Location: Genetics and Animal Breeding
2018 Annual Report
Objectives
Objective 1: Utilize next-generation sequencing technologies to improve the contiguity of the swine genome assembly and better characterize genomic variation in pigs.
Subobjective 1.A: Utilize segregation analysis to improve the porcine genome assembly.
Subobjective 1.B: Develop more comprehensive gene models for the swine genome.
Subobjective 1.C: Develop an electronic warehouse of genomic variants that can be utilized by the swine genomics research community.
Objective 2: Develop genotyping products for commercial swine producers to increase the efficiency of swine production.
Subobjective 2.A: Identify predictive genetic markers for traits associated with production efficiency in commercial swine populations.
Subobjective 2.B: Develop strategies for inclusion of predictive markers in selection programs.
Approach
The principal goals of this project are to enhance our understanding of the biological processes important to swine production and provide the U.S. swine industry with genetic tools that will ensure that it remains the global leader in providing safe, nutritious, and economic pork products. The swine industry has been faced with significant challenges, many of which revolve around the production and performance of feeder pigs. The environment in which females are housed is continually evolving, and the increasing cost of feed has resulted in continuous shifts in the utilization of feed stuffs. Each new challenge requires an assessment of potential solutions. Genetic selection can be used to address many production issues. If DNA variants associated with changes in phenotype can be identified, then marker assisted selection can be implemented to expedite genetic progress. Predictive genetic markers need to be transferred to commercial entities that will rapidly evaluate and adopt them. The increasing improvements to the porcine genome, better annotations of genes from model organisms, and enhanced bioinformatics technologies provide researchers with the necessary tools to identify functional genetic variants.
Objective 1 focuses on improving the porcine genome assembly and detecting polymorphisms from data generated by next-generation sequencing.
Objective 2 will strive to effectively transfer the results of the research from Objective 1 to producers. Development of marker panels along with economical genotyping platforms will be essential. Our research will focus on the evaluation of genetic markers based on their predicted effects on gene products to discover causal genetic variants of phenotypic variation. This will lead to the development of marker panels and economical genotyping platforms for industry applications.
Progress Report
Research on the project objectives has been very productive. Evaluation of the new swine genome build, based on long-read sequencing methods, revealed that the new build is extremely high quality (Objective 1A). USMARCv1.0, was assembled using 65× coverage of PacBio long reads from DNA of lung tissue of a cross-bred pig (½ Landrace- ¼ Duroc- ¼ Yorkshire) and is available under Accession No. PRJNA392765. There is also a build produced by Roslin Institute based on a Duroc sow (Sus scrofa 11.1). The mapping of single nucleotide markers (SNP) to the new build resulted in nearly all SNP having a unique position. Markers with issues typically are due to inaccurate marker sequence or markers that map to redundant regions of the genome. To date, we are unable to identify any regions where linkage analysis can improve the genome assembly.
To develop more comprehensive porcine gene models (Objective 1B), sequencing the transcriptome of several animals and tissues has been conducted. Iso-Seq data were collected from several tissues of the pig sequenced for USMARCv1.0 and analyzed through a collaborative effort with Iowa State University. RNA-Seq libraries were constructed from Major Olfactory Epithelium (MOE) from gilts not showing estrus at 240 days and normal gilts displaying estrus (controls). Nonestrus gilts were subdivided into delayed puberty (no ovulation events) and behavioral anestrus (no estrus with ovulation event). Differential gene expression (DGE) showed only a small number of genes differentially expressed nonestrus gilts and controls, while normal cycling gilts had over 2,800 genes differentially expressed depending upon their stage of the estrus cycle. Over 18,000 genes were expressed in the MOE at measurable levels. Iso-Seq sequencing was completed on MOE and arcuate nucleus from noncycling gilts and controls. Similarly, RNA-Seq was performed on fresh and post mortem loin muscle from five gilts to determine DGE during the conversion of muscle to meat. Of over 14,000 genes expressed, 136 were differentially expressed between 0 and 24 hrs. post mortem and over 4,000 showed DGE at 48 hrs. Nearly half of these DEGs were more highly expressed at 48 hrs. post mortem.
A total of 240 animals from our resource populations have been sequenced using short-read technologies to detect segregating genetic variation. These data have been used to identify SNP, small insertions and deletions (INDELs) as well as copy number variation (CNVs) of larger fragments of DNA and are facilitating the creation of a variant warehouse for commercial pigs (Objective 1C)). A total of 21,961,591 high confidence SNP was identified. Variation was detected in the coding sequence or untranslated regions (UTR) of 90.3% of the genes in the porcine genome (Objective 2A): 1,374 loss-of-function variants were predicted in 950 genes, 11,723 genes contained 54,281 nonsynonymous variants, 219,183 variants were present in UTR of 15,572 genes, and 14,447 genes contained 77,843 synonymous variants. In total, approximately 275,000 SNP were classified as being of high to moderate impact (i.e. loss-of-function, nonsynonymous, or regulatory). These high to moderate impact SNP will be the focus of future genome-wide association studies. Additionally, 2,920 CNV were identified, covering approximately 1.8% of the porcine genome and spanning 1,521 protein-coding genes. Gene ontology analysis identified that CNV genes were enriched for functions related to neurophysiological processing of environmental stimuli, including sensory perception, signal transduction, and olfactory receptor activity. CNV were also found to overlap many known quantitative trait loci (QTL) for traits including average daily gain, drip loss, average backfat thickness, loin muscle area, and backfat at last rib.
