BackgroundMaize (Zea mays) is a globally produced crop with broad genetic and phenotypic variation. New tools that improve our understanding of the genetic basis of quantitative traits are needed to guide predictive crop breeding. We have produced the first balanced multi-parental population in maize, a tool that provides high diversity and dense recombination events to allow routine quantitative trait loci (QTL) mapping in maize.ResultsWe produced 1,636 MAGIC maize recombinant inbred lines derived from eight genetically diverse founder lines. The characterization of 529 MAGIC maize lines shows that the population is a balanced, evenly differentiated mosaic of the eight founders, with mapping power and resolution strengthened by high minor allele frequencies and a fast decay of linkage disequilibrium. We show how MAGIC maize may find strong candidate genes by incorporating genome sequencing and transcriptomics data. We discuss three QTL for grain yield and three for flowering time, reporting candidate genes. Power simulations show that subsets of MAGIC maize might achieve high-power and high-definition QTL mapping.ConclusionsWe demonstrate MAGIC maize’s value in identifying the genetic bases of complex traits of agronomic relevance. The design of MAGIC maize allows the accumulation of sequencing and transcriptomics layers to guide the identification of candidate genes for a number of maize traits at different developmental stages. The characterization of the full MAGIC maize population will lead to higher power and definition in QTL mapping, and lay the basis for improved understanding of maize phenotypes, heterosis included. MAGIC maize is available to researchers.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0716-z) contains supplementary material, which is available to authorized users.

the genome of the allotetraploid species Coffea arabica L. was sequenced to assemble independently the two component subgenomes (putatively deriving from C. canephora and C. eugenioides) and to perform a genome-wide analysis of the genetic diversity in cultivated coffee germplasm and in wild populations growing in the center of origin of the species. We assembled a total length of 1.536 Gbp, 444 Mb and 527 Mb of which were assigned to the canephora and eugenioides subgenomes, respectively, and predicted 46,562 gene models, 21,254 and 22,888 of which were assigned to the canephora and to the eugeniodes subgenome, respectively. Through a genome-wide SNP genotyping of 736 C. arabica accessions, we analyzed the genetic diversity in the species and its relationship with geographic distribution and historical records. We observed a weak population structure due to lowfrequency derived alleles and highly negative values of Taijma's D, suggesting a recent and severe bottleneck, most likely resulting from a single event of polyploidization, not only for the cultivated germplasm but also for the entire species. This conclusion is strongly supported by forward simulations of mutation accumulation. However, PCA revealed a cline of genetic diversity reflecting a west-toeast geographical distribution from the center of origin in East Africa to the Arabian Peninsula. The extremely low levels of variation observed in the species, as a consequence of the polyploidization event, make the exploitation of diversity within the species for breeding purposes less interesting than in most crop species and stress the need for introgression of new variability from the diploid progenitors.

Changes in the performance of genotypes in different environments are defined as genotype × environment (G×E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G×E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G×E-related. The G×E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G×E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.