Identification of SNP Markers and Candidate Genes Associated with Major Agronomic Traits in <i>Coffea arabica</i>
Ruane Alice da Silva,
Eveline Teixeira Caixeta,
Letícia de Faria Silva,
Tiago Vieira Sousa,
Pedro Ricardo Rossi Marques Barreiros,
Antonio Carlos Baião de Oliveira,
Antonio Alves Pereira,
Cynthia Aparecida Valiati Barreto,
Moysés Nascimento
Affiliations
Ruane Alice da Silva
Biotechnology Applied to Agriculture Institute (Bioagro), Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
Eveline Teixeira Caixeta
Biotechnology Applied to Agriculture Institute (Bioagro), Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
Letícia de Faria Silva
Biotechnology Applied to Agriculture Institute (Bioagro), Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
Tiago Vieira Sousa
Biological Sciences Center, Iturama University Campus, Universidade Federal do Triângulo Mineiro (UFTM), Iturama 38025-180, Brazil
Pedro Ricardo Rossi Marques Barreiros
Biotechnology Applied to Agriculture Institute (Bioagro), Federal University of Viçosa (UFV), Viçosa 36570-900, Brazil
Antonio Carlos Baião de Oliveira
Embrapa Coffee, Brazilian Agricultural Research Corporation (Embrapa), Brasília 70770-901, Brazil
Antonio Alves Pereira
Agricultural Research Company of Minas Gerais (EPAMIG), Viçosa 36571-000, Brazil
Cynthia Aparecida Valiati Barreto
Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, Brazil
Moysés Nascimento
Laboratory of Intelligence Computational and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, Brazil
Genome-wide association studies (GWASs) allow for inferences about the relationships between genomic variants and phenotypic traits in natural or breeding populations. However, few have used this methodology in Coffea arabica. We aimed to identify chromosomal regions with significant associations between SNP markers and agronomic traits in C. arabica. We used a coffee panel consisting of 195 plants derived from 13 families in F2 generations and backcrosses of crosses between leaf rust-susceptible and -resistant genotypes. The plants were phenotyped for 18 agronomic markers and genotyped for 21,211 SNP markers. A GWAS enabled the identification of 110 SNPs with significant associations (p C. arabica: plant height, plagiotropic branch length, number of vegetative nodes, canopy diameter, fruit size, cercosporiosis incidence, and rust incidence. The effects of each SNP marker associated with the traits were analyzed, such that they can be used for molecular marker-assisted selection. For the first time, a GWAS was used for these important agronomic traits in C. arabica, enabling applications in accelerated coffee breeding through marker-assisted selection and ensuring greater efficiency and time reduction. Furthermore, our findings provide preliminary knowledge to further confirm the genomic loci and potential candidate genes contributing to various structural and disease-related traits of C. arabica.