346 Investigating the potential of incorporating indirect genetic effects into genetic evaluations of dairy calf disease traits
Notice bibliographique
Résumé
Abstract The goal of infectious disease control is often local eradication, but this is theoretically impossible to achieve based on classical quantitative genetic theory. Current methods focus on the susceptibility of individuals to disease and assume that exposure to a pathogen is 1) constant over time, 2) equal among individuals, and (3) due entirely to the environment. For this reason, it is likely that conventional genetic methods are capturing only a fraction of genetic variation in disease occurrence. The incorporation of epidemiological theory into quantitative genetics provides an opportunity to better determine the level of genetic variation in infectious disease traits. This stems from the ability to include the positive feed-back dynamics of infectious disease transmission. From an epidemiolocal perspective, both susceptibility and infectivity also have indirect genetic effects (IGE), because the genotype of one individual impacts the risk of infection of other individuals, and this can drastically affect the rate and direction of response to selection. Epidemiological models unravel the genetic heterogeneity in both susceptibility and infectivity traits, and account for the impact each animal has on its contemporaries compared with conventional quantitative genetic approaches. From a calf health perspective, there is a shift towards group housing dairy calves, primarily due to welfare and social acceptability. In turn, this will lead to increased animal interactions from a young age. Therefore, the aim of this study was to investigate the potential of incorporating IGE into current quantitative approaches to determine its impact on selection potential. Our study looked at two common infectious diseases in calves on dairy farms: respiratory problems (RESP) and diarrhea (DIAR). Producer-recorded data comprised of 19,445 records collected on 34 herds that group housed calves between 2007 and 2020. Calves were allocated into pen groups based on birth dates and herd specific housing practices. Original phenotypes were split into 10 records, each representing a week of life, to determine when exactly calves became sick and infective. Several scenarios were investigated with respect to the time an animal was infective for following a disease case (1 or 2 wk), and the maximum difference in weeks between animal birth dates to be included within the same pen (2 to 5 wk). Variance components were estimated using a generalized linear mixed model fitting a complementary-log-log link function with offset for exposure. Initial heritability estimates for susceptibility on the observed scale for DIAR ranged from 0.03 to 0.07, and from 0.02 to 0.03 for RESP across scenarios. While heritability estimates for infectivity are currently being investigated, the results for susceptibility highlight the potential for incorporating IGE into genetic evaluation of disease traits and provide an introduction towards incorporating epidemiological theory into classical quantitative approaches to better respect the transmission dynamics of infections.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».