Spinoso-Castillo José Luis1, Pérez-Rodríguez Paulino2, Jericó Jabín Bello-Bello3, Escamilla-Prado Esteban4, Aguilar-Rincón Víctor Heber2, Corona-Torres Tarsicio2, García-de Los Santos Gabino2, Morales-Ramos Victorino5. 1. Colegio de Postgraduados Campus Montecillo, Carretera Federal México-Texcoco km 36.5, 56230, Texcoco, Estado de México, México. jlspinoso@gmail.com. 2. Colegio de Postgraduados Campus Montecillo, Carretera Federal México-Texcoco km 36.5, 56230, Texcoco, Estado de México, México. 3. CONACYT-Colegio de Postgraduados Campus Córdoba, Carretera Federal Córdoba-Veracruz km 348, Amatlán de los Reyes 94946, Veracruz, México. 4. Universidad Autónoma Chapingo, Centro Regional Universitario Oriente, Carretera Huatusco-Xalapa Km 6, 94100, Huatusco, Veracruz, México. 5. Colegio de Postgraduados Campus Córdoba, Carretera Federal Córdoba-Veracruz km 348, Amatlán de los Reyes, 94946, Veracruz, México.
Abstract
BACKGROUND: Coffee quality is an important selection criterion for coffee breeding. Metabolite profiling and Genome-Wide Association Studies (GWAS) effectively dissect the genetic background of complex traits such as metabolites content (caffeine, trigonelline, and 5-caffeoylquinic acid (5-CQA)) in coffee that affect quality. Therefore, it is important to determine the metabolic profiles of Coffea spp. genotypes. This study aimed to identify Single Nucleotide Polymorphisms (SNPs) within Coffea spp. genotypes through GWAS and associate these significant SNPs to the metabolic profiles of the different genotypes. METHODS AND RESULTS: A total of 1,739 SNP markers were obtained from 80 genotypes using the DArTseq™ method. Caffeine, trigonelline, and 5-CQA content were determined in coffee leaves using Ultra-Performance Liquid Chromatography/tandem mass spectrometry (UPLC-MS/MS) analyses. The GWAS was carried out using the Genome Association and Prediction Integrated Tool (GAPIT) software and a compressed mixed linear model. Finally, a total of three significant SNP markers out of ten were identified. One SNP, located in the coffee chromosome (Chr) 8, was significantly associated with caffeine. The two remaining SNPs, located in Chr 4 and 5, were significantly associated with trigonelline and six SNPs markers were associated with 5-CQA in Chr 1, 5 and 10, but these six markers were not significant. CONCLUSIONS: These significant SNP sequences were associated with protein ubiquitination, assimilation, and wall receptor kinases. Therefore, these SNPs might be useful hits in subsequent quality coffee breeding programs.
BACKGROUND: Coffee quality is an important selection criterion for coffee breeding. Metabolite profiling and Genome-Wide Association Studies (GWAS) effectively dissect the genetic background of complex traits such as metabolites content (caffeine, trigonelline, and 5-caffeoylquinic acid (5-CQA)) in coffee that affect quality. Therefore, it is important to determine the metabolic profiles of Coffea spp. genotypes. This study aimed to identify Single Nucleotide Polymorphisms (SNPs) within Coffea spp. genotypes through GWAS and associate these significant SNPs to the metabolic profiles of the different genotypes. METHODS AND RESULTS: A total of 1,739 SNP markers were obtained from 80 genotypes using the DArTseq™ method. Caffeine, trigonelline, and 5-CQA content were determined in coffee leaves using Ultra-Performance Liquid Chromatography/tandem mass spectrometry (UPLC-MS/MS) analyses. The GWAS was carried out using the Genome Association and Prediction Integrated Tool (GAPIT) software and a compressed mixed linear model. Finally, a total of three significant SNP markers out of ten were identified. One SNP, located in the coffee chromosome (Chr) 8, was significantly associated with caffeine. The two remaining SNPs, located in Chr 4 and 5, were significantly associated with trigonelline and six SNPs markers were associated with 5-CQA in Chr 1, 5 and 10, but these six markers were not significant. CONCLUSIONS: These significant SNP sequences were associated with protein ubiquitination, assimilation, and wall receptor kinases. Therefore, these SNPs might be useful hits in subsequent quality coffee breeding programs.
Authors: Gustavo C Sant'Ana; Luiz F P Pereira; David Pot; Suzana T Ivamoto; Douglas S Domingues; Rafaelle V Ferreira; Natalia F Pagiatto; Bruna S R da Silva; Lívia M Nogueira; Cintia S G Kitzberger; Maria B S Scholz; Fernanda F de Oliveira; Gustavo H Sera; Lilian Padilha; Jean-Pierre Labouisse; Romain Guyot; Pierre Charmetant; Thierry Leroy Journal: Sci Rep Date: 2018-01-11 Impact factor: 4.379
Authors: Hue T M Tran; Thiruvarangan Ramaraj; Agnelo Furtado; Leonard Slade Lee; Robert J Henry Journal: Plant Biotechnol J Date: 2018-04-13 Impact factor: 9.803
Authors: Fernando E Vega; Lewis H Ziska; Ann Simpkins; Francisco Infante; Aaron P Davis; Joseph A Rivera; Jinyoung Y Barnaby; Julie Wolf Journal: Sci Rep Date: 2020-04-03 Impact factor: 4.379