Short communication: The importance of random effects in detecting purging of inbreeding depression: A model comparison in Pannon White rabbits

•Importance of random dam effects in modelling inbreeding load purging.•Assessment of predictive power in modelling by Receiver-operating characteristic curve.•The impact of inbreeding on kits survival at birth in Pannon White rabbits.•Power similarity of “ancestral inbreeding” and “Inbreeding-Purging” models.•Superior predictive power of models including random dam effects. Inbreeding depression (ID) is a well-documented phenomenon associated with reduced fitness and possible extinction. However, ID can be mitigated or even eliminated through the interplay of inbreeding and selection, a process known as purging. The aim of this study was to compare the predictive power of two commonly used approaches in models with and without random dam effects to detect purging (full and reduced models). Specifically, we compared the full and reduced models based on the Kalinowski ancestral inbreeding coefficient (KAL) with the full and reduced inbreeding-purging (IP) models based on the purged inbreeding coefficient. Our analysis utilised kindling records from 1 379 Pannon rabbits born between 1992 and 1997. We modelled the effects of dam and litter inbreeding on kit survival at birth (zero/one), an important fitness trait, along with the effects of parity and the effects of kindling season. Random dam effects were only considered in the full KAL and IP models. We assessed the classification abilities (predictive power) of these models using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, where larger areas under the curve (AUC) indicate better classification performance. The full KAL model (AUC-ROC = 0.8156, AUC-PR = 0.9861) and the full IP model (AUC-ROC = 0.8152, AUC-PR = 0.9860), both of which include random dam effects, demonstrated high predictive power based on both methods. In contrast, the reduced KAL model (AUC-ROC = 0.5730, AUC-PR = 0.9553) and the reduced IP model (AUC-ROC = 0.5686, AUC-PR = 0.9546), which did not include random dam effects, had significantly lower AUC values. Based on our empirical results using the receiver ROC and PR curves, it could be concluded that the inclusion of random dam effects significantly increased the predictive power of the KAL and IP approaches. This finding has high importance as the inclusion of ’polygenic’ random effects is not standard - and possibly never applied - in the IP models, unlike in KAL models where their use is more common.