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United States Department of Agriculture

Agricultural Research Service

Research Project: GENETIC AND GENOMIC APPROACHES TO IMPROVE EFFICIENCY OF SWINE PRODUCTION AND PRODUCT QUALITY
2012 Annual Report


1a.Objectives (from AD-416):
Objective 1: Develop genetic tests which can be used as tools to improve selection in commercial swine populations for important production traits.

Objective 2: Determine interactions among traits, parental origin of alleles, loci and/or environment to better understand the basis of genetic correlations, inheritance of complex traits and to more accurately formulate selection plans in swine.

Objective 3: Utilize the knowledge gained from objective 2 and from USMARC collaborators in conjunction with the swine genome sequence to identify the causative genes underlying QTL.


1b.Approach (from AD-416):
The goal of this research is to ensure US swine producers are a competitive source of pork products by providing the genetic information necessary to maintain superior production levels. The approach will use genetic markers and genomic technologies to understand how the genome regulates animal performance and determine the molecular basis behind non-additive genetic effects. Availability of the draft swine genome sequence will allow continuation of research on genomic regions affecting components of reproductive performance, growth, and carcass quality to move faster and more efficiently. Future studies will include a broader list of phenotypes including metabolic parameters to understand nutrient utilization, animal disposition and incidence of disease during natural outbreaks in the population.

This project will use genomic approaches in combination with extensively phenotyped swine populations to identify genetic markers associated with production traits and understand these complex biological processes. Our approach will be to conduct genome-wide QTL scans and then fine map these QTL and develop SNP markers in tight linkage with the causative polymorphisms. QTL scans will be conducted in subsets of the USMARC BX swine population that have been extensively phenotyped for a wide variety of traits. This will permit a more complete biological understanding of each QTL region. Follow-up studies on QTL will be conducted in the BX population on larger groups of animals that may be phenotyped for a specific set of traits.

Standard QTL analyses will first be conducted followed by statistical models to identify components to nonadditive genetic variation affecting performance such as intra-locus (dominance and imprinting) and inter-locus (epistatic) interactions. These analyses will also yield valuable information about pleiotropic effects to understand the molecular bases of genetic correlations. A high density SNP map (5-20 SNP/cM) will be developed for the studied regions and genotyped across additional generations of BX animals to fine map QTL. Significant SNP markers developed from these approaches will be evaluated in additional commercially relevant lines of pig to ensure their applicability in commercial pigs. Markers that exhibit useful predictive genetic information will be disseminated to the swine industry.

Finally with all of the genetic and phenotypic knowledge in hand, we should be well-equipped to determine the causative gene for some QTL and greatly improve our understanding of the physiological effects of these QTL. A precise location of the causative gene as predicted from fine mapping studies, knowledge about different biological pathways affected from the extensively phenotyped population and knowledge about the genes located in the region from the swine genome sequence should allow selection of positional candidate gene to study for causative variation. These studies will be supplemented with functional genomic and marker-assisted animal experimentation.


3.Progress Report:
Final report for this project, which was replaced by 5438-31000-089-00D. Significant accomplishments have been achieved to address the NP Action Plan’s Problem Statements 1A) Develop Genome-Enabling Tools and Reagents, 1B) Identify Functional Genes and Their Interactions and 1D) Develop and Implement Genome-Enabled Genetic Improvement Programs. In the past five years, we have contributed to Problem Statement 1A by developing a comprehensive physical map integrated with the linkage map for the porcine genome and developing the Illumina PorcineSNP60 BeadChip. Problem Statement 1B was fortified with discoveries of variant forms of calpastatin and dystrophin that affect tenderness and survival, respectively. Problem Statement 1D was the target for the genome-wide association studies completed for female reproduction and pork quality. In total, this research has reported 10 accomplishments and produced 30 refereed publications. Due to an earlier critical vacancy (FY2009), the availability of the Illumina PorcineSNP60 BeadChip and changes to the management of the swine population, our research was modified to efficiently address more critical issues to swine producers. We have conducted a thorough assessment of genetic markers within the calpastatin gene in multiple commercial populations to determine the most effective set of markers to improve tenderness. This assessment has likely identified the causative genetic variant(s) in the calpastatin gene. We have identified a variant in dystrophin, which may cause stress-associated deaths. Finally, we identified two genes (LIMA1 and TMEM138) differentially expressed in placenta after selection for uterine capacity. Differences in haplotype frequency between lines and within population associations were detected for ovulation rate and average placental weight. We have made significant progress in all of our research objectives by completing numerous scans of the swine genome for loci of economic importance with the Illumina PorcineSNP60 BeadChip. Genome-wide association analyses were completed for all measures of meat quality as well as female fertility collected in our population, including age at puberty, ovulation rate and litter traits. Novel approaches to identify candidate regions affecting performance were employed by pooling DNA samples of animals with extreme phenotypes and then assaying the DNA pools with the Illumina PorcineSNP60 BeadChip. This approach was used to study a piglet’s ability to acquire immunoglobulin within 24 hours after birth, lung lesion scores on slaughter pigs, tenderness and novel pork quality phenotypes related to color stability. Finally, we have continued our research on a novel porcine stress syndrome caused by a mutation in dystrophin to determine its impact on commercial swine production. We have also joined a group of university scientists in a NIFA grant proposal to further study female reproduction in commercial populations, which will grant us access to >10,000 phenotyped females from multiple commercial populations. These records will enhance our ability to develop markers predictive of reproductive performance and sow longevity in commercial pigs.


