Potential negative effects of genomic selection

Abstract

Abstract Initial findings on genomic selection (GS) indicated substantial improvement for major traits, such as performance, and even successful selection for antagonistic traits. However, recent unofficial reports indicate an increased frequency of deterioration of secondary traits. This phenomenon may arise due to the mismatch between the accelerated selection process and resource allocation. Traits explicitly or implicitly accounted for by a selection index move toward the desired direction, whereas neglected traits change according to the genetic correlations with selected traits. Historically, the first stage of commercial genetic selection focused on production traits. After long-term selection, production traits improved, whereas fitness traits deteriorated, although this deterioration was partially compensated for by constantly improving management. Adding these fitness traits to the breeding objective and the used selection index also helped offset their decline while promoting long-term gains. Subsequently, the trend in observed fitness traits was a combination of a negative response due to genetic antagonism, positive response from inclusion in the selection index, and a positive effect of improving management. Under GS, the genetic trends accelerate, especially for well-recorded higher heritability traits, magnifying the negatively correlated responses for fitness traits. Then, the observed trend for fitness traits can become negative, especially because management modifications do not accelerate under GS. Additional deterioration can occur due to the rapid turnover of GS, as heritabilities for production traits can decline and the genetic antagonism between production and fitness traits can intensify. If the genetic parameters are not updated, the selection index will be inaccurate, and the intended gains will not occur. While the deterioration can accelerate for unrecorded or sparsely recorded fitness traits, GS can lead to an improvement for widely recorded fitness traits. In the context of GS, it is crucial to look for unexpected changes in relevant traits and take rapid steps to prevent further declines, especially in secondary traits. Changes can be anticipated by investigating the temporal dynamics of genetic parameters, especially genetic correlations. However, new methods are needed to estimate genetic parameters for the last generation with large amounts of genomic data.