The PI and collaborators will utilize high-throughput (next-generation) sequencing technologies to elucidate the genetic components of eyes and vision in multiple, diverse species of invertebrate animals. The animals to be studied were chosen because their vision and/or eyes are well-studied functionally, but lack extensive genetic data. Leveraging new sequencing technologies will enable the rapid collection of vast genetic data that will complement existing bodies of knowledge on visual function (especially physiology, development and evolution), and will facilitate an integrative understanding of animal vision from genotype to phenotype to evolution in multiple species. The new genetic data will be made available in public databases (e.g. www.ncbi.nlm.nih.gov/ ). The project will also have multiple broader impacts, including the education and participation of graduate students, postdoctoral researchers, and junior and senior faculty, including individuals with a strong record of service in broadening participation of under-represented groups in science. The research will network vision research labs across the US together and with one international lab (Denmark). Eyes are a fascinating evolutionary innovation, and are, therefore, a persistent topic of debate between scientists and critics of Darwinian evolution. This project will provide additional, key data that will help to promote understanding, and educate the public about eye evolution.
An integrated understanding of the function and evolution of complex biological traits – such as eyes – is a major goal for biologists. Eyes are complex traits with established links between genotype and phenotype because their genetic components tend to be well-characterized in model organisms and deeply conserved. Still, scientists lack genomic resources for many species that have eyes amenable for evolutionary, physiological, or behavioral study. In this study, collaborators from multiple institutions worked together to address this shortcoming to produce new data and new tools for studying vision genes. They used high-throughput sequencing to discover genes expressed in 28 vision-related tissues from mollusks, arthropods, and cnidarians. These genetic data are now available to vision researchers. In addition, they created computational tools for analyzing high-throughput genetic data. The researchers first calculated a set of 109 gene-trees to understand the evolutionary history of a collection of animal genes known to be involved with the function or development of light-interacting structures like eyes. These gene trees can now be used to identify sequences of new genes in high throughput sequence data with evolutionary similarity to known genes, which are the strongest candidates for light-interacting genes in the new data sets. These tools and analyses can be implemented by anyone using a set of online, flexible, user-friendly workflows, implemented in an open-source bioinformatics platform. These new data and new tools will accelerate the understanding of connections between genotype and phenotype in a diversity of animal visual systems.
