Handling errors in the response: Considerations for leveraging unsupervised or incomplete data for genetic evaluations

Accurate genetic evaluations rely on high-quality phenotypic data; however, measurement errors and data inconsistencies-such as those arising from unsupervised or incomplete sources-pose challenges to their reliability. This study investigates the effect of response errors on genetic evaluations across continuous and categorical traits. We introduce an additive measurement error model to illustrate how phenotypic errors influence genetic effects and variance estimation. Next, we examine a binary trait scenario, demonstrating the utility of sensitivity and specificity in adjusting observed incidence rates for misclassified data. To further illustrate genetic evaluation in the presence of misclassifications, we proposed a mixed effects liability model assuming unequal sensitivity and specificity or varied false-positive and false-negative rates. Our findings underscore the necessity of integrating measurement error models into genetic evaluation frameworks to reduce bias and enhance predictive accuracy.