Data from: Digging for gold nuggets: uncovering novel candidate genes for variation in gastrointestinal nematode burden in a wild bird species
Wenzel, Marius A.; Piertney, Stuart B. (2015), Data from: Digging for gold nuggets: uncovering novel candidate genes for variation in gastrointestinal nematode burden in a wild bird species, Dryad, Dataset, https://doi.org/10.5061/dryad.d51j3
The extent to which genotypic variation at a priori identified candidate genes can explain variation in complex phenotypes is a major debate in evolutionary biology. Whereas some high-profile genes such as the MHC or MC1R clearly do account for variation in ecologically relevant characters, many complex phenotypes such as response to parasite infection may well be underpinned by a large number of genes, each of small and effectively undetectable effect. Here, we characterize a suite of novel candidate genes for variation in gastrointestinal nematode (Trichostrongylus tenuis) burden among red grouse (Lagopus lagopus scotica) individuals across a network of moors in north-east Scotland. We test for associations between parasite load and genotypic variation in twelve genes previously identified to be differentially expressed in experimentally infected red grouse or genetically differentiated among red grouse populations with overall different parasite loads. These genes are associated with a broad physiological response including immune system processes. Based on individual-level generalized linear models, genotypic variants in nine genes were significantly associated with parasite load, with effect sizes accounting for differences of 514–666 worms per bird. All but one of these variants were synonymous or untranslated, suggesting that these may be linked to protein-coding variants or affect regulatory processes. In contrast, population-level analyses revealed few and inconsistent associations with parasite load, and little evidence of signatures of natural selection. We discuss the broader significance of these contrasting results in the context of the utility of population genomics and landscape genomics approaches in detecting adaptive genomic signatures.