DOI: 10.5061/dryad.14853
title: Data from: Bayesian inference of selection in a heterogeneous environment from genetic time-series data

description: Evolutionary geneticists have sought to characterize the causes and
 molecular targets of selection in natural populations for many years.
 Although this research program has been somewhat successful, most
 statistical methods employed were designed to detect consistent, weak to
 moderate selection. In contrast, phenotypic studies in nature show that
 selection varies in time and that individual bouts of selection can be
 strong. Measurements of the genomic consequences of such fluctuating
 selection could help test and refine hypotheses concerning the causes of
 ecological specialization and the maintenance of genetic variation in
 populations. Herein, I proposed a Bayesian non-homogenous hidden Markov
 model to estimate effective population sizes and quantify variable
 selection in heterogeneous environments from genetic time-series data. The
 model is described and then evaluated using a series of simulated data,
 including cases where selection occurs on a trait with a simple or
 polygenic molecular basis. The proposed method accurately distinguished
 neutral loci from non-neutral loci under strong selection, but not from
 those under weak selection. Selection coefficients were accurately
 estimated when selection was constant or when the fitness values of
 genotypes varied linearly with the environment, but these estimates were
 less accurate when fitness was polygenic or the relationship between the
 environment and the fitness of genotypes was non-linear. Past studies of
 temporal evolutionary dynamics in lab populations have been remarkably
 successful. The proposed method makes similar analyses of genetic
 time-series data from natural populations more feasible, and thereby could
 help answer fun damental questions about the causes and consequences of
 evolution in the wild.
description: simulated data
 setsGenetic data (*G) and
 environmental data (*E) infiles that were analyzed in this manuscript.
 These data sets were simulated in R according to the Wright-Fisher model
 with selection. Genetic data files with the 'sub' prefix include
 subsets of individuals and thus have lower sample sizes than the other
 files (2N = 50 or 100 instead of 200).wfsims.tar.gzdata sets simulated with quantiNEMOGenetic data (geno*) and environmental data (env*) infiles that were analyzed in this manuscript. These data sets were simulated using quantiNEMO.qnemosims.tar.gzperl script to run quantiNEMO simulationsThis is a wrapper script that I wrote to run quantiNEMO to generate the simulated data files that were analyzed in the paper.runSims.plsource code and binaries for spatpg softwareThis compressed directory contains the source C++ code for the software described in the paper. A compiled binary for Linux is also included. An up-to-date version of this software can be downloaded from SourceForge (http://sourceforge.net/projects/spatpg/).source.tar.gzwrapper perl scripts for simulationsThis compressed directory contains a set of perl scripts that were used to run the R Wright-Fisher simulations and generate the data sets analyzed in this paper.simScripts.tar.gz