DOI: 10.5061/dryad.78642
title: Data from: Controlling false discoveries in genome scans for selection

description: Population differentiation (PD) and ecological association (EA) tests have
 recently emerged as prominent statistical methods to investigate
 signatures of local adaptation using population genomic data. Based on
 statistical models, these genome-wide testing procedures have attracted
 considerable attention as tools to identify loci potentially targeted by
 natural selection. An important issue with PD and EA tests is that
 incorrect model specification can generate large numbers of false positive
 associations. Spurious association may indeed arise when shared
 demographic history, patterns of isolation by distance, cryptic
 relatedness or genetic background are ignored. Recent works on PD and EA
 tests have widely focused on improvements of test corrections for those
 confounding effects. Despite significant algorithmic improvements, there
 is still a number of open questions on how to check that false discoveries
 are under control and implement test corrections, or how to combine
 statistical tests from multiple genome scan methods. This tutorial paper
 provides a detailed answer to these questions. It clarifies the
 relationships between traditional methods based on allele frequency
 differentiation and EA methods, and provides a unified framework for their
 underlying statistical tests. We demonstrate how techniques developed in
 the area of genome-wide association studies, such as inflation factors and
 linear mixed models, benefit genome scan methods, and provide guidelines
 for good practice while conducting statistical tests in landscape and
 population genomic applications. Finally, we highlight how the combination
 of several well-calibrated statistical tests can increase the power to
 reject neutrality, improving our ability to infer patterns of local
 adaptation in large population genomic datasets.
description: Example 1
 genotypesA matrix of genotypes
 for 200 diploid individuals genotyped at 1000 loci. These data are used in
 the first tutorial example of our review paper. They are also required
 when reproducing the results reported in the supplementary file
 1.ex1geno.lfmmFDIST values for example 1Significance values computed from the program FDIST for the genotypic data presented in example 1. These p-values were obtained by setting Fst at a level of 5 percent and by using 20,000 simulation replicates.FDISTpv.datGenotypes for 170 European lines of A. thaliana (Chromosome 1)Genotypes for European lines of A. thaliana (Chromosome 1) genotyped at a density of one SNP per kb. These data are used in our tutorial example 2 and supplementary file 2. Data extracted from Atwell et al. 2010 (publicly availlable dataset).chr1geno.lfmmEnvironmental variable for 170 European lines of A. thalianaEnvironmental data for 170 European lines of A. thaliana summarized from 18 bioclimatic variables. These data are used in our tutorial example 2 and supplementary file 2.chr1var.envGenetic map for A. thaliana chromosome 1Genetic map for A. thaliana chromosome 1. Markers were genotyped at a density on one SNP per kb. These data are used in out tutorial example 2 and in the supplementary file 2.poschr1.txt