The repeated, rapid and often pronounced patterns of evolutionary divergence observed in insular plants, or the ‘plant island syndrome’, include changes in leaf phenotypes, growth, as well as the acquisition of a perennial lifestyle. Here, we sequence and describe the genome of the critically endangered, Galápagos-endemic species Scalesia atractyloides Arnot., obtaining a chromosome-resolved, 3.2-Gbp assembly containing 43,093 candidate gene models. Using a combination of fossil transposable elements, k-mer spectra analyses and orthologue assignment, we identify the two ancestral genomes, and date their divergence and the polyploidization event, concluding that the ancestor of all extant Scalesia species was an allotetraploid. There are a comparable number of genes and transposable elements across the two subgenomes, and while their synteny has been mostly conserved, we find multiple inversions that may have facilitated adaptation. We identify clear signatures of selection across genes associated with vascular development, growth, adaptation to salinity and flowering time, thus finding compelling evidence for a genomic basis of the island syndrome in one of Darwin’s giant daisies.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here, we analyzed the genomic and phenotypic impacts of wild introgressions in inbred lines of cultivated sunflower to estimate the impacts of linkage drag. First, we generated reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge-regression best linear unbiased prediction (BLUP) model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including >3,000 new genes. While introgressions reduced genetic load at protein-coding sequences, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low-frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here we comprehensively analyzed the genomic and phenotypic impacts of wild introgressions into cultivated sunflower to estimate the impacts of linkage drag. First, we generated new reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge regression model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including > 3,000 new genes. While introgressions reduced genetic load at protein-coding sequences and positively affected traits associated with abiotic stress resistance, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, pre-breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

No abstract

BackgroundRetinal diseases associated with the dysfunction or death of photoreceptors are a major cause of blindness around the world, improvements in genetics tools, like next generation sequencing (NGS) allows the discovery of genes and genetic changes that lead to many of those retinal diseases. Though, there very few databases that explores a wide spectrum of retinal diseases, phenotypes, genes, and proteins, thus creating the need for a more comprehensive database, that groups all these parameters.MethodsMultiple open access databases were compiled into a new comprehensive database. A biological network was then crated, and organized using Cytoscape. The network was scrutinized for presence of hubs, measuring the concentration of grouped nodes. Finally, a trace back analysis was performed in areas were the power law reports a high r-squared value near one, that indicates high nodes density.ResultsThis work leads to creation of a retinal database that includes 324 diseases, 803 genes, 463 phenotypes, and 2461 proteins. Four biological networks (1) a disease and gene network connected by common phenotypes, (2) a disease and phenotype network connected by common genes, (3) a disease and gene network with shared disease or gene as the cause of an edge, and (4) a protein and disease network. The resulting networks will allow users to have easier searching for retinal diseases, phenotypes, genes, and proteins and their interrelationships.ConclusionsThese networks have a broader range of information than previously available ones, helping clinicians in the comprehension of this complex group of diseases.