Conditioning on the causal network prevents indirect response to selection

BONAMY, M; FERNANDEZ, ME; GIOVAMBATTISTA, G; MUNILLA, S

JOURNAL OF ANIMAL BREEDING AND GENETICS-ZEITSCHRIFT FUR TIERZUCHTUNG UND ZUCHTUNGSBIOLOGIE

WILEY-BLACKWELL PUBLISHING, INC

Lugar: Londres; Año: 2023 vol. 141 p. 42 - 51

0931-2668

Multiple trait animal models (MTM) allow to estimate the breeding values (BV)of several traits simultaneously while accounting for genetic and environmentalcorrelations among them. However, relationships among traits may not be reciprocalbut rather causal in nature. In these cases, and given a causal network,structural equations models (SEM) arise as a more appropriate methodology.Although MTM and SEM have been shown to be parametrically equivalent, theestimated breeding value (EBV) obtained from either one or the other should beinterpreted differently. In this study, we investigated the impact of using theseestimates on the response to selection for a causal network comprising five differenttraits through a stochastic simulation experiment. Three different selectiontargets were assayed, involving traits located upstream, midstream and downstreamthis causal network. We first considered the case in which traits werecausally related but not genetically correlated. The current results support ourhypothesis that MTM will absorb causal relationships as genetic correlations and,consequently, change the response to selection achieved as compared with SEM.We found no differences on the response to selection when the target trait was locatedat the top of the causal network, but noticeable differences were detected onupstream traits when selection pressure was placed on midstream or downstreamtraits. We also assayed a scenario in which causal effects and genetic correlationsact simultaneously and found that selection based on BVs estimated using SEMdiminished the indirect response in traits upstream the causal network.