Over the years, breeding programs have sought efficient strategies to select genotypes with superior performance. Genome-wide selection (GWS) emerged in 2001, aiming to increase efficiency and accelerate selection gain. This technique is considered essential in the breeding of perennial species, such as Coffea canephora, mainly due to the potential to increase the gain per unit of time. Thus, this study aimed to apply the GWS principle, evaluate the efficiency of this technique in C. canephora population using SNP molecular markers, and evaluate eight main phenotypic traits. A total of 165 genotypes were evaluated, being 51 of varietal group of Conilon, 32 of Robusta, and 82 intervarietal hybrids. Through the sequencing of the RAPiD Genomics company, 18,111 SNP markers were identified, of which 14,429 were used after quality analysis. All traits showed good predictive capacity, except for fruit maturation time, fruit size, and yield per plant. The lower values of genomic heritability found for these traits may justify the low values of predictive capacity obtained. The accuracy values estimated were considered as moderate to high, ranging from 67 to 82%. By shortening the cycle time from 6 to 3 years, GWS provided selective efficiency ranging from 22 to 146%. Results revealed that GWS provides higher gains per unit of time. Therefore, GWS proved to be a useful and promising tool for the breeding of C. canephora for accurately predicting the individuals’ genotypes, shortening the time required to complete the selection cycle and providing gains in selective efficiency per unit of time.

多年来，育种计划一直在寻求有效的策略来选择具有优异性能的基因型。全基因组选择（GWS）于2001年问世，旨在提高效率并加快选择增益。该技术被认为是多年生物种（例如Coffea canephora）的育种中必不可少的，主要是因为它有可能增加单位时间的增益。因此，本研究旨在应用GWS原理，评估该技术在C. canephora中的效率。人群使用SNP分子标记，并评估八个主要表型性状。共评估了165个基因型，其中Conilon品种组为51个，Robusta品种为32个，杂种间82个。通过RAPiD Genomics公司的测序，鉴定出18,111个SNP标记，在质量分析后使用了14,429个。除果实成熟时间，果实大小和单株产量外，所有性状均具有良好的预测能力。这些性状的基因组遗传力值较低可能证明所获得的预测能力值较低。估计的准确性值被认为是中等到较高，范围从67％到82％。通过将周期时间从6年缩短到3年，GWS提供了22至146％的选择性效率。结果表明，GWS每单位时间提供了更高的收益。因此，C. canephora用于准确预测个体的基因型，缩短完成选择周期所需的时间，并提高单位时间的选择效率。