Low-density marker panels for genomic prediction in Coffea arabica L.

Abstract

Developing new cultivars, particularly in perennial species like Coffea arabica, can be a time-consuming process. Employing molecular markers in genome-wide selection (GWS) for predicting genetic values offers an alternative to accelerate this process. However, implementing GWS typically involves genotyping many markers for both training and candidate individuals, which can increase the total genotyping cost for the breeding program. Therefore, this study aimed to assess the feasibility of using low-density marker panels to predict the genetic merit of C. arabica for a range of desirable agronomic traits. For this purpose, GWS analyses were performed using the G-BLUP method with panels of varying marker densities, selected based on marker effect magnitude. The results indicate that employing lower-density panels might be advantageous for this species' improvement. Models based on these panels yielded accurate predictions for various traits and demonstrated high agreement in terms of selected individuals compared to more complex models.