4.Accomplishments
1. Genome-wide association analyses identify numerous quantitative trait loci (QTL) affecting female reproductive performance in swine. Reproductive efficiency has a great impact on the success of pork production, but improvements through traditional selection practices are small. ARS researchers at Clay Center, Nebraska, used the Illumina PorcineSNP60 Beadchip, which they co-developed as members of the International Porcine SNP Consortium, to scan the genome of over 1,500 female pigs with various measures of reproductive performance. Traits measured included age at first estrus, ovulation rate, litter size, pre-weaning mortality, and maternal influence on birth weight. Significant QTL were identified on every chromosome and these QTL typically accounted for approximately 50% or more of the additive genetic variation. Swine producers can now use either marker-assisted or genomic selection procedures to improve reproductive efficiency and sustainability of their herds.

2. Genetic markers associated with pork quality detected in a commercial-type population. Maintaining pork quality while applying direct selection for increased lean muscle mass is critical to ensure continued demand for pork products. However, selection for traits that can only be measured in carcasses is difficult with traditional selection. ARS researchers at Clay Center, Nebraska, interrogated the genome of 1,237 pigs with the Illumina PorcineSNP60 BeadChip and conducted association analyses with measures of pork tenderness, marbling, color, waterholding capacity, and pH. Eighty-nine significant associations were detected. Most associations were with marbling or waterholding capacity. These results will enable the swine industry to monitor or improve pork quality while focusing most selection pressure on the more economical traits, like lean muscle mass accretion.


Review Publications
Mousel, M.R., Leymaster, K.A., Christenson, R.K., Nonneman, D.J., Rohrer, G.A. 2012. Validation and fine mapping of a QTL for ovulation rate on swine chromosome 3. Animal Genetics. 43(2):220-224.

Schneider, J.F., Rempel, L.A., Rohrer, G.A., Brown Brandl, T.M. 2011. Genetic parameter estimates among scale activity score and farrowing disposition with reproductive traits in swine. Journal of Animal Science. 89(11):3514-3521.

Safranski, T.J., Ford, J.J., Rohrer, G.A., Guthrie, H.D. 2011. Plenary contribution to International Conference on Boar Semen Preservation 2011. Genetic selection for freezability and its controversy with selection for performance. Reproduction of Domestic Animals. 46(Suppl. 2):31-34.

Nonneman, D.J., Lindholm-Perry, A.K., Shackelford, S.D., King, D.A., Wheeler, T.L., Rohrer, G.A., Bierman, C.D., Schneider, J.F., Miller, R.K., Zerby, H., Moeller, S.J. 2011. Predictive markers in calpastatin for tenderness in commercial pig populations. Journal of Animal Science. 89:2663-2672.

Cepica, S., Ovilo, C., Masopust, M., Knoll, A., Fernandez, A., Lopez, A., Rohrer, G.A., Nonneman, D. 2012. Four genes located on a SSC2 meat quality QTL region are associated with different meat quality traits in Landrace x Chinese-European crossbred population. Animal Genetics. 43(3):333-336.

Last Modified: 10/22/2014
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