Robert Verity | Search bioRxiv

bioRxiv2016

MINOTAUR: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny

Genome scans are widely used to identify 'outliers' in genomic data: loci with different patterns compared with the rest of the genome due to the action of selection or other non-adaptive forces of evolution. These genomic datasets are often high-dimensional, with complex correlation structures among variables, making it a challenge to identify outliers in a robust way. The Mahalanobis distance has been widely used for this purpose, but has the major limitation of assuming that data follow a simple parametric distribution. Here we develop three new metrics that can be used to identify outliers in multivariate space, while making no strong assumptions about the distribution of the data. These metrics are implemented in the R package MINOTAUR, which also includes an interactive web-based application for visualizing outliers in high-dimensional datasets. We illustrate how these metrics can be used to identify outliers from simulated genetic data, and discuss some of the limitations they may face in application.

Genomics↗

bioRxiv2015

Estimating K in Genetic Mixture Models

A key quantity in the analysis of structured populations is the parameter K, which describes the number of subpopulations that make up the total population. Inference of K ideally proceeds via the model evidence, which is equivalent to the likelihood of the model. However, the evidence in favour of a particular value of K cannot usually be computed exactly, and instead programs such as SO_SCPLOWTRUCTUREC_SCPLOW make use of simple heuristic estimators to approximate this quantity. We show - using simulated data sets small enough that the true evidence can be computed exactly - that these simple heuristics often fail to estimate the true evidence, and that this can lead to incorrect conclusions about K. Our proposed solution is to use thermodynamic integration (TI) to estimate the model evidence. After outlining the TI methodology we demonstrate the effectiveness of this approach using a range of simulated data sets. We find that TI can be used to obtain estimates of the model evidence that are orders of magnitude more accurate and precise than those based on simple heuristics. Furthermore, estimates of K based on these values are found to be more reliable than those based on a suite of model comparison statistics. Our solution is implemented for models both with and without admixture in the software TO_SCPLOWRUEC_SCPLOWK.

Evolutionary Biology↗