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Predicting vaccine effectiveness in livestock populations: A theoretical framework applied to PRRS virus infections in pigs.

ABSTRACT: Vaccines remain one of the main tools to control infectious diseases in domestic livestock. Although a plethora of veterinary vaccines are on the market and routinely applied to protect animals against infection with particular pathogens, the disease in question often continues to persist, sometimes at high prevalence. The limited effectiveness of certain vaccines in the field leaves open questions regarding the required properties that an effective vaccine should have, as well as the most efficient vaccination strategy for achieving the intended goal of vaccination programmes. To date a systematic approach for studying the combined effects of different types of vaccines and vaccination strategies is lacking. In this paper, we develop a theoretical framework for modelling the epidemiological consequences of vaccination with imperfect vaccines of various types, administered using different strategies to herds with different replacement rates and heterogeneity in vaccine responsiveness. Applying the model to the Porcine Reproductive and Respiratory Syndrome (PRRS), which despite routine vaccination remains one of the most significant endemic swine diseases worldwide, we then examine the influence of these diverse factors alone and in combination, on within-herd virus transmission. We derive threshold conditions for preventing infection invasion in the case of imperfect vaccines inducing limited sterilizing immunity. The model developed in this study has practical implications for the development of vaccines and vaccination programmes in livestock populations not only for PRRS, but also for other viral infections primarily transmitted by direct contact.

SUBMITTER: Bitsouni V

PROVIDER: S-EPMC6716781 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Predicting vaccine effectiveness in livestock populations: A theoretical framework applied to PRRS virus infections in pigs.

Bitsouni Vasiliki V Lycett Samantha S Opriessnig Tanja T Doeschl-Wilson Andrea A

PloS one 20190830 8

Vaccines remain one of the main tools to control infectious diseases in domestic livestock. Although a plethora of veterinary vaccines are on the market and routinely applied to protect animals against infection with particular pathogens, the disease in question often continues to persist, sometimes at high prevalence. The limited effectiveness of certain vaccines in the field leaves open questions regarding the required properties that an effective vaccine should have, as well as the most effic ...[more]

PMID: 31469850

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Project description:BackgroundHeritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.ResultsHere, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).ConclusionsHeritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Project description:ObjectivePropose a theoretical framework for retinal biomarkers of Alzheimer's disease (AD).BackgroundThe retina and brain share important biological features that are relevant to AD. Developing retinal biomarkers of AD is a strategic priority but as yet none have been validated for clinical use. Part of the reason may be that fundamental inferential assumptions have been overlooked. Failing to recognize these assumptions will disadvantage biomarker discovery and validation, but incorporating them into analyses could facilitate translation.New theoryThe biological assumption that a disease causes analogous effects in the brain and retina can be expressed within a Bayesian network. This allows inferences about abstract theory and individual events, and provides an opportunity to falsify the foundational hypothesis of retina-brain analogy. Graphical representation of the relationships between variables simplifies comparison between studies and facilitates judgements about whether key assumptions are valid given the current state of knowledge.Major challengesThe framework provides a visual approach to retinal biomarkers and may help to rationalize analysis of future studies. It suggests possible reasons for inconsistent results in existing literature on AD biomarkers.Linkage to other theoriesThe framework can be modified to describe alternative theories of retinal biomarker biology, such as retrograde degeneration resulting from brain disease, and can incorporate confounding factors such as co-existent glaucoma or macular degeneration. Parallels with analogue confirmation theory and surrogate marker validation suggest strengths and weaknesses of the framework that can be anticipated when developing analysis plans.HighlightsRetinal biomarkers hold great promise for Alzheimer's disease (AD), but none are currently used clinically.Assumptions about the cause of retinal and brain changes are often overlooked, and this may disadvantage biomarker discovery and validation.We present a new approach to retinal biomarkers that describes cause and effect graphically in a Bayesian network.We show how this allows a more complete assessment of how well a biomarker might reflect the brain, and how data from right and left eyes can be used to rule out poor biomarker candidates.

Dataset Information

Predicting vaccine effectiveness in livestock populations: A theoretical framework applied to PRRS virus infections in pigs.

Publications

Predicting vaccine effectiveness in livestock populations: A theoretical framework applied to PRRS virus infections in pigs.

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