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Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine-mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci, functional pathways, and differentially expressed genes subject to physiological states. Multiple quantitative trait loci were detected for all recorded performance and clinical disease traits. The major histocompatibility complex region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified 4 quantitative trait loci in the major histocompatibility complex region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects, explaining 0.8-27.1% of genetic variance for growth rate and for multiple clinical disease traits. This single-nucleotide polymorphism was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The major histocompatibility complex region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the major histocompatibility complex region with treatment or mortality rates (-0.62 to -0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity quantitative trait loci, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the major histocompatibility complex and other quantitative trait loci that harbor immune-related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The major histocompatibility complex region was pleiotropic for growth rate under challenge and for clinical disease traits. Four quantitative trait loci were identified across the class I, II, and III subregions of the major histocompatibility complex for nursery growth rate under challenge, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects. The major histocompatibility complex and other quantitative trait loci identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified single-nucleotide polymorphism in the major histocompatibility complex class I subregion.

Telemetry and mark-recapture provide movement information, but each approach comes with tradeoffs, potentially producing conflicting understandings of fish movement patterns. Using a Bayesian framework that allows exchanging priors from either method may help assess these inconsistencies. We evaluated channel catfish Ictalurus punctatus movements in the Red River of the North and Lake Winnipeg system, which impacts harvest management across different jurisdictions and affects different ecosystems (e.g., lotic and lentic). Channel catfish were tagged with T-bar tags or acoustic transmitters. The resulting movement data were modeled using a Bayesian multi-state Cormack-Jolly-Seber model to estimate survival, movement, and recapture probabilities. Model estimates with uninformative priors showed a greater tendency of downstream movement from the Red River into Lake Winnipeg for the T-bar tags. In contrast, the telemetry method showed fish predominantly stay in the river. However, exchanging increasingly stronger prior information from the alternative method's model revealed that telemetry movement estimates were less sensitive than the T-bar model to informative priors. Using priors from both methods provided a transparent means to assessing tagging approach tradeoffs quantitatively. Key words: Bayesian, multi-state Cormack-Jolly-Seber model, informative priors, telemetry, mark-recapture Si les techniques de telemetrie et de marquage-recapture fournissent de l'information sur les deplacements, des compromis sont associes a chacune de ces approches, qui peuvent mener a des interpretations contradictoires des motifs de deplacements des poissons. L'utilisation d'un cadre bayesien qui permet l'echange de renseignements prealables de l'une a l'autre de ces methodes peut aider a evaluer ces incoherences. Nous avons evalue les deplacements de barbues de riviere Ictalurus punctatus dans le reseau de la riviere Rouge et du lac Winnipeg, qui ont une incidence sur la gestion des prises dans differents territoires et sur differents ecosystemes (p. ex. lotiques et lentiques). Des barbues de riviere ont ete dotees d'etiquettes en T ou d'emetteurs acoustiques. Les donnees de deplacement resultantes ont ete modelisees en utilisant un modele bayesien multi-etats de Cormack-Jolly-Seber afin d'estimer la survie, les deplacements et les probabilites de recapture. Les estimations du modele integrant des renseignements prealables non informatifs montrent une plus forte tendance de deplacements vers l'aval de la riviere Rouge vers le lac Winnipeg pour les etiquettes en T. La telemetrie montre en revanche que les poissons restent de maniere preponderante dans la riviere. L'echange de renseignements prealables de plus en plus forts entre les modeles des deux methodes revele que les estimations des deplacements par telemetrie sont moins sensibles aux renseignements prealables informatifs que celles du modele pour les etiquettes en T. L'utilisation de renseignements prealables issus des deux methodes offre une approche transparente pour evaluer de maniere quantitative les compromis associes aux differentes methodes de marquage. [Traduit par la Redaction] Mots-cles : bayesien, modele mutli-etats de Cormack-Jolly-Seber, renseignements prealables informatifs, telemetrie, marquage-recapture, 1. Introduction Incorporating the spatial dynamics of fisheries into decision-making remains a challenge for fisheries management as management perspectives transition from local to ecosystem scales (Berkeley et al. 2004; Ciannelli [...]

[This corrects the article DOI: 10.3389/fgene.2020.00362.].

