Resumo: Epidemics can significantly affect animal production and generate large economic impacts. Furthermore, current practices for treating and controlling infectious diseases in farmed animals do not always show the desired effectiveness. In these cases, quantitative genetics offers a viable alternative through the study of genetic variations of host characteristics that affect disease transmission, especially susceptibility and infectivity. In this study, we propose a flexible dynamic non-linear indirect genetic effects model (dnIGE) which better captures the dynamics of infectious diseases transmission and consequently improves the estimation of genetic effects on infectivity. Results show that the proposed model accurately estimates heritabilities and genetic values associated with susceptibility and infectivity, even when there is a genetic correlation between these traits. Furthermore, we analyze the impact of different experimental designs in the accuracy of estimates using synthetic data sets. Joint work with Milena Nascimento Lima e Osvaldo Anacleto
Bio: Daiane é bacharel e mestre em Estatística pela UFSCar e doutora em Estatística pelo PIPGEs com período sanduíche na Universidade do Texas em Austin. Há 7 anos é professora do Departamento de Estatística da UFSCar e agora coordenadora do PIPGEs. Tem atuado principalmente na seleção e estimação de modelos de mistura independente ou com dependência (HMM), mapeamento de QTL com dependência familiar, inferência Bayesiana paramétrica e não-paramétrica e métodos computacionais MCMC com aplicação, principalmente, em Genômica e Biologia Molecular.
