A deregression-based approach to integrate multitrait information into a single pooled phenotype for genetic evaluation

Abstract Background Genetic evaluation of complex traits is often challenged by heterogeneous, incomplete or costly phenotyping. Many biological processes require multiple traits to be characterized. Moreover, combining several traits becomes difficult when the configuration of available traits is complex (e.g., increasing number, complex missing patterns), leading to more demanding computations. To overcome these limitations, a deregression-based pooling method was developed to pre-process multitrait information into a single pseudo-phenotype of the target trait (i.e., a pooled phenotype). This method was assessed in a terminal crossbreeding situation in pigs. Results The behavior of estimated breeding values and the accuracy of reliabilities systems were studied on different datasets (i.e., different data availability scenarios - a real and 3 simulated). Three modeling steps were used: (1) a multitrait model generating input for deregressions, (2) an adjusted scalar deregression, combining information into a pooled phenotype using (3) a single trait model applied to this pooled phenotype to assess how these results match the multitrait predictions from step (1). In all data structure scenarios, unadjusted deregression underestimates breeding values and distorts reliabilities. In contrast, applying expansion and reliability-alignment factors as adjustments restored the distributions and scale of estimated breeding values, yielding a near-perfect agreement with the multitrait model. Across all simulated scenarios, including the one with the complete absence of the target trait, the adjusted deregression process achieved the highest correlations between true and estimated breeding values. Conclusions The deregression-based process provides an effective and flexible approach for combining heterogeneous multitrait information into a unique pooled phenotype in a single trait setting, while maintaining genetic scale and reliability of the target trait in a multiple trait setting. Unlike classical deregression, the adjusted deregression process prevents overshrinking, maintains accuracy and reproduces the behavior of a multitrait evaluation. This pre-processing would be a practical solution for integrating complex or partially recorded traits into routine genetic evaluation pipelines. This method is applicable even if the main target trait is limited or missing, provided that moderately to highly correlated traits are available. The strategy could be extended to other situations as long as any double-counting can be avoided.