James E. Koltes

Putative Regulatory Factors Associated with Intramuscular Fat Content

Intramuscular fat (IMF) content is related to insulin resistance, which is an important prediction factor for disorders, such as cardiovascular disease, obesity and type 2 diabetes in human. At the same time, it is an economically important trait, which influences the sensorial and nutritional value of meat. The deposition of IMF is influenced by many factors such as sex, age, nutrition, and genetics. In this study Nellore steers (Bos taurus indicus subspecies) were used to better understand the molecular mechanisms involved in IMF content. This was accomplished by identifying differentially expressed genes (DEG), biological pathways and putative regulatory factors. Animals included in this study had extreme genomic estimated breeding value (GEBV) for IMF. RNA-seq analysis, gene set enrichment analysis (GSEA) and co-expression network methods, such as partial correlation coefficient with information theory (PCIT), regulatory impact factor (RIF) and phenotypic impact factor (PIF) were utilized to better understand intramuscular adipogenesis. A total of 16,101 genes were analyzed in both groups (high (H) and low (L) GEBV) and 77 DEG (FDR 10%) were identified between the two groups. Pathway Studio software identified 13 significantly over-represented pathways, functional classes and small molecule signaling pathways within the DEG list. PCIT analyses identified genes with a difference in the number of gene-gene correlations between H and L group and detected putative regulatory factors involved in IMF content. Candidate genes identified by PCIT include: ANKRD26, HOXC5 and PPAPDC2. RIF and PIF analyses identified several candidate genes: GLI2 and IGF2 (RIF1), MPC1 and UBL5 (RIF2) and a host of small RNAs, including miR-1281 (PIF). These findings contribute to a better understanding of the molecular mechanisms that underlie fat content and energy balance in muscle and provide important information for the production of healthier beef for human consumption.

A nonsense mutation in cGMP-dependent type II protein kinase (PRKG2) causes dwarfism in American Angus cattle.

Historically, dwarfism was the major genetic defect in U.S. beef cattle. Aggressive culling and sire testing were used to minimize its prevalence; however, neither of these practices can eliminate a recessive genetic defect. We assembled a 4-generation pedigree to identify the mutation underlying dwarfism in American Angus cattle. An adaptation of the Elston-Steward algorithm was used to overcome small pedigree size and missing genotypes. The dwarfism locus was fine-mapped to BTA6 between markers AFR227 and BM4311. Four candidate genes were sequenced, revealing a nonsense mutation in exon 15 of cGMP-dependant type II protein kinase (PRKG2). This C/T transition introduced a stop codon (R678X) that truncated 85 C-terminal amino acids, including a large portion of the kinase domain. Of the 75 mutations discovered in this region, only this mutation was 100% concordant with the recessive pattern of inheritance in affected and carrier individuals (log of odds score = 6.63). Previous research has shown that PRKG2 regulates SRY (sex-determining region Y) box 9 (SOX9)-mediated transcription of collagen 2 (COL2). We evaluated the ability of wild-type (WT) or R678X PRKG2 to regulate COL2 expression in cell culture. Real-time PCR results confirmed that COL2 is overexpressed in cells that overexpressed R678X PRKG2 as compared with WT PRKG2. Furthermore, COL2 and COL10 mRNA expression was increased in dwarf cattle compared with unaffected cattle. These experiments indicate that the R678X mutation is functional, resulting in a loss of PRKG2 regulation of COL2 and COL10 mRNA expression. Therefore, we present PRKG2 R678X as a causative mutation for dwarfism cattle.

Gene Co-Expression Network Analysis Provides Novel Insights into Myostatin Regulation at Three Different Mouse Developmental Timepoints

Myostatin (Mstn) knockout mice exhibit large increases in skeletal muscle mass. However, relatively few of the genes that mediate or modify MSTN effects are known. In this study, we performed co-expression network analysis using whole transcriptome microarray data from MSTN-null and wild-type mice to identify genes involved in important biological processes and pathways related to skeletal muscle and adipose development. Genes differentially expressed between wild-type and MSTN-null mice were further analyzed for shared DNA motifs using DREME. Differentially expressed genes were identified at 13.5 d.p.c. during primary myogenesis and at d35 during postnatal muscle development, but not at 17.5 d.p.c. during secondary myogenesis. In total, 283 and 2034 genes were differentially expressed at 13.5 d.p.c. and d35, respectively. Over-represented transcription factor binding sites in differentially expressed genes included SMAD3, SP1, ZFP187, and PLAGL1. The use of regulatory (RIF) and phenotypic (PIF) impact factor and differential hubbing co-expression analyses identified both known and potentially novel regulators of skeletal muscle growth, including Apobec2, Atp2a2, and Mmp13 at d35 and Sox2, Tmsb4x, and Vdac1 at 13.5 d.p.c. Among the genes with the highest PIF scores were many fiber type specifying genes. The use of RIF, PIF, and differential hubbing analyses identified both known and potentially novel regulators of muscle development. These results provide new details of how MSTN may mediate transcriptional regulation as well as insight into novel regulators of MSTN signal transduction that merit further study regarding their physiological roles in muscle and adipose development.

Evaluation of variant identification methods for whole genome sequencing data in dairy cattle

BackgroundAdvances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays.ResultsThe total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio.ConclusionsOur findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection.

Optimizing the PCIT algorithm on stampede's Xeon and Xeon Phi processors for faster discovery of biological networks

The PCIT method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re-implemented with exemplary parallel, vector, I/O, memory and instruction optimizations for todays multi- and many-core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer, but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component comprised of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi.

