DOI: 10.5061/dryad.x3ffbg7jz
title: Whole-genome sequencing reveals asymmetric introgression between two sister species of cold-resistant leaf beetles

description: A large number of genetic variation studies have identified cases of
 mitochondrial genome introgression in animals, indicating that
 reproductive barriers among closely related species are often permeable.
 Because of its sheer size, the impact of hybridization on the evolution of
 the nuclear genome is more difficult to apprehend. Only a few studies have
 explored it recently thanks to recent progresses in DNA sequencing and
 genome assembly. Here, we analysed whole-genome sequence variation among
 multiple individuals of two sister species of leaf beetles inside their
 hybrid zone, in which asymmetric mitochondrial genome introgression had
 previously been established. We used a machine learning approach based on
 computer simulations for training to identify regions of the nuclear
 genome that were introgressed. We inferred asymmetric introgression of ≈2%
 of the genome, in the same direction than was observed for the
 mitochondrial genome. Because a previous study based on a
 reduced-representation sequencing approach was not able to detect this
 introgression, we conclude that whole-genome sequencing is necessary when
 the fraction of the introgressed genome is small. We also analysed the
 whole-genome sequence of a hybrid individual, demonstrating that hybrids
 have the capacity to backcross with the species for which virtually no
 introgression was observed. Our data suggest that one species has recently
 invaded the range of the other and/or that positive selection has favoured
 the mitochondrial genes of foreign origin for the invading species, along
 with a series of nuclear genes, some of which possibly being co-adapted
 with the mitochondrial genes.
description: The reference genome of <em>G. quinquepunctata</em>
 (<strong>gq__wtdbg2.fa) </strong>was assembled from a single
 individual (pupa). We used 2 sequencing libraries: 85x Illumina 2x250 bp
 and 27x ONT. The ONT reads were assembled using wtdbg2 and the resulting
 assembly was polished twice using the Illumina sequences.
 The file <strong>gq__repeats.gff</strong> contains
 the coordinates of the repeated regions identified within the reference
 genome of <em>G. quinquepunctata</em>. It was computed by
 first creating a species-specific repeat library using RepeatModeler
 v.1.0.11 and then combining this library with the Repbase library
 (RepeatMasker edition 20181026) to identify the coordinates of the
 repeated regions using RepeatMasker v.4.0.9. To detect
 introgression, we used 10 <em>G. intermedia </em>individuals,
 9 <em>G. quinquepunctata</em> individuals and one hybrid. The
 corresponding Illumina libraries are available on the NCBI Sequence Read
 Archive (SRA) under the accession number XXXX. The VCF
 files were computed by aligning these reads to the reference assembly and
 performing the variant calling using GATK v.4.1.0.0. The file
 <strong>full-dataset.vcf</strong> contains the variant calls
 computed for all the individuals. The files
 <strong>gi__snp.vcf</strong> and
 <strong>gq__snp.vcf</strong> contain the SNPs computed
 separately for <em>Gonioctena intermedia</em> and
 <em>Gonioctena quinquepunctata</em> individuals (hybrid
 excluded). The files <strong>gi__all-positions.vcf</strong>
 and <strong>gq__all-positions.vcf</strong> were computed with
 the option "--emitRefConfidence" and contain confidence scores
 for each site the the genome, computed separately for <em>Gonioctena
 intermedia</em> and <em>Gonioctena quinquepunctata</em>
 individuals (hybrid excluded). The simulated datasets
 (<strong>training__no-introgression.txt,
 training__introgression12.txt,
 training__introgression21.txt)</strong> were generated using msmove.
 The first dataset contains simuated data without introgression, the second
 dataset contains simulated data with the introgression in the direction
 <em>G. intermedia</em> -&gt; <em>G.
 quinquepunctata</em> and the last dataset contains simulated data
 with the introgression in the direction <em>G.
 quinquepunctata</em> -&gt; <em>G. intermedia.</em>
 For each parameter provided to msmove, we defined an interval around the
 estimated value v as follows: [v - v/2; v + v/2] and generated values
 uniformly distributed across this interval. The parameter values
 (estimated by GADMA) were the following: 
 Ancestral population size (Nanc) = 9,808,986
 Divergence time (Tdiv) = 501,675 Initial
 population size of <em>G. intermedia</em> (Nint0) =
 9,673,164 Final population size of <em>G.
 intermedia </em>(Nint) = 107,758 Initial
 population size of <em>G. quinquepunctata</em> (Nqui0) =
 135,823 Final population size of <em>G.
 quinquepunctata</em> (Nqui) = 1,292,448 
 For the datasets with introgression, we defined an interval of
 [0; Tdiv/4] for the introgression time and in interval of [0.01; 1] for
 the probability of introgression. The file
 <strong>filet-classification.zip</strong> contains the final
 classification produced by FILET. FILET was run following the example
 given at https://github.com/kr-colab/FILET/blob/master/examplePipeline.sh.
 We used the sliding windows approach. The window size was set to 10k, the
 sliding step to 1k.
description: The introgression detection was performed using FILET by following the procedure explained here. The reference genome, the file containing repeats coordinates and the four VCF files computed separately for <em>G. intermedia</em> and <em>G. quinquepunctata</em> are used during the first two steps of the data masking procedure explained here: https://github.com/kr-colab/FILET/tree/master/maskingScripts . These files should be used as follows: Step 1: <code>python makeMaskArmFilesFromQualAndRM.py gi__all-positions.vcf gq__all-positions.vcf gq__repeats.gff gq__wtdbg2.fa 20 0.7 maskedRefFaFileName</code> Step 2<code>: </code> <code>python makeFilteredSnpVcfsForEachArm.py gi__snp.vcf gq__snp.vcf gi__all-positions.vcf gq__all-positions.vcf maskedRefFaFileName 20 filteredVcfPathPrefix_</code>