ONeSAMP 3.0: Effective Population Size via SNP Data for One Population Sample

Abstract

O_LIThe genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Genetic estimators of (Ne) increasingly popular tools in population and conservation genetic studies. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. C_LIO_LIWe present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism (SNP) data collected from a single population sample using Approximate Bayesian Computation and local linear regression. C_LIO_LIWe demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is robust to the number of individual samples and number of loci included in and appears accurate even if the range of true Ne values is large. C_LIO_LIThis method is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU license at https://github.com/AaronHong1024/ONeSAMP_3 and also available with Bioconda (https://bioconda.github.io/index.html). C_LI