Descoberta e genotipagem por sequenciamento de SNPs em Coffea canephora e aplica????es em estudos de diversidade e estrutura gen??tica - Carneiro, Fernanda de Ara??jo (2014)
Of all the different activities related to agricultural industry worldwide, coffee agribusiness is among the most important ones, both economically and socially, being the main livelihood for more than 125 million people in more than 60 countries. Commercial coffee production is mostly based on two species, Coffea arabica and C. canephora. The high genetic variability of C. canephora, due to its level of allogamy, is of great importance for breeding programs of coffee as a source of novel alleles and co-evolved genetic combinations. The molecular analysis of genetic diversity has become increasingly efficient, necessary and refined, as molecular techniques have evolved and morphological traits do not supply sufficient information to fully describe an individual. An important tool in the study of genetic diversity is the use of SNP (Single Nucleotide Polymorphism) based molecular markers. In the case of C. canephora diversity studies may benefit breeding programs in selecting individuals to optimize mating schemes or assess the diversity and relatedness of clonal varieties. This study aimed to (i) evaluate and characterize the diversity and the genetic structure of C. canephora Conilon individuals belonging to a population located at Embrapa Cerrados, by nextRAD genotyping (reductively-amplified Nextera tagmented-DNA); (ii) identify potential parents of these individuals and (iii) validate genotyping technique in genomic scale. A total of 11,230 SNPs were obtained for C. canephora Conilon individuals by technique nextRAD, of these, 573 markers were selected for the diversity analysis, kinship and genetic structure using the Cervus, adegenet and Structure. The genetic diversity (0.405) and the mean observed heterozygosity (0:41) were high considering the bi-allelic markers and more than 64% of the SNPs showed PIC values higher than 0.3. In terms of population, the two methodologies used (Bayesian method and DAPC) identified different numbers of groups formed for the same set of data. In kinship analysis some parents are more frequent in the population. The data generated for the population will be useful in upcoming association studies the development of genomic prediction models.