Assessment of SNPs for linkage mapping in Eucalyptus: construction of a consensus SNP/microsatellite map from two unrelated pedigrees

Financial support. Brazilian Ministry of Science and Technology (CNPq Grant 577047-2008-6), FAP-DF NEXTREE Grant 193.000.570/2009 and EMBRAPA Macroprogram 2 project grant 02.07.01.004

Genomic selection in Coffea canephora.

Coffee is one of the most heavily globally traded commodities and its production is based on Coffea arabica and Coffea canephora and Brazil being the world's largest coffee producer. It is believed that all this production will be affected due to climatic changes, with low flower viability, fruit development, yield and beverage quality. An alternative to assist in obtaining coffee plants more adapted to future climatic conditions would be genomic selection (GS). The implementation of these programs requires a lot of genetic markers, which are more readily discovered now after the reference genome of C. canephora became available. Another important factor is the high genetic variability of C. canephora, due to its level of allogamy, being of great importance for breeding programs of coffee

Development and validation of a 26K Axiom® SNP array for Coffea canephora.

World coffee production is higly affected by climate changes due to the occurrence of severe droughts and high temperatures resulting in low flow er viability, fruit development and yield. Faster breeding methods are required to obtain adapted coffee plants to a changed climate cenario, as conventional breeding in perennial crops such as coffee, requires a long time. With the recent advances in coffee genomics, such as the availability of a C. canephora reference genome , the objective of this work was to develop and validate a 26K Axiom SNP array for C. canephora aiming at a reliable high throughput genotyping platform to be used in the breeding programmes of the species. The chip design was based on a whole - genome resequencing panel comprised by DNA pools of C. canephora Conilon and pools formed by individuals representing the different genetic diversity groups of C. canephora

A genotyping array of 3,400 Single Nucleotide Polymorphisms (SNPs) advances the genetic analysis of the iconic tree Araucaria angustifolia, showing that the natural populations ar e more differentiated than previously reported.

Edição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding

Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding