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A survey of single nucleotide polymorphisms identified from whole‐genome sequencing and their functional effect in the porcine genome,

B. N. Keel, D. J. Nonneman, G. A. Rohrer
Animal genetics 2017 v.48 no.4 pp. 404-411
Duroc, Yorkshire (swine breed), boars, genes, genetic variation, genomics, herds, landraces, loss-of-function mutation, phenotypic variation, sequence analysis, single nucleotide polymorphism, sows, surveys
Genetic variants detected from sequence have been used to successfully identify causal variants and map complex traits in several organisms. High and moderate impact variants, those expected to alter or disrupt the protein coded by a gene and those that regulate protein production, likely have a more significant effect on phenotypic variation than do other types of genetic variants. Hence, a comprehensive list of these functional variants would be of considerable interest in swine genomic studies, particularly those targeting fertility and production traits. Whole‐genome sequence was obtained from 72 of the founders of an intensely phenotyped experimental swine herd at the U.S. Meat Animal Research Center (USMARC). These animals included all 24 of the founding boars (12 Duroc and 12 Landrace) and 48 Yorkshire–Landrace composite sows. Sequence reads were mapped to the Sscrofa10.2 genome build, resulting in a mean of 6.1 fold (×) coverage per genome. A total of 22 342 915 high confidence SNPs were identified from the sequenced genomes. These included 21 million previously reported SNPs and 79% of the 62 163 SNPs on the PorcineSNP60 BeadChip assay. Variation was detected in the coding sequence or untranslated regions (UTRs) of 87.8% of the genes in the porcine genome: loss‐of‐function variants were predicted in 504 genes, 10 202 genes contained nonsynonymous variants, 10 773 had variation in UTRs and 13 010 genes contained synonymous variants. Approximately 139 000 SNPs were classified as loss‐of‐function, nonsynonymous or regulatory, which suggests that over 99% of the variation detected in our pigs could potentially be ignored, allowing us to focus on a much smaller number of functional SNPs during future analyses.