EigenGWAS: An online visualizing and interactive application for detecting genomic signatures of natural selection.

Detecting genetic regions under selection in structured populations is of great importance in ecology, evolutionary biology, and breeding programs. We recently proposed EigenGWAS, an unsupervised genomic scanning approach that is similar to Fst but does not require grouping information of the population, for detection of genomic regions under selection. The original EigenGWAS is designed for the random mating population, in this study, we extend its use to the inbred populations. We also show in theory and simulation that eigenvalues, the previous corrector for genetic drift in EigenGWAS, are overcorrected for genetic drift, and the genomic inflation factor is found a better option for the adjustment. Applying the updated algorithm, we introduce the new EigenGWAS online platform with highly efficient core implementation. Our online computational tool accepts PLINK data in a standard binary format that can be easily converted from the original sequencing data, provides the users graphical results with R-Shiny user-friendly interface. We applied the proposed method and tool in various datasets, biologically interpretable results as well as caveats that may lead to an unsatisfactory outcome are given. EigenGWAS online platform is available at www.eigengwas.com, and can be localized and scaled up via R (recommended) or Docker.