Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

Abstract

Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUP method, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

“…The increase in coverage is another highlighted aspect of GS [60,61]. However, Sousa et al (2019) [62] applied GS in polyploid hybrids of Coffea spp. and suggested that once the optimal number of SNPs is reached, a plateau in terms of the increase in selective accuracy is observed, and then decreases.…”

Functional Markers for Precision Plant Breeding

“…Different types of molecular markers have been developed: restriction fragment length polymorphism (RFLP) [ 14 ], random amplification of polymorphic DNA (RAPD) [ 15 , 16 ], amplified fragment length polymorphism (AFLP) [ 17 ], microsatellite or simple sequence repeat (SSR) [ 18 , 19 ], sequence characterized amplified region (SCARs) [ 20 ], cleaved amplified polymorphic sequences (CAPS) [ 21 ], single nucleotide polymorphism (SNP) [ 22 ], and diversity arrays technology (DArT) markers [ 23 ]. Among these, SNP markers have gained the widest use [ 24 ]. Most of these marker types are random DNA markers (RDMs), which may be lost during recombination.…”

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

“…Trait data must be in Linkage Disequilibrium-LD or genetic auto-correlation (e.g., Kelleher et al, 2012), with the molecular markers or with the samples' genetic co-ancestry. GP utility has been demonstrated ( Table 1) in model forest tree species such as Eucalyptus Suontama et al, 2019), and conifers as Pinus Li et al, 2019) and Douglas-fir (Thistlethwaite et al, 2017(Thistlethwaite et al, , 2019b, but also in non-model perennial crops such as coffee (Sousa et al, 2018), rubber (Cros et al, 2019;Souza et al, 2019) and oil palm (Cros et al, 2015). GP may even fit epigenetics (Roudbar et al, 2020), as well as multi-trait genomic models as was recently confirmed in Norway spruce for growth, wood quality and weevil resistance traits (Lenz et al, 2020).…”