Cyberinfrastructure for Life Sciences - iAnimal Resources for Genomics and Other Data Driven Biology

Whole genome sequence, SNPs, copy number variation, phenotypes and other “-omics” data underlie evidence-based estimations of breeding value. Unfortunately, the computational resources (data storage, high-performance computing, analysis pipelines, etc.) that exploit this knowledge are limited in availability – many investigations are therefore restricted to the commercial sector or well-funded academic programs. Cyberinfrastructure developed by the iPlant Collaborative (NSF-#DBI0735191) and its extension iAnimal (USDA-#2013-67015-21231) provides the animal breeding community a comprehensive and freely available platform for the storage, sharing, and analyses of large datasets – from genomes to phenotype data. iPlant/iAnimal tools support a variety of genotype-phenotype related analyses in a platform that accommodates every level of user – from breeder to bioinformatician. These tools have been used to develop scalable, accessible versions of common workflows required for applying sequencing to livestock genomics

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

Population genomics of local adaptation versus speciation in coral reef fishes (Hypoplectrus spp, Serranidae)

Are the population genomic patterns underlying local adaptation and the early stages of speciation similar? Addressing this question requires a system in which (i) local adaptation and the early stages of speciation can be clearly identified and distinguished, (ii) the amount of genetic divergence driven by the two processes is similar, and (iii) comparisons can be repeated both taxonomically (for local adaptation) and geographically (for speciation). Here, we report just such a situation in the hamlets (Hypoplectrus spp), brightly colored reef fishes from the wider Caribbean. Close to 100,000 SNPs genotyped in 126 individuals from three sympatric species sampled in three repeated populations provide genome-wide levels of divergence that are comparable among allopatric populations (Fst estimate = 0.0042) and sympatric species (Fst estimate = 0.0038). Population genetic, clustering, and phylogenetic analyses reveal very similar patterns for local adaptation and speciation, with a large fraction of the genome undifferentiated (Fst estimate ≈ 0), a very small proportion of Fst outlier loci (0.05–0.07%), and remarkably few repeated outliers (1–3). Nevertheless, different loci appear to be involved in the two processes in Hypoplectrus, with only 7% of the most differentiated SNPs and outliers shared between populations and species comparisons. In particular, a tropomyosin (Tpm4) and a previously identified hox (HoxCa) locus emerge as candidate loci (repeated outliers) for local adaptation and speciation, respectively. We conclude that marine populations may be locally adapted notwithstanding shallow levels of genetic divergence, and that from a population genomic perspective, this process does not appear to differ fundamentally from the early stages of speciation

Single nucleotide variant discovery of highly inbred Leghorn and Fayoumi chicken breeds using pooled whole genome resequencing data reveals insights into phenotype differences

Background Analyses of sequence variants of two distinct and highly inbred chicken lines allowed characterization of genomic variation that may be associated with phenotypic differences between breeds. These lines were the Leghorn, the major contributing breed to commercial white-egg production lines, and the Fayoumi, representative of an outbred indigenous and robust breed. Unique within- and between-line genetic diversity was used to define the genetic differences of the two breeds through the use of variant discovery and functional annotation. Results Downstream fixation test (F ST ) analysis and subsequent gene ontology (GO) enrichment analysis elucidated major differences between the two lines. The genes with high F STvalues for both breeds were used to identify enriched gene ontology terms. Over-enriched GO annotations were uncovered for functions indicative of breed-related traits of pathogen resistance and reproductive ability for Fayoumi and Leghorn, respectively. Conclusions Variant analysis elucidated GO functions indicative of breed-predominant phenotypes related to genomic variation in the lines, showing a possible link between the genetic variants and breed traits

Genomic analysis of Ugandan and Rwandan chicken ecotypes using a 600 k genotyping array

Background Indigenous populations of animals have developed unique adaptations to their local environments, which may include factors such as response to thermal stress, drought, pathogens and suboptimal nutrition. The survival and subsequent evolution within these local environments can be the result of both natural and artificial selection driving the acquisition of favorable traits, which over time leave genomic signatures in a population. This study’s goals are to characterize genomic diversity and identify selection signatures in chickens from equatorial Africa to identify genomic regions that may confer adaptive advantages of these ecotypes to their environments. Results Indigenous chickens from Uganda (n = 72) and Rwanda (n = 100), plus Kuroilers (n = 24, an Indian breed imported to Africa), were genotyped using the Axiom® 600 k Chicken Genotyping Array. Indigenous ecotypes were defined based upon location of sampling within Africa. The results revealed the presence of admixture among the Ugandan, Rwandan, and Kuroiler populations. Genes within runs of homozygosity consensus regions are linked to gene ontology (GO) terms related to lipid metabolism, immune functions and stress-mediated responses (FDR \u3c 0.15). The genes within regions of signatures of selection are enriched for GO terms related to health and oxidative stress processes. Key genes in these regions had anti-oxidant, apoptosis, and inflammation functions. Conclusions The study suggests that these populations have alleles under selective pressure from their environment, which may aid in adaptation to harsh environments. The correspondence in gene ontology terms connected to stress-mediated processes across the populations could be related to the similarity of environments or an artifact of the detected admixture

Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection

Citation: Schroyen, M., Steibel, J. P., Koltes, J. E., Choi, I., Raney, N. E., Eisley, C., . . . Tuggle, C. K. (2015). Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection. Bmc Genomics, 16(1). doi:10.1186/s12864-015-1741-8Background: The presence of variability in the response of pigs to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recent demonstration of significant genetic control of such responses, leads us to believe that selection towards more disease resistant pigs could be a valid strategy to reduce its economic impact on the swine industry. To find underlying molecular differences in PRRS susceptible versus more resistant pigs, 100 animals with extremely different growth rates and viremia levels after PRRSv infection were selected from a total of 600 infected pigs. A microarray experiment was conducted on whole blood RNA samples taken at 0, 4 and 7 days post infection (dpi) from these pigs. From these data, we examined associations of gene expression with weight gain and viral load phenotypes. The single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) on the porcine 60 K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so the effect of the WUR10000125 (WUR) genotype on expression in whole blood was also examined. Results: Limited information was obtained through linear modeling of blood gene differential expression (DE) that contrasted pigs with extreme phenotypes, for growth or viral load or between animals with different WUR genotype. However, using network-based approaches, molecular pathway differences between extreme phenotypic classes could be identified. Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4dpi contrasted with 0dpi data. The expression pattern of one such cluster of genes correlated with weight gain and WUR genotype, contained numerous immune response genes such as cytokines, chemokines, interferon type I stimulated genes, apoptotic genes and genes regulating complement activation. In addition, Partial Correlation and Information Theory (PCIT) identified differentially hubbed (DH) genes between the phenotypically divergent groups. GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways. Conclusion: There are molecular differences in blood RNA patterns between pigs with extreme phenotypes or with a different WUR genotype in early responses to PRRSv infection, though they can be quite subtle and more difficult to discover with conventional DE expression analyses. Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups. © 2015 Schroyen et al

Deriving Gene Networks from SNP Associated with Triacylglycerol and Phospholipid Fatty Acid Fractions from Ribeyes of Angus Cattle

The fatty acid profile of beef is a complex trait that can benefit from gene-interaction network analysis to understand relationships among loci that contribute to phenotypic variation. Phenotypic measures of fatty acid profile from triacylglycerol and phospholipid fractions of longissimus muscle, pedigree information, and Illumina 54 k bovine SNP genotypes were utilized to derive an annotated gene network associated with fatty acid composition in 1,833 Angus beef cattle. The Bayes-B statistical model was utilized to perform a genome wide association study to estimate associations between 54 k SNP genotypes and 39 individual fatty acid phenotypes within each fraction. Posterior means of the effects were estimated for each of the 54 k SNP and for the collective effects of all the SNP in every 1-Mb genomic window in terms of the proportion of genetic variance explained by the window. Windows that explained the largest proportions of genetic variance for individual lipids were found in the triacylglycerol fraction. There was almost no overlap in the genomic regions explaining variance between the triacylglycerol and phospholipid fractions. Partial correlations were used to identify correlated regions of the genome for the set of largest 1 Mb windows that explained up to 35% genetic variation in either fatty acid fraction. SNP were allocated to windows based on the bovine UMD3.1 assembly. Gene network clusters were generated utilizing a partial correlation and information theory algorithm. Results were used in conjunction with network scoring and visualization software to analyze correlated SNP across 39 fatty acid phenotypes to identify SNP of significance. Significant pathways implicated in fatty acid metabolism through GO term enrichment analysis included homeostasis of number of cells, homeostatic process, coenzyme/cofactor activity, and immunoglobulin. These results suggest different metabolic pathways regulate the development of different types of lipids found in bovine muscle tissues. Network analysis using partial correlations and annotation of significant SNPs can yield information about the genetic architecture of complex traits