DOI: 10.5061/dryad.ffbg79ctg
title: Regarding the F-word: the effects of data Filtering on inferred genotype-environment associations

description: Genotype-environment association (GEA) methods have become part of the
 standard landscape genomics toolkit, yet, we know little about how to best
 filter genotype-by-sequencing data to provide robust inferences for
 environmental adaptation. In many cases, default filtering thresholds for
 minor allele frequency and missing data are applied regardless of sample
 size, having unknown impacts on the results. These effects could be
 amplified in downstream predictions, including management strategies.
 Here, we investigate the effects of filtering on GEA results and the
 potential implications for assessment of adaptation to environment. We use
 empirical and simulated datasets derived from two widespread tree species
 to assess the effects of filtering on GEA outputs. Critically, we find
 that the level of filtering of missing data and minor allele frequency
 affect the identification of true positives. Even slight adjustments to
 these thresholds can change the rate of true positive detection. Using
 conservative thresholds for missing data and minor allele frequency
 substantially reduces the size of the dataset, lessening the power to
 detect adaptive variants (i.e. simulated true positives) with strong and
 weak strengths of selection. Regardless, strength of selection was a good
 predictor for GEA detection, but even some SNPs under strong selection
 went undetected across all methods and filtering parameters. The rate of
 false positives varied markedly depending on the species and GEA method
 but scaled with the number of SNPs in the dataset, indicating that false
 positives are sensitive to the interaction between demography, sampling
 design, and filtering parameters.  We further show that filtering
 can significantly impact the predictions of adaptive capacity of species
 in downstream analyses. We make several recommendations regarding
 filtering for GEA methods. Ultimately, there is no filtering panacea, but
 some choices are better than others, depending largely on the study
 system, availability of genomic resources, and desired objectives of the
 study.
description: Original datasets were collected from published work and provided
 here. The demographic history from these empirical
 datasets were simulated using Baypass.simulation in R. Then missind data
 was added after simulation. Then filtering took place to create 150
 separate but linked datasets. The code that was used to create these
 datasets are provided here.
description: R was used for the pipeline. All R code is provided for the
 creation of simulated datasets and filtering of those datasets. 
 We've also provide .012 data input files (.txt) with their
 env files (.env) and the outputs of baypass (.csv) and lfmm
 (calpval).   The name of the outputs
 look like
 this: <strong>emsim_156_6_0.5_0.1.txt.lfmm_env_2.calpval</strong>  This naming convention is the same throughout. emsim = name of the datastet E. microcarpa simulation 156 = # of individuals i.e., sample size 6 = number of individuals per population 0.5 = the missing data threshold (note, for coding purposes this is actually the % of data kept : 10% missing data will be 0.9) (one of 0.5, 0.6, 0.7 0.8, or 0.9) 0.1 = minor allele frequency (one of 0.1, 0.05, or 0.01) Associated SNPs  V#####MT - SNPs associated with BIO5 V#####MP - SNPs associated with BIO14