that zero-inflated Poisson was the most parsimonious model for the analysis of tick count data. The resulting estimates of variance components and high heritability (0.32) led us to conclude that genetic determinism is relevant on tick count. A reduction of the breeding values for tick-count by one sire-dam genetic standard deviation on the liability scale will reduce the number of tick counts below an average of 1. An appropriate breeding scheme could control tick-count and, as a consequence, probably reduce TBF in sheep.

Assessing the mandatory bovine abortion notification system in France using unilist capture-recapture approach

The mandatory bovine abortion notification system in France aims to detect as soon as possible any resurgence of bovine brucellosis. However, under-reporting seems to be a major limitation of this system. We used a unilist capture-recapture approach to assess the sensitivity, i.e. the proportion of farmers who reported at least one abortion among those who detected such events, and representativeness of the system during 2006-2011. We implemented a zero-inflated Poisson model to estimate the proportion of farmers who detected at least one abortion, and among them, the proportion of farmers not reporting. We also applied a hurdle model to evaluate the effect of factors influencing the notification process. We found that the overall surveillance sensitivity was about 34%, and was higher in beef than dairy cattle farms. The observed increase in the proportion of notifying farmers from 2007 to 2009 resulted from an increase in the surveillance sensitivity in 2007/2008 and an increase in the proportion of farmers who detected at least one abortion in 2008/2009. These patterns suggest a raise in farmers' awareness in 2007/2008 when the Bluetongue Virus (BTV) was detected in France, followed by an increase in the number of abortions in 2008/2009 as BTV spread across the country. Our study indicated a lack of sensitivity of the mandatory bovine abortion notification system, raising concerns about the ability to detect brucellosis outbreaks early. With the increasing need to survey the zoonotic Rift Valley Fever and Q fever diseases that may also cause bovine abortions, our approach is of primary interest for animal health stakeholders to develop information programs to increase abortion notifications. Our framework combining hurdle and ZIP models may also be applied to estimate the completeness of other clinical surveillance systems.

Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

Background

Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis.

Methods

Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations.

Results

Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process.

Conclusions

The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health.

Utilizing prospective sequence analysis of SHH, ZIC2, SIX3 and TGIF in holoprosencephaly probands to describe the parameters limiting the observed frequency of mutant gene×gene interactions

Clinical molecular diagnostic centers routinely screen SHH, ZIC2, SIX3 and TGIF for mutations that can help to explain holoprosencephaly and related brain malformations. Here we report a prospective Sanger sequence analysis of 189 unrelated probands referred to our diagnostic lab for genetic testing. We identified 28 novel unique mutations in this group (15%) and no instances of deleterious mutations in two genes in the same subject. Our result extends that of other diagnostic centers and suggests that among the aggregate 475 prospectively sequenced holoprosencephaly probands there is negligible evidence for direct gene-gene interactions among these tested genes. We model the predictions of the observed mutation frequency in the context of the hypothesis that gene×gene interactions are a prerequisite for forebrain malformations, i.e. the "multiple-hit" hypothesis. We conclude that such a direct interaction would be expected to be rare and that more subtle genetic and environmental interactions are a better explanation for the clinically observed inter- and intra-familial variability.

Comparison of different models for genetic analysis of clinical mastitis in Austrian Fleckvieh dual-purpose cows

The performance of different models for genetic analyses of clinical mastitis in Austrian Fleckvieh dual-purpose cows was evaluated. The main objective was to compare threshold sire models (probit and logit) with linear sire and linear animal models using REML algorithm. For comparison, data were also analyzed using a Bayesian threshold sire model. The models were evaluated with respect to ranking of sires and their predictive ability in cross-validation. Only minor differences were observed in estimated variance components and heritability from Bayesian and REML probit models. Heritabilities for probit and logit models were 0.06 and 0.08, respectively, whereas heritabilities for linear sire and linear animal models were lower (0.02). Correlations among ranking of sires from threshold and linear sire models were high (>0.99), whereas correlations between any sire model (threshold or linear) and the linear animal model were slightly lower (0.96). The worst sires were ranked very similar across all models, whereas for the best sires some reranking occurred. Further, models were evaluated based on their ability to predict future data, which is one of the main concerns of animal breeders. The predictive ability of each model was determined by using 2 criteria: mean squared error and Pearson correlation between predicted and observed value. Overall, the 5 models did not differ in predictive ability. In contrast to expectations, sire models had the same predictive ability as animal models. Linear models were found to be robust toward departures from normality and performed equally well as threshold models.

Robust QTL fine mapping by applying a quantitative transmission disequilibrium test to the Mendelian sampling term

In many farm animal populations, high-density single nucleotide polymorphism (SNP) genotypes are becoming available on a large scale, and routine estimation of breeding values is implemented for a multiplicity of traits. We propose to apply the basic principle of the quantitative transmission disequilibrium test (QTDT) to estimated Mendelian sampling terms. A two-step procedure is suggested, where in the first step additive breeding values are estimated with a mixed linear model and the Mendelian sampling terms are calculated from the estimated breeding values. In the second step, the QTDT is applied to these estimated Mendelian sampling terms. The resulting test is expected to yield significant results if the SNP is in sufficient linkage disequilibrium and linkage with quantitative trait loci (QTL). This principle is illustrated with a simulated data set comprising 4665 individuals genotyped for 6000 SNP and 15 true QTL. Thirteen of the fifteen QTL were significant on a genome-wide 0.1% error level. Results for the empirical power are derived from repeated samples of 1000 and 3000 genotyped individuals, respectively. General properties and potential extensions of the methodology are indicated. Owing to its computational simplicity and speed, the suggested procedure is well suited to scan whole genomes with high-density SNP coverage in samples of substantial size and for a multiplicity of different traits.

A genetic analysis of mortality in pigs

An analysis of mortality is undertaken in two breeds of pigs: Danish Landrace and Yorkshire. Zero-inflated and standard versions of hierarchical Poisson, binomial, and negative binomial Bayesian models were fitted using Markov chain Monte Carlo (MCMC). The objectives of the study were to investigate whether there is support for genetic variation for mortality and to study the quality of fit and predictive properties of the various models. In both breeds, the model that provided the best fit to the data was the standard binomial hierarchical model. The model that performed best in terms of the ability to predict the distribution of stillbirths was the hierarchical zero-inflated negative binomial model. The best fit of the binomial hierarchical model and of the zero-inflated hierarchical negative binomial model was obtained when genetic variation was included as a parameter. For the hierarchical binomial model, the estimate of the posterior mean of the additive genetic variance (posterior standard deviation in brackets) at the level of the logit of the probability of a stillbirth was 0.173(0.039) in Landrace and 0.202(0.048) in Yorkshire. The implications of these results from a breeding perspective are briefly discussed.

Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows

Clinical mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of mastitis, it has rarely been used for studying the genetics of mastitis. Many models have been proposed for genetic analysis of mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for mastitis in dairy cattle. The response variables were clinical mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, ..). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy cows and had a similar performance between them.