Data and code from: A multifaceted approach reveals complex genomic mediation of white-nose syndrome adaptative response in the little brown bat (Myotis lucifugus)

Abstract

Novel pathogens have become a major challenge faced by wildlife in the Anthropocene. White-nose syndrome (WNS) has decimated bat populations across North America over the last two decades. Demographic and physiological evidence of adaptation in one heavily affected species, Myotis lucifugus, has prompted multiple attempts to delineate the genomic underpinnings, but these studies show little congruence in their findings. This may be due, in part, to the limitations of the genomic resources utilized and/or analytical approaches employed. Here, we performed high-coverage whole-genome resequencing of M. lucifugus sampled prior to (n = 29), and 10 years after the local arrival of WNS (n = 30), aligned to a new reference genome to identify signatures of selection associated with adaptive responses. Using 41.9 million SNPs, we implemented a combination of hard and soft sweep detection analyses, leading to discovery of 382 genes with robust evidence of selection. Of these, 202 (49.9 %) were associated with enriched gene ontology (GO) terms, many of which were tied to neuron development, organization, and function. Further, approximately half (107) of genes associated with enriched GO terms interact with genes identified by previous studies. Our findings suggest reduced susceptibility to WNS is mediated through highly complex, polygenic mechanisms. Further, we demonstrate there are far more connections among WNS selection study results than previously recognized. We believe that the methods employed by our study illustrate a need for a paradigm shift in non-model selection studies and further highlight the value of genomics as a tool for conservation management.

Dataset DOI: 10.5061/dryad.ncjsxkt66

Code & scripts: GitHub repository

Description of the data and file structure

This dataset contains data files associated with the analytical code for this manuscript, two versions of the final VCF, and selection statistic output files. All analytical code can be found with the the associated GitHub repository or the associated Zenodo repository (DOI: 10.5281/zenodo.17636150). The genome annotation used for this data is available through https://github.com/docmanny/myotis-gene-annotations. Raw data for this study are available through the NCBI Short Read Archive (BioProject PRJNA1353610, accession nos. SAMN52933055–SAMN52933113). GO enrichment and gene network analyses were performed with Cytoscape (Shannon et al. 2003), a GUI application, using the ClueGO (Bindea et al. 2009) and Cluepedia (Bindea et al. 2013) plugins, respectively.

Data Files

VCFs:

gatk.snp.qual_hard_filtered_autosomes.vcf.gz -- full set of filtered SNPs
gatk.snp.qual_hard_filtered_autosomes_thin.vcf.gz -- filtered SNPs thinned by 10 Kbp distance

SNP Calling Pipeline Input Files:

RG_info.tsv -- read group information for variant calling
all_samples.txt -- sample IDs for all individual FASTQ files
all_samples.merge.txt -- sample IDs merging all files for each individual

Selection Statistic Output Files:

ANGSD_Fst_10kbp_windows.zip -- final output files from ANGSD_Fst.md
XP-CLR_10kbp_windows.zip -- final output files from XP-CLR_outliers.R
REHH_Rsb_10_Kbp_windows.zip -- final output files from REHH_Rsb_outliers.R

Sample and Population Text Files for Scripts:

pops.txt -- text file of all population designations
Population_Map.tsv -- population and sampling site information for each individual
pop_comps.tsv -- text file of all pairwise comparisons
PRE.ind -- all pre-WNS individuals
pop_scaff.tsv -- text file input for phasing genotypes

Alignment and Variant Statistics:

hicov_coverage.tsv -- per-scaffold alignment statistics for "high coverage" individuals
lowcov_coverage.tsv -- per-scaffold alignment statistics for "shallow coverage" individuals
SNP_filtering.txt -- Type and number of variants filtered per scaffold

Scripts & Code (available through GitHub)

SNP Calling Pipeline:

call_SNPs_pipeline.zip
Components:
- clean_align_callSNPs.sbatch -- master script
- HTS_preproc.slurm -- clean FASTQs; pipeline component
- hashDRAGMAP.slurm -- build reference genome hash table; pipeline component
- alignDRAGMAP.slurm -- align FASTQs to reference genome; pipeline component
- samtools_merge.slurm -- merge sample bams; pipeline component
- genome_wins.slurm -- create .bed for genome windows; pipeline component
- align_stats.slurm -- calculate alignment statistics; pipeline component
- STRtable.slurm -- create STR table; pipeline component
- bam_to_gvcf.slurm -- call variants per individual; pipeline component
- gvcf_to_vcf_scaff.slurm -- joint call variants; pipeline component
- vcf_scaff_to_snp.vcf.slurm -- combine VCFs and filter; pipeline component
Dependencies: HTStream, DRAGMAP v1.2.1, picard, GATK4, samtools, bedtools, and bcftools
Inputs: raw sequencing files (NCBI BioProject PRJNA1353610), M. lucifugus reference genome, RG_info.tsv, all_samples.txt, all_samples.merge.txt
For more details see the associated github repository

Selection Statistic Calculation:

ANGSD_Fst.md -- calculate population pariwise F_ST in 10 Kbp windows for all population comparisons
- Dependencies: ANGSD v0.940 and bcftools
- Inputs: gatk.snp.qual_hard_filtered_autosomes.vcf.gz, pops.txt, Population_Map.txt, pop_comps.tsv
XP-CLR.sbatch -- calculate XP-CLR in 10 Kbp windows for all population comparisons
- Dependencies: XP-CLR, bcftools, and tabix
- Inputs: gatk.snp.qual_hard_filtered_autosomes.vcf.gz, pop_comps.tsv
rehh.md -- polarize VCF alleles, phase genotypes, and calculate Rsb 10 Kbp windows for all population comparisons
- Dependencies: java 17, bcftools, vcftools, vcffilterjdk, beagle v5.4, R, rehh, and strigr
- Inputs: gatk.snp.qual_hard_filtered_autosomes.vcf.gz, PRE.ind, pop_scaff.tsv, pop_comps.tsv

Identify Selection Statistic Outlier Windows:

ANGSD_Fst_outliers.R -- identify outlier windos from final ANGSD_Fst.md output
- Dependencies: R and dplyr
- Input: ANGSD_Fst_10kbp_windows.zip (unzipped)
XP-CLR_outliers.R -- identify outlier windos from final XP-CLR.sbatch output
- Dependencies: R and dplyr
- Input: XP-CLR_10kbp_windows.zip (unzipped)
REHH_Rsb_outliers.R -- identify outlier windos from final rehh.sbatch output
- Dependencies: R and dplyr
- Input: REHH_Rsb_10_Kbp_windows.zip (unzipped)

Other Scripts:

SNPRelate.R -- Calculate pairwise kinship using SNPRelate's IBD MLE method
- Requirements: R, SNPRelate, and gdsfmt
- Input: gatk.snp.qual_hard_filtered_autosomes_thin.vcf.gz