Flowering time is a key life-history trait in the plant life cycle. Most studies to unravel the genetics of flowering time in Arabidopsis thaliana have been performed under greenhouse conditions. Here, we describe a study about the genetics of flowering time that differs from previous studies in two important ways: first, we measure flowering time in a more complex and ecologically realistic environment; and, second, we combine the advantages of genome-wide association (GWA) and traditional linkage (QTL) mapping. Our experiments involved phenotyping nearly 20,000 plants over 2 winters under field conditions, including 184 worldwide natural accessions genotyped for 216,509 SNPs and 4,366 RILs derived from 13 independent crosses chosen to maximize genetic and phenotypic diversity. Based on a photothermal time model, the flowering time variation scored in our field experiment was poorly correlated with the flowering time variation previously obtained under greenhouse conditions, reinforcing previous demonstrations of the importance of genotype by environment interactions in A. thaliana and the need to study adaptive variation under natural conditions. The use of 4,366 RILs provides great power for dissecting the genetic architecture of flowering time in A. thaliana under our specific field conditions. We describe more than 60 additive QTLs, all with relatively small to medium effects and organized in 5 major clusters. We show that QTL mapping increases our power to distinguish true from false associations in GWA mapping. QTL mapping also permits the identification of false negatives, that is, causative SNPs that are lost when applying GWA methods that control for population structure. Major genes underpinning flowering time in the greenhouse were not associated with flowering time in this study. Instead, we found a prevalence of genes involved in the regulation of the plant circadian clock. Furthermore, we identified new genomic regions lacking obvious candidate genes.

The Pacific cupped oyster is genetically subdivided into two sister taxa, Crassostrea gigas and Crassostrea angulata, which are in contact in the north-western Pacific. The nature and origin of their genetic and taxonomic differentiation remains controversial due the lack of known reproductive barriers and the high degree of morphologic similarity. In particular, whether the presence of ecological and/or intrinsic isolating mechanisms contributes to species divergence is unknown. The recent co-introduction of both taxa into Europe offers a unique opportunity to test how genetic differentiation is maintained under new environmental and demographic conditions. We generated a pseudochromosome assembly of the Pacific oyster genome using a combination of BAC-end sequencing and scaffold anchoring to a new high-density linkage map. We characterized genome-wide differentiation between C. angulata and C. gigas in both their native and introduced ranges, and showed that gene flow between species has been facilitated by their recent co-introductions in Europe. Nevertheless, patterns of genomic divergence between species remain highly similar in Asia and Europe, suggesting that the environmental transition caused by the co-introduction of the two species did not affect the genomic architecture of their partial reproductive isolation. Increased genetic differentiation was preferentially found in regions of low recombination. Using historical demographic inference, we show that the heterogeneity of differentiation across the genome is well explained by a scenario whereby recent gene flow has eroded past differentiation at different rates across the genome after a period of geographical isolation. Our results thus support the view that low-recombining regions help in maintaining intrinsic genetic differences between the two species.

Use of SNPs has been favoured due to their abundance in plant and animal genomes, accompanied by the falling cost and rising throughput capacity for detection and genotyping. Here, we present in vitro (obtained from targeted sequencing) and in silico discovery of SNPs, and the design of medium-throughput genotyping arrays for two oyster species, the Pacific oyster, Crassostrea gigas, and European flat oyster, Ostrea edulis. Two sets of 384 SNP markers were designed for two Illumina GoldenGate arrays and genotyped on more than 1000 samples for each species. In each case, oyster samples were obtained from wild and selected populations and from three-generation families segregating for traits of interest in aquaculture. The rate of successfully genotyped polymorphic SNPs was about 60% for each species. Effects of SNP origin and quality on genotyping success (Illumina functionality Score) were analysed and compared with other model and nonmodel species. Furthermore, a simulation was made based on a subset of the C. gigas SNP array with a minor allele frequency of 0.3 and typical crosses used in shellfish hatcheries. This simulation indicated that at least 150 markers were needed to perform an accurate parental assignment. Such panels might provide valuable tools to improve our understanding of the connectivity between wild (and selected) populations and could contribute to future selective breeding programmes.