DOI: 10.5061/dryad.tqjq2bw48
title: Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)

description: The rapid expansion of wild pigs (Sus scrofa) throughout the United States
 (US) has been fueled by unlawful introductions, with invasive populations
 causing extensive crop losses, damaging native ecosystems, and serving as
 a reservoir for disease. Multiple states have passed laws prohibiting the
 possession or transport of wild pigs. However, genetic and phenotypic
 similarities between domestic pigs and invasive wild pigs – which
 overwhelmingly represent domestic pig-wild boar hybrids – pose a challenge
 for the enforcement of such regulations. We sought to exploit wild boar
 ancestry as a common attribute among the vast majority of invasive wild
 pigs as a means of genetically differentiating wild pigs from breeds of
 domestic pigs found within the US. We organized reference high-density
 single nucleotide polymorphism genotypes (1,039 samples from 33 domestic
 breeds and 382 samples from 16 wild boar populations) into five
 genetically cohesive reference groups: mixed-commercial breeds, Durocs,
 heritage breeds, primitive breeds, and wild boar. Building upon
 well-established genetic clustering approaches, we structured the test
 statistic to describe the difference in the likelihood of a given
 genotype’s ancestry vectors (sensu genetic clustering analysis) if derived
 strictly from the four described domestic pig reference groups versus
 allowing for admixture from the wild boar group. By fitting statistical
 distributions to test statistics of reference domestic pigs, we
 characterized the distribution of the null hypothesis – that a given
 genotype descends strictly from domestic pig reference groups. We tested
 the approach with simulated genotypes and empirical data from an
 additional 29 breeds of domestic pig represented by 435 unique genotypes;
 all associated test statistics for simulated and empirical domestic pig
 challenge sets fell within the distribution of reference domestic pigs. We
 then evaluated 6,566 invasive wild pigs sampled across the contiguous
 United States, of which 63% exceeded the maximum threshold for domestic
 pigs and could be statistically classified as possessing wild boar
 ancestry. This approach provides a scientific foundation to enforce
 regulations prohibiting the possession of this destructive invasive
 species. Further, this computationally efficient and generalizable
 approach could be readily adapted to quantify gene flow among ecological
 systems of conservation or management concern.
description: We assembled the Sus scrofa reference set from previously
 published high-resolution SNP genotypes, restricting analysis to genotypes
 produced with Illumina BeadArray technology (San Diego, California) across
 multiple commercially available arrays (Illumina PorcineSNP60, Illumina
 PorcineSNP60 v2, Genomic Profiler for Porcine HD, licensed exclusively to
 GeneSeek, a Neogen Corporation, Lansing, Michigan; Ramos et al., 2009). We
 augmented previously published genotypes (detailed in Smyser et al., 2020)
 with a subset of novel genotypes produced for this study (Appendix S1:
 Table S1). We restricted our analyses to loci that were available across
 all datasets (influenced by loci shared across arrays and the extent to
 which publicly available datasets were filtered by authors prior to
 publication) and mapped to autosomes (Sscrofa11.1 genome assembly; Warr et
 al., 2020). In sum, we included 33 breeds and 16 populations of European
 wild boar, representing a total of 1,421 reference samples (Table 1)
 genotyped at 28,545 biallelic loci.
description: Files are presented as a PLINK binary file family
 (*.bed/*.bim/*.fam).  Files can be opened with PLINK (freely available at
 https://www.cog-genomics.org/plink/) or with many other software platforms.
description: We present data files and computer code necessary to recreate the analyses
 described in: "Probabilistic genetic identification of wild boar
 hybridization to support control of invasive wild pigs (Sus scrofa)"
 by Timothy J. Smyser, Peter Pfaffelhuber, Rachael M. Giglio, Matthew G.
 DeSaix, Amy J. Davis, Courtney F. Bowden, Michael A. Tabak, Arianna
 Manunza, Valentin Adrian Balteanu, Marcel Amills, Laura Iacolina, Pamela
 Walker, Carl Lessard, and Antoinette J. Piaggio and published in Ecosphere
 The files include genotypes for 8,422 Sus scrofa, representing 1,421
 reference samples organized into K5 reference groups (mixed-commercial
 breeds, Duroc, heritage breeds, primitive breeds, and European wild boar),
 6,566 wild pigs sampled across the invaded range within the contiguous
 United States, and 435 domestic pigs sampled from 29 Western breeds that
 were excluded from the reference set to serve as a test set, with
 genotypes compiled in a *.bed/*.bim/*.fam (binary PED file) file format.
 All genotypes were produced with Illumina BeadChip microarrays (San Diego,
 California) developed for porcine (PorcineSNP60 v1 and 2 or Genomic
 Profiler for Porcine HD, exclusively licensed to GeneSeek, a Neogen
 Corporation, Lincoln, Nebraska). Many of the reference genotypes have been
 published previously (full citations below with abbreviated citations
 listed in metadata file
 "All_DeltaLikelihoodGenotypes_MetadataFile.csv"). Additionally,
 we provide the following R scripts, necessary to recreate our analyses:
 01_DeltaLikelihood_empirical.Rmd - This R markdown (Rmd) file has R code
 that performs the linkage disequilibrium pruning and Delta Likelihood
 calculation for the empirical data. As a component of this script, we read
 in a file presenting all evaluated loci (file
 "assignPop_ranking_InformativeLoci_1k_iterations_K5_referencePops.csv", columns represent locus name and rank order, from most informative [1] to least informative [28545]) ranked with respect to their relative informativeness in differentiating the 5 reference groups as delineated in the manuscript. Ranking of loci was performed in a separate analysis using the R package assignPOP (function assign.MC; Chen et al., 2018; R Core Team 2023; Appendix S1: Figure S1). Specifically, we conducted 1,000 Monte Carlo bootstrap iterations, retaining 90% of reference genotypes as training data to rank loci by FST within each iteration and concatenating all iterations to rank the informativeness of loci. 02_1_Bootstrap_prep.Rmd - This Rmd file has R code that prepares an Rdata file used in the subsequent file for running the bootstrap iterations. 02_2_Bootstrap_run.R - This R code performs the bootstrap calculation of the Delta Likelihood statistics and is intended to be run across many computers or cores. 03_1a_get-gscramble-HPC.sh - This Bash script submits the code to a Slurm cluster to run the gscramble simulations for Delta Likelihood calculations. 03_1b_run-gscramble-HPC.R - This R code performs the gscramble simulations for Delta Likelihood calculations. 03_1c_gscramble_deltaLikelihood_results.csv - This text file presents the results of the simulations described in the manuscript, listing by row the individual ID for a simulated genotype (column IndID), the domestic pig reference group from which empirical genotypes were drawn for the creation of the 16 member pedigree (ReferenceGroup), the number of empirical domestic pig genotypes included in the pedigree (# Dom Pig), the number of empirical wild boar genotypes included in the pedigree (# Wild Boar), the proportion of the simulated genome originating from the empirical domestic pig genotypes following four generations of recombination as described in the pedigree (Dom Pig Proportion), the proportion of the given simulated genome originating from the empirical wild boar genotypes (Wild Boar Proportion), and the corresponding value for the Delta Likelihood statistic (DeltaLikelihood Value). 03_2_gscramble-DL-summary-paper.Rmd - This Rmd file has the R code to produce the plots used in the manuscript from the simulations. 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