Linkage disequilibrium (LD), often expressed in terms of the squared correlation (r 2) between allelic values at two loci, is an important concept in many branches of genetics and genomics. Genetic drift and recombination have opposite effects on LD, and thus r 2 will keep changing until the effects of these two forces are counterbalanced. Several approximations have been used to determine the expected value of r 2 at equilibrium in the presence or absence of mutation. In this paper, we propose a probability-based approach to compute the exact distribution of allele frequencies at two loci in a finite population at any generation t conditional on the distribution at generation t - 1. As r 2 is a function of this distribution of allele frequencies, this approach can be used to examine the distribution of r 2 over generations as it approaches equilibrium. The exact distribution of LD from our method is used to describe, quantify, and compare LD at different equilibria, including equilibrium in the absence or presence of mutation, selection, and filtering by minor allele frequency. We also propose a deterministic formula for expected LD in the presence of mutation at equilibrium based on the exact distribution of LD.

The LEE pathogenicity island has been acquired on multiple occasions within the different lineages of enteropathogenic and enterohemorrhagic Escherichia coli. In each lineage, LEE expression is regulated by complex networks of pathways, including core pathways shared by all lineages and lineage-specific pathways. Within the O157:H7 lineage of enterohemorrhagic E. coli, strain-to-strain variation in LEE expression has been observed, implying that expression patterns can diversify even within highly related subpopulations. Using comparative genomics of E. coli O157:H7 subpopulations, we have identified one source of strain-level variation affecting LEE expression. The variation occurs in prophage-dense regions of the genome that lie immediately adjacent to the late regions of the pch prophage carrying pchA, pchB, pchC, and a newly identified pch gene, pchX. Genomic segments extending from the holin S region to the pchA, pchB, pchC, and pchX genes of their respective prophage are highly conserved but are nonetheless embedded within adjacent genomic segments that are extraordinarily variable, termed pch adjacent genomic regions (pch AGR). Despite the remarkable degree of variation, the pattern of variation in pch AGR is highly correlated with the distribution of phylogenetic markers on the backbone of the genome. Quantitative analysis of transcription from the LEE1 promoter further revealed that variation in the pch AGR has substantial effects on absolute levels and patterns of LEE1 transcription. Variation in the pch AGR therefore serves as a mechanism to diversify LEE expression patterns, and the lineage-specific pattern of pch AGR variation could ultimately influence ecological or virulence characteristics of subpopulations within each lineage.

Thirteen different serotypes of Listeria monocytogenes can be distinguished on the basis of variation in somatic and flagellar antigens. Although the known virulence genes are present in all serotypes, greater than 90% of human cases of listeriosis are caused by serotypes 1/2a, 1/2b, and 4b and nearly all outbreaks of food-borne listeriosis have been caused by serotype 4b strains. Phylogenetic analysis of these three common clinical serotypes places them into two different lineages, with serotypes 1/2b and 4b belonging to lineage I and 1/2a belonging to lineage II. To begin examining evolution of the genome in these serotypes, DNA microarray analysis was used to identify lineage-specific and serotype-specific differences in genome content. A set of 44 strains representing serotypes 1/2a, 1/2b, and 4b was probed with a shotgun DNA microarray constructed from the serotype 1/2a strain 10403s. Clones spanning 47 different genes in 16 different contiguous segments relative to the lineage II 1/2a genome were found to be absent in all lineage I strains tested (serotype 4b and 1/2b) and an additional nine were altered exclusively in 4b strains. Southern hybridization confirmed that conserved alterations were, in all but two loci, due to absence of the segments from the genome. Genes within these contiguous segments comprise five functional categories, including genes involved in synthesis of cell surface molecules and regulation of virulence gene expression. Phylogenetic reconstruction and examination of compositional bias in the regions of difference are consistent with a model in which the ancestor of the two lineages had the 1/2 somatic serotype and the regions absent in the lineage I genome arose by loss of ancestral sequences.

Survival or failure time traits such as herd life and days open are both important economically and pose a number of challenges to an analysis based on linear mixed models. The main features of a survival trait are that it is the time until some event occurs, and some of the observations are censored. Survival models and the associated estimation procedures provide a flexible means of modeling survival traits. In this paper I will discuss the application of survival analysis based on the Weibull distribution. The components that make up a survival model will be presented along with their interpretation. Issues related to the model construction and estimation will be presented. Key Words: survival, failure time, mixed model)