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.

Heritability Estimated Using 50K SNPs Indicates Missing Heritability Problem in Holstein Breeding

Previous studies in Holstein have shown 35% to 51.8% heritability in milk production traits, such as milk yield, fat, and protein, using pedigree data. Other studies in complex human traits could be captured by common single-nucleotide polymorphisms (SNPs), and their genetic variations, attributed to chromosomes, are in proportion to their length. Using genome-wide estimation and partitioning approaches, we analyzed three quantitative Holstein traits relevant to milk production in Korean Holstein data harvested from 462 individuals genotyped for 54,609 SNPs. For all three traits (milk yield, fat, and protein), we estimated a nominally significant (p = 0.1) proportion of variance explained by all SNPs on the Illumina BovineSNP50 Beadchip (h(2)(G)). These common SNPs explained approximately most of the narrow-sense heritability. Longer genomic regions tended to provide more phenotypic variation information, with a correlation of 0.46~0.53 between the estimate of variance explained by individual chromosomes and their physical length. These results suggested that polygenicity was ubiquitous for Holstein milk production traits. These results will expand our knowledge on recent animal breeding, such as genomic selection in Holstein.

The Usage of an SNP-SNP Relationship Matrix for Best Linear Unbiased Prediction (BLUP) Analysis Using a Community-Based Cohort Study

Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.

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.

Functional Prediction of Imprinted Genes in Chicken Based on a Mammalian Comparative Expression Network

Little evidence supports the existence of imprinted genes in chicken. Imprinted genes are thought to be intimately connected with the acquisition of parental resources in mammals; thus, the predicted lack of this type of gene in chicken is not surprising, given that they leave their offspring to their own heritance after conception. In this study, we identified several imprinted genes and their orthologs in human, mouse, and zebrafish, including 30 previously identified human and mouse imprinted genes. Next, using the HomoloGene database, we identified six orthologous genes in human, mouse, and chicken; however, no orthologs were identified for SLC22A18, and mouse Ppp1r9a was not included in the HomoloGene database. Thus, from our analysis, four candidate chicken imprinted genes (IGF2, UBE3A, PHLDA2, and GRB10) were identified. To expand our analysis, zebrafish was included, but no probe ID for UBE3A exists in this species. Thus, ultimately, three candidate imprinted genes (IGF2, PHLDA2, and GRB10) in chicken were identified. GRB10 was not significant in chicken and zebrafish based on the Wilcoxon-Mann-Whitney test, whereas a weak correlation between PHLDA2 in chicken and human was identified from the Spearman's rank correlation coefficient. Significant associations between human, mouse, chicken, and zebrafish were found for IGF2 and GRB10 using the Friedman's test. Based on our results, IGF2, PHLDA2, and GRB10 are candidate imprinted genes in chicken. Importantly, the strongest candidate was PHLDA2.

Functional Prediction of Imprinted Genes in Chicken Based on a Mammalian Comparative Expression Network

Little evidence supports the existence of imprinted genes in chicken. Imprinted genes are thought to be intimately connected with the acquisition of parental resources in mammals; thus, the predicted lack of this type of gene in chicken is not surprising, given that they leave their offspring to their own heritance after conception. In this study, we identified several imprinted genes and their orthologs in human, mouse, and zebrafish, including 30 previously identified human and mouse imprinted genes. Next, using the HomoloGene database, we identified six orthologous genes in human, mouse, and chicken; however, no orthologs were identified for SLC22A18, and mouse Ppp1r9a was not included in the HomoloGene database. Thus, from our analysis, four candidate chicken imprinted genes (IGF2, UBE3A, PHLDA2, and GRB10) were identified. To expand our analysis, zebrafish was included, but no probe ID for UBE3A exists in this species. Thus, ultimately, three candidate imprinted genes (IGF2, PHLDA2, and GRB10) in chicken were identified. GRB10 was not significant in chicken and zebrafish based on the Wilcoxon-Mann-Whitney test, whereas a weak correlation between PHLDA2 in chicken and human was identified from the Spearman's rank correlation coefficient. Significant associations between human, mouse, chicken, and zebrafish were found for IGF2 and GRB10 using the Friedman's test. Based on our results, IGF2, PHLDA2, and GRB10 are candidate imprinted genes in chicken. Importantly, the strongest candidate was PHLDA2.