Terminal and rotaterminal crossbreeding systems for pork producers (1995)

Crossbreeding is a widely established management practice among commercial pork producers. Over the years, the industry has used rotational crossbreeding programs extensively. Rotational programs are relatively easy to operate, enable pork producers to develop their own females and exploit most of the possible heterosis.Reprint 6/95/5M

Purchasing a herd boar for commercial swine production

AGRICULTURALMU GuidePUBLISHED BY UNIVERSITY EXTENSION, UNIVERSITY OF MISSOURI-COLUMBIASwine BreedingSuccess in commercial swine production is determined by a pork producer's ability to use resources efficiently. However, the level of efficiency that can be obtained is set by the genetic merit of the breeding herd. The genetic merit of the breeding herd is established by the crossbreeding program used and the introduction of new seedstock either by purchasing females or boars. The most popular crossbreeding programs have been rotational programs that produce replacement females so that only boars need to be brought into the herd. Farmers can use other more specialized crossbreeding programs, only purchase boars as well. For more information, see MU publication G 2311, Terminal and Rotaterminal Crossbreeding Systems for Pork Producers.Ronald O. Bates (Department of Animal Sciences)Reviewed November 2018 -- websit

Purchasing a herd boar for commercial swine production

"Success in commercial swine production is determined by a pork producer's ability to use resources efficiently. However, the level of efficiency that can be obtained is a set by the genetic merit of the breeding herd. The genetic merit of the breeding herd is established by the crossbreeding program used and the introduction of new seedstock either by purchasing females or boars."--First page.Ronald O. Bates (Department of Animal Science, College of Agriculture)New 6/90/5

Terminal and rotaterminal crossbreeding systems for pork producers

"Crossbreeding is a widely established management practice among commercial pork producers. Over the years, the industry has extensively used rotational crossbreeding programs. Rotational programs are relatively easy to operate, enable pork producers to develop their own females and exploit most of the possible heterosis."--First page.Ronald O. Bates (Animal Science Department College of Agriculture)New 5/90/5

By-products, damaged feeds and nontraditional feed sources for swine

"In Missouri, corn and soybean oil meal are the principal feed ingrents used in formulating swine rations. Rations using corn and soybean meal remain the standard to which other ingredients are compared. COnsiderable use is made of other ingredients, however, depending on cost."--First page.John C. Rea, Ronald O. Bates and Trygve L. Veum (Department of Animal Science, College of Agriculture)Revised 10/87/6

Expanding Effective Behavioral Health Literacy Programs to Address Farm Stress

Attention to stress and mental health among agricultural producers has increased over recent years, and Cooperative Extension has been active in offering educational workshops and resources to agricultural audiences. This article describes the process and effectiveness of expanding two (university) Extension farm stress management programs to Cooperative Extension in other states through a national Farm Stress Management Summit. The two-day training Summit provided deeper knowledge about farm stress issues and prepared Extension professionals to offer behavioral health programs in their own communities and respective states. Evaluation findings highlight effective aspects of the Summit and next steps

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.Fil: Gualdron Duarte, Jose Luis. Michigan State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, Ronald O.. Michigan State University; Estados UnidosFil: Ernst, Catherine W.. Michigan State University; Estados UnidosFil: Raney, Nancy E.. Michigan State University; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, Juan P.. Michigan State University; Estados Unido

Estimation of linkage disequilibrium in four US pig breeds

<p>Abstract</p> <p>Background</p> <p>The success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.</p> <p>Results</p> <p>Average <it>r²</it>between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, <it>r²</it>ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average <it>r²</it>ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.</p> <p>Conclusions</p> <p>Our estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (<it>r²</it>> 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.</p> <p>Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.</p

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F2 animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F2 animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three different Mendelian models using least-squares for QTL analysis were applied for the second scan: a LC model, a half-sib (HS) model, and a combined LC and HS model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL using the LC model, 38 QTL using the HS model, and 3 additional QTL using the combined LC and HS model were identified (q < 0.05). The LC and HS models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10th-rib backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01), respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent (SSC12), CIE a* (SSC16), and carcass length and spareribs weight (SSC18) were also significant (q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL detection, and applying different analysis models allowed confirmation of QTL and detection of new QTL

Application of alternative models to identify QTL for growth traits in an F2 Duroc x Pietrain pig resource population

<p>Abstract</p> <p>Background</p> <p>A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in experimental populations. The initial genome scan of our Duroc x Pietrain F₂resource population included 510 F₂animals genotyped with 124 microsatellite markers and analyzed using a line-cross model. For the second scan, 20 additional markers on 9 chromosomes were genotyped for 954 F₂animals and 20 markers used in the first scan were genotyped for 444 additional F₂animals. Three least-squares Mendelian models for QTL analysis were applied for the second scan: a line-cross model, a half-sib model, and a combined line-cross and half-sib model.</p> <p>Results</p> <p>In total, 26 QTL using the line-cross model, 12 QTL using the half-sib model and 3 additional QTL using the combined line-cross and half-sib model were detected for growth traits with a 5% false discovery rate (FDR) significance level. In the line-cross analysis, highly significant QTL for fat deposition at 10-, 13-, 16-, 19-, and 22-wk of age were detected on SSC6. In the half-sib analysis, a QTL for loin muscle area at 19-wk of age was detected on SSC7 and QTL for 10th-rib backfat at 19- and 22-wk of age were detected on SSC15.</p> <p>Conclusions</p> <p>Additional markers and animals contributed to reduce the confidence intervals and increase the test statistics for QTL detection. Different models allowed detection of new QTL which indicated differing frequencies for alternative alleles in parental breeds.</p