DOI: 10.5061/dryad.m37pvmdg9
title: Population genetics, trait mapping, and fungal pathogen surveillance using untargeted sequencing in timber rattlesnakes (<em>Crotalus horridus</em>)

description: Timber rattlesnakes (Crotalus horridus) face escalating threats in the
 Northeastern Appalachians, including habitat fragmentation, human
 encroachment, and the fungal pathogen Ophidiomyces ophiodiicola. Using
 untargeted sequencing of DNA extracted from scale clips, we generated both
 host whole-genome and metagenomic data for 97 snakes from eight
 populations. Analysis of the snake genomes shows the populations surveyed
 exhibit relatively low levels of inbreeding and are genetically distinct,
 but the degree of separation correlates only weakly with geographic
 distance. A genome-wide association analysis identified a locus associated
 with black-to-yellow color variation that contains an aldehyde
 dehydrogenase gene (ALDH4A1) related to genes involved in hair color
 differences in humans. Metagenomic analysis showed that O. ophiodiicola
 read counts were generally higher in snakes exhibiting clinical signs of
 Snake Fungal Disease, but some visually asymptomatic snakes had high
 pathogen loads. Together, these findings highlight the dual utility of
 untargeted sequencing for population genetics and pathogen surveillance,
 providing a foundation for future studies of adaptation, disease dynamics,
 and conservation in this declining species.
description: # Population genetics, trait mapping, and fungal pathogen surveillance
 using untargeted sequencing in timber rattlesnakes (*Crotalus horridus*)
 Dataset DOI:
 [10.5061/dryad.m37pvmdg9](https://doi.org/10.5061/dryad.m37pvmdg9) ##
 Description of the data and file structure This repository contains
 sequencing output files and reference annotations in the study titled:
 “Population genetics, trait mapping, and fungal pathogen surveillance
 using untargeted sequencing in timber rattlesnakes (*Crotalus horridus*).”
 It comprises data from scale clipping samples of *C. horridus* collected
 at multiple locations throughout the Northeastern United States. A total
 of 111 bracken output files are included. Additionally, we have included
 liftoff output GTF used in our GWAS analyses and raw ANGD-assoc GWAS data.
 The data mainly consist of Bracken output tables generated after running
 Kraken2 + Bracken with default settings for metagenomic taxonomic
 classification. These tables show microbial taxa's relative abundance
 and read assignments at the genus level for each snake sample. The dataset
 also features a liftover GTF annotation from *Crotalus tigris* to
 *Crotalus adamanteus*, created to support future genomic or gene-mapping
 comparisons for GWAS analyses performed in the paper, and identifying the
 overlapping genes. Overall, this dataset enables replication and reuse in
 metagenomic analysis of microbiota and pathogens (including *Ophidiomyces
 ophiodiicola*) and future comparative annotation using viper references.
 ### Files and variables #### File:
 lifted_tigris_to_adamanteus.gtf_polished **Description:** The file is a
 cross-species annotation file aligning *C. tigris* gene coordinates to the
 *C. adamanteus* reference assembly. #### File: GWAS_output.lrt0-2.gz
 **Description:** Compressed raw output file containing results from
 genome-wide association analyses performed using the ANGSD-assoc module of
 ANGSD. Each record reports likelihood ratio test (LRT) statistics,
 p-values, and associated summary metrics for all tested loci before
 post-processing or multiple-testing correction. These results represent
 the unfiltered association output directly generated from the `-doAsso`
 step. #### File: bracken_files.zip **Description:** Zipped directory
 containing 111 `.bracken` files generated by **Kraken2 + Bracken**. Each
 file corresponds to a unique *C. horridus* sample. Files include taxonomic
 assignments at the genus level, estimated read counts, and relative
 abundances. #### File: pairwise_bray_distances.csv **Description:** This
 was generated from the following code:  bray_dist &lt;- vegdist(otu_rel,
 method = "bray")  bray_mat &lt;- as.matrix(bray_dist)  The first
 line uses `vegdist()` to compute pairwise community dissimilarities
 between samples in `otu_rel` (relative abundances of the identified taxa),
 and the second converts that distance object into a full square matrix for
 easier inspection or downstream analysis. ## Code/software  #### File:
 liftoff.sh The script to generate the liftoff runs on an HPC scheduler
 (SLURM), producing a GTF of lifted features and a GTF listing features
 that failed to map. This was specifically: Lift over gene annotations from
 *Crotalus tigris* to the *Crotalus adamanteus* reference using Liftoff
 (genome-to-genome annotation transfer). #### GWAS_output.lrt0-2.gz: The
 analyses examined the relationships between genotype likelihoods and the
 animal’s color morph phenotype using an additive genetic model (model 1).
