Biology subjects

Doeschl-Wilson, A.

Publications and source records attributed to Doeschl-Wilson, A..

5 recordsLinked to original sources

bioRxiv2025

Revealing the hidden social structure of pigs with AI assisted automated monitoring data and social network analysis

Social interactions of farm animals affect their performance, health and welfare. The recent advances in AI-automated monitoring technologies offer digital phenotypes, at low-cost, that record the animals in real-time. This proof-of-concept study addresses, for the first time, the hypothesis that applying social network analysis (SNA) on automated data could potentially facilitate the analysis of social structures of farm animals. Data was collected using automated recording systems that captured 2D camera images and videos of pigs in six pens (16-19 animals each) on a PIC breeding company farm (USA). The system provided real-time data, including ear-tag readings, elapsed time, posture (standing, lying, sitting), and XY coordinates of the shoulder and rump for each pig. Weighted SNA was performed, based on the proximity of "standing" animals, for two 3-day periods: the early growing period (first month after mixing) and the later period (60 days post-mixing). Group level degree, betweenness, and closeness centralization showed a significant increase from the early growing period to the later one (p<0.02), highlighting the pig[s] social dynamics over time. Largest clique size remained unchanged (p=0.28), but the number of maximal cliques significantly decreased from the early to late growing period (p=0.007). Individual SNA traits were stable over these periods, except for closeness centrality and clustering coefficient which significantly increased (p<0.00001). This study demonstrates that combining AI-assisted monitoring technologies with SNA offers an efficient, real-time approach to gain novel insights into animal social interactions. This approach can optimize on-farm management or breeding practices, leading to improved animal performance, health, and welfare. HighlightsO_LISocial network analysis (SNA) applied to automated monitoring data provides novel insight into social interactions of pigs. C_LIO_LIGroup level SNA centralization traits highlighted pigs social dynamics over time. C_LIO_LIThe stability of key individual SNA traits can be leveraged for breeding purposes. C_LIO_LIAI-assisted monitoring combined with SNA help optimizing management practices on commercial farms. C_LI

animal behavior and cognition↗

bioRxiv2022

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

BackgroundInfectious disease spread in populations is controlled by individuals susceptibility (propensity to acquire infection), infectivity (propensity to pass on infection to others) and recoverability (propensity to recover/die). Estimating the effects of genetic risk factors on these host epidemiological traits can help reduce disease spread through genetic control strategies. However, the effects of previously identified disease resistance SNPs on these epidemiological traits are usually unknown. Recent advances in computational statistics make it now possible to estimate the effects of single nucleotide polymorphisms (SNPs) on these traits from longitudinal epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known how to optimally design disease transmission experiments or field studies to maximise the precision at which pleiotropic SNP effects estimates for susceptibility, infectivity and recoverability can be estimated. ResultsWe develop and validate analytical expressions for the precision of SNP effects estimates on the three host traits assuming a disease transmission experiment with one or more non-interacting contact groups. Maximising these leads to three distinct experimental designs, each specifying a different set of ideal SNP genotype compositions across groups: a) appropriate for a single contact-group, b) a multi-group design termed "pure", and c) a multi-group design termed "mixed", where pure and mixed refer to contact groups consisting of individuals with the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than infectivity. Data from multiple groups were found more informative about infectivity effects than from a single group containing the same number of individuals. Whilst the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferable because it uses information from naturally occurring infections rather than those artificially induced. The same optimal design principles apply to estimating other categorical fixed effects, such as vaccinations status, helping to more effectively quantify their epidemiological impact. An online software tool SIRE-PC has been developed which calculates the precision of estimated substitution and dominance effects of a single SNP (or vaccine status) associated with all three traits depending on experimental design parameters. ConclusionsThe developed methodology and software tool can be used to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables underlying host susceptibility, infectivity and recoverability.

genetics↗

bioRxiv2020

A putative genomic map for resistance of Bos indicus cattle in Cameroon to bovine tuberculosis

Bovine Tuberculosis (bTB) caused by Mycobacterium bovis is a livestock disease of global economic and public health importance. There are currently no effective vaccines available for livestock and so control relies on animal level surveillance and pasteurisation of dairy products. A new alternative control approach is to exploit the genetic variability of the host; recent studies have demonstrated that breeding Bos taurus cattle for increased resistance to bTB is feasible. The utility of such an approach is still unknown for the Bos indicus cattle population. This study aims to assess genetic variation in bTB resistance and the underlying genomic architecture in Bos indicus breeds in Cameroon. We conducted a cross-sectional study of slaughter cattle in Cameroon and genotyped a sample of 213 cattle. Their genomic diversity was characterised using PCA, hierarchical clustering and admixture analysis. We assessed genetic variation in bTB resistance using heritability analysis and compared quantitative trait loci. Previous studies had found that breed was an important factor in explaining the epidemiology of bTB, with Fulani cattle appearing to be more susceptible than mixed breeds. However, we show that the apparent phenotypic differences in visual appearance between the breeds was not reflected by clear genomic differences. At the genetic level, cattle belonging to different hierarchical genomic clusters differed in their susceptibility to bTB. There was evidence of a genomic association between M. bovis infection status with specific SNPs. We highlight the need to understand the challenges faced by livestock in specific settings both in terms of pathogens and the environment, in addition to their intended purpose and how they fit into a defined management system. It is only at this point livestock keepers can then make informed breeding choices, not only for resistance to disease but also for increasing production.

genetics↗

bioRxiv2019

Transmission from vaccinated hosts can cause dose-dependent reduction in pathogen virulence

Many livestock and human vaccines are leaky as they block symptoms but do not prevent infection or onward transmission. This leakiness is concerning as it increases vaccination coverage required to prevent disease spread, and can promote evolution of increased pathogen virulence. Despite leakiness, vaccination may reduce pathogen load, affecting disease transmission dynamics. However, the impacts on post-transmission disease development and infectiousness in contact individuals are unknown. Here, we use transmission experiments involving Mareks disease virus in chickens to show that vaccination with a leaky vaccine substantially reduces viral load in both vaccinated individuals and unvaccinated contact individuals they infect. Consequently, contact birds are less likely to develop disease symptoms or die, show less severe symptoms, and shed less infectious virus themselves, when infected by vaccinated birds. These results highlight that even partial vaccination with a leaky vaccine can have unforeseen positive consequences in controlling the spread and symptoms of disease.

microbiology↗

bioRxiv2019

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

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.

epidemiology↗