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Data from: Parallel evolution of gene classes, but not genes: evidence from Hawai’ian honeycreeper populations exposed to avian malaria

By Loren Cassin-Sackett, Taylor E. Callicrate and Robert C. Fleischer


Adaptation in nature is ubiquitous, yet characterizing its genomic basis is difficult because population demographics cause correlations with non-adaptive loci. Introduction events provide opportunities to observe adaptation over known spatial and temporal scales, facilitating the identification of genes involved in adaptation. The pathogen causing avian malaria, Plasmodium relictum, was introduced to Hawai’i in the 1930s and elicited extinctions and precipitous population declines in native honeycreepers. After a sharp initial population decline, the Hawai’i ‘amakihi (Chlorodrepanis virens) has evolved tolerance to the parasite at low elevations where P. relictum exists, and can sustain infection without major fitness consequences. High-elevation, unexposed populations of ‘amakihi display little to no tolerance. To explore the genomic basis of adaptation to P. relictum in low-elevation ‘amakihi, we genotyped 125 ‘amakihi from the island of Hawai’i via hybridization capture to 40,000 oligonucleotide baits containing SNPs, and used the reference ‘amakihi genome to identify genes potentially under selection from malaria. We tested for outlier loci between low- and high-elevation population pairs and identified loci with signatures of selection within low-elevation populations. In some cases, genes commonly involved in the immune response (e.g., major histocompatibility complex) were associated with malaria presence in the population. We also detected several novel candidate loci that may be implicated in surviving malaria infection (e.g., beta defensin, glycoproteins, and interleukin-related genes). Our results suggest that rapid adaptation to pathogens may occur through changes in different immune genes, but in the same classes of genes, across populations

Topics: Hawai'ian honeycreepers, amakihi, disease resistance, outliers, adaptation
Year: 2018
DOI identifier: 10.5061/dryad.hs5jt80
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