Phenotypic traits may exert causal effects between them. For example, on the one hand,
high yield in dairy cows may increase the liability to certain diseases and, on the other hand …

Structural equation models allow causal effects between 2 or more variables to be
considered and can postulate unidirectional (recursive models; RM) or bidirectional …

Integrating genetic perturbations with gene expression data not only improves accuracy of
regulatory network topology inference, but also enables learning of causal regulatory …

Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by
multiple-trait random regression models (RRM). The reliability and bias of genomic …

The aim of this study was to compare genetic (co) variance components and prediction
accuracies of breeding values from genomic random regression models (gRRM) and …

We conducted a longitudinal study to evaluate the effect of non-aureus staphylococci (NAS)
causing subclinical intramammary infections (IMI) on quarter milk somatic cell count (qSCC) …

Structural equation models (SEMs) are multivariate specifications capable of conveying
causal relationships among traits. Although these models offer insights into how phenotypic …

Test‐day (TD) records of milk, fat‐to‐protein ratio (F: P) and somatic cell score (SCS) of first‐
lactation Canadian Holstein cows were analysed by a three‐trait finite mixture random …

An accurate and efficient statistical method for repeated measures analysis and selection of
scion/rootstock combinations (SRC) is a fundamental prerequisite for genetic progress in …

The link between phenotypic plasticity and heterosis is a broad fundamental question, with
stakes in breeding. We report a case-study evaluating temporal series of wood ring traits of …