Data from: Accurate genomic predictions for chronic wasting disease in North American elk
Data files
Apr 06, 2026 version files 107.88 MB
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AF1_NoCovs_CorrectMales-NoDupesEMMAX_NumericGenos.xlsx
31.14 MB
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AF2_SexAgeRegion_CorrectMales-NoDupesEMMAX_NumericGenos.xlsx
46.01 MB
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AF3_K3_NoCovs_CorrectMales_GRM_NumericGenos_50Iters.tsv
6.82 KB
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AF4_K3_SexAgeRegion_CorrectMales_GRM_NumericGenos_50Iters.tsv
6.81 KB
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AF5_K5_NoCovs_CorrectMales_GRM_NumericGenos_50Iters.tsv
6.82 KB
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AF6_K5_SexAgeRegion_CorrectMales_GRM_NumericGenos_50Iters.tsv
6.81 KB
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DRYAD_ElkGenosMeta_2025.zip
30.55 MB
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ReadMe_Seabury2025Elk.txt
1.72 KB
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README.md
2.80 KB
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SupplementalFiguresS1-S3_ElkRevised.docx
123.28 KB
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TableS1_Final.xlsx
21.82 KB
Abstract
The geographic expansion of chronic wasting disease (CWD) in North American elk (Cervus canadensis) has not been well-mitigated by best management practices, diagnostic surveillance, and depopulation of positive herds. Using a custom Affymetrix Axiom® genetic variant array, we demonstrate that differential susceptibility to CWD is highly heritable ( among farmed North American elk; with loci other than PRNP involved. Genome-wide association analyses using 173,674 quality filtered variants for a geographically diverse cohort of 904 farmed North American elk (n = 357 CWD positive; n = 547 CWD non-detect) confirmed the prion gene (PRNP codon 132 MetàLeu and promoter variants) as a large-effect risk locus (P-value < 5.135E-08), as evidenced by the estimated proportion of phenotypic variance explained (PVE ≥ 0.032). However, more phenotypic variance was collectively explained by loci other than PRNP.** Genomic best linear unbiased prediction (GBLUP; n = 173,674 markers) with k-fold cross validation (k = 3; k = 5) and random sampling (n = 50 iterations) for the same cohort of 904 farmed North American elk produced mean genomic prediction accuracies ≥ 0.791; thereby providing a foundation to explore a genomically-estimated CWD genetic improvement program.
https://doi.org/10.5061/dryad.p2ngf1w57
Summary of Additional (AF) and/or Supplemental (S) Files:
AF1_NoCovs_CorrectMales-NoDupesEMMAX_NumericGenos.xlsx: EMMAX output for Genome-Wide Association Analysis (GWAA) with full dosage compensation and no fixed-effect covariates.
AF2_SexAgeRegion_CorrectMales-NoDupesEMMAX_NumericGenos.xlsx: EMMAX output for Genome-Wide Association Analysis (GWAA) with full dosage compensation and no fixed-effect covariates (sex, age, geographic region of origin).
AF3_K3_NoCovs_CorrectMales_GRM_NumericGenos_50Iters.tsv: Iteration summary (n = 50 iterations) of k = 3 cross-validation with full dosage compensation and no fixed-effect covariates. This file can be opened in Excel as a tab-delimited text file.
AF4_K3_SexAgeRegion_CorrectMales_GRM_NumericGenos_50Iters.tsv: Iteration summary (n = 50 iterations) of k = 3 cross-validation with full dosage compensation and fixed-effect covariates (sex, age, geographic region of origin). This file can be opened in Excel as a tab-delimited text file.
AF5_K5_NoCovs_CorrectMales_GRM_NumericGenos_50Iters.tsv: Iteration summary (n = 50 iterations) of k = 5 cross-validation with full dosage compensation and no fixed-effect covariates. This file can be opened in Excel as a tab-delimited text file.
AF6_K5_SexAgeRegion_CorrectMales_GRM_NumericGenos_50Iters.tsv: Iteration summary (n = 50 iterations) of k = 5 cross validation with full dosage compensation and fixed-effect covariates (sex, age, geographic region of origin). This file can be opened in excel as a tab-delimited text file.
DRYAD_ElkGenosMeta_2025.zip: Primary genotype and meta data (sex (1= Female and 0 = Male), age (years), geographic region of origin) used for the study. Note, the X chromosome
is labeled as "34" in the Manhattan plot.
ReadMe_Seabury2025Elk.txt: Original ReadMe compiled by CM Seabury at TAMU describing all files here.
SupplementalFiguresS1-S3_ElkRevised.docx: Supplemental Figures S1-S3. Consistent with a prior study involving farmed white-tailed deer (Seabury et al. 2020), analytical support for rounding in the present study is similarly apparent by the histograms representing the frequency distributions of the predicted elk CWD binary phenotypes (Fig. S1), the relevant elk GEBVs (Fig. S2), and the relationship between them (Fig. S3); with a discernible break that occurs at 0.50 (Fig. S1-S3).
TableS1_Final.xlsx: Summary data for AF1 and AF2; focusing only on markers that meet or exceed the Wellcome Trust significance threshold for
polygenic traits. A second tab in this table also provides detailed estimates of linkage disequilibrium related to elk PRNP variants.
