Prediction of Genes Related to Positive Selection Using Whole-Genome Resequencing in Three Commercial Pig Breeds

Selective sweep can cause genetic differentiation across populations, which allows for the identification of possible causative regions/genes underlying important traits. The pig has experienced a long history of allele frequency changes through artificial selection in the domestication process. We obtained an average of 329,482,871 sequence reads for 24 pigs from three pig breeds: Yorkshire (n = 5), Landrace (n = 13), and Duroc (n = 6). An average read depth of 11.7 was obtained using whole-genome resequencing on an Illumina HiSeq2000 platform. In this study, cross-population extended haplotype homozygosity and cross-population composite likelihood ratio tests were implemented to detect genes experiencing positive selection for the genome-wide resequencing data generated from three commercial pig breeds. In our results, 26, 7, and 14 genes from Yorkshire, Landrace, and Duroc, respectively were detected by two kinds of statistical tests. Significant evidence for positive selection was identified on genes ST6GALNAC2 and EPHX1 in Yorkshire, PARK2 in Landrace, and BMP6, SLA-DQA1, and PRKG1 in Duroc.These genes are reportedly relevant to lactation, reproduction, meat quality, and growth traits. To understand how these single nucleotide polymorphisms (SNPs) related positive selection affect protein function, we analyzed the effect of non-synonymous SNPs. Three SNPs (rs324509622, rs80931851, and rs80937718) in the SLA-DQA1 gene were significant in the enrichment tests, indicating strong evidence for positive selection in Duroc. Our analyses identified genes under positive selection for lactation, reproduction, and meat-quality and growth traits in Yorkshire, Landrace, and Duroc, respectively.

Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples

Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes were identified as follows: 22 genes in the USA (CEU) individuals, 25 genes in the Japanese (JPT) individuals, and 332 genes in the African (YRI) individuals. To analyze the biologically functional implications for ethnicity-specific SNPs, we focused on constructing a semantic network model. Entities for the network represented by "Gene," "Pathway," "Disease," "Chemical," "Drug," "ClinicalTrials," "SNP," and relationships between entity-entity were obtained through curation. Our semantic modeling for ethnicity-specific SNPs showed interesting results in the three categories, including three diseases ("AIDS-associated nephropathy," "Hypertension," and "Pelvic infection"), one drug ("Methylphenidate"), and five pathways ("Hemostasis," "Systemic lupus erythematosus," "Prostate cancer," "Hepatitis C virus," and "Rheumatoid arthritis"). We found ethnicity-specific genes using the semantic modeling, and the majority of our findings was consistent with the previous studies - that an understanding of genetic variability explained ethnicity-specific disparities.