 We chose this model because additive genetic variance chiefly influences
 the genetic basis of complex traits like color morphs (Hill et al., 2008).
 Initially, genotype likelihoods were computed in ANGSD and then refined
 with BEAGLE to enhance genotype accuracy before testing for associations.
 The output includes unfiltered likelihood ratio test (LRT) statistics and
 p-values for all loci considered, prior to any filtering or correction for
 multiple testing. To address multiple comparisons, a Bonferroni correction
 established a genome-wide significance threshold based on a family-wise
 error rate of α = 0.05 and the total loci count (n = 1,159,481). This
 resulted in a significance cutoff of p &lt; 4.31 × 10⁻⁸. ####
 kraken_run_array_bacteria_horridus.sh,
 kraken_run_array_fungus_horridus.sh, bracken_run_array_fungus.sh,
 bracken_run_array_bacteria.sh These scripts were used for running Kraken2
 and then Bracken against a database containing standard bacteria, fungi,
 and viral sequences. We also included the *Ophidiomyces ophiodiicola* and
 human genomes (this is to remove contamination). References: Hill, W. G.,
 Goddard, M. E., & Visscher, P. M. (2008). Data and theory point to
 mainly additive genetic variance for complex traits. *PLoS Genetics*,
 *4*(2), e1000008.
 [https://doi.org/\[10.1371/journal.pgen.1000008](https://doi.org/[10.1371/journal.pgen.1000008)]([http://dx.doi.org/10.1371/journal.pgen.1000008](http://dx.doi.org/10.1371/journal.pgen.1000008)) Korneliussen, T. S., Albrechtsen, A., & Nielsen, R. (2014). ANGSD: Analysis of Next Generation Sequencing Data. *BMC Bioinformatics*, *15*(1), 356. [https://doi.org/\[10.1186/s12859-014-0356-4](https://doi.org/[10.1186/s12859-014-0356-4)]([http://dx.doi.org/10.1186/s12859-014-0356-4](http://dx.doi.org/10.1186/s12859-014-0356-4)) Lu, J., Breitwieser, F. P., Thielen, P., & Salzberg, S. L. (2017). Bracken: estimating species abundance in metagenomics data. *PeerJ. Computer Science*, *3*(e104), e104. [https://doi.org/\[10.7717/peerj-cs.104](https://doi.org/[10.7717/peerj-cs.104)]([http://dx.doi.org/10.7717/peerj-cs.104](http://dx.doi.org/10.7717/peerj-cs.104)) Shumate, A., & Salzberg, S. L. (2021). Liftoff: accurate mapping of gene annotations. *Bioinformatics (Oxford, England)*, *37*(12), 1639–1643. *vegan: R package for community ecologists: popular ordination methods, ecological null models & diversity analysis*. (n.d.). Github. Retrieved June 2, 2025, from [https://github.com/vegandevs/vegan](https://github.com/vegandevs/vegan) Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. *Genome Biology*, *20*(1), 257. [https://doi.org/\[10.1186/s13059-019-1891-0](https://doi.org/[10.1186/s13059-019-1891-0)]([http://dx.doi.org/10.1186/s13059-019-1891-0](http://dx.doi.org/10.1186/s13059-019-1891-0)) ## Access information Other publicly accessible locations of the data: Additional data will be included in the supplemental data included in our manuscript. These will contain the final outputs from all tools used in the publication.  Data was derived from the following sources: Most data was generated using the ANGSD pipeline and related tools. We used Linux and SLURM for processing the data for job scheduling on the Unity HPC.  ANGSD software website: [https://www.popgen.dk/angsd/index.php/ANGSD](https://www.popgen.dk/angsd/index.php/ANGSD) HPC used: [https://docs.unity.rc.umass.edu](https://docs.unity.rc.umass.edu)