Data from: Risk alleles for tuberculosis infection associate with reduced immune reactivity in a wild mammalian host
Tavalire, Hannah F.
Hoal, Eileen G.
le Roex, Nikki
van Helden, Paul D.
Ezenwa, Vanessa O.
Jolles, Anna E.
Published Jun 26, 2019 on Dryad.
Cite this dataset
Tavalire, Hannah F. et al. (2019). Data from: Risk alleles for tuberculosis infection associate with reduced immune reactivity in a wild mammalian host [Dataset]. Dryad. https://doi.org/10.5061/dryad.3c45k28
Integrating biological processes across scales remains a central challenge in disease ecology. Genetic variation drives differences in host immune responses, which, along with environmental factors, generates temporal and spatial infection patterns in natural populations that epidemiologists seek to predict and control. However, genetics and immunology are typically studied in model systems, whereas population-level patterns of infection status and susceptibility are uniquely observable in nature. Despite obvious causal connections, organizational scales from genes to host outcomes to population patterns are rarely linked explicitly. Here we identify two loci near genes involved in macrophage (phagocyte) activation and pathogen degradation that additively increase risk of bovine tuberculosis infection by up to 9-fold in wild African buffalo. Furthermore, we observe genotype-specific variation in IL-12 production indicative of variation in macrophage activation. Here we provide measurable differences in infection resistance at multiple scales by characterizing the genetic and inflammatory variation driving patterns of infection in a wild mammal.
This file contains unfiltered SNPs obtained by 2bRAD genomic sequencing methods. Each row contains one SNP with marker metadata in the first five columns, followed by animal genotypes in subsequent columns.
This file contains the phenotypic data used in Tavalire et al. 2019 for the time to onset of bTB GWAS analysis, final time-to-event multi-locus genotype models, and longitudinal cytokine production models. Each row corresponds to a single capture time point for an animal identified by a unique Animal.ID, followed by month, year, and season of capture and demographic and phenotypic information. Some variables (e.g., bTB status) are repeated across all captures for each animal and can be extracted for single-observation analyses (e.g., time to onset of bTB GWAS analysis). All animals are female.
National Science Foundation, Award: EF-0723918, DEB-1102493/EF-0723928