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Using diverse U.S. beef cattle genomes to identify missense mutations in EPAS1, a gene associated with high-altitude pulmonary hypertension
Ist Teil von
F1000 research, 2016, Vol.5, p.2003-2003
Ort / Verlag
London: Faculty of 1000 Ltd
Erscheinungsjahr
2016
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
The availability of whole genome sequence (WGS) data has made it possible to discover protein variants
in silico
. However, existing bovine WGS databases do not show data in a form conducive to protein variant analysis, and tend to under represent the breadth of genetic diversity in U.S. beef cattle. Thus, our first aim was to use 96 beef sires, sharing minimal pedigree relationships, to create a searchable and publicly viewable set of mapped genomes relevant for 19 popular breeds of U.S. cattle. Our second aim was to identify protein variants encoded by the bovine endothelial PAS domain-containing protein 1 gene (
EPAS1
), a gene associated with high-altitude pulmonary hypertension in Angus cattle. The identity and quality of genomic sequences were verified by comparing WGS genotypes to those derived from other methods. The average read depth, genotype scoring rate, and genotype accuracy exceeded 14, 99%, and 99%, respectively. The 96 genomes were used to discover four amino acid variants encoded by
EPAS1
(E270Q, P362L, A671G, and L701F) and confirm two variants previously associated with disease (A606T and G610S). The six
EPAS1
missense mutations were verified with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry assays, and their frequencies were estimated in a separate collection of 1154 U.S. cattle representing 46 breeds. A rooted phylogenetic tree of eight polypeptide sequences provided a framework for evaluating the likely order of mutations and potential impact of
EPAS1
alleles on the adaptive response to chronic hypoxia in U.S. cattle. This public, whole genome resource facilitates
in silico
identification of protein variants in diverse types of U.S. beef cattle, and provides a means of translating WGS data into a practical biological and evolutionary context for generating and testing hypotheses.