Discovery of biological networks using an optimized partial correlation coefficient with information theory algorithm on Stampede's Xeon and Xeon Phi processors

The partial correlation coefficient with information theory (PCIT) method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re‐implemented with exemplary parallel, vector, input/output (I/O), memory, and instruction optimizations for todays multi‐core and many‐core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component consist of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi. Copyright

Differences in the skeletal muscle transcriptome profile associated with extreme values of fatty acids content

BackgroundLipids are a class of molecules that play an important role in cellular structure and metabolism in all cell types. In the last few decades, it has been reported that long-chain fatty acids (FAs) are involved in several biological functions from transcriptional regulation to physiological processes. Several fatty acids have been both positively and negatively implicated in different biological processes in skeletal muscle and other tissues. To gain insight into biological processes associated with fatty acid content in skeletal muscle, the aim of the present study was to identify differentially expressed genes (DEGs) and functional pathways related to gene expression regulation associated with FA content in cattle.ResultsSkeletal muscle transcriptome analysis of 164 Nellore steers revealed no differentially expressed genes (DEGs, FDR 10%) for samples with extreme values for linoleic acid (LA) or stearic acid (SA), and only a few DEGs for eicosapentaenoic acid (EPA, 5 DEGs), docosahexaenoic acid (DHA, 4 DEGs) and palmitic acid (PA, 123 DEGs), while large numbers of DEGs were associated with oleic acid (OA, 1134 DEGs) and conjugated linoleic acid cis9 trans11 (CLA-c9t11, 872 DEGs). Functional annotation and functional enrichment from OA DEGs identified important genes, canonical pathways and upstream regulators such as SCD, PLIN5, UCP3, CPT1, CPT1B, oxidative phosphorylation mitochondrial dysfunction, PPARGC1A, and FOXO1. Two important genes associated with lipid metabolism, gene expression and cancer were identified as DEGs between animals with high and low CLA-c9t11, specifically, epidermal growth factor receptor (EGFR) and RNPS.ConclusionOnly two out of seven classes of molecules of FA studied were associated with large changes in the expression profile of skeletal muscle. OA and CLA-c9t11 content had significant effects on the expression level of genes related to important biological processes associated with oxidative phosphorylation, and cell growth, survival, and migration. These results contribute to our understanding of how some FAs modulate metabolism and may have protective health function.

High-performance epistasis detection in quantitative trait GWAS

epiSNP is a program for identifying pairwise single nucleotide polymorphism (SNP) interactions (epistasis) in quantitative-trait genome-wide association studies (GWAS). A parallel MPI version (EPISNPmpi) was created in 2008 to address this computationally expensive analysis on large data sets with many quantitative traits and SNP markers. However, the falling cost of genotyping has led to an explosion of large-scale GWAS data sets that challenge EPISNPmpi’s ability to compute results in a reasonable amount of time. Therefore, we optimized epiSNP for modern multi-core and highly parallel many-core processors to efficiently handle these large data sets. This paper describes the serial optimizations, dynamic load balancing using MPI-3 RMA operations, and shared-memory parallelization with OpenMP to further enhance load balancing and allow execution on the Intel Xeon Phi coprocessor (MIC). For a large GWAS data set, our optimizations provided a 38.43× speedup over EPISNPmpi on 126 nodes using 2 MICs on TACC’s Stampede Supercomputer. We also describe a Coarray Fortran (CAF) version that demonstrates the suitability of PGAS languages for problems with this computational pattern. We show that the Coarray version performs competitively with the MPI version on the NERSC Edison Cray XC30 supercomputer. Finally, the performance benefits of hyper-threading for this application on Edison (average 1.35× speedup) are demonstrated.

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

BackgroundAnalyses 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.ResultsDownstream fixation test (FST) analysis and subsequent gene ontology (GO) enrichment analysis elucidated major differences between the two lines. The genes with high FST values 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.ConclusionsVariant 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.

Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis).

Background The availability of the bovine genome sequence and SNP panels has improved various genomic analyses, from exploring genetic diversity to aiding genetic selection. However, few of the SNP on the bovine chips are polymorphic in buffalo, therefore a panel of single nucleotide DNA markers exclusive for buffalo was necessary for molecular genetic analyses and to develop genomic selection approaches for water buffalo. The creation of a 90K SNP panel for river buffalo and testing in a genome wide association study for milk production is described here. Methods The genomes of 73 buffaloes of 4 different breeds were sequenced and aligned against the bovine genome, which facilitated the identification of 22 million of sequence variants among the buffalo genomes. Based on frequencies of variants within and among buffalo breeds, and their distribution across the genome, inferred from the bovine genome sequence, 90,000 putative single nucleotide polymorphisms were selected to create an Axiom® Buffalo Genotyping Array 90K. Results This 90K “SNP-Chip” was tested in several river buffalo populations and found to have ∼70% high quality and polymorphic SNPs. Of the 90K SNPs about 24K were also found to be polymorphic in swamp buffalo. The SNP chip was used to investigate the structure of buffalo populations, and could distinguish buffalo from different farms. A Genome Wide Association Study identified genomic regions on 5 chromosomes putatively involved in milk production. Conclusion The 90K buffalo SNP chip described here is suitable for the analysis of the genomes of river buffalo breeds, and could be used for genetic diversity studies and potentially as a starting point for genome-assisted selection programmes. This SNP Chip could also be used to analyse swamp buffalo, but many loci are not informative and creation of a revised SNP set specific for swamp buffalo would be advised.