(untitled) (Indian Journal of Animal Production and Management, 2024)
Exploring the realm of behavioral genetics holds profound significance in research, given the intricate and varied behavioral manifestations displayed by domestic animals. These behaviors, marked by both commonalities and distinctions across species, wield considerable influence over animal well-being and productivity. This comprehensive review delves into the genetic dimensions of behavior, elucidating the application of behavioral genetics in breeding initiatives and its pivotal role in unravelling the inherent genetic diversity within these traits. In the context of cattle, temperament traits are pivotal components of their overall well-being, encompassing responses to various activities such as handling, milking, and challenges like human interactions during calving or engagements with conspecifics. The genetic foundations of these traits have been subjects of rigorous study, with heritabilities estimated, and in some cases, quantitative trait loci (QTL) pinpointed. Despite the notable variability in these traits, certain handling temperament traits exhibit moderate heritabilities, rendering them amenable to selective breeding. However, the integration of temperament traits into selection programs remains somewhat limited. Animals typically undergo screening for behaviors like aggression or excessive fear during handling or milking, resulting in the removal of extreme cases. While estimated breeding values (EBVs) for temperament are occasionally determined, these traits are not consistently incorporated into selection indices, despite their potential economic, welfare, and human safety implications. Challenges include the collection of behavioral data on sufficiently large animal populations for estimating genetic parameters and the complexity of assigning economic values to temperament traits. The discourse also considers the implications of primarily selecting for productivity traits on temperament and welfare. Moving forward, opportunities lie in capitalizing on automated data collection methods and more extensive utilization of genomic information in the selection process.