30 research outputs found
SNP Discovery and Genomic Architecture of Highly Inbred Leghorn and Fayoumi Chicken Breeds Using Whole Genome Resequencing
Advances in the use of next generation sequencing (NGS) and ability to pool individuals into groups that represent distinct livestock populations has made it possible to examine trait differences between breeds of chicken. The breeds examined are very divergent when compared on their history of laying ability and immune response. The long-term objective is to understand the genetic differences between the Leghorn and Fayoumi breeds for use in developing more productive and disease resistant chickens. Statistical testing of the sequence of the two breeds along with Gene set enrichment analysis (GSEA) to make connections between the genetic variation seen in the NGS data and the breed specific traits of egg laying and heightened immune response can be used to characterize these two breeds. Genetic terms having the highest level of differentiation between the lines appear to group into metabolic processes, with terms over-enriched for immune system process, sexual reproduction, and growth for variants examined between lines. Terms for functions within the Fayoumi and Leghorn populations aligned to immune function and reproductive function, respectively. These results are consistent with known breed phenotypes and provide a means to focus on specific DNA variations and the birds’ genetic diversity that are potentially of more commercial importance
Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection
Blood transcriptomics in response to porcine reproductive and respiratory syndrome (PRRS).
Pursuing porcine reproductive and respiratory syndrome (PRRS) control alternatives with translational genomics.
Gene Set Enrichment Analysis, significant functional classes and pathways (p < 0.10) are shown from DEG (FDR < 0.20) comparing high and low IMF GEBV animals.
<p><sup><b>1</b></sup> padj—p value adjusted for multiple testing with the Benjamini-Hochberg procedure, which controls false discovery rate (FDR).</p><p>Gene Set Enrichment Analysis, significant functional classes and pathways (p < 0.10) are shown from DEG (FDR < 0.20) comparing high and low IMF GEBV animals.</p
Top 10 genes identified by Regulatory Impact Factor 2 (RIF2) score by contrasting high minus low IMF groups.
<p>*Note: RIF2 scores are presented as Z score normalized values.</p><p>Top 10 genes identified by Regulatory Impact Factor 2 (RIF2) score by contrasting high minus low IMF groups.</p
Top 10 genes identified by Regulatory Impact Factor 1 (RIF1) score by contrasting high minus low IMF groups.
<p>*Note: RIF1 scores are presented as Z score normalized values.</p><p>Top 10 genes identified by Regulatory Impact Factor 1 (RIF1) score by contrasting high minus low IMF groups.</p
Negative differential hubbing (DH) between the high and low groups for IMF GEBV.
<p>The center spot represents the gene with high value of DH, the red edges represent the positive DH and blue edges represent the negative DH. Other spots represent the connections.</p
Top 10 positive differentially hubbed (DH) genes comparing H and L groups of GEBV for IMF.
<p>Top 10 positive differentially hubbed (DH) genes comparing H and L groups of GEBV for IMF.</p