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      dartr : An r package to facilitate analysis of SNP data generated from reduced representation genome sequencing

      1 , 1 , 2 , 1
      Molecular Ecology Resources
      Wiley

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          Abstract

          Although vast technological advances have been made and genetic software packages are growing in number, it is not a trivial task to analyse SNP data. We announce a new r package, dartr, enabling the analysis of single nucleotide polymorphism data for population genomic and phylogenomic applications. dartr provides user-friendly functions for data quality control and marker selection, and permits rigorous evaluations of conformation to Hardy-Weinberg equilibrium, gametic-phase disequilibrium and neutrality. The package reports standard descriptive statistics, permits exploration of patterns in the data through principal components analysis and conducts standard F-statistics, as well as basic phylogenetic analyses, population assignment, isolation by distance and exports data to a variety of commonly used downstream applications (e.g., newhybrids, faststructure and phylogeny applications) outside of the r environment. The package serves two main purposes: first, a user-friendly approach to lower the hurdle to analyse such data-therefore, the package comes with a detailed tutorial targeted to the r beginner to allow data analysis without requiring deep knowledge of r. Second, we use a single, well-established format-genlight from the adegenet package-as input for all our functions to avoid data reformatting. By strictly using the genlight format, we hope to facilitate this format as the de facto standard of future software developments and hence reduce the format jungle of genetic data sets. The dartr package is available via the r CRAN network and GitHub.

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          Most cited references20

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          Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power.

          Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.
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            Diversity arrays: a solid state technology for sequence information independent genotyping.

            Here we present the successful application of the microarray technology platform to the analysis of DNA polymorphisms. Using the rice genome as a model, we demonstrate the potential of a high-throughput genome analysis method called Diversity Array Technology, DArT'. In the format presented here the technology is assaying for the presence (or amount) of a specific DNA fragment in a representation derived from the total genomic DNA of an organism or a population of organisms. Two different approaches are presented: the first involves contrasting two representations on a single array while the second involves contrasting a representation with a reference DNA fragment common to all elements of the array. The Diversity Panels created using this method allow genetic fingerprinting of any organism or group of organisms belonging to the gene pool from which the panel was developed. Diversity Arrays enable rapid and economical application of a highly parallel, solid-state genotyping technology to any genome or complex genomic mixtures.
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              Diversity arrays technology: a generic genome profiling technology on open platforms.

              In the last 20 years, we have observed an exponential growth of the DNA sequence data and simular increase in the volume of DNA polymorphism data generated by numerous molecular marker technologies. Most of the investment, and therefore progress, concentrated on human genome and genomes of selected model species. Diversity Arrays Technology (DArT), developed over a decade ago, was among the first "democratizing" genotyping technologies, as its performance was primarily driven by the level of DNA sequence variation in the species rather than by the level of financial investment. DArT also proved more robust to genome size and ploidy-level differences among approximately 60 organisms for which DArT was developed to date compared to other high-throughput genotyping technologies. The success of DArT in a number of organisms, including a wide range of "orphan crops," can be attributed to the simplicity of underlying concepts: DArT combines genome complexity reduction methods enriching for genic regions with a highly parallel assay readout on a number of "open-access" microarray platforms. The quantitative nature of the assay enabled a number of applications in which allelic frequencies can be estimated from DArT arrays. A typical DArT assay tests for polymorphism tens of thousands of genomic loci with the final number of markers reported (hundreds to thousands) reflecting the level of DNA sequence variation in the tested loci. Detailed DArT methods, protocols, and a range of their application examples as well as DArT's evolution path are presented.
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                Author and article information

                Contributors
                Journal
                Molecular Ecology Resources
                Mol Ecol Resour
                Wiley
                1755098X
                May 2018
                May 2018
                January 15 2018
                : 18
                : 3
                : 691-699
                Affiliations
                [1 ]Institute for Applied Ecology; University of Canberra; Canberra ACT Australia
                [2 ]CSIRO Environomics Future Science Platform; Indian Ocean Marine Research Centre; The University of Western Australia (M097); Crawley WA Australia
                Article
                10.1111/1755-0998.12745
                29266847
                95073ae7-d79b-4fde-b109-1939a81543a1
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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