A genome-wide association study was completed for sow lifetime productivity traits using a principle component approach (Objective 2A). The primary principle component which assessed lifetime productivity identified six QTL with relatively large effects and several other regions with smaller but important effects. Other principle components with QTL detected were related to growth rate, pre-weaning survival and peri-natal survival.
In addition, a scan was also conducted for concentration of myoglobin in longissimus tissue in response to industry concerns of light colored loins (Objective 2A). This study found several important genomic regions associated with myoglobin concentration that contain genes regulating iron or calcium level in muscle cells.
Accomplishments
1. Genetic factors are associated with myoglobin concentration of porcine longissimus muscle. A recent increase in consumer complaints about light colored pork muscles is a growing concern to the pork industry. Consumers prefer a redder-colored lean, especially consumers in Asian export markets. As myoglobin is the primary red pigmentation in porcine muscles, ARS scientists at Clay Center, Nebraska conducted a genome-wide association analysis to identify genetic markers associated with this important trait. Results indicate there are at least two major regions of the genome affecting myoglobin concentration as well as several other regions with minor effects. These results indicate a primary factor associated with myoglobin concentration is the percentage of different fiber types represented in the lean tissue, which contradicts a common belief that fiber type distribution within muscles is similar across animals within a species. With our enhanced knowledge of myoglobin concentration in muscles, selection methods can now be developed to improve pork color which may subsequently improve muscle pH as well as other quality attributes, and improve overall pork quality and subsequent consumer demand.
2. Copy number variation (CNV) of genomic DNA segregating in commercial pigs was quantified. Genome-wide association studies have made thousands of connections between single nucleotide polymorphisms (SNP) and phenotypes, but this type of variation only represents a portion of the total heritable genetic variation. Hence, determining other types of DNA variation that may make substantial contributions to variation in complex traits is a meaningful goal. Copy number variations (CNV) are gains and losses of large regions of genomic sequence between individuals of a species (ranging from thousands to nearly a million bases) and have been associated with phenotypic differences in humans and mouse. CNV can disrupt genes or enhance the amount of gene product if a complete gene is within a CNV region, and has been duplicated one or more times. Utilizing 240 sequenced genomes of commercial pigs, ARS scientists at Clay Center, Nebraska, identified 2,920 CNV covering approximately 1.8% of the swine genome. A larger than expected number of genes overlapped by CNV were found to be involved in the neurophysiological processing of environmental stimuli. Additionally, CNV were shown to overlap several known regions of DNA that correlate with variation in economically relevant swine phenotypes. The focus of future work will be to discover the extent to which CNV affect traits of economic interest and how to incorporate them into genomic selection systems.
Review Publications
Oliver, W.T., Keel, B.N., Lindholm-Perry, A.K., Horodyska, J., Foote, A.P. 2018. The effects of Capn1 gene inactivation on the differential expression of genes in skeletal muscle. Gene. 668:54-58. https://doi.org/10.1016/j.gene.2018.05.040.
Keel, B.N., Deng, B., Moriyama, E.N. 2018. MOCASSIN-prot: A multi-objective clustering approach for protein similarity networks. Bioinformatics. 34(8):1270-1277. https://doi.org/10.1093/bioinformatics/btx755.
Cross, A.J., Keel, B.N., Brown-Brandl, T.M., Cassady, J.P., Rohrer, G.A. 2018. Genome-wide association of changes in swine feeding behaviour due to heat stress. Genetics Selection Evolution. 50:11. https://doi.org/10.1186/s12711-018-0382-1.
Keel, B.N., Zarek, C.M., Keele, J.W., Kuehn, L.A., Snelling, W.M., Oliver, W.T., Freetly, H.C., Lindholm-Perry, A.K. 2018. RNA-seq meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers. BMC Genomics. 19:430. https://doi.org/10.1186/s12864-018-4769-8.
Keel, B.N., Snelling, W.M. 2018. Comparison of Burrows-Wheeler transform-based mapping algorithms used in high-throughput whole-genome sequencing: application to Illumina data for livestock genomes. Frontiers in Genetics. 9:35. https://doi.org/10.3389/fgene.2018.00035.
Cross, A.J., King, D.A., Shackelford, S.D., Wheeler, T.L., Nonneman, D.J., Keel, B.N., Rohrer, G.A. 2018. Genome-wide association of myoglobin concentrations in pork loins. Meat and Muscle Biology. 2(1):189-196. https://doi.org/10.22175/mmb2017.08.0042.
