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

Edilaine Silva  Arcanjo; Moyses Nascimento; Camila Ferreira Azevedo; Eveline Teixeira  Caixeta; Antônio Carlos Baião de  Oliveira; Antonio Alves  Pereira; Ana Carolina Campana Nascimento

doi:10.4025/actasciagron.v47i1.69698

Edilaine Silva Arcanjo Universidade Federal de Viçosa https://orcid.org/0000-0003-2862-1020
Moyses Nascimento Universidade Federal de Viçosa https://orcid.org/0000-0001-5886-9540
Camila Ferreira Azevedo Universidade Federal de Viçosa https://orcid.org/0000-0003-0438-5123
Eveline Teixeira Caixeta Instituto de Biotecnologia Aplicada à Agropecuária / Empresa Brasileira de Pesquisa Agropecuária https://orcid.org/0000-0001-8850-6273
Antônio Carlos Baião de Oliveira Empresa Brasileira de Pesquisa Agropecuária / Empresa de Pesquisa Agropecuária de Minas Gerais https://orcid.org/0000-0003-0348-8657
Antonio Alves Pereira Empresa de Pesquisa Agropecuária de Minas Gerais https://orcid.org/0000-0002-4252-3913
Ana Carolina Campana Nascimento Universidade Federal de Viçosa https://orcid.org/0000-0002-6985-1490

DOI: https://doi.org/10.4025/actasciagron.v47i1.69698

Keywords: genetic improvement; coffee; genomic selection; G-BLUP.

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.

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

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