Data Mining in Networks of Differentially Expressed Genes during Sow Pregnancy

Small to moderate gains in Pig fertility can mean large returns in overall efficiency, and developing methods to improve it is highly desirable. High fertility rates depend on completion of successful pregnancies. To understand the molecular signals associated with pregnancy in sows, expression profiling experiments were conducted to identify differentially expressed genes in ovary and myometrium at different pregnancy periods using the Affymetrix Porcine GeneChipTM. A total of 974, 1800, 335 and 710 differentially expressed transcripts were identified in the myometrium during early pregnancy (EP) and late pregnancy (LP), and in the ovary during EP and LP, respectively. Self-Organizing Map (SOM) clusters indicated the differentially expressed genes belonged to 7 different functional groups. Based on BLASTX searches and Gene Ontology (GO) classifications, 129 unique genes closely related to pregnancy showed differential expression patterns. GO analysis also indicated that there were 21 different molecular function categories, 20 different biological process categories, and 8 different cellular component categories of genes differentially expressed during sow pregnancy. Gene regulatory network reconstruction provided us with an interaction model of known genes such as insulin-like growth factor 2 (IGF2) gene, estrogen receptor (ESR) gene, retinol-binding protein-4 (RBP4) gene, and several unknown candidate genes related to reproduction. Several pitch point genes were selected for association study with reproduction traits. For instance, DPPA5 g.363 T>C was found to associate with litter born weight at later parities in Beijing Black pigs significantly (p < 0.05). Overall, this study contributes to elucidating the mechanism underlying pregnancy processes, which maybe provide valuable information for pig reproduction improvement

Genome-wide Association Study of Porcine Hematological Parameters in a Large White × Minzhu F2 Resource Population

Hematological traits, which are important indicators of immune function in animals, have been commonly examined as biomarkers of disease and disease severity in humans and animals. Genome-wide significant quantitative trait loci (QTLs) provide important information for use in breeding programs of animals such as pigs. QTLs for hematological parameters (hematological traits) have been detected in pig chromosomes, although these are often mapped by linkage analysis to large intervals making identification of the underlying mutation problematic. Single nucleotide polymorphisms (SNPs) are the common form of genetic variation among individuals and are thought to account for the majority of inherited traits. In this study, a genome-wide association study (GWAS) was performed to detect regions of association with hematological traits in a three-generation resource population produced by intercrossing Large White boars and Minzhu sows during the period from 2007 to 2011. Illumina PorcineSNP60 BeadChip technology was used to genotype each animal and seven hematological parameters were measured (hematocrit (HCT), hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC) and red blood cell volume distribution width (RDW)). Data were analyzed in a three step Genome-wide Rapid Association using the Mixed Model and Regression-Genomic Control (GRAMMAR-GC) method. A total of 62 genome-wide significant and three chromosome-wide significant SNPs associated with hematological parameters were detected in this GWAS. Seven and five SNPs were associated with HCT and HGB, respectively. These SNPs were all located within the region of 34.6-36.5 Mb on SSC7. Four SNPs within the region of 43.7-47.0 Mb and fifty-five SNPs within the region of 42.2-73.8 Mb on SSC8 showed significant association with MCH and MCV, respectively. At chromosome-wide significant level, one SNP at 29.2 Mb on SSC1 and two SNPs within the region of 26.0-26.2 Mb were found to be significantly associated with RBC and RDW, respectively. Many of the SNPs were located within previously reported QTL regions and appeared to narrow down the regions compared with previously described QTL intervals. In current research, a total of seven significant SNPs were found within six candidate genes SCUBE3, KDR, TDO, IGFBP7, ADAMTS3 and AFP. In addition, the KIT gene, which has been previously reported to relate to hematological parameters, was located within the region significantly associated with MCH and MCV and could be a candidate gene. These results of this study may lead to a better understanding of the molecular mechanisms of hematological parameters in pigs

Genome-Wide Association Analysis of Meat Quality Traits in a Porcine Large White &#215; Minzhu Intercross Population

Pork quality is an economically important trait and one of the main selection criteria for breeding in the swine industry. In this genome-wide association study (GWAS), 455 pigs from a porcine Large White &#215; Minzhu intercross population were genotyped using the Illumina PorcineSNP60K Beadchip, and phenotyped for intramuscular fat content (IMF), marbling, moisture, color L*, color a*, color b* and color score in the longissimus muscle (LM). Association tests between each trait and the SNPs were performed via the Genome Wide Rapid Association using the Mixed Model and Regression-Genomic Control (GRAMMAR-GC) approach. From the Ensembl porcine database, SNP annotation was implemented using Sus scrofa Build 9. A total of 45 SNPs showed significant association with one or multiple meat quality traits. Of the 45 SNPs, 36 were located on SSC12. These significantly associated SNPs aligned to or were in close approximation to previously reported quantitative trait loci (QTL) and some were located within introns of previously reported candidate genes. Two haplotype blocks ASGA0100525-ASGA0055225-ALGA0067099-MARC0004712-DIAS0000861, and ASGA0085522-H3GA0056170 were detected in the significant region. The first block contained the genes MYH1, MYH2 and MYH4. A SNP (ASGA0094812) within an intron of the USP43 gene was significantly associated with five meat quality traits. The present results effectively narrowed down the associated regions compared to previous QTL studies and revealed haplotypes and candidate genes on SSC12 for meat quality traits in pigs.</p

Genome-Wide Copy Number Variations Inferred from SNP Genotyping Arrays Using a Large White and Minzhu Intercross Population

<div><p>Copy number variations (CNVs) are one of the main contributors to genetic diversity in animals and are broadly distributed in the genomes of swine. Investigating the performance and evolutionary impacts of pig CNVs requires comprehensive knowledge of their structure and function within and between breeds. In the current study, 4 different programs (i.e., GADA, PennCNV, QuantiSNP, and cnvPartition) were used to analyze Porcine SNP60 genotyping data of 585 pigs from one Large White × Minzhu intercross population to detect copy number variant regions (CNVRs). Overlapping CNVRs recalled by at least 2 programs were used to construct a powerful and comprehensive CNVR map, which contained249 CNVRs (i.e., 70 gains, 43 losses, and 136 gains/losses) and covered 26.22% of the regions in the swine genome. Ten CNVRs, representing different predicted statuses, were selected for validation <i>via</i> quantitative real-time PCR (QPCR); 9/10 CNVRs (i.e., 90%) were validated. When being traced back to the F0 generation, 58 events were identified in only Minzhu F0 parents and 2 events were identified in only Large White F0 parents. A series of CNVR function analyses were performed. Some of the CNVRs functions were predicted, and several interesting CNVRs for meat quality traits and hematological parameters were obtained. A comprehensive and lower false rate genome-wide CNV map was constructed for Large White and Minzhu pig genomes in this study. Our results may provide an important basis for determining the relationship between CNVRs and important qualitative and quantitative traits. In addition, it can help to further understand genetic processes in pigs.</p></div

Results of QPCR Validation.

<p><i>TIAM2</i> is T-cell lymphoma invasion and metastasis 2; <i>ELL3</i> is elongation factor RNA polymerase II-like 3; <i>C1orf150</i> is chromosome 1 open reading frame 150; <i>F1SGK0_PIG</i> is an Uncharacterized gene; <i>HMGA2</i> is high mobility group AT-hook 2; <i>CES1</i> is liver carboxylesterase; <i>ECI2</i> is enoyl-CoA delta isomerase 2.</p

Unique CNVRs in F0 Minzhu pig and F0 Large-White.

<p>Unique CNVR means CNVR only detected in this breed.</p><p>Positions are retrieved from the swine genome sequence assembly (9.2) (<a href="http://www.ensembl.org/Sus_scrofa/Info/Index" target="_blank">http://www.ensembl.org/Sus_scrofa/Info/Index</a>).</p

Sample sizes and the CNVR numbers detected in F0 and F2 generation.

<p>Unique CNVR means CNVR only detected in this breed.</p

Relative quantification (RQ) value by Quantitative PCR (QPCR) for CNVR64.

<p>Twenty animals with Relative quantification (RQ) value are showed in this figure. Each dot represents the relative copy number in comparison to the reference individual. Y-axis shows the RQ obtained by QPCR. Samples with RQ about 1 denote normal individuals (two copy), samples with RQ below 0.59 (ln^1.5) denote copy number loss individuals, and samples with RQ about 1.59 (ln³) or more denote copy number gain individuals (≧three copy).</p

Relative quantification (RQ) value by Quantitative PCR (QPCR) for CNVR79.

<p>Twenty animals with Relative quantification (RQ) value are showed in this figure. Each dot represents the relative copy number in comparison to the reference individual. Y-axis shows the RQ obtained by QPCR. Samples with RQ about 1 denote normal individuals (two copy), samples with RQ below 0.59 (ln^1.5) denote copy number loss individuals.</p