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Data from: SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate


Roffler, Gretchen H. et al. (2016), Data from: SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate, Dryad, Dataset,


Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5,321 candidate adaptive genes associated with known immunological, metabolic, and growth functions in ovids and other ungulates. We selectively targeted 8,161 exons in protein-coding and nearby 5′ and 3′ untranslated regions of chosen candidate genes. Targeted sequences were taken from bighorn sheep (O. canadensis) exon capture data and directly from the domestic sheep genome (O. aries v. 3; oviAri3). The bighorn sheep sequences used in the Dall's sheep (Ovis dalli dalli) exon capture aligned to 2,350 genes on the oviAri3 genome with an average of 2 exons each. We developed a microfluidic qPCR-based SNP-chip to genotype 476 Dall's sheep from locations across their range and test for patterns of selection. Using multiple corroborating approaches (LOSITAN and BayeScan), we detected 28 SNP loci potentially under selection. We additionally identified candidate loci significantly associated with latitude, longitude, precipitation, and temperature, suggesting local environmental adaptation. The 3 methods demonstrated consistent support for natural selection on 9 genes with immune and disease-regulating functions (e.g., Ovar-DRA, APC, BATF2, MAGEB18), cell regulation signaling pathways (e.g., KRIT1, PI3K, ORRC3), and respiratory health (CYSLTR1). Characterizing adaptive allele distributions from novel genetic techniques will facilitate investigation of the influence of environmental variation on local adaptation of a northern alpine ungulate throughout its range. This research demonstrated the utility of exon capture for gene-targeted SNP discovery and subsequent SNP-chip genotyping using low quality samples in a non-model species.

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