DOI: 10.5061/dryad.fbg79cnwx
title: Faster‐haplodiploid evolution under divergence‐with‐gene‐flow: Simulations and empirical data from pine‐feeding hymenopterans

description: Although haplodiploidy is widespread in nature, the evolutionary
 consequences of this mode of reproduction are not well characterized.
 Here, we examine how genome-wide hemizygosity and a lack of recombination
 in haploid males affects genomic differentiation in populations that
 diverge via natural selection while experiencing gene flow. First, we
 simulated diploid and haplodiploid “genomes” (500-kb loci) evolving under
 an isolation-with-migration model with mutation, drift, selection,
 migration, and recombination; and examined differentiation at neutral
 sites both tightly and loosely linked to a divergently selected site. So
 long as there is divergent selection and migration, sex-limited
 hemizygosity and recombination cause elevated differentiation (i.e.,
 produce a “faster-haplodiploid effect”) in haplodiploid populations
 relative to otherwise equivalent diploid populations, for both recessive
 and codominant mutations. Second, we used genome-wide SNP data to model
 divergence history and describe patterns of genomic differentiation
 between sympatric populations of Neodiprion lecontei and N. pinetum, a
 pair of pine sawfly species (order: Hymenoptera; family: Diprionidae) that
 are specialized on different pine hosts. These analyses support a history
 of continuous gene exchange throughout divergence and reveal a pattern of
 heterogeneous genomic differentiation that is consistent with divergent
 selection on many unlinked loci. Third, using simulations of haplodiploid
 and diploid populations evolving according to the estimated divergence
 history of N. lecontei and N. pinetum, we found that divergent selection
 would lead to higher differentiation in haplodiploids. Based on these
 results, we hypothesize that haplodiploids undergo
 divergence-with-gene-flow and sympatric speciation more readily than
 diploids.
description: DNA was extracted and ddRAD libraries were prepared for 23
 <em>Neodiprion pinetum </em>larvae and 44 <em>N.
 lecontei </em>larvae collected from multiple locations in Kentucky,
 as well as 4 interspecific hybrids and an additional 18 <em>N.
 lecontei </em>samples from an allopatric population in Michigan.
 These libraries were sequenced using 150-bp paired end reads on an
 Illumina HiSeq 4000. We also collected whole-genome resequncing data from 
 a <em>N. virginiana </em>sample to be used as an outgroup in
 demographic analyses. <em>Neodiprion</em> sequencing reads are
 available via the NCBI SRA, accession numbers: SAMN23893940-SAMN23893944,
 SAMN23893948, SAMN23893960-SAMN23893963, SAMN23893965, and
 SAMN25157024-SAMN25157101. We aligned demultiplexed
 ddRAD reads to the <em>N. lecontei</em> reference genome
 (Nlec1.1 GenBank assembly accession number- GCA_001263575.2) using the very sensitive setting in bowtie2. We only retained reads that aligned to one locus in the reference genome and had a Phred score greater than 30. For the ddRAD dataset, we removed PCR duplicates using a custom script. We called SNPs in samtools. We required all sites to have a minimum of 7x coverage and 50% missing data or less. We also removed SNPs with significantly more heterozygotes than expected under Hardy-Weinberg equilibrium (an indicator of genotyping/mapping error). We removed any individual that was missing more than 70% of the data. We performed all filtering in VCFtools v0.1.13. We created several datasets with subsets of individuals and additional filtering for each of the population genetic analyses. We generated three data sets with minor allele filtering (MAF, SNPs &lt;0.01 removed): 1) sympatric <em>N. pinetum</em> and <em>N. lecontei</em> for genome-wide patterns of divergence (36,935 SNPs), 2) sympatric <em>N. pinetum</em>, <em>N. lecontei, </em>and hybrids for admixture analysis (35,649 SNPs), and 3) sympatric <em>N. pinetum</em>, <em>N. lecontei, </em>allopatric <em>N. lecontei</em>, and outgroup <em>N. virginiana </em>for ABBA- BABA tests (12,905 SNPs). We also generated a down-sampled dataset (described below) without a MAF filter for estimating site-frequency spectra (SFS) that included sympatric <em>N. pinetum</em>, <em>N. lecontei</em>, and <em>N. virginiana</em> for demographic analyses.
description: ZIP files contains all input files (dryad) and scripts (zenodo)
 needed to run a set of population genetic analyses (plus a README
 file). <br><br><strong>ABBA-BABA:</strong><br><br>Data and scripts used to perform the ABBA-BABA tests (D-statistic), including the following files:<br><br>DATA:<br>- data_files folder: VCF and individual info files<br><br>SCRIPTS:<br>- ProcessData_Neodiprion_filterDPperind.sh:  bash script to filter the VCF file based on DP and HWE and obtain a genotype matrix file<br>- analysis_NeoLecPin_feb2017.r:  R script to read the genotype matrix file and compute the ABBA-BABA tests (D-statistics) and compute their significance using block-jackniffe<br>- RScripts folder: files with definition of functions used by the bash script ProcessData_Neodiprion_filterDPperind.sh and R script analysis_NeoLecPin_feb2017.r<br><br><strong>ADMIXTURE:</strong><br><br>Data and scripts to run the admixture analysis for the hybrids, <em>N. pinetum</em> and <em>N.lecontei</em> in Kentucky, including the following files:<br><br>DATA:<br>- KY_Pin_F1_nohet_7x_0.5miss_0.01maf.recode.vcf is the vcf file containing the SNPS used in this analyses<br>- KY_pin_F1_sitesincommon.ped and KY_pin_F1_sitesincommon.map are the input files for this analysis<br><br>SCRIPTS:<br>- test_run_admixture.sh is the script to run the admixture analysis<br><br><strong>Demography_Fastsimcoal: </strong><br><br>Data and scripts to perform demographic analyses of <em>Neodiprion</em> sawflies using fastsimcoal2<br><br>DATA:<br>- Define_NeutralFstThreshold folder: outputfiles to obtain the threshold FST based on simulations<br>- input_data folder: contains vcf files that are conversted to 2DSFS, the 2DSFS files, and the fastsimcoal input files<br><br>SCRIPTS:<br>- Build_2DSFS folder: scripts  to obtain the 2D SFS from VCF files<br>- Define_NeutralFstThreshold folder: scripts to obtain the threshold FST based on simulations<br>- ScriptsLaunchFastsimcoal2 folder: scripts to launch fastsimcoal2 analyses<br><br><strong>Genomewide_patterns_of_divergence:</strong><br><br>Data for calculating pi and Fst for <em>N. lecontei</em> and <em>N. pinteum</em><br><br>DATA:<br>- KY.text and Pintetum.txt contain the sample names for <em>N. lecontei </em>and <em>N. pinetum</em> respectively<br>- for all files in this folder KY refers to the Kentucky population of <em>N. lecontei</em><br>- KY_Pin_7x_50%miss_nothetexc_0.01maf.recode.vcf is the input vcf file for all three analyses<br>- The .log files contain the command line code that was used to run the three analyses<br>- .pi and .fst files are the output files containing the raw pi and Fst values<br><br><strong>Process_Neodiprion_data: </strong><br><br>Data and scripts to go from raw fastq reads to vcf files. <br><br>DATA:<br>- Deduplication folder contains fastq_files folder with one fastq file per lane per library for removal of PCR duplicates<br><br>SCRIPTS:<br>- Deduplication folder contains Python_scripts folder includes the python script for removal of PCR duplicates. There is a single python script for each lane and library.<br>- Process_fastq.sh has the command line input to go from fastq files to BAM files. The output is a single filtered BAM